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A review of wetlands remote sensing and defining new considerations
Abstract
Significant progress has been made in using remote sensing as a means of acquiring information about wetlands. This research provides a brief review of selected previous works, which address the issues of wetland identification, classification, biomass measurement, and change detection. Suggested new research emphases include compiling basic spectral‐reflectance characteristics for individual wetland species by means of close‐range instrumentation, analyzing canopies architectures to facilitate species identification, and assessing the impact on composite spectral signatures of wet soils and variable depths of standing water beneath emergent canopies. These research foci are justifiable when considered in the context of environmental change / variability and the production of trace gases.
... Wetlands are important transitional ecosystems that remove substantial amounts of nitrogen and phosphorus from upland runoff [1,2]. They play a critical role in recharging water tables, retaining nutrients, absorbing pollutants from runoff, filtering sediment, and sequestering atmospheric carbon [3][4][5][6]. ...
... Historically, wetlands were considered unproductive agricultural lands. They were often drained and manipulated to allow different agricultural activities to be conducted, but in the late 20th century, the importance of wetlands and ecosystem services was recognized, triggering wide-ranging mapping efforts [1,2]. The early mapping efforts started with identifying the boundaries of wetlands, vegetation mapping, and periodic surveillance and change detection [7][8][9][10]. ...
... We conducted a comprehensive literature review on remote sensing techniques for tidal or coastal wetland SOC prediction and mapping and synthesized our findings, as part of this study. Our objectives for this study were: (1) to provide an in-depth review of existing remote sensing models developed and used to predict SOC in tidal wetlands; (2) to synthesize the standard environmental covariables and propose novel covariables needed to develop accurate, remote-sensing-based SOC prediction models; (3) to analyze the feasibility of modeling subsurface SOC (defined in this review as soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) from remotely estimated surface SOC; and (4) to propose future-facing remote-sensing-based research questions to advance the field of tidal wetland SOC monitoring and mapping. We also include some preliminary data analysis from tidal wetlands in the United States and mangrove wetlands in eastern India, to provide a rationale to support the questions raised as part of future research directions. ...
Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.
... Wetland conservation is well established as a matter of national and international public policy. Accurate maps of wetland boundaries and their changes are essential for effective monitoring, and remotely sensed imagery provides researchers with a means to achieve those goals [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. ...
... Remotely sensed imagery has been used to generate wetland maps with various levels of success [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. High-spatial-resolution remotely sensed imagery has created some of the most accurate wetland maps with the disadvantages of limited coverage and large time and resource demands; turnaround times for these products can be years [4]. ...
The goal of this research was to improve wetland classification by fully exploiting multi-source remotely sensed data. Three distinct classifiers were designed to distinguish individual or compound wetland categories using random forest (RF) classification. They were determined, in part, to best use the available remotely sensed features in order to maximize that information and to maximize classification accuracy. The results from these classifiers were integrated according to Dempster–Shafer theory (D–S theory). The developed method was tested on data collected from a study area in Northern Alberta, Canada. The data utilized were Landsat-8 and Sentinel-2 (multi-spectral), Sentinel-1 (synthetic aperture radar—SAR), and digital elevation model (DEM). Classification of fen, bog, marsh, swamps, and upland resulted in an overall accuracy of 0.93 using the proposed methodology, an improvement of 5% when compared to a traditional classification method based on the aggregated features from these data sources. It was noted that, with the traditional method, some pixels were misclassified with a high level of confidence (>85%). Such misclassification was significantly reduced (by ~10%) by the proposed method. Results also showed that some features important in separating compound wetland classes were not considered important using the traditional method based on the RF feature selection mechanism. When used in the proposed method, these features increased the classification accuracy, which demonstrated that the proposed method provided an effective means to fully employ available data to improve wetland classification.
... There are a number of reviews on the use of remote sensing data and techniques for generating information about wetland systems (e.g. Rundquist et al., 2001;Ozesmi and Bauer, 2002;Ritchie and Das, 2015). Rundquist et al. (2001) have shown that remote sensing can be widely used for wetland inventory and mapping, wetland classification, wetland ecological change detection, and estimating wetland plant biomass. ...
... Rundquist et al., 2001;Ozesmi and Bauer, 2002;Ritchie and Das, 2015). Rundquist et al. (2001) have shown that remote sensing can be widely used for wetland inventory and mapping, wetland classification, wetland ecological change detection, and estimating wetland plant biomass. Aerial photographs, multispectral-satellite images, radar data, and ancillary data such as edaphic and elevation data are the most widely used datasets. ...
Due to wetland inaccessibility and limited wetland geomorphological studies, there is limited information on the geomorphological origin and hydrological functioning of different types of wetlands in Africa's elevated drylands. As a result, there is limited information for the development of a comprehensive wetland classification system that classifies wetlands based on long-term geomorphic processes that determine their formation and shape, their structure and hydrological functioning. Therefore, the current study was designed to classify large wetlands in Africa's elevated drylands based on processes that determine their formation, and shape their structure and hydrological functioning using remote sensing and Geographic Information System (GIS) techniques. Although wetlands perform a number of hydrological functions including groundwater recharge and water purification, the current study focuses mainly on their flood attenuation function. Detailed analysis of topographic information was undertaken using Shuttle Radar Topographic Mission (SRTM) elevations measured at the scale of 30 m x 30 m. LandsatLook and Google Earth images, tectonic as well as geological data were used as supplementary data for developing an understanding of the origin, structure and hydrological characteristics of wetlands. The Principal Component Analysis (PCA) of wetland environmental variables was used to identify and explain wetland heterogeneity. The results of the study showed that fluvial processes, tectonic history and the evolution of Africa's landscape played a fundamental role in the formation and evolution of wetlands. This study demonstrates a wide range of processes that contribute to wetland formation, structure and functioning. At one extreme it is clear that tectonic processes may be primarily responsible for the creation of basins that host wetlands. At another extreme, wetlands may be structured primarily by fluvial processes. At a third extreme are wetlands that superficially appear to be structured by fluvial processes, but which have their structures modified by gradual rising of the base level at their distal ends, either through marginal uplift adjacent to rift valleys, or through aggradation of a floodplain that blocks a tributary valley. Overall, the classification of wetlands considered in this study can be summarised into four distinct groupings, with two of these divided further into two groupings each: (1) Tectonic basins with little or no indication of fluvial development (Bahi and Wembere wetlands), (2) Tectonic basins evolving towards a wetland with a structure increasingly shaped by fluvial characteristics (Usangu wetland), (3) Fluvially modified valleys with a local base level at the toe of the wetland such as a resistant lithology or a tectonic control that limits the rate of incision of easily weathered and eroded lithologies, leading to valley widening and Page | ii longitudinal slope reduction, which are of two distinct types: (a) With a catchment on Kalahari Group sediment that is transported fluvially as bedload, and therefore with no prominent alluvial ridge or backwater depressions (Upper Zambezi and Barotse wetlands), (b) With a catchment that produces abundant fine sediment that is deposited as overbank sediments, leading to channel migration via meandering and to the construction of an elevated alluvial ridge (Lufira wetland), (4) Fluvially modified basins with evidence of gradual elevation of the base level at the toe of the wetland, which are of two types: (a) Tectonic marginal rift valley uplift such that they behave more as depression wetlands rather than as wetlands shaped by fluvial processes (Kafue and Luapula wetlands), (b) Tributary valley wetlands blocked by aggradation of the trunk valley (Lukanga wetland). In conclusion, although few geomorphological studies have been conducted on southern African wetlands because of their inaccessibility, Africa's surface topography and its historical evolution, as well as aridity, provide an opportunity for illustrating the important role that the long-term tectonic, geological and geomorphological processes play in determining wetland origin, structure and dynamics. GIS methodology and Earth Observation (EO) data on the other hand, provide a practical means for acquiring information on inaccessible and hard to traverse wetland systems. A novel cut-and-fill approach for delineating wetlands from a Digital Elevation Model (DEM) was presented as another way in which GIS methodology and Earth
... A variety of change detection techniques and algorithms have been developed and reviewed for their advantages and disadvantages. Among these unsupervised classification or clustering, Supervised classification, PCA, Hybrid classification and Fuzzy classification are the most commonly applied techniques used in classification ( Lu et al., 2004;Rundquist et al., 2001; Zhang et al., 2000). ...
... ha) area was under forest in 1991. Landsat Image, 1991, 2001and 2011 In tehsil 13270.09 ha area under forest, out of that 86.95% (8713.67 ha) area belongs to Sangavi, Boradi and Holnanthe circles. ...
The general land use of region is control by various factors i.e. physical, cultural, social,
environmental etc. economic activities, agricultural practices and their development depend on land use and
intensity of land use. The temporal changes in land use pattern of Shirpur tehsil have studied for the period
of 1991 to 2011 to find out the trends of variation in general land use and to identify the reasons for the
changes. Spatial variation in land use changes studied on circle level. Last three decades land satellite images
have been used. These satellite images further processed and analyzed by GIS software. Total population
density of Shirpur Tehsil was 405 in 1991 that increase up to 507 in 2011. The total area of Shirpur tehsil is
83307.61hectares out of that 1000.72 hectares area under built up in 1991 that increases by 1152.22 hectares
in 2011. The agricultural land was 540.33 hectares in 1991 that has been decline 298.72 hectares in 2011.
Forest area also decline by 3196.27 hectares in last three decades. It is concluded that in Shirpur tehsil
Barren land and forest area decline and built up area, agricultural land, area under water bodies has
increasing because of increasing population demands.
... Thus, only small areas can be mapped precisely, and extrapolation could be erroneous (Millar 1973;Harvey and Hill 2001;Morrison et al. 2020;Morrison 2021). Remote sensing technology offers a less intrusive (Rundquist et al. 2001;Lane et al. 2014Lane et al. , 2015 and more scalable approach (Rebelo et al. 2009;Adam et al. 2010;Yan et al. 2017;Moity et al. 2019) for mapping wetland plant species. Also, repeated coverage can facilitate the thorough detection of temporal changes in wetlands (Ramsey Elijah et al. 2009;Sica et al. 2016;Jia et al. 2020;Hasan et al. 2023), although the mapping capability of remote sensing technologies can be hampered by coarse spatial resolution (De Roeck et al. 2007). ...
Wetlands harbour a wide range of vital ecosystems. Hence, mapping wetlands is essential to conserving the ecosystems that depend on them. However, the physical nature of wetlands makes fieldwork difficult and potentially erroneous. This study used multispectral UAV aerial photography to map ten wetland plant species in the Fynbos Biome in the Steenbras Nature Reserve. We developed a methodology that used K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Random Forest (RF) machine learning algorithms to classify ten wetland plant species using the preselected bands and spectral indices. The study identified Normalized green red difference index (NGRDI), Red Green (RG) index, Green, Log Red Edge (LogRE), Normalized Difference Red-Edge (NDRE), Chlorophyll Index Red-Edge (CIRE), Green Ratio Vegetation Index (GRVI), Normalized Difference Water Index (NDWI), Green Normalized Difference Vegetation Index (GNDVI) and Red as pertinent bands and indices for classifying wetland plant species in the Proteaceae, Iridaceae, Restionaceae, Ericaceae, Asteraceae and Cyperaceae families. The classification had an overall accuracy of 87.4% and kappa accuracy of 0.85. Thus, the findings are pertinent to understanding the spectral characteristics of these endemic species. The study demonstrates the potential for UAV-based remote sensing of these endemic species.
... All these techniques are commonly applied for studying various LULC change patterns [14][15][16]. The aim of present study was to analyze the spatio-temporal changes occurred in last two decade (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) in Quetta watershed using the Landsat imageries. ...
The changes in land use and land cover are the most considerable factors for the decision makers, which can be precisely evaluated by using Geographic Information System (GIS) and Satellite Remote Sensing (SRS) technique. The present study was carried out in the Quetta Basin Watershed for evaluating the land use change of two decades during 1999-2009 and 2009-2018, using the Landsat imageries. The main land use types of study area include water body, agriculture, vegetation, barren and built-up land area. All these land use types were mapped for each time period. The technique of hybrid classification was used for the classification of the imageries by using ISODATA and Maximum Likelihood Algorithm (MLA) in ERDAS Imagine software 2011 v, while change detection maps of different land use land cover classes was generated by using the ArcGIS 10.2 v. The finding of study indicated that widespread and significant changes has occurred in study area during two decades in which remarkable increase occur in built up and vegetation land area, on the other hand reduction was observed in water body, barren and agriculture land area from year 1999 to 2018. During the whole study period, major land shifting was observed in agriculture to vegetation and vegetation to barren. The transformation in land cover use may pose severe risk to watershed resources. Therefore, proper management, effective conservation plans and strategies are necessary to designed for the protection of this natural resource.
... Donc, elle nécessite l'utilisation d'une masse de données diversifiée. La diversité des données offerte par la télédétection présente un potentiel important pour l'étude, la caractérisation et le suivi des écosystèmes constituant le paysage à différentes échelles (Rundquist et al, 2001 ;Bamba et al, 2008). Elle permet le suivi du changement de l'occupation du sol et l'étude des phénomènes dynamiques qui affectent ces écosystèmes (Mas, 2000). ...
L’étude de la dynamique de l’occupation du sol est d’une importance primordiale pour le suivi et la gestion d’un territoire. C’est dans ce contexte que cette étude a été menée dans le but d’évaluer l’impact de l’évolution de l’urbain sur la diversité de l’occupation du sol dans le grand Sfax au centre-Est de la Tunisie.L’approche méthodologique adoptée est basée sur le traitement des images satellites. Trois images satellitaires Landsat, téléchargées du site USGS provenant respectivement des capteurs L5 MSS/TM, L7 ETM+ et L8 OLI/TIRS et datant de 1990, 2003 et 2015, ont été utilisées et traitées par la classification supervisée pour la production des cartes multi-dates illustrant la dynamique spatio-temporelle. Huit classes ont été définies: l’urbain, les champs d’olivier, les jardins, les parcelles irriguées, les marais saumâtres saisonniers (sebkha), les salines et le phosphogypse. L’analyse des résultats indiquent une croissance des superficies bâties de 18% et une régression de 20 % de terres agricoles entre 1900 et 2015. Pour chaque année étudiée, une matrice de confusion a été conçue afin d’évaluer la classification. L’indice de succès global ‘SG’, de 1990 à 2015, il augmente de 78% à 96%, cela est peut-être expliqué par l’amélioration de la qualité de l’image satellitaire liée à l’évolution des capteurs Landsat. La précision des classifications se jugent bonne et acceptable. Elle a été évalué quantitativement à l’aide des indices de kappa qui sont >0,7 à travers la matrice de confusion.
... Wetland studies with traditional approaches that were based on a field survey of wetland ecosystems were time-and cost-consuming. Photogrammetry (Cox, 1992) and satellite imaging (Rundquist et al., 2001) gradually replaced these traditional approaches, and use of earth observation approaches in different applications, such as water level monitoring (Wdowinski et al., 2008), classification (Chopra et al., 2001), and change detection (Munyati, 2000), has been well illustrated. ...
Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth’s surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologies capturing high-resolution and multi-temporal views of landscapes are incredibly beneficial in wetland mapping compared to traditional methods. Taking advantage of the Google Earth Engine’s computational power and multi-source earth observation data from Sentinel-1 multi-spectral sensor and Sentinel-2 radar, we generated a 10 m nationwide wetlands inventory map for Iran. The whole country is mapped using an object-based image processing framework, containing SNIC superpixel segmentation and a Random Forest classifier that was performed for four different ecological zones of Iran separately. Reference data was provided by different sources and through both field and office-based methods. Almost 70% of this data was used for the training stage and the other 30% for evaluation. The whole map overall accuracy was 96.39% and the producer’s accuracy for wetland classes ranged from nearly 65 to 99%. It is estimated that 22,384 km² of Iran are covered with water bodies and wetland classes, and emergent and shrub-dominated are the most common wetland classes in Iran. Considering the water crisis that has been started in Iran, the resulting ever-demanding map of Iranian wetland sites offers remarkable information about wetland boundaries and spatial distribution of wetland species, and therefore it is helpful for both governmental and commercial sectors.
... Leurs études supposent le recours à des méthodes diversifiées et adaptées (PNRZH, 2005) dont la plus utilisée est la télédétection. Elle offre une gamme de données, des photographies aériennes et des images satellitaires, de plus en plus couramment utilisées pour identifier, délimiter, caractériser les zones humides à différentes échelles en allant du paysage, bassin versant, à la région (Rundquist et al, 2001). Des photographies aériennes panchromatiques, couleur et infra-rouge couleur ont été utilisées comme documents de base dans plusieurs études pour identifier les zones humides (Anderson et Wobber, 1973 ;Cowardin et Myers, 1974 ;Provencher et Dubois, 2007). ...
ABSRACT In this paper, we present the different steps to study the wetlands of the big Sfax, from identification to caracteriziation. Identification consists of locating and delimiting wetlands. The approch used is the interpretation assisted with computer of panchromatic aerial photographs. The wetlands identified are classified according to the Ramsar typology into three categories: marine wetlands, continental welnads and artificial wetlands. Ground prospecting was then used to caracterize the wetlands through searching for traces of hydromorphy and halophytic vegetation. Soils and vegetation develop in a specific way and persist after periods of waterlogging and land developpement. They constitue reliable criteria for diagnostic, identification and delimitation of wetlnads. 46 RESUME Dans cet article nous présentons les différentes étapes suivies dans l'étude des zones humides du grand Sfax, de l'identification à la caractérisation. L'identification consiste à localiser et à délimiter les zones humides. La démarche employée est la photo-interprétation assistée par ordinateur des photos aériennes panchromatiques. Les zones humides identifiées sont classées selon la typologie Ramsar en trois catégories : zones humides marines, zones humides continentales et zones humides artificielles. Puis, la prospection du terrain a été utilisée pour la caractérisation des zones moyennant la recherche des traces d'hydromorphie et de la végétation halophyte. Les sols et la végétation se développent de manière spécifique et persistent après les périodes d'engorgement en eau et d'aménagement du terrain. Ils constituent des critères fiables de diagnostic, d'identification et de délimitation des zones humides.
... Wetland mapping using remote sensing has engaged the scientific community for several decades (Guo et al., 2017;Rundquist et al., 2001), but many challenges still remain despite great advances in sensor capabilities and methods for EO data processing and interpretation (L. A. Gallant, 2015;Ozesmi & Bauer, 2002). One of the main inherent challenges is that wetlands often are dynamic with potentially large fluctuations in extent and water levels through time, which results in constantly changing spectral signatures between seasons and years. ...
Mapping wetland types in northern‐latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel‐1 radar data, Sentinel‐2 optical data and terrain derivatives derived from Copernicus digital elevation model to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regional classification models constructed from region‐specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel‐1, Sentinel‐2 and terrain derivatives). Overall accuracy for the individual sites ranged between 79.7% and 90.3%. Class specific accuracies for the peatland types were also high overall but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that regional classification models consistently outperformed the global classification model by producing significantly higher classification accuracies for all five sites. This suggests for progress in identifying effective approaches for continental scale peatland mapping to improve scaling of for example, hydrological‐ and greenhouse gas‐related processes in Earth system models.
... As a result of the variety of natural and man-made processes, the land is always transforming. Understanding the evolution of land use/cover systems and studying spatio-temporal patterns of intra-and inter-land use/cover systems are still important goals in land use studies [31]. The method of detecting changes in the process of an object or situation by observing it at different times is known as land use/land cover change detection [32][33][34]. ...
One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area.
... Thus, satellite images with their unique characteristics including multi-temporal, multispectral, repeated and synoptic view (Ozesmi and Bauer, 2002) can be used for wetland mapping and change detection (Han et al., 2015) with an improved result. With the rapid development of remote sensing technology, abundant remote sensing data are available for wetland mapping, including the freely available Landsat images, and the moderate resolution imaging spectroradiometer data (Rundquist et al., 2001). Recent technological advances in space observation have facilitated the collection of very detailed images that can characterize large areas of wetlands with high spatial resolution. ...
Satellite-based analysis of wetland is an effective alternative to the costly ground-based surveys. The aim of this study is to conduct a spatial and temporal investigation of wetland of Southsouth zone of Nigeria using Time-series satellite datasets. For this study, MODIS–NDVI datasets covering Southsouth zone of Nigeria were acquired for 2000, 2010, and 2020. This was followed by image reprojection to WGS 84 and clipping of the study area. Also, the clipped images were classified and change detection was conducted. The result is a map and statistics wetlandof Southsouth zone of Nigeria.
... Recently, several changes in detection techniques and algorithms have been developed and reviewed for their advantages and disadvantages. Of these techniques, including unsupervised classifications or clustering, supervised classification, principal component analysis, hybrid classification and fuzzy classification, are often the most widely applied land-cover classification techniques (Zhang et al., 2000;Rundquist et al., 2001;Lu et al., 2004;Muke and Haile, 2018;Mohamed and El-Raey, 2018). ...
Coastal dune landscapes are subject to morphological and ecological changes. In many parts of the world, coastal dunes are under severe pressure. The present study illustrates an integrated remote sensing and Geographical Information System (GIS) approach, i.e., geospatial techniques for assessing land-cover dynamics in Zouaraa coastal dunes, located in northwest Tunisia. As a main result, the analysis of the situation in the past six decades indicates that the dune area showed a decreasing trend with up to 31% (i.e., 6198 ha) in favour of forest area, which has increased by up to 6485 ha. The geo-spatial analysis revealed that restoration works have positively contributed to stabilize coastal dune systems with a substantial increase in vegetation cover. An increase in drought frequency and intensity was detected during the 1952-2017 period using the SPEI index, which enhanced the vegetation activity and growth in the study area. The SPEI significantly correlated with vegetation greenness on the 12- and 24-months’ time scales. The croplands, water and buildings in the study area have increased respectively by 6% (i.e., 1256 ha), 13% (i.e., 3073 ha) and 3% (i.e., 719 ha). In contrast, land cover like shrub and bare soil has decreased respectively by 13% (i.e., 3073 ha) and 2% (i.e., 1831 ha) during the same period. Furthermore, this study highlights the importance of the revegetation techniques undertaken for conserving coastal dune systems. The findings of this study allow land-use planning decision makers to manage and improve situations in similar coastal regions.
... Wetlands are a valuable natural resource of considerable scienti c interest for being associated with biological diversity, functions and processes important to ecosystems (Rundquist et al. 2001). These habitats are among the most threatened in the world, despite their recognized ecological value and the institution of several international treaties that recommend their regular inventory and efforts for their protection (Junk et al 2014). ...
Veredas are wetlands of relevant ecological and social value that may be closely related to the maintenance of the water regime of the springs. Remotely Piloted Aircraft Systems (RPAS) have proved to be great allies in the space-time monitoring of wetlands. This study evaluates the effectiveness of multispectral sensors attached to an RPAS to discriminate habitats from paths through the Object-Based Image Analysis (OBIA) approach. Multispectral camera overflights were performed on September 25, 2020 (dry) and January 28, 2021 (wet). Radiometrically corrected orthomosaics were generated with five spectral bands. Multiscale segmentations were applied, and later the classification by the OBIA approach through the classifier of the nearest neighbor, the results were post-processed by applying the algorithm of a class assignment. The classification separated the objects into 14 and 12 classes with an overall accuracy of 92.21% and 88.01% (kappa 0.92 and 0.87), for September and January, respectively. Among these, are the phytophysiognomies of Cerrado stricto sensu (surrounding) and Gallery forest (centralized), in addition to eight classes of habitats in the vereda. The multispectral sensor was sensitive to differentiate these habitats in the vereda and the occurrence of areas covered by the pteridophyte Dicranopteris flexuosa , its distribution, and physiological stages. The classification of two seasonal seasons made it possible to characterize the behavior of habitats according to water availability. The multispectral sensor on board the RPAS is a powerful tool to determine the diagnosis and management of wetlands, contributing to the establishment of public policies for the conservation of vereda environments.
... The use of remote sensing in wetland research has been widespread, and numerous studies and reviews have examined this method [41,[43][44][45][46][47][48][49][50][51]. Rundquist et al. [52] examined the issues of wetland identification, classification, change detection, and biomass and discussed them all in detail in a review of wetland remote sensing. Keeping this in view, the present study was conducted for the analysis and estimation of India's monthly rainfall, temperature, and vegetation pattern over 41 years. ...
Methane is produced by various natural processes that directly or indirectly contribute to the entire Earth’s methane budget. If the Earth’s overall methane budget becomes imbalanced, CH4 has an impact on climate change. Wetlands, rice fields, animals, factories, and fossil fuels are major sources of methane emissions. Among all the resources, wetlands and rice fields are more prominent factors in methane emission, dependent on the water table, temperature, and vegetation. Our study employed the GIS remote sensing technique to analyze methane emissions from 2003 to 2021 in the northern part of India, East Uttar Pradesh and Bihar, and the northeast region of India that is Assam. We also predicted the water table, temperature, and vegetation as raw materials for methane creation. Water table, temperature, and vegetation are essential for wetland ecosystem life, particularly for methanogenic organisms; however, the water table and temperature are critical for rice plant growth and development. With the help of GIS remote sensing, India’s monthly rainfall pattern and the water table, vegetation, and temperature pattern over 41 years were analyzed. Our key findings highlight the importance of GIS remote-sensing-based monitoring of methane gas emissions from wetlands and rice fields for their management.
... While field surveys provide the most accurate wetland identification, they are usually costly, seasonal, time consuming, and spatially limited. Efforts to develop remote sensingbased approaches are ongoing among interdisciplinary scientific communities (4). ...
Wetlands and channels provide significant ecological and societal services. Mapping their locations and types at high resolution remains a challenge for scientific communities and regulatory agencies. In the U.S.A., the National Wetland Inventory (NWI) provides the best nationwide wetland maps, but the NWI for some locations has not been updated for up to four decades and includes omission errors. To address these problems, we developed a deep learning framework for identifying wetlands and channels from lidar point clouds and 1 m aerial images. Both deep learning and terrain analysis were applied to classify wetlands and identify channels. The deep learning classifier was a convolutional neural network that utilized both image color information and lidar-derived canopy height. When tested on a 94 km ² Ohio watershed, the method achieved a classification accuracy of 85.6%. Compared with the NWI maps, the results included 76% more forested wetlands by area and 168% more channels by length. It was also found that only leaf-off images were useful for detecting forested wetlands and typical commission errors (i.e., false positives) were attributed to tree shadows. The study demonstrates the advantages of combining lidar structural and aerial spectral information over the use of each alone and exemplifies the utility of deep learning as an effective means to leverage open-source data for high resolution mapping of wetlands.
... Climatic changes and human activities are significant for managing water resources for sustainable socioeconomic development [1]. At the same time, wetlands are a valuable natural resource for groundwater recharge and flood control [2]. However, the reduction and destabilization of wetlands pose a significant threat to biodiversity conservation and the ecological environment [3]. ...
It is essential to monitor the changes in wetlands on the earth's surface to understand the impact of global climate changes and human activities on water resources. Remote Sensing (RS) techniques are beneficial in monitoring and mapping the dynamics of changes in wetlands. Although RS techniques seem practical in monitoring water surfaces, traditional RS methods require a high amount of workforce, software, hardware, and especially data storage needs. For this purpose, in this study, the change in water surface area of Marmara Lake, located within the borders of Manisa Province, between 2013-2022, was investigated with Google Earth Engine (GEE). The change in the water surface area was analyzed for four different seasons using Landsat-8 (OLI) images. The Normalized Difference Water Index (NDWI) was used in the study. The study is divided into four different classes according to the land use conditions of the region: vegetation, water surface, bare lands, and agricultural lands.Support Vector Machines (SVM),a machine learning algorithm, were used for classification. According to the analyzes made, it has been determined that a wetland of 3,975.78 ha has dried up in the lake surface area in the last eight years. This calculated area corresponds to an area of 75.04%, according to the average of all areas.
... Published resources about vegetation cover in Schiermonnikoog and its ecology [31,[42][43][44]. ...
In complex classification tasks, such as the classification of heterogeneous vegetation covers, the high similarity between classes can confuse the classification algorithm when assigning the correct class labels to unlabelled samples. To overcome this problem, this study aimed to develop a classification method by integrating graph-based semi-supervised learning (SSL) and an expert system (ES). The proposed method was applied to vegetation cover classification in a wetland in the Netherlands using Sentinel-2 and RapidEye imagery. Our method consisted of three main steps: object-based image analysis (OBIA), integration of SSL and an ES (SSLES), and finally, random forest classification. The generated image objects and the related features were used to construct the graph in SSL. Then, an independently developed and trained ES was used in the labelling stage of SSL to reduce the uncertainty of the process, before the final classification. Different spectral band combinations of Sentinel-2 were then considered to improve the vegetation classification. Our results show that integrating SSL and an ES can result in significantly higher classification accuracy (83.6%) compared to a supervised classifier (64.9%), SSL alone (71.8%), and ES alone (69.5%). Moreover, utilisation of all Sentinel-2 red-edge spectral band combinations yielded the highest classification accuracy (overall accuracy of 83.6% with SSLES) compared to the inclusion of other band combinations. The results of this study indicate that the utilisation of an ES in the labelling process of SSL improves the reliability of the process and provides robust performance for the classification of vegetation cover.
... Seasonal and annual variations in hydrology, such as high precipitation years that increase surface water extent and water depth, drive plant mortality and conversely drought facilitates establishment of shrubs and trees in palustrine systems [62,63]. Subsequently, the success of remote-sensing approaches to palustrine wetland mapping rests on high spatial resolution data available over time and accurate classification approaches that take advantage of spectral, textural, geophysical, and geomorphological characteristics of wetlands [64][65][66][67]. Similar to the issues raised with mapping wetlands, the accuracies of young forest and shrubland classes were relatively low. ...
Many remote sensing studies have individually addressed afforestation, forest disturbance and forest regeneration, and considered land use history. However, no single study has simultaneously addressed all of these components that collectively constitute successional stages and pathways of young forest and shrubland at large spatial extents. Our goal was to develop a multi-source, object-based approach that utilized the strengths of Landsat (large spatial extent with good temporal coverage), LiDAR (vegetation height and vertical structure), and aerial imagery (high resolution) to map young forest and shrubland vegetation in a temperate forest. Further, we defined young forest and shrubland vegetation types in terms of vegetation height and structure, to better distinguish them in remote sensing for ecological studies. The multi-source, object-based approach provided an area-adjusted estimate of 42,945 ha of young forest and shrubland vegetation in Connecticut with overall map accuracy of 88.2% (95% CI 2.3%), of which 20,953 ha occurred in complexes ≥2 ha in size. Young forest and shrubland vegetation constituted 3.3% of Connecticut’s total land cover and 6.3% of forest cover as of 2018. Although the 2018 estimates are consistent with those of the past 20 years, concerted efforts are needed to restore, maintain, or manage young forest and shrubland vegetation in Connecticut.
... Remotely sensed imagery provides a practical, economical approach to monitor and measure biogeophysical factors. Hence, it is efficient for large-area monitoring [19][20][21]. Satellite imagery from multispectral and hyperspectral sensors (Landsat, Sentinel, SPOT, MODIS, and HyMap) as well as LiDAR and RADAR data have been extensively used for land cover mapping at various scales [17,[22][23][24][25][26][27][28][29] Fine-scale mapping is critical to locate and map endangered habitats, particularly with escalating global climate change impacts. Hence, high spatial resolution imagery, such as that of the National Agriculture Imagery Program (NAIP), is important. ...
Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral enhancement techniques such as principal component analysis (PCA) and independent component analysis (ICA) were integrated. The study evaluated the importance of biogeophysical parameters such as topography, soil characteristics and gray level co-occurrence matrix (GLCM) textures, together with the normalized difference vegetation index (NDVI) and NAIP water index (WINAIP) spectral indices, using the joint mutual information maximization (JMIM) feature selection method and various machine learning algorithms (MLAs) to accurately map the natural habitat communities. Individual habitat community classification user’s accuracies (UA) ranged from 60 to 100%. An overall accuracy (OA) of 79.45% (kappa coefficient (k): 0.75) with random forest (RF) and an OA of 75.85% (k: 0.70) with support vector machine (SVM) were achieved. The analysis showed that the use of the biogeophysical ancillary data layers was critical to improve interclass separation and classification accuracy. Utilizing widely available free high-resolution NAIP imagery coupled with an integrated classification approach using MLAs, fine-scale natural habitat communities were successfully delineated in a spatially and spectrally complex Laurentian Mixed Forest environment.
... The research capacity for detecting and characterising peatland ecosystems, and monitoring their dynamics, is often hindered by limited access to the site, risk of disruption of sensitive habitats and species, and a high surface complexity due to varied topography, hydrological properties and vegetation [8][9][10]. Remote sensing (RS) offers the benefit of capturing extensive research areas featuring the same state of plant phenology or flooding and a greater repeatability of data collection compared to field studies [11][12][13][14]. Furthermore, the high spectral sensitivity of sensors enables the observation of detailed changes in the composition of the peatland surface. ...
In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods in studies on peatlands and investigates publications from the period 2010–2021. Based on fifty-nine case studies from different climatic zones (from subarctic to subtropical), we can indicate an increase in the use of RS methods in peatland research during the last decade, which is likely a result of the greater availability of new remote sensing data sets (Sentinel 1 and 2; Landsat 8; SPOT 6 and 7) paired with the rapid development of open-source software (ESA SNAP; QGIS and SAGA GIS). In the studied works, satellite data analyses typically encompassed the following elements: land classification/identification of peatlands, changes in water conditions in peatlands, monitoring of peatland state, peatland vegetation mapping, Gross Primary Productivity (GPP), and the estimation of carbon resources in peatlands. The most frequently employed research methods, on the other hand, included: vegetation indices, soil moisture indices, water indices, supervised classification and machine learning. Remote sensing data combined with field research is deemed helpful for peatland monitoring and multi-proxy studies, and they may offer new perspectives on research at a regional level.
... Microwave remote sensing is not typically affected by clouds or vegetation. Many researchers report that this sensing method is the best means of monitoring flood disasters [9,10]. However, the spatial or temporal resolution of the data provided by existing microwave remote sensing satellites is limited [11]. ...
On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.
... Therefore, their mapping and monitoring have become increasingly important with the development of remote sensing technologies [6]. Indeed, remote sensing offers a range of diverse data, aerial photographs and satellite imagery, which are increasingly used to study wetlands at different scales [10]. Panchromatic, color and infrared aerial photographs are used as background materials in several studies to identify, delineate and map wetlands ( [7], [11], [12]). ...
ARTICLE INFO ABSTRACT Article history: Wetlands are characterized by temporary alterations in their structure and composition, and by multifunctionality. Therefore, identifying the boundaries of these zones is essential for appropriate characterization. In this context, this applied research work focuses on the wetlands of Large Sfax in the eastern center of Tunisia. The adopted methodology is based on a combined approach based on multivariate analysis and multi-dates analysis for the identification and the spatial delimitation of wetlands in the study area. The radiometric indexes of humidity NDWI, vegetation NDVI and brightness IB were calculated for the years 2003 and 2015 by using satellite imagery coming from Landsat ETM+7 and Landsat OLI 8. The classification maps of the calculated indexes enabled the identification and spectral delimitation of the wetlands of the study area. The multi-dates analysis was based on the visual interpretation of the panchromatic aerial photographs and the Google Earth snippets for the update of the results. This allowed the spatial delineation and the monitoring of marine, inland, and artificial wetlands in the study area. The importance of using the combined approach is that it allows a better characterization of wetlands.
... Unmanned aerial vehicle-based HTPPs are able to assess a large number of plots within a short time to minimize the effect of varied environmental conditions such as cloud cover, wind speed, and solar radiation (Chapman et al., 2014;Haghighattalab et al., 2016). Based on reflectance of visible/near-infrared radiation and emission of far-infrared wave by plants (Berger et al., 2010;Vadivambal & Jayas, 2011;Zia et al., 2013), remote sensing of vegetation is nondestructive and resource conservative, which allows repeated inventories and measurements of physiological characteristics to detect changes over time (Rundquist et al., 2001). When collected from UAV-HTPPs, remote sensing allows synoptic visualization, mapping, assessment, and quantification of physiological characteristics of vegetation like biomass and relative stress or vigor (Yang et al., 2017). ...
Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high‐throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle‐based high‐throughput phenotyping platforms (UAV‐HTPPs) provide novel opportunities for large‐scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV‐based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV‐HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized‐splines (P‐splines) model was used to obtain genotype‐specific curve parameters. Genome‐wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P‐splines models revealed the dynamic change of genetic effects, indicating the role of gene–environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra‐high spatial resolution multispectral imagery, as that acquired using a UAV‐based remote sensing, for genetic dissection of NDVI.
... Satellite remote sensing and GIS are common methods for mapping and detection of land use/cover and its changes [2, [5][6][7][8], which can provide timely and visual geospatial information [9][10][11][12][13][14]. Due to the wide availability of satellite images, especially medium spatial resolution satellite images such as Landsat images, using satellite images to detect the spatial and temporal variation of land use/cover has been a subject undergoing intense study in remote sensing and GIS [12]. Many techniques have been developed to classify land use/cover classes from satellite images, including pixel-based classification methods and object-based classification methods [5,[15][16][17]. The advantages and disadvantages of those techniques have been discussed in many studies [18,19]. ...
Land use/land cover maps derived from remotely sensed imagery are often insufficient in quality for some quantitative application purposes due to a variety of reasons such as spectral confusion. Although object-based classification has some advantages over pixel-based classification in identifying relatively homogeneous land use/cover areas from medium resolution remotely sensed images, the classification accuracy is usually still relatively low. In this study, we aimed to test whether the recently proposed Markov chain random field (MCRF) post-classification method, that is, the spectral similarity-enhanced MCRF co-simulation (SS-coMCRF) model, can effectively improve object-based land use/cover classifications on different landscapes. Four study areas (Cixi, Yinchuan and Maanshan in China and Hartford in USA) with different landscapes and classification schemes were chosen for case studies. Expert-interpreted sample data (0.087% to 0.258% of total pixels) were obtained for each study area from the original Landsat images used in object-based pre-classification and other sources (e.g., Google satellite imagery). Post-classification results showed that the overall classification accuracies of the four cases were obviously improved over the corresponding pre-classification results by 14.1% for Cixi, 5% for Yinchuan, 11.8% for Maanshan and 5.6% for Hartford, respectively. At the meantime, SS-coMCRF also reduced the noise and minor patches contained in pre-classifications. This means that the Markov chain geostatistical post-classification method is capable of improving the accuracy and quality of object-based land use/cover classification from medium resolution remotely sensed imagery in various landscape situations.
... Including all campuses, U-M represents the fourth-largest public university in the USA. As the Internal Analysis Team for the U-M President's Commission on Carbon Neutrality, we compared three methods to create basemaps from which to assess baseline carbon storage and biosequestration, namely, Method 1) unsupervised land-use land-cover (LULC) classification, allowing the computer to decide, which cover classes are presently based on pixel differentiation, Method 2) supervised LULC classification, where the user "trains" the program to differentiate classes based on pixel or segment differentiation of known areas and Method 3) supervised LULC classification with the inclusion of expert delineated wetland data, as wetlands are the critical habitat to identify and protect to support carbon storage and sequestration in our region and wetland bounds are not easily differentiated from neighboring habitat via aerial imagery (Rundquist et al., 2009). ...
Purpose
The University of Michigan (U-M) is planning its course toward carbon neutrality. A key component in U-M carbon accounting is the calculation of carbon sinks via estimation of carbon storage and biosequestration on U-M landholdings. Here, this paper aims to compare multiple remote sensing methods across U-M natural lands and urban campuses to determine the accurate and efficient protocol for land assessment and ecosystem service valuation that other institutions may scale as relevant.
Design/methodology/approach
This paper tested three remote sensing methods to determine land use and land cover (LULC), namely, unsupervised classification, supervised classification and supervised classification incorporating delineated wetlands. Using confusion matrices, this paper tested remote sensing approaches to ground-truthed data, the paper obtained via field-based vegetation surveys across a subset of U-M landholdings.
Findings
In natural areas, supervised classification incorporating delineated wetlands was the most accurate and efficient approach. In urban settings, maps incorporating institutional knowledge and campus tree surveys better estimated LULC. Using LULC and literature-based carbon data, this paper estimated that U-M lands store 1.37–3.68 million metric tons of carbon and sequester 45,000–86,000 Mt CO 2 e/yr, valued at $2.2m–$4.3m annually ($50/metric ton, social cost of carbon).
Originality/value
This paper compared methods to identify an efficient and accurate remote sensing methodology to identify LULC and estimate carbon storage, biosequestration rates and economic values of ecosystem services provided.
... Wetland is a valued resource for the groundwater recharge and flood control (Rundquist et al., 2001). The shrinkage and degradation of wetlands are major threat for the conservation of biodiversity and the ecological environment. ...
Recent initiatives using the GIS tools for mapping and analysis of wetland dynamics bring the hope for an effective wetland management. Degraded or modified wetlands are more sensitive and less resilient to the climate change. This paper focusses on a study of water bodies in Sirmaur District, Uttarakhand, India with the special focus on Renuka wetland. The Renuka wetland, a Ramsar site is affected by global warming, floods and storms and changes due to precipitation. The updated or accurate maps and spatial and temporal changes of Renuka wetland are not available. Only the details about the flora and fauna have been recorded in this region. Traditional methods in mapping and monitoring, like field survey and sampling, are usually labour intensive, time consuming, and expensive and often fail to detect the changes over the regions of wetland zones. The present study is carried using the online interactive cloud-based planetary processing open source platform, Google Earth Engine (GEE). The wetland maps considered are Landsat 7 and 8 images with cloud cover less than 30% during the summer (May) and winter (September) seasons. The suitability index is also calculated based on the area and perimeter of the water body in study region to demonstrate the characteristic change of Renuka wetland over the selected time period. The missing data of NDVI and NDWI is fitted using the harmonic time series and the resultant map using supervised classification shows a high overall accuracy of 95% with a reasonable Kappa coefficient of 0.8. Using the Global Water Occurrence Explorer, the water changes intensity and the seasonality water change over the wetland for the period of 1984 to 2019 are studied. The results showed the significant insight into the changes over the study region in terms of pixel range with the presence of water and the change in locations in terms of seasonality and persistence.
... Satellite remote sensing detecting methods have many advantages in providing multiscale, multi-spectral and multi-temporal imagery for mapping wetland dynamics compared with the traditional field survey [5]. For example, multi-spectral sensors (Landsat multispectral scanner (MSS), Landsat-5 thematic mapper (TM), Landsat-7 enhanced thematic mapper plus (ETM+), Landsat-8 operational land imager (OLI) and SPOT's high resolution visible range instruments (HRV)) have been widely used to discriminate wetlands from the other land cover types at various scales [6][7][8]. ...
In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation.
... Some studies on wetland database and change have been done using the remote sensing and GIS technique because of the ability for wetland identification, classification, change detection and biomass identification (Rundquist et al., 2001;Guo et.al, 2017;Kumar et al, 2013). The multi-dated nature of remotely sensed images allows for monitoring the dynamic features of landscape environments and thus provides a medium for detecting major landcover changes and quantification of the observed rate of change (Kumar et al., 2013) The Convention on Wetland (Ramsar Convention; an intergovernmental treaty that provides the framework for national action and international cooperation for the conservation and sustainable use of wetlands and their resources) is the oldest multilateral international conservation convention and the only one dealing with wetlands and has resulted in the protection of over 200 million ha of wetland globally covering over 2,000 sites, 11 of which are in Nigeria. ...
Wetlands are essential components of the ecosystem and play significant roles as habitats and are reservoirs of biodiversity. This paper was an attempt to inferentially study the degradation and devaluation of marshland in a rapidly developing coastal area like the University of Lagos, Akoka Campus. Object based classification of high-resolution imagery and aerial photography of the Akoka area was used to determine the degradation and of the marshland and wetland in the study between 1983 and 2015. The result showed a significant change in the natural marshland from the 1980's till date in response to the extensive alterations of the campus landscape to meet with the rapid population increase. The result is the degradation of marshland which serves as habitat to numerous organisms that are vital to the ecosystem of the area. The study recommends the preservation of the marshland habitat by the promulgation and implementation of legislation and policies that allows for protection of selected wetland area against urbanisation.
... These distinct phenological stages provide an opportunity to strategically employ satellite imagery from specific seasons to detect wildrice and differentiate it from other aquatic vegetation. Earlier attempts to map wildrice utilized in-situ data collection and have improved throughout the past 30 years by incorporating high-resolution aerial imagery [11,12]. Recent studies have mapped rice (Oryza L.) paddies in Asia using the suite of Landsat satellites [13][14][15], but were challenged by nearshore canopy cover, species differentiation, and cloud cover [16]. ...
Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during the last century drives the demand for new and innovative techniques to support monitoring of this culturally and ecologically significant crop wild relative. We trained three wildrice detection models in R and Google Earth Engine using data from annual aquatic vegetation surveys in northern Minnesota. Three different training datasets, varying in the definition of wildrice presence, were combined with Landsat 8 Operational Land Imager (OLI) and Sentinel-1 C-band synthetic aperture radar (SAR) imagery to map wildrice in 2015 using random forests. Spectral predictors were derived from phenologically important time periods of emergence (June-July) and peak harvest (August-September). The range of the Vertical Vertical (VV) polarization between the two time periods was consistently the top predictor. Model outputs were evaluated using both point and area-based validation (polygon). While all models performed well in the point validation with percent correctly classified ranging from 83.8% to 91.1%, we found polygon validation necessary to comprehensively assess wildrice detection accuracy. Our practical approach highlights a variety of applications that can be applied to guide field excursions and estimate the extent of occurrence at landscape scales. Further testing and validation of the methods we present may support multiyear monitoring which is foundational for the preservation of wildrice for future generations.
... Numerous effective methods and advanced classifiers have been applied to improve the performance of land use and land cover classification that is based on moderate resolution data. Researchers have used various methods to incorporate Landsat data into land-use change analyses Ozesmi and Bauer (2002); Lu et al. (2004); Rundquist et al. (2001); Zhang et al. (2000). The complexity of the landscape, the selected remote sensing data, image processing, and classification methods, make it difficult to obtain reliable and accurate land use and land cover information Manandhar et al. (2009). ...
The land use and land cover map plays a significant role in agricultural, water resources planning, management, and monitoring programs at regional and national levels and is an input to various hydrological models. Land use and land cover maps prepared using satellite remote sensing techniques in conjunction with landform-soil-vegetation relationships and ground truth are popular for locating suitable sites for the construction of water harvesting structures, soil and water conservation measures, runoff computations, irrigation planning and agricultural management, analyzing socio-ecological concerns, flood controlling, and overall watershed management. Here we use a novel approach to analyze Sentinel–2 multispectral satellite data using traditional and principal component analysis based approaches to evaluate the effectiveness of maximum likelihood estimation, random forest tree, and support vector machine classifiers to improve land use and land cover categorization for Soil Conservation Service Curve Number model. Additionally, we use stratified random sampling to evaluate the accuracies of resulted land use and land cover maps in terms of kappa coefficient, overall accuracy, producer's accuracy, and user's accuracy. The classifiers were used for classifying the data into seven major land use and land cover classes namely water, built-up, mixed forest, cultivated land, barren land, fallow land with vertisols dominance, and fallow land with inceptisols dominance for the Vishwamitri watershed. We find that principal component analysis with support vector machine is able to produce highly accurate land use and land cover classified maps. Principal component analysis extracts the useful spectral information by compressing redundant data embedded in each spectral channel. The study highlights the use of principal component analysis with support vector machine classifier to improve land use and land cover classification from which policymakers can make better decisions and extract basic information for policy amendments.
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... Satellite image has been used so far to classify and map land cover and land use changes with different techniques and data sets. Unsupervised and supervised approaches are the most commonly adopted for satellite images classification (Butt, Shabbir, Ahmad, & Aziz, 2015;Lu, Mausel, Brondizio, & Moran, 2004;Rundquist, Narumalani, & Narayanan, 2001;Zhang, Zhang, & Zhang, 2000). ...
Land disputes are considered both key sources and perpetuating factors of conflict in the eastern Democratic Republic of the Congo (DRC). Existing literature demonstrates that remote sensing (RS) is a useful tool for systematically monitor the spatial-temporal land use/land cover dynamics in many regions of the world. For this reason, in this paper we propose a methodology for the integration of different sources of information, namely satellite imagery and census information, in order to set up a Spatial Decision Support System aimed at Multi-Criteria Evaluation of potential pilot sites for agricultural development and refugees resettlement
... Satellite image has been used so far to classify and map land cover and land use changes with different techniques and data sets. Unsupervised and supervised approaches are the most commonly adopted for satellite images classification (Butt, Shabbir, Ahmad, & Aziz, 2015;Lu, Mausel, Brondizio, & Moran, 2004;Rundquist, Narumalani, & Narayanan, 2001;Zhang, Zhang, & Zhang, 2000). ...
... Scientists and Resource managers are therefore interested in deltas using such data, because this method provides continuous data for desired extents. Several studies represented that examination of medium resolution satellite data (such as Landsat TM, SPOT) can successfully provide the ecologic conditions of deltas (Zhang et al., 2011;Ghioca-Robrect et al., 2008;Gilmore et al., 2008;Hardisky et al., 1986;Lunetta and Barlogh, 1999;Rundquist et al., 2001;Tsai et al., 2007;Silva et al., 2008;Adam et al., 2010;De Roeck et al., 2008;Mishra et al., 2006). Nevertheless, over heterogeneous or non-uniform delta areas, remote sensing techniques are delimiting to only rely on common classification methods (supervised and unsupervised) to collect information about land use/cover changes (Ullah et al., 2000;Özesmi and Bauer, 2002;Artigas and Yang, 2006). ...
... Wetlands provide essential ecological services, including filtering and purifying water, preventing flooding, protecting shorelines, controlling erosion, and storing carbon produced by human activities (Barbier et al., 1988;Rundquist et al., 2001;Nyarko et al., 2015). Wetlands are also highly productive environments in terms of both land and water habitats for various plants and animals . ...
Wetlands provide many services to the environment and humans. They play a pivotal role in water quality, climate change, as well as carbon and hydrological cycles. Wetlands are environmental health indicators because of their contributions to plant and animal habitats. While a large portion of Newfoundland and Labrador (NL) is covered by wetlands, no significant efforts had been conducted to identify and monitor these valuable environments when I initiated this project. At that time, there were only two small areas in NL that had been classified using basic Remote Sensing (RS) methods with low accuracies. There was an immediate need to develop new methods for conserving and managing these vital resources using up-to-date maps of wetland distributions. In this thesis, object- and pixel-based classification methods were compared to show the high potential of the former method when medium or high spatial resolution imagery were used to classify wetlands. The maps produced using several classification algorithms were also compared to select the optimum classifier for future experiments. Moreover, a novel Multiple Classifier System (MCS), which combined several algorithms, was proposed to increase the classification accuracy of complex and similar land covers, such as wetlands. Landsat-8 images captured in different months were also investigated to select the time, for which wetlands had the highest separability using the Random Forest (RF) algorithm. Additionally, various spectral, polarimetric, texture, and ratio features extracted from multi-source optical and Synthetic Aperture Radar (SAR) data were assessed to select the most effective features for discriminating wetland classes. The methods developed during this dissertation were validated in five study areas to show their effectiveness. Finally, in collaboration with a team, a website (http://nlwetlands.ca/) and a software package were developed (named the Advanced Remote Sensing Lab (ARSeL)) to automatically preprocess optical/SAR data and classify wetlands using advanced algorithms. In summary, the outputs of this work are promising and can be incorporated into future studies related to wetlands. The province can also benefit from the results in many ways.
... The third key approach to the analysis of environmental condition is compiling the water balance and the balance of chemical composition in a river water [8][9]. The fourth approach is based on an ecological assessment of areas, built on the analysis of remote sensing data [10][11]. The fifth approach to the assessment of the ecological state of a territory is to evaluate the key components of river runoffwater and chemical components [12][13]. ...
This work is devoted to the environmental assessment and zoning of the Chaya River basin in Western Siberia using the data of mire water chemical composition and changes in the water level. Investigations showed that the features of the environmental state of the basin are largely determined by the total bogging of the catchment area, as well as by the spread of mires and ridge-hollow-pool complexes and swamp landscapes. Mire waters are characterized by low mineralization. The inflow of low-mineralized water from vast waterlogged interfluvial spaces reduces the flow of ions, enhancing total iron and organic matter flow. In addition, widespread swamp and ridge-hollow-pool bog complexes significantly reduce the intensity of the water exchange of the area, but significantly increase the ecosystem's stability in terms of anthropogenic stress. In general, high waterlogging of the southern taiga subzone of Western Siberia determines the stability of the hydrological catchment areas in the conditions of the observed climatic changes and the increasing anthropogenic influence.
... This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality. 2 of 28 resolution, and very high resolution remotely sensed imagery [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Some of the most widely used maps have been created by expert photo-interpreters using high spatial resolution imagery [1,10]. ...
Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality.
... However, the global extent of wetlands is still poorly constrained, with estimates of maximum annual extent ranging from 0.54 to 21.26 × 10 6 km 2 (Hu et al., 2017;Melton et al., 2013). Particularly large uncertainties in the wetland extent have been estimated in northern latitude areas (> 60°N) where peatland is Satellite remote sensing is arguably the only feasible alternative for regional scale wetland mapping and monitoring since it enables repeated coverage over large and inaccessible areas at low cost (Guo et al., 2017;Rundquist et al., 2001). Much progress in remote sensingbased mapping of wetland environments has been achieved in recent years following the improvement in sensing and data processing capabilities, and the theoretical understanding of the signal-target interaction (Ozesmi and Bauer, 2002;Silva et al., 2015). ...
The spatial extent of northern peatlands remains highly uncertain in spite of rapidly developing satellite observation datasets. This is limiting progress in the understanding of fundamental biogeochemical processes, such as the global carbon (C) cycle and climate feedback effects on C fluxes.
In this study, we evaluated the capabilities of two new satellite datasets that enable regional scale mapping of peatland extent at high spatial resolution, including Sentinel-1 synthetic aperture radar (SAR) and the Arctic digital elevation model (ArcticDEM). Terrain indices and temporal features derived from these datasets provided input to Random Forest models for delineating four main land cover classes (forest, open upland, water and peatland) in an area in northern Sweden consisting of both lowland and mountainous terrain. The contribution of ArcticDEM to the classification accuracy was assessed by comparing the results with those derived when a high quality LiDAR based DEM (LiDEM) was used as alternative model input.
This study shows that multi-seasonal SAR alone can produce reasonable classification results in terms of overall accuracy (OA; 81.6%), but also that it has limitations. The inclusion of terrain indices improved classification performance substantially. OA increased to 87.5% and 90.9% when terrain indices derived from ArcticDEM and LiDEM were included, respectively. The largest increase in accuracy was achieved for the peatland class, which suggests that terrain indices do have the ability to capture the features in the geographic context that aid the discrimination of peatland from other land cover classes.
The relatively small difference in classification accuracy between LiDEM and ArcticDEM is encouraging since the latter provides circumpolar coverage. Thus, the combination of Sentinel-1 time series and terrain indices derived from ArcticDEM presents opportunities for substantially improving regional estimates of peatland extent at high latitudes.
This study marks one of the pioneering efforts to compile comprehensive information on Ramsar sites globally. It delves into the significance of wetlands and the designation of Ramsar sites across various countries, incorporating a concise exploration of the utilization of Unmanned Aerial Vehicles (UAVs) for wetland monitoring and assessment. Additionally, the study conducts a comparative evaluation of Ramsar sites, analyzing their percentage area and overall coverage worldwide. Incorporating a Scientometric analysis utilizing the Scopus database, the study features a co-occurrence map, thematic map, thematic evolution trend, and country collaboration map. Emphasizing the interconnection between wetlands and Sustainable Development Goals (SDGs), particularly SDG6 (Clean Water & Sanitation), SDG12 (Responsible Consumption & Production), SDG13 (Climate-Action), SDG14 (Life Below Water) and SDG15 (Life on Land), the study delves into associated targets and indicators. Targets such as 6.1, 6.2, 6.3, 6.4, 6.5, 6a, 6b of SDG-6, 12.1, 12.2, 12.4 of SDG-12, and 13.2, 13.3 of SDG-13 align with wetland management and conservation. Moreover, it affirms the role of wetlands in supporting targets 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14a-c of SDG-14, and 15.1, 15.5, 15.6, 15.7, 15.8, and 15.8 of SDG-15. Policies, regulations and management plans of different countries relevant for supporting and establishing relationship with SDGs are discussed in details. The study offers a detailed exploration of these targets, elucidating indicator types associated with each SDG target. By doing so, it provides valuable insights for future researchers and policymakers, underlining the indispensable contribution of wetlands to the direct and indirect fulfillment of SDGs 6,12,13,14,15 and 17.
Global Navigation Satellite System Reflectometry (GNSS-R) technology is gaining more and more attention from the scientific community due to its advantages of being all-weather, unaffected by clouds and rainfall, and low cost. The Cyclone Global Navigation Satellite System (CYGNSS), NASA’s first constellation of small satellites with space borne GNSS-R, was launched in late 2016, and CYGNSS data has now been shown to be useful for flood detection, in addition to the designed mission of inversion of sea surface wind fields. However, the quasi-random sampling of the surface by the CYGNSS constellation limits its potential for flood detection. Spatial interpolation techniques can bridge this gap and provide a complete coverage of high-resolution daily flood monitoring. In this paper we first introduce the CYGNSS surface reflectivity (SR) calculation method, secondly introduce a new spatial interpolation method (POBI) based on the interpolation of previously observed behavior and finally analyses the performance of CYGNSS high-resolution flood monitoring based on POBI using the 2022 Pakistan catastrophic floods as an example. The results show that compared with the common spatial interpolation methods, the CYGNSS observations based on the POBI method can not only obtain high-resolution flood monitoring results (daily, 3km), but also preserve the surface heterogeneity and discontinuity much better. The comparison with flood monitoring results obtained using microwave remote sensing data also demonstrates the feasibility of CYGNSS high spatial and temporal resolution flood monitoring based on POBI interpolation.
Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes.
Over the last century, direct human modification has been a major driver of coastal wetland degradation, resulting in widespread losses of wetland vegetation and a transition to open water. High-resolution satellite imagery is widely available for monitoring changes in present-day wetlands; however, understanding the rates of wetland vegetation loss over the last century depends on the use of historical panchromatic aerial photographs. In this study, we compared manual image thresholding and an automated machine learning (ML) method in detecting wetland vegetation and open water from historical panchromatic photographs in the Florida Everglades, a subtropical wetland landscape. We compared the same classes delineated in the historical photographs to 2012 multispectral satellite imagery and assessed the accuracy of detecting vegetation loss over a 72 year timescale (1940 to 2012) for a range of minimum mapping units (MMUs). Overall, classification accuracies were >95% across the historical photographs and satellite imagery, regardless of the classification method and MMUs. We detected a 2.3–2.7 ha increase in open water pixels across all change maps (overall accuracies > 95%). Our analysis demonstrated that ML classification methods can be used to delineate wetland vegetation from open water in low-quality, panchromatic aerial photographs and that a combination of images with different resolutions is compatible with change detection. The study also highlights how evaluating a range of MMUs can identify the effect of scale on detection accuracy and change class estimates as well as in determining the most relevant scale of analysis for the process of interest.
This chapter gives an overview of the types of (semi-) constructed wetlands, and how these can be applied for the management and, specifically, the retention of agricultural diffuse loads so that they do not reach inland surface waters.
To achieve an effective wetland design, a multi-faceted approach is required, which accounts for the location of the wetland in the landscape (including riparian wetlands) and the compounds subjected to removal/retention, e.g., inorganic nutrients, pesticides. Agricultural diffuse loads of nonpoint source-nature will be introduced which are critical regarding (semi-)constructed wetlands, and an outlook given to related ecological issues as ecosystem services and the phenomenon of eutrophication. This chapter does not provide a detailed technical description of the processes operating in wetlands, rather an overview of their functionality and key considerations.
The chapter is primarily a review of relevant literature (seminal papers and most recent ones) focusing on the retention of inorganic nutrients from reaching surface waters.
An overview is provided on the most important characteristics of (semi-)constructed wetlands in retaining diffuse loads from entering inland surface waters. Wetlands, and their location, is among the utmost capable tools of handling these emerging problems, thus it would be strongly preferable to preserve and protect existing wetlands rather than to let them degrade and then create artificial wetlands as a subsequent mitigation strategy.
Coastal wetlands as the potential ecosystems, provide a wide range of benefits counting from ecosystem services to livelihood opportunities. Wetlands are continuously being degraded throughout the world especially in coastal ecosystems due to intensifying pressure on these resources and changing global climatic patterns. The wetlands in Sundarban Biosphere Reserve (SBR) are unique in character as these are surrounded by numerous rivers, creaks and mangrove forest. The present work is a concerted attempt to assess the health conditions of coastal wetland ecosystem in the SBR, India. Coastal wetlands of SBR were delineated during 1989–2017. Geospatial layers of site-specific indicators were prepared and were applied to the PSR model to examine the health of the wetland ecosystem. The model was also utilized to examine the impact of anthropogenic activities on wetland ecosystem and to ascertain the relationship among ecological pressure-state-response of ecosystem. The findings revealed that area under agriculture has been transformed into aquaculture. The shortage of fresh water supply for agriculture and increased salinity transformed many croplands to seasonal wetlands throughout the SBR. The overall ecosystem health of Sundarban has been decreased during the last two decades due to anthropogenic pressure and climate change issues. The result also indicated that the ecosystem fragmentation and human interference rate are two main dominating factors for declining wetland health in SBR. The findings of the study may help in formulating policy for the management of coastal wetland ecosystem.
Methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from remotely sensed data using advanced classification algorithms through two hierarchical approaches. The data utilized included multispectral optical and thermal data (Landsat-5, and Landsat-8), radar imagery (Sentinel-1), and a digital elevation model. Goals were to determine the best way to combine imagery to classify wetlands through hierarchically based classification approaches to produce more accurate and efficient maps compared to standard classification. Algorithms used were Random Forest (RF), and Naïve Bayes. A hierarchically based RF classification methodology produced the most accurate classification result (91.94%). The hierarchically based approaches also improved classification accuracies for low-quality data, as defined through feature analysis, when compared to a nonhierarchical classifier. The hierarchical approaches also produced a significant increase in classification accuracy for the Naïve Bayes classifier versus the standard approach (∼12% increase) while not significantly increasing computation time – comparable in accuracy to the RF tests for around 20% the computational effort. Preselection of spectral bands, polarizations and other input parameters (Normalized Difference Vegetation Index, Normalized Difference Water Index, albedo, slope, etc.) using log-normal or RF variable importance analysis was very effective at identifying low-quality features and features which were of higher quality.
As critical habitats for migratory birds in the East Asia-Australasia Flyway, coastal wetlands bordering the Yellow Sea have experienced prominent losses and threats triggered by human activities. In this study, the spatially and temporally variable changes in wetland extent and anthropogenic threats bordering the Yellow Sea from 1978 to 2018 were examined by utilizing Landsat observations. A fishnet and an anthropogenic threat index (ATI) were applied to document the patterns of wetland changes and anthropogenic threats and further to reveal their interactions. The results indicated that natural wetlands bordering the Yellow Sea have undergone consistent losses during the past four decades, with a net decline rate of 17.2% (7361.2 km²). The ATI pattern suggested that direct anthropogenic threats from agriculture and urbanization distributed like a new Great Wall affecting the natural wetlands along the coastline, while built-up land consistently expanded on each side of the three countries (China, North Korea, and South Korea). The spatial distribution of bird number, night light data, and major ports, as well as the Landsat images, demonstrated that notable anthropogenic threats have affected coastal wetlands and biodiversity. Given these comparative findings, the study is expected to support policy optimization and international corporations in coastal wetland management to achieve the twin goals of biodiversity conservation and socioeconomic development.
Wetlands are the transitional areas between terrestrial and aquatic systems who provide the world with natural storm barriers, environmental cleansers, and food and water resources for many forms of life. However, human interventions have disrupted these ecosystems and caused an important environment damage and 48% of wetlands in the Mediterranean basin have disappeared since 1970. This research aims to study the spatial and temporal evolution of a Tunisian wetland located in the Central-East using earth observation and GIS tools. The diachronic analysis of LULC temporal series of aerial photographs and satellite imagery between 1963 and 2018 showed that the wetlands of big Sfax city underwent important changes explained by coastal industrial activities and the urban sprawl. These factors have led, for example, to the reduction of 85% in the area of the seasonal brackish swamps in the north side of the study area and 55% in the south one, then the transformation of ‘Ezzit’ and ‘El Haffera’ streams to concrete canals.
Available for download from
http://wrcwebsite.azurewebsites.net/mdocs-posts/establishing-remote-sensing-toolkits-for-monitoring-freshwater-ecosystems-under-global-change/
EXECUTIVE SUMMARY:
BACKGROUND
Freshwater ecosystems are globally considered one of the most threatened ecosystems in the world. More than > 85% of wetlands are estimated to be lost or degraded since the 1700s and the rates of decline in their extent and ecological condition have increased in the past 50 years. Increasing pressures related to water abstraction, water pollution, habitat degradation, the fragmentation that results from these, as well as invasive species and climate change, have a multiplying effect on the degradation of wetlands. Owing to the inadequate connectivity of wetlands in the landscape, the consequences are severe. Efforts initiated within the past 50 years to ensure sustainable use of resources therefore needs to be intensified to avoid further losses.
Similarly to the global assessments, fine-scale studies in South Africa have also showed high levels of wetland degradation with an estimated 50% of wetlands transformed by 1988. Several climate change parameters are likely to exacerbate the current pressures on wetlands. The concerns around climate change include the increase in temperatures observed within the past 50 years, predictions of further temperature increases between now and 2050 and the increasing intensity of storms and droughts. These changes have a multiplying effect on the intensity of current pressures and their interactive effect. In South Africa, the last two National Biodiversity Assessments of 2011 (NBA 2011) and 2018 (NBA 2018) showed that wetlands are inadequately mapped, highly threatened and poorly protected. Inventorying and monitoring of this ecosystem should therefore be a top priority. For nearly 70% of the extent of South Africa, it is estimated that the latest National Wetland Map version 5 (NWM5) represents only 50% of the wetlands, and that these may be mostly vegetated or arid.
Remote sensing has for the past 50 years been an important data source in mapping wetland extent, either through heads-up digitising or image classification. New sensors launched since 2008 have improved the capability of remote sensing to detect wetland vegetation and separate it from upland vegetation, through improved spatial resolution and extra bands representing the red-edge and shortwave infrared regions of the electromagnetic spectrum. These space-borne sensors provided additional capability, beyond that of the freely available Landsat images. Since 2014, the European Space Agency (ESA) has been launching the Sentinel-1, -2 and -3 sensors. The data from the new sensors are freely available to the public, with free software such as the Sentinel Application Platform (SNAP) for preprocessing and analysis. Conditions are therefore ideal for assessing the capabilities of these sensors for aiding in mapping and monitoring of wetlands in South Africa.
AIMS
The aim of this research project was to assess the ability of the new space-borne sensors, such as Sentinel, for the inventory and long-term monitoring of vegetated wetlands, as part of freshwater ecosystems, in the face of global change. The approach was to assess the capabilities of the new multispectral and SAR space-borne sensors in combination with a variety of wetland vegetation indices, for reporting on structure, function and condition of vegetated freshwater ecosystems.
The objectives of the project were to:
1. Ascertain the range of the feature extents of wetlands that could be detected by various types of sensors;
2. Calculate the contribution of field visits to improving the representation of the extent and HGM units of inland wetlands;
3. Determine the capability of the new space-borne sensors to separate wetlands from uplands, using either vegetation or thresholding of soil moisture content;
4. Assess the capability of SAR technology to detect the temporal dynamics of vegetation structure (above ground biomass);
5. Investigate whether optical imagery can detect the seasonal or annual variation of freshwater ecosystems (reporting variation in function); and
6. Evaluate the utility of remote sensing tools for the inventory and monitoring of freshwater ecosystems under global change as part of the national programmes such as the National Wetland Monitoring Programme.
METHODOLOGY
1. The degree to which sensors would be able to detect different wetland sizes was evaluated for three regions of South Africa in the Grassland, Fynbos and arid (Nama-Karoo) biomes, as well as using all wetlands mapped in the National Wetland Map version 5 (NWM5).
2. In-field visits and desktop mapping using the WorldView-3 images acquired for the Tevreden Pan (Mpumalanga Province) and Hogsback (Eastern Cape) study areas were used to improve the extent and hydrogeomorphic unit classification of wetlands. The results were compared to the wetlands from the National Freshwater Ecosystems Priority Areas (NFEPA) project, which were also used in the National Biodiversity Assessment of 2011 (NBA 2011). The output of these datasets were contributed to the NWM5 and used for the NBA 2018 assessment of wetland ecosystem types.
3. The Random Forest classification algorithm was used to assess the classification errors and accuracies of the Sentinel-2 and WorldView-3 images for the Tevreden Pan (Mpumalanga Province) and Hogsback (Eastern Cape) study areas to determine whether wetland and upland vegetation were separable, and whether different wetland vegetation communities could be mapped.
4. Empirical regression modelling was used to compare the ability of the Sentinel sensors to that of WorldView-3 in the estimation and mapping of the above ground biomass of wetland and upland vegetation for the Tevreden Pan (Mpumalanga Province) and Hogsback (Eastern Cape) study areas.
5. The monthly extent of inundation of depressions in the Mpumalanga Lakes District (MLD) was mapped from the Sentinel-2 sensors. Thereafter the maximum extent of inundation and hydroperiod categories were derived by processing statistical information in Excel.
6. A literature review was done to (a) assess the impact of climate change on wetlands in South Africa and (b) determine the capabilities of remote sensing for quantifying and monitoring wetland attributes (Chapter 2). Thereafter a number of themes were listed as priorities for implementation in the National Wetland Monitoring Programme (NWMP) that is about to commence.
An additional chapter (Chapter 7) was included based on a student project funded by the Water Research Commission and associated with this project, which determined whether Soil Moisture Content can be predicted from the Sentinel sensors and used for inventorying and monitoring of wetlands. For this study, empirical modelling was used to determine the correlation between in-field measurements of Volumetric Water Content and the Sentinel images for the Colbyn Wetland in Pretoria, South Africa.
RESULTS AND DISCUSSION
The results and discussion of the various objectives are summarised below as key findings of the literature and subsequently this report:
Key findings from the literature review:
• Remote sensing has been an important data source during the past 50 years for the mapping and monitoring of wetland extent.
• Land cover datasets dominate the product range that was derived from remote sensing sensors in South Africa, with fewer products related to the mapping of wetland vegetation.
• The Landsat sensors are amongst the most used space-borne sensors for the mapping of wetlands and inferring the ecological condition of wetlands.
• The Sentinel optical sensors, launched since 2015, now contribute to finer spatial resolution of mapping and monitoring features in South Africa.
• To date, open water or inundated wetlands were more easily mapped than palustrine wetlands.
• More effort is required to map and monitor palustrine wetlands.
• Unmanned Airborne Vehicle (UAV or ‘drone’) images can contribute to specific interventions required in wetlands, such as thermal imagery of sub-surface fires in peat wetlands (Grundling et al., 2019).
• Research funding contributes to significant discoveries relating to the use of remote sensing for wetland mapping and monitoring.
• Further work will be required for determining how remote sensing indices can be used to represent and monitor ecological condition of wetlands over time.
• Several global and South African products derived from remote sensing that are under development could benefit both the wetland and other realms.
Key contributions and knowledge generated from this study:
• Sentinel sensors can make a valuable contribution to the mapping and monitoring of wetlands in South Africa.
• Wetland vegetation is highly separable from upland vegetation in the Grassland biome for two study areas, including Tevreden Pan and Hogsback.
• Wetland vegetation communities were more separable using WorldView-3 images than with Sentinel-2 images.
• Theoretically, current, freely available multispectral space-borne sensors with a spatial resolution of ≥ 10 m are able to detect > 69% of the aerial extent of South African wetlands as represented in the NWM5.
• Fine-scale studies showed that wetlands on slopes greater than 10% will likely require images of higher spatial resolution compared to those on slopes less than 10%.
• The Sentinel sensors were able to predict above ground biomass (AGB) of wetland vegetation with accuracies comparable to those of WorldView-3.
• The Sentinel optical and Synthetic Aperture Radar (SAR) sensors show potential for estimating soil moisture content (SMC) across a wetland-upland gradient.
• The increased temporal frequency of Sentinel-2 compared to Landsat images, refines the determination of the maximum extent of inundated depressions for reporting to SDG 6.6, as well as characterising hydroperiod classes for the inventory of wetlands.
GENERAL
The Sentinel sensors provide an improved data source for the quantification and monitoring of wetland characteristics vulnerable to global and climate change.
CONCLUSIONS
In conclusion, remote sensing has demonstrated its usefulness for the inventorying and monitoring of wetlands in South Africa. South Africa also has the expertise to facilitate implementation and further research to optimise the use of the now freely available Sentinel sensors for this purpose. Earlier studies were constrained by data processing capabilities, storage and cost of images (e.g. Thompson et al., 2002), but imagery and software are now more freely available for processing, while the capabilities of information systems for processing large images have improved. In our opinion, the only challenge we have today, is to secure funding for implementation and operation of monitoring systems for wetlands.
RECOMMENDATIONS
In summary, we suggest that managers of the National Wetland Monitoring Programme (NWMP) consider the use of remote sensing in five areas of response, related to:
A) the improvement of the representation of wetland extent in future versions of the NWMP and for Sustainable Development Goal (SDG) reporting;
B) Improved characterisation and description of biodiversity of wetland ecosystem types in the NWMP;
C) Intervention strategies that will be increasingly required under predicted climate change scenarios;
D) The modelling of ecological condition, required for assessment and planning; and
E) Monitoring of wetland attributes.
Different themes are summarised, which we consider a top priority, ranked according to the ease of implementation and with recommended sensors. Reporting channels have also been identified through known national departments and international reporting obligations.
This study evaluates the utility of synthetic aperture radar (SAR) imagery collected by the ERS-1 satellite for monitoring wetland vegetation communities in southwestern Florida. Two images were analyzed, one collected at the end of the dry season in April 1994 and one collected at the end of the wet season in October 1994. The range of image intensity values from the different test sites varied by a factor of 6.2 (7.9 dB) on the dry season ERS-I SAR image and by a factor of 2.6 (4.1 dB) for the wet season ERS-1 SAR image. The re-sults from the radar observations were found to be consistent with theoretical micro wave scattering models that predict variations in backscatter as a function of vegetation struc-ture, soil moisture, surface roughness, and the presence or absence of standing water. Both the radar data and models show that, in wetlands dominated by herbaceous vegetation, the presence of standing water results in a decrease in back-scatter. Conversely, in wetlands with woody plants (trees and shrubs), the radar data and models show that the presence of water results in an increase in backscatter. The results of this study illustrate that radar imagery is uniquely suited to detect and monitor changes in soil moisture, flooding, and aboveground biomass in these wetland ecosystems.
Satellite radar was used in a Florida Jancus roemerianus marsh to map
tidal flooding, a critical control of coastal vegetation distribution. Radar images
taken during a time of near-continuous recordings of ground-based hydrology
measurements directly linked marsh flooding to lowered radar returns and
indicated a negative covariation between flood frequency and radar return.
Flood-extent contours extracted from the radar images and calibrated with point
depth measurements showed marsh elevation could be estimated to about 8cm
compared to the 150cm topographic contours currently available.
Spectral radiance indices (vegetation index and infrared index) were highly correlated with canopy biomass parameters like live leaf biomass, percent live biomass, and a live-dead biomass ratio. Regression models equating spectral radiance index with the different canopy biomass parameters suggested that spectral data explained from 97-88% of the variation observed in the biomass data for short-form communities. Tall form communities were generally less accurately described by spectral radiance data. Seasonal changes in S. alterniflora biomass were readily detected using spectral data.-from Authors
Overlay maps of Delaware's wetlands have been prepared, showing the dominant species or group of species of vegetation present. Five such categories of vegetation were used indicating marshes dominated by specific groups of species. In addition, major secondary species were indicated where appropriate. Small, representative areas of each of the major marsh regions were analyzed and enhanced to show detailed growth patterns not shown on the small-scale maps. The mapping technique utilized the General Electric Multispectral Data Processing System (GEMSDPS) to analyze NASA RB-57 color-infrared imagery. The GEMSDPS is a hybrid analog-digital system designed as an analysis tool to be used by an operator whose own judgment and knowledge of ground truth can be incorporated at any time into the analyzing process. The result is a high-speed, cost-effective method for producing enhanced photomaps showing a number of spectral classes.
Net aerial primary productivity is the rate of storage of organic matter in above-ground plant issues exceeding the respiratory use by the plants during the period of measurement. It is pointed out that this plant tissue represents the fixed carbon available for transfer to and consumption by the heterotrophic organisms in a salt marsh or the estuary. One method of estimating annual net aerial primary productivity (NAPP) required multiple harvesting of the marsh vegetation. A rapid nondestructive remote sensing technique for estimating biomass and NAPP would, therefore, be a significant asset. The present investigation was designed to employ simple regression models, equating spectral radiance indices with Spartina alterniflora biomass to nondestructively estimate salt marsh biomass. The results of the study showed that the considered approach can be successfully used to estimate salt marsh biomass.
Spectral measurements were recorded for leaves from two monospecific stands ofAcer rubrum in an attempt to characterize leaf reflectance at different stages of flooding. The stands occupied two different soil types
possessing different soil moisture regimes. Leaves were excised from different parts of the trees, and their reflectance properties
were measured with a hand-held spectroradiometer recording from 400 to 900 nanometers in 3-nm increments. Soil redox potentials
were recorded at the sites in an attempt to characterize stress as a function of the soil reducing conditions. Spectral curves,
reflectance peaks, soil moisture observations, and redox potentials were plotted and analyzed to document the conditions of
the trees during a two-and-a-half month period in the early local growing season. Compared to non-flooded trees, spectral
measurements for flooded trees showed elevated reflectance in both the green spectral region at 550 nm as well as the near
infrared region at 770 nm. In addition, the reflectance measurements were strongly related (r >- 0.80) to redox potentials
recorded during the same period. The results indicated that spectrally detectable changes in visible and near infrared leaf
reflectance may be more influenced by prolonged flooding than saturation. This suggests that where remote sensing is used
for wetland mapping, there may be optimal times to spectrally separate stands of forested wetlands during the growing season.
We analyzed airborne synthetic aperture radar (AIRSAR) imagery of forest, wetland, and agricultural ecosystems in northern Belize, Central America. Our analyses are based upon four biophysical indices derived from the fully polarimetric SAR data: the volume scattering index (VSI), canopy structure index (CSI), biomass index (BMI), calculated from the backscatter magnitude data, and the interaction type index (ITI), calculated from the backscatter phase data. We developed a four-level landscape hierarchy based upon clustering analyses of the 12 index parameters (four indices each for P, L, and C band) from two test site images. Statistical analyses were used to examine the relative importance of the 12 parameters for discriminating ecosystem characteristics at various landscape scales. We found that ITI was the most important index (primarily C band = CITI) for level, vegetated terrain at all levels of the hierarchy. BMI was most important for differentiating between vegetated and nonvegetated areas and between sloping and level terrain. These findings indicate that upper canopy spatial characteristics and flooding in marshlands (reflected in the CITI) are more important than biomass in differentiating many tropical ecosystems with radar data. The relative importance of the indices varied with vegetation type; for example, PVSI was the most important for distinguishing between upland forests and regrowth, and PCSI was the most important for differentiating swamp forest types. Finally, we evaluated the potential of present and future spaceborne SARs for tropical ecosystem studies based on our results. Most of these SARs are single channel systems and will provide limited capability for characterizing biomass and structure of tropical vegetation. This is especially true for C band systems, which produce data similar to our CBMI parameter, which was one of the least important in our analyses. The SIR-C/X-SAR and proposed EOS SAR are future spaceborne multifrequency fully polarimetric SAR systems, and they will provide a significant contribution to tropical ecosystem studies.
Traditional field sampling approaches for ecological studies of restored habitat can only cover small areas in detail, con be time consuming, and are often invasive and destructive. Spatially extensive and non-invasive remotely sensed data can make field sampling more focused and efficient. The objective of this work was to investigate the feasibility and accuracy of hand-held and airborne remotely sensed data to estimate vegetation structural parameters for an indicator plant species in a restored wetland. High spatial resolution, digital, multispectral camera images were captured from an aircraft over Sweetwater Marsh (San Diego County, California) during each growing season between 1992-1996. Field data were collected concurrently, which included plant heights, proportional ground cover and canopy architecture type, and spectral radiometer measurements. Spartina foliosa (Pacific cordgrass) is the indicator species for the restoration monitoring. A conceptual model summarizing the controls on the spectral reflectance properties of Pacific cordgrass was established. Empirical models were developed relating the stem length, density, and canopy architecture of cordgrass to normalized-difference-vegetation-index values. The most promising results were obtained from empirical estimates of total ground cover using image data that had been stratified into high, middle, and low marsh zones. As part of on-going restoration monitoring activities, this model is being used to provide maps of estimated vegetation cover.
Reflectance of red (656-705 nm) and infrared (776-826 nm) solar radiation and standing crop biomass were measured in three salt marsh communities at intervals of approximately 2 weeks between February and August 1974. Red reflectance declined at the onset of greening in each community and was correlated with standing crop of green biomass. Infrared reflectance increased substantially in the shrub community but less in the grass and sedge communities. The inverse of red reflectance was found to be a reliable predictor of green biomass in sedge and grass communities, but not in a shrub community.
In 1979, when the National Wetlands Inventory (NWI) became operational, it was clear that two very different kinds of information were needed. First, detailed wetland maps were needed to support site-specific decisions. Secondly, national statistics on the current status and trends of wetlands were needed in order to provide information supporting the development or alteration of Federal programs and policies. The national scope and required level of detail dictated that a remote sensing tool, combined with field work, be used to conduct the project. Both high altitude aerial photography and satellite imagery were investigated as possible data sources. The results of these investigations showed that high altitude color infrared photography provided the best possible data because of it's spatial resolution and the classification accuracy it allowed. NWI continues to investigate other remote sensing data sources for updating it's products. Several past and ongoing studies are discussed. To date, wetland maps have been produced for 70% of the lower 48 states at a scale of 1:24 000 and for 22% of Alaska at a scale of 1:63 360. Maps covering 11.5% of the US have been converted to a digital form for distribution. -Authors
The US Geological Survey has prepared three experimental wetland maps for the Auburndale, Florida 1:24,000-scale quadrangle. Wetland classes and boundaries were interpreted from quad centered high altitude color infrared and superwide black and white panchromatic photographs onto a black and white orthophoto base map made from a color infrared photograph. - from Authors
Aerial photographs were used to measure historical areas of wetlands at Pointe Mouillee in Monroe County, Michigan, and these results were compared with Lake Erie water levels. Measurements indicated a decrease of 836.4 hectares in total wetlands between 1935 and 1980. This was a reduction of 80%. The majority of wetland losses occurred between 1940 and 1950, and may be partially attributed to the construction of dams across the Huron River prior to 1950. After losses of 1940 to 1950, wetlands area varied inversely with water levels. Regression of total wetlands and water levels between 1950 and 1980 demonstrated an inverse relationship, with a coefficient of determination (R2) of 0.8722. This result is similar to conditions found in other Great Lakes areas. -Authors
A wetland classification system was developed for the Tennessee Valley Region based primarily on vegetation, and on frequency and duration of inundation. Using this new classification system, wetlands at four sites were mapped at 1:24 000 scale as overlays on U. S. Geological Survey 7. 5-minute topographic maps. Adjacent land use was also mapped, but in less detail than wetlands. The methodology for separating and delineating wetland classes was carefully documented. Overlays for separate dates were combined to make the final camera-ready composite overlay. A lithographed map of wetlands and land use was made of one of the five quadrangles covering the Reelfoot Lake site. At the Reelfoot Lake and Hatchie River sites, the stage at time of photography was referenced to a stage-duration curve, placed on the map collar, to show that boundaries are representative of average water levels rather than extreme highs or lows.
IntroductionThe Role of the Oceans in Ameliorating Global WarmingThe Role of Others Gases in Global WarmingEffects of Climate Change on the World's Oceans and Fresh WatersAcid Rain and Freshwater EcosystemsEvidence for Past and Present Coupling between Oceans and ClimateFurther Reading
Recent and historical satellite remote sensor data were used to inventory aquatic macrophyte changes within the Florida Everglades Water Conservation Area 2A using Landsat Multispectral Scanner (MSS) and Spot High Resolution Visible (HRV) multispectral data. The method required a single base year of remotely sensed data with adequate ground reference information. Historical remotely sensed data were 'normalized' to the base year's radiometric characteristics. Statistical clusters extracted from each data of imagery were found in relatively consistent regions of multispectral feature space and labeled using a 'core cluster approach'. Wetland classification maps of each year were analyzed using 'post classification comparison' change detection techniques to produce maps of 1) cattail change and 2) change in the 'sawgrass/cattail mixture' class.
Data were analyzed using digital image processing techniques to inventory the spatial distribution of cattail and waterlily beds in a freshwater reservoir located on the Savannah River Site in South Carolina. Creation of a multiple date color composite using October 1988, 1989, and 1990 SPOT panchromatic data proved to be a very effective method to visually identify the change in aquatic macrophyte distribution through time. -from Authors
The NOAA sponsored CoastWatch Change Analysis Project (C-CAP) will utilize remote sensing technology to monitor changes in coastal wetland habitats and adjacent uplands on a cycle of 1 to 5 years. Two study areas in South Carolina were selected to test various C-CAP change detection protocols using near-anniversary Landsat Thematic Mapper data obtained in 1982 and 1988. Fort Moultrie (dominated by salt and brackish marsh) and Kittredge (40 river miles inland and dominated by bottomland hardwoods and riverine aquatic beds) study areas were used to evaluate a modified C-CAP classification scheme, image classification procedures, change detection algorithm alternatives, and the impact of tidal stage on coastal change detection.
Remote sensing was examined as a tool to describe the spectral and structural changes within and between mangrove species and community types. To accomplish this goal, high-resolution canopy reflectance spectra were obtained at 21 mangrove sites in southwest Florida. This was in addition to leaf spectra, canopy closure, height, and species composition from a number of these sites. High relative variability typified measurements of canopy reflectance spectra, canopy height, and percent species composition, while leaf reflectance variances within species (black or red, about 0.04 to 0.06 percent) were higher than between species (about 0.02 percent). Mean reflectances were generated for the blue, green, red, and near-infrared (NIR) wavelength regions from the obtained canopy reflectance spectra by using either user-defined bandwidths or bandwidths defined for the Advanced Very High Resolution Radiometer, Thematic Mapper, and XMS (SPOT) sensors.
The ability to map open surface water is integral to many hydrologic and agricultural models, wildlife management programmes, and recreational and natural resource studies. Open surface water is generally regarded as easily detected on radar imagery. However, this view is an oversimplification. This study used X-band HH polarized airborne Synthetic Aperture Radar ( SAR) imagery to examine the potential of SAR data to map open fresh water areas extant on 1:100000 USGS topographic maps. Seven study sites in the U.S.A. with a combined area of over 68000km2were analysed. Detection accuracies and minimum size for detection varied among the seven locations. Size and shape of water bodies and radar shadow all affected detection. However, environmental modulation factors including vegetation and forest cover, moisture, and landscape composition and morphology had the greatest influence and exhibited the most complex role in explaining variability
A self-contained, portable, hand-radiometer designed for field usage was constructed and tested. The device, consisting of a hand-held probe containing three sensors and a strap supported electronic module, weighs 4 1/2 kilograms. It is powered by flashlight and transistor radio batteries, utilizes two silicon and one lead sulfide detectors, has three liquid crystal displays, sample and hold radiometric sampling, and its spectral configuration corresponds to LANDSAT-D's thematic mapper bands. The device was designed to support thematic mapper ground-truth data collection efforts and to facilitate 'in situ' ground-based remote sensing studies of natural materials. Prototype instruments were extensively tested under laboratory and field conditions with excellent results. Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Keywords (in text query field) Abstract Text Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints
Wetlands are important as both sources and sinks for methane, a trace gas implicated in greenhouse warming. Information necessary for estimating fluxes of methane includes plant species and primary production. Measuring and monitoring the spatial and temporal variations in these parameters should facilitate modeling fluctuations in global greenhouse gas amounts. Three ERS-1 and two Landsat-TM datasets, acquired over large wetland sites in the Western Sandhills of Nebraska, were obtained during the 1995 growing season. The SAR images were despeckeled, and both ERS and TM were resampled to a 30m spatial resolution, rectified to a UTM coordinate system, and segmented to exclude uplands using a digital National Wetlands Inventory (NWI) dataset. In-situ reference data were obtained at various sites within the study area in conjunction with satellite overpasses. The ERS data were classified in an attempt to identify and map the distribution of wetlands at two levels: 1) specificity roughly equivalent to that of the NWI; and 2) at the species level, with emphasis on Typha, Scirpus, and Phragmites. The TM scenes were also classified and compared to the SAR result. Finally, a combined ERS and TM dataset was classified. Both SAR backscatter and TM reflectance were correlated to field measures of above-ground biomass. Results were considered with regard to C- and L-band (W and VH) scatterometer data acquired at close-range over experimental plots containing Typha and Phragmites. Landsat-TM seems better than ERS for generalized classification and mapping, and for emulating the NWI product. However, TM is limited with regard to identification of individual wetland species, and sparse stands of emergent, floating, and submergent macrophytes. ERS seems better than TM for detecting individual species, and sparse stands of emergent, floating, and submergent macrophytes. But, the ERS multi-temporal classification is not easily adapted for most practical mapping applications. Wind must be considered when classifying ERS in shallow lake systems. Potential exists for ERS/TM classification, but more work is needed. Biomass estimation, while encouraging, needs refinement. C- and L-band scatterometer data suggest that polarization is more important in distinguishing Typha and Phragmites than frequency.
A global data base of wetlands at 1 degree resolution was developed from the integration of three independent global, digital sources: (1) vegetation, (2) soil properties and (3) fractional inundation in each 1 degree cell. The integration yielded a global distribution of wetland sites identified with in situ ecological and environmental characteristics. The wetland sites were classified into five major groups on the basis of environmental characteristics governing methane emissions. The global wetland area derived in this study is 5.3 trillion sq m, approximately twice the wetland area previously used in methane emission studies. Methane emission was calculated using methane fluxes for the major wetland groups, and simple assumptions about the duration of the methane production season. The annual methane emission from wetlands is about 110 Tg, well within the range of previous estimates. Tropical/subtropical peat-poor swamps from 20 degrees N to 30 degrees S account from 30% of the global wetland area and 25% of the total methane emission. About 60% of the total emission comes from peat-rich bogs concentrated from 50-70 degrees N, suggesting that the highly seasonal emission from these ecosystems is the major contributor to the large annual oscillations observed in atmospheric methane concentrations at these latitudes. 78 refs., 6 figs., 5 tabs.
One way to estimate spatial distribution of water content at the soil surface consists of using active microwave remote sensing. It has been theoretically and experimentally demonstrated for unsaturated conditions. Nevertheless, radar data are ambiguous when ponding conditions occur, as in variable source areas, due to the contradictory influence of the dielectric effect and the specular effect on the baekscattering attenuation coefficient, σ0. A procedure is considered, based on a topographic analysis, to take into account the influence of the two effects on radar response. The present results stress the capabilities of the ERS (European Remote Sensing Satellite) radar to survey saturated areas in time and space.
A review of remote sensing techniques in change detection studies is presented. Due to the nature of imagery examined, most change detection studies have involved the use of Advanced Very High Resolution Radiometer (AVHRR), Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS), SPOT data, and aerial photography. Change detection techniques have included transparency compositing, image differencing, classification, band ratioing, and principal components analysis. This paper will focus on a review of the utility of these methods for various resource applications.
IntroductionProperties of Coastal EnvironmentsBiological Components of Coastal EnvironmentsGeomorphic Features of Coastal EnvironmentsHydrograpic FeaturesContrasts of Coastal and Open Water Column EcosystemsControls of Production and Abundance in Coastal EnvironmentsFurther Reading
The Sandhills region represents a tremendous water resource for the State of Nebraska. Small shallow lakes, marshes, and subirrigated meadows are abundant due to interactions between ground water and surface water. One theory relating ground water to lake-flow systems in the Sandhills has been termed the “flow-through” concept. Thermal-infrared remotely acquired images document the flow-through model for a test site in Western Nebraska.
We studied the reflectance spectra of the aquatic vegetation of Searsville Lake in coastal central California using a high spectral resolution hand-held spectroradiometer. The three aquatic types—submerged, floating, and emergent—exhibited clear differences in their spectral reflectance and can be distinguished on the basis of discriminant analysis using reflectance parameters. This technique can be used in large-area mapping of aquatic plants. The normalized difference vegetation index (NDVI) and the simple ratio (SR) were well correlated with chlorophyll content, photosynthetic efficiency, and biomass in the emergent species. New, narrow-bandwidth indices and reflectance indices calculated from first and second derivative spectra were strongly correlated with the ratio of secondary and protective pigments to chlorophyll a and with epoxidation state of the xanthophyll cycle pigments, and therefore, with photosynthetic efficiency. These new indices may be useful in the remote sensing of plant physiological status.
High resolution multispectral scanner (MSS) imagery of Savannah River nontidal wetlands were analyzed to identify 1) useful spectral bands for discriminating among National Wetland Inventory classes, 2) where the classes cluster in n-dimensional feature space, and 3) what wetland classification accuracies can be expected. Spectral measurements in the green (0.55–0.60 μm), red (0.65–0.70 μm), and near-infrared wavelengths (0.70–0.79 μm and 0.92–1.10 μm) provided the most useful information. Emergent marsh (both persistent and nonpersistent), scrub-shrub, mixed deciduous swamp forest, and mixed deciduous upland forest were found to cluster in somewhat predictable regions of 2- and 3-dimensional feature space. The overall classification accuracy of the Steel Creek delta study area was 83% and was assessed by comparing the remote sensing derived thematic map with 1325 linear meters of transects sampled in situ. These results suggest that high resolution aircraft MSS data can provide detailed vegetation type information for mapping both thermally affected and rejuvenating nontidal wetland in the South Carolina Savannah River Swamp System.
Radar backscatter measurements were made of experimental prairie on Campus West of the University of Kansas, Lawrence, Kansas, during the period from July to November 1978. Experiments were performed with a surface-based FM—CW scatterometer that swept from 8.5–17.5 GHz. Natural grass field and mowed grass fields were observed. Results indicate that the plant moisture content has somewhat close correlation with the differential cross-section of σ° of VV polarization, and plant biomass might be able to be estimated from σ° of HH polarization. Also, σ°dB was found to have an almost linearly decreasing angular response.
Identification of hydrophytes will improve the delineation and classification of wetlands on remotely sensed imagery. Spectral reflectance measurements of 10 species of hydrophytes were made with an Exotech radiometer during 3 phenological stages, flowering and early seed, senescent, and early emergent. Reflectance data were analyzed to determine significant (P < 0.5) differences between species in each of 4 spectral regions during each phenological stage. Eight species had significantly (P < 0.05) different reflectances during the flower and early seed stage. Among the 10 species, only one could not be spectrally isolated during at least 1 phenological stage. The results indicate that films sensitive to both visible and infrared spectra (e.g., Ektachrome infrared) should enable recognition of different species of hydrophytes.
This paper examines the relationship between the microwave backscattering coefficient of a vegetation canopy, σcan0, and the canopy's leaf area index (LAI). The relationship is established through the development of one model for corn and sorghum and another for wheat. Both models are extensions of the cloud model of Attema and Ulaby (1978). Analysis of experimental data measured at 8.6, 13.0, 17.0, and 35.6 GHz indicates that most of the temporal variations of σcan0 can be accounted for through variations in green LAI alone, if the latter is greater than 0.5.
Since microwave remote sensing techniques are insensitive to cloud cover, they can overcome this strong limitation of optical remote sensing. As in the optical domain, their use for monitoring vegetation canopies requires the development of suitable inversion algorithms. These would allow the estimation of variables such as LAI from radar data. This article investigates the possible use of a semiempirical water-cloud model in an inversion scheme. Using radar data obtained with a ground-based dual-frequency (C and X bands, 5.7 and 3.3 cm wavelength, respectively) scatterometer on experimental winter wheat fields, it is first verified that a semiempirical water-cloud model can adequately simulate the backscattering coefficients obtained over the growing season, as a function of LAI and surface soil moisture. Then it is shown that the model can be numerically inverted. This yields simultaneous estimation of LAI and surface soil moisture, the standard deviations of the residuals being respectively 0.64 m2 m−2 and 0.065 cm3 cm−3. Finally, the influence of radar measurement errors on the inversion scheme is quantified by means of a simulation study. This shows that a 1 dB accuracy of the radar is required for a 1 m2 m−2 precision of the estimated LAI.
There is no clear consensus on how environmental and biotic factors control microbiallymediated methane production in wetlands, as well as emission of this important ‘greenhouse gas’ from wetlands into the atmosphere. To provide insight, I studied rates of methane production and emission into the atmosphere, as well as factors controlling those rates, along a toposequence from non-flooded to seasonally flooded in a coastal meadow and in a fen in Denmark. Methane production was estimated from anaerobic soil slurries while emission was estimated from static flux chambers. Methane emission into the atmosphere averaged 0.04 μg C-CH4 dm−2 h-−1 in the coastal meadow and 1.9 μg C-CH4 dm−2 h−1 in the fen. A comparison of potential CH4 production and CH4 emission into the atmosphere showed that in the coastal meadow, but not in the fen, emission increased when production increased during summer. Relationships between potential CN4 production and soil water content as well as soil temperature are discussed. Arrhenius plots indicated strikingly similar temperature responses of CH4 production in the two wetlands. Also, both wetlands showed different temperature responses in saturated soils (Q10 = 3.1 and 3.6; Eh=79 and 84 kJ mol−1) compared to unsaturated soils (Q10 = 8.1 and 8.7; eh = 138 and 142 kJ mol−1). My results suggest that different types of methanogens inhabit saturated and unsaturated soils in both a coastal meadow and a fen. Overall, the study indicates that CH4 production in wetlands and CH4 emission into the atmosphere from wetlands are controlled by a complex set of environmental and biotic factors which differ between wetlands.
In October 1984, space-borne multiple incidence angle synthetic aperture radar (SAR) data acquired by the Shuttle Imaging Radar-B Program were used to delineate pools of standing water beneath a 12.5 m tall 100% closed forest canopy and to map flood boundaries for the assessment of flood damage in the Peoples Republic of Bangladesh. The projects, as described here, demonstrate the potential use of SAR systems as input to disease and disease vector control programs in monsoon countries.
This classification, to be used in a new inventory of wetlands and deepwater habitats of the United States, is intended to describe ecological taxa, arrange them in a system useful to resource managers, furnish units for mapping, and provide uniformity of concepts and terms.Wetlands are defined by plants (hydrophytes), soils (hydric soils), and frequency of flooding. Ecologically related areas of deep water, traditionally not considered wetlands, are included in the classification as deepwater habitats. Systems form the highest level of the classification hierarchy; five are defined—Marine, Estuarine, Riverine, Lacustrine, and Palustrine. Marine and Estuarine Systems each have two Subsystems, Subtidal and Intertidal; the Riverine System has four Subsystems, Tidal, Lower Perennial, Upper Perennial, and Intermittent; the Lacustrine has two, Littoral and Limnetic; and the Palustrine has no Subsystems.
Factors responsible for the alteration of CH 4 isotopic composition during gas transport by emergent macrophytes were investigated in Pontederia cordata (Pickerelweed) and Sagittaria lancifolia (Bull tongue). Measured rates of CH 4 emission from petioles and leaves indicated that the locus of gas release from these plants is the petiole and not the leaf. Methane concentration profiles of gases within petioles further indicated that most CH 4 is emitted from the lower portion of the petiole near the waterline. Ethane and propane tracer experiments confirmed mass‐dependent fractionation during gas transport through these plants. After injection with a gas mixture, both plant types emitted ethane 12% faster than propane. Twenty‐five minutes or more after injection, ethane was found to be depleted in gas within petioles relative to the injected gas. The degree of fractionation observed for the ¹³ CH 4 / ¹² CH 4 and ethane/propane pairs was similar to values predicted by kinetic gas theory.
A method is described for estimating wetland abundance in the 700,000 sq km prairie pothole region of North America. A double sampling procedure is described, incorporating the use of high resolution aircraft imagery, capable of delineating ponds as small as 5 m across, as a means of adjusting the count of surface water features derived from the low-resolution Landsat census over a 38,876 sq km area in east-central North Dakota. The regression expansion formula used to estimate the actual number of total wetlands is also presented.
Field measurements of wetland spectral canopy reflectance in the Landsat-MSS wavebands were correlated with biotic factors. The highest single band correlations were observed between visible (MSS Band 4: 0.5 to 0.6 micron and Band 5: 0.6 to 0.7 micron) canopy reflectance and the percentage, by weight, of live (green) vegetation in the canopies of Spartina alterniflora (salt marsh cordgrass), Spartina patens (salt meadow grass), and Distichlis spicata (spike grass). Infrared canopy reflectance displayed significant but weaker dependence on canopy parameters such as live and total biomass and canopy height. The Band 7 (0.8 to 1.1 microns)/Band 5 (0.6 to 0.7 micron) reflectance ratio was found to be highly correlated with green biomass for S. alterniflora. Highest spectral separability between the 'low marsh' S. alterniflora and the 'high marsh' Salt Hay (S. patens and D. spicata) communities in Delaware occurs during December.
Progress on research designed to test the usability of multispectral, high altitude, remotely sensed data to analyze ecological and hydrological conditions in estuarine environments is presented. Emphasis was placed on data acquired by NASA aircraft over the Patuxent River Chesapeake Bay Test Site, No. 168. Missions were conducted over the Chesapeake Bay at a high altitude flight of 18,460 m and a low altitude flight of 3070. The principle objectives of the missions were: (1) to determine feasibility of identifying source and extent of water pollution problems in Baltimore Harbor, Chesapeake Bay and major tributaries utilizing high altitude, ERTS analogous remote sensing data; (2) to determine the feasibility of mapping species composition and general ecological condition of Chesapeake Bay wetlands, utilizing high altitude, ERTS analogous data; (3) to correlate ground spectral reflectance characteristics of wetland plant species with tonal characteristics on multispectral photography; (4) to determine usefulness of high altitude thermal imagery in delinating isotherms and current patterns in the Chesapeake Bay; and (5) to investigate automated data interpretive techniques which may be usable on high altitude, ERTS analogous data.
Examination of Seasat SAR images of eastern Maryland and Virginia reveals botanical distinctions between vegetated lowland areas and adjacent upland areas. Radar returns from the lowland areas can be either brighter or darker than returns from the upland forests. Scattering models and scatterometer measurements predict an increase of 6 dB in backscatter from vegetation over standing water. This agrees with the 30-digital number (DN) increase observed in the digital Seasat data. The brightest areas in the Chickahominy, Virginia, drainage, containing P. virginica about 0.4 m high, contrast with the brightest areas in the Blackwater, Maryland, marshes, which contain mature loblolly pine in standing water. The darkest vegetated area in the Chickahominy drainage contains a forest of Nyssa aquatica (water tupelo) about 18 m high, while the darkest vegetated area in the Blackwater marshes contains the marsh plant Spartina alterniflora, 0.3 m high. The density, morphology, and relative geometry of the lowland vegetation with respect to standing water can all affect the strength of the return L band signal.
Synthetic aperture radar remote sensing is a promising tool for detection of flooding on forested floodplains. The brigtht appearance of flooded forests on radar images results from double-bounce reflections between smooth water surfaces and tree trunks or branches. Enhanced backscattering at L-band has been shown to occur in a wide variety of forest types, including cypress-tupelo swamps, temperate bottomland hardwoods, spruce bogs, mangroves and tropical floodplain forests. Lack of enhancement is a function of both stand density and branching structure. According to models and measurements, the magnitude of the enhancement is about 3 to 10 dB. Steep incidence angles (20-30 deg) are optimal for detection of flooding, since some forest types exhibit bright returns only at steeper angles. P-band should prove useful for floodwater mapping in dense stands, and multifrequency polarimetric analysis should allow flooded forests to be distinguished from marshes.
ERTS-1 data were used in mapping open surface water features in the glaciated prairies. Emphasis was placed on the recognition of these features based upon water's uniquely low radiance in a single near-infrared waveband. On the basis of these results, thematic maps and statistics relating to open surface water were obtained. In a related effort, the added information content of multiple spectral wavebands was used for discriminating surface water at a level of detail finer than the virtual resolution of the data. The basic theory of this technique and some preliminary results are described.
In a Delaware salt marsh, total S. alterniflora live aerial biomass for the marsh was estimated from satellite-gathered radiance data to be 1.70 × 109 dry weight (gdw) distributed over 580 ha, for a mean of 294 gdw/m2 (one standard deviation of the mean = 76 gdw/m2). Such biomass estimates were within 13% of those derived from ground-gathered radiance and harvest data. -from Authors
Spectral characteristics of the 6 major wetland types found in NE Indiana were measured during the summer of 1978 using an EXOTECH- 100 radiometer mounted in a helicopter. The spectral measurements were compared to a computer classification of Landsat multispectral scanner data. Analysis of the spectral characteristics indicated that deep marshes and open water can be separated based on spectral reflectance. Shallow marshes, shrub swamps, and hardwood swamps were spectrally similar to each other and to upland cover types and were difficult to separate using spectral responses alone. -from Authors