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Application of multi-temporal LANDSAT 5 TM imagery for wetland identification
Abstract
Multi-temporal Landsat 5 Thematic Mapper (TM) imagery was evaluated for the identification and monitoring of potential jurisdictional wetlands located in the states of Maryland and Delaware. A wetland map prepared from single-date TM imagery was compared to a hybrid map developed using two dates of imagery. The basic approach was to identify land-cover vegetation types using spring leaf-on imagery, and identify the location and extent of the seasonally saturated soil conditions and areas exhibiting wetland hydrology using spring leaf-off imagery. The accuracy of the wetland maps produced from both single- and multiple-date TM imagery were assessed using reference data derived from aerial photographic interpretations and field observation data. Subsequent to the merging of wetland forest and shrub categories, the overall accuracy of the wetland map produced from two dates of imagery was 88 percent compared to the 69 percent result from single-date imagery. A Kappa Test Z statistic of 5.8 indicated a significant increase in accuracy was achieved using multiple-date TM images. Wetland maps developed from multi-temporal Landsat TM imagery may potentially provide a valuable tool to supplement existing National Wetland Inventory maps for identifying the location and extent of wetlands in northern temperate regions of the United States.
... La résolution temporelle fait référence à l'intervalle de temps dans lequel un satellite revisite la même zone géographique. L'utilisation de données de télédétection recueillies au cours des différentes saisons s'est avéré utile dans l'amélioration de la précision de la classification, en particulier pour la classification des cultures et de la végétation (El kharki el al., 2012b ;Wolter et al., 1995 ;Ducrot, 2005 ;Lunetta et Balogh, 1999 ;Oetter et al., 2000 ;Guerschman et al., 2003). Par exemple, Lunetta et Balogh (1999) ont comparé des images Landsat 5 TM, monodate et deux dates pour la cartographie des zones humides dans le Maryland et le Delaware. ...
... L'utilisation de données de télédétection recueillies au cours des différentes saisons s'est avéré utile dans l'amélioration de la précision de la classification, en particulier pour la classification des cultures et de la végétation (El kharki el al., 2012b ;Wolter et al., 1995 ;Ducrot, 2005 ;Lunetta et Balogh, 1999 ;Oetter et al., 2000 ;Guerschman et al., 2003). Par exemple, Lunetta et Balogh (1999) ont comparé des images Landsat 5 TM, monodate et deux dates pour la cartographie des zones humides dans le Maryland et le Delaware. Ils ont constaté que les images multitemporelles fournies de meilleurs taux de classification par rapport à l'image monodate. ...
Au cours des dernières années, de nombreuses approches avancées de classification, tels que les réseaux de neurones artificiels, arbres de décision, les ensembles flous, etc. ont été largement appliquées à la classification des images satellites. Chaque méthode de classification a son propre mérite. Sélectionner une approche de classification appropriée pour une étude spécifique n'est pas facile. Différents résultats de classification peuvent être obtenus selon le(s) classificateur(s) choisi(s). Dans cet article, nous passons en revue diverses méthodes de classification avec une analyse et étude comparative. Nous présentons également les techniques pour améliorer la précision de la classification de la couverture terrestre. Mots-clés : Télédétection, images satellites, classification, SVM, réseaux de neurones, classification floue, arbre de décision. Abstract In recent years, many advanced classification approaches, such as artificial neural networks, decision trees, fuzzy sets, have been widely applied for image classification. Each classification method has its own merits. Select an appropriate classification approach for a specific study is not easy. Different classification results can be obtained according to the selected classifier(s). In this paper, we review various methods of classification with an analysis and comparative study. We also present techniques to improve the accuracy of the classification of land cover.
... La résolution temporelle fait référence à l'intervalle de temps dans lequel un satellite revisite la même zone géographique. L'utilisation de données de télédétection recueillies au cours des différentes saisons s'est avéré utile dans l'amélioration de la précision de la classification, en particulier pour la classification des cultures et de la végétation (El kharki el al., 2012b ;Wolter et al., 1995 ;Ducrot, 2005 ;Lunetta et Balogh, 1999 ;Oetter et al., 2000 ;Guerschman et al., 2003). Par exemple, Lunetta et Balogh (1999) ont comparé des images Landsat 5 TM, monodate et deux dates pour la cartographie des zones humides dans le Maryland et le Delaware. ...
... L'utilisation de données de télédétection recueillies au cours des différentes saisons s'est avéré utile dans l'amélioration de la précision de la classification, en particulier pour la classification des cultures et de la végétation (El kharki el al., 2012b ;Wolter et al., 1995 ;Ducrot, 2005 ;Lunetta et Balogh, 1999 ;Oetter et al., 2000 ;Guerschman et al., 2003). Par exemple, Lunetta et Balogh (1999) ont comparé des images Landsat 5 TM, monodate et deux dates pour la cartographie des zones humides dans le Maryland et le Delaware. Ils ont constaté que les images multitemporelles fournies de meilleurs taux de classification par rapport à l'image monodate. ...
Au cours des dernières années, de nombreuses approches avancées de classification, tels que les réseaux de neurones artificiels, arbres de décision, les ensembles flous, etc. ont été largement appliquées à la classification des images satellites. Chaque méthode de classification a son propre mérite. Sélectionner une approche de classification appropriée
pour une étude spécifique n'est pas facile. Différents résultats de classification peuvent être obtenus selon le(s) classificateur(s) choisi(s). Dans cet article, nous passons en revue diverses méthodes de classification avec une analyse et étude comparative. Nous présentons également les techniques pour améliorer la précision de la classification de la
couverture terrestre.
... Numerous methods have been developed to automatically identify and classify wetlands from multisource remotely sensed imagery; they include unsupervised/supervised classification, spectral angle mapping (SAM), artificial neural networks (ANN), Support vector machine (SVM), and "TUPU" coupled hybrid classification [26][27][28][29][30][31][32][33][34][35][36][37]. The automatic extraction technique has been pursued by researchers to extract the geographic area of wetlands. ...
... Due to the complexity, wetland remote sensing has undergone manual interpretation, semi-automatic, and intelligent extraction periods [14,29,36,[38][39][40][41][42][43][44]. Wetland remote sensing has experienced changes in terms of data sources; it has gone from a single data source to multi-phase, multi-angle, and multi-data sources for wetland detection [36,37,45,46]. In terms of the scale of information extraction, wetland classifications have been performed from the individual pixel level to the level of objects, which are equivalent to wetland patches [2,28]. ...
Yellow River Delta wetlands are essential for the migration of endangered birds and breeding. The wetlands, however, have been severely damaged during recent decades, partly due to the lack of wetland ecosystem protection by authorities. To have a better historical understanding of the spatio-temporal dynamics of the wetlands, this study aims to map and characterize patterns of the loss and degradation of wetlands in the Yellow River Delta using a time series of remotely sensed images (at nine points in time) based on object-based image analysis and knowledge transfer learning technology. Spatio-temporal analysis was conducted to document the long-term changes taking place in different wetlands over the four decades. The results showed that the Yellow River Delta wetlands have experienced significant changes between 1973 and 2013. The total area of wetlands has been reduced by 683.12 km2 during the overall period and the trend of loss continues. However, the rates and trends of change for the different types of wetlands were not the same. The natural wetlands showed a statistically significant decrease in area during the overall period (36.04 km2·year−1). Meanwhile, the artificial wetlands had the opposite trend and showed a statistically significant increase in area during the past four decades (18.96 km2·year−1). According to the change characteristics revealed by the time series wetland classification maps, the evolution process of the Yellow River Delta wetlands could be divided into three stages: (1) From 1973–1984, basically stable, but with little increase; (2) from 1984–1995, rapid loss; and (3) from 1995–2013, slow loss. The area of the wetlands reached a low point around 1995, and then with a little improvement, the regional wetlands entered a slow loss stage. It is believed that interference by human activities (e.g., urban construction, cropland creation, and oil exploitation) was the main reason for wetland degradation in the Yellow River Delta over the past four decades. Climate change also has long-term impacts on regional wetlands. In addition, due to the special geographical environment, the hydrological and sediment conditions and the location of the Yellow River mouth also have a significant influence on the evolution process of the wetlands.
... Besides, conventional wetland mapping is usually based on mono temporal Landsat spectral data (Niu et al. 2012;Michishita et al. 2012;Zhu et al. 2014). Therefore, the spectral similarities of different land covers such as cropland, wetland plant and natural vegetation make it difficult to achieve high classification accuracy by using only mono temporal Landsat spectral data (Lunetta and Balogh 1999;Kayastha et al. 2012). Further, the wetland mapping usually uses pixel-based classification approaches Laba et al. 2010;Niu et al. 2012;Zhu et al. 2014;Betbeder et al. 2015;Whiteside and Bartolo 2015), which tend to result in 'salt-and-pepper' effects and reduce the accuracy of wetland extraction. ...
... The first category used mono temporal spectral images with pixel-based or object-based method (Baker et al. 2007;Conchedda et al. 2008;Gong et al. 2010;Laba et al. 2010;Dronova et al. 2011;Niu et al. 2012;Han et al. 2015). The second one employed multi-temporal images combining with a variety of classifiers (such as, classification tree and regression models (CART) and other pixel-based approaches) (Lunetta and Balogh 1999;Davranche et al. 2010;Poulin et al. 2010;Corcoran et al. 2013;Reschke and Hüttich 2014;Betbeder et al. 2015;Kontgis et al. 2015;Zhang and Zeng 2015). The third category considered phenology with pixel-based method, in which wetlands with vegetation are identified as individual pixels based on signs before and after flood or phonological features the using normalized difference water index (NDWI) and the spectral bands or vegetation index (NDVI/EVI) (Xiao et al. 2005;Son et al. 2014;Huang et al. 2014;Singha et al. 2016). ...
Wetlands, as an important part of urban landscape, provide diverse ecological and social services to cities. It is therefore essential to monitor wetlands in urban areas for ecosystem conservation and sustainable development. Remote sensing techniques have been affirmed promising in wetland extent mapping. However, conventional methods of mapping wetlands in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM+ images, which have great uncertainty due to the spectral similarity of different land covers. Further, traditional pixel-based classifications are unable to meet the accuracy requirement of wetland mapping in such environment. This study proposes an approach that combines spatiotemporal fusion and object-based image analysis for improving wetland mapping in a complex and heterogeneous urban landscape. Firstly, the spatial and temporal adaptive reflectance fusion model (STARFM) was employed to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice. Secondly, phenological parameters were calculated using the time series of MODIS NDVI. Thirdly, using the time series of Landsat 8 OLI and the phenological parameters, wetlands were identified using an object-based method with random tree classifier. A case study of the Changsha-Zhuzhou-Xiangtan urban agglomeration, located in the middle reaches of the Yangtze River in China, was conducted to test this approach. The results indicate that different types of wetlands can be successfully identified using our approach. The overall accuracy and the Kappa coefficient were 92.38% and 0.85, respectively, and the user’s accuracies of sedge swamp and paddy were higher than 85% and 90%, respectively. The phenology- and object-oriented wetland mapping approach with fused time series of Landsat 8 OLI and MODIS is very promising for identifying wetlands with high degree of spatial heterogeneity.
... Several studies in Sudanian savannas found that multitemporal images improved LULC classification accuracy than monotemporal images (Key et al., 2001;Lunetta and Balogh, 1999;Zoungrana et al., 2015). The highest overall accuracy was achieved with multi-temporal images, leading to an improvement of ca.7% over that achieved with mono-temporal data. ...
Burkina Faso's W National Park (WNPB) is one of the country's biodiversity hotspots. Despite this Park's protection, illegal human activities threaten its vegetation, and little information about the on-site causes of degradation is available. Here we explored the extent and drivers of changes in land use/land cover (LULC) in the WNPB. For this purpose, we used Landsat (1988, 2001 and 2016), and ancillary data to determine the spatial structure and changes in LULC with a random forest classifier. We identified socio-economic factors influencing LULC by conducting household surveys, focus groups, and field observations. We used the ranking and binary logistic regression methods to analyze the socio-economic data. A logistic regression model assessed the relationship between land cover change and socio-economic factors. The results showed that multi-temporal classification performed significantly better than mono-temporal one in the study area. However, the combination of mono-temporal reflectances and vegetation indices for LULC classification significantly enhanced the accuracy to the level of multi-temporal classification. This result indicates that this combination is an efficient alternative to multi-temporal classification in the study area where cloud-free imagery is rare. In the WNPB, LULC dynamics from 1988 to 2001 was mainly characterized by reduction of tree savannas (9.4%) and woodland (8.8%) and expansion of bare soil (0.1%) and mixed vegetation (18.1%). From 2001 to 2016, croplands increased by 102.1%. Bush fire (77.19%), wood harvesting (48.25%), lack of rain (48.25%) and elephants’ impact (20.18%) were the leading direct causes of LULC change. In general, the perceptions of local populations about vegetation trends were in line with the observations from remote sensing. Altogether, the tree vegetation in the study area decreased significantly between 2001 and 2016, and the cause of this change appears to be unsustainable land-use rather than rainfall conditions. According to these findings, land-use management practices in this park should be more sustainable and provide education (and changing habits) to the surrounding population. It is therefore suggested that the government regulate illegal activities through forest law enforcement to protect the WNPB from over-exploitation.
... Previous studies such as [8,11,22] used satellite imagery to identify and map wetlands. Landsat imagery, owing to its time scales, spectral bands, and spatial resolution, holds great potential for wetland studies [2]. ...
Wetlands are a distinctive terrestrial ecosystem that benefits living things, including people, in various ways. Sustainable wetland ecosystem resources are needed to protect the global environment. Wetlands in China have undergone positive and negative changes in response to several factors, but studies documenting their long-term dynamicity have been few, particularly in Guangling County. This study examines the change of wetlands area based on remotely sensed data while exploring trends associated with climate variations and economic growth in Guangling County, China. Analysis of remotely sensed imagery, mainly in hilly and nonhomogeneous environments is problematic, largely as a result of interference and their high spectral non-homogeneity. We conducted experiments using five classical machine learning algorithms based on the Google Earth Engine (GEE) and obtained the greatest robustness and accuracy using a Support Vector Machine (SVM)—Radial Basis Function (RBF) kernel approach, with overall accuracy and kappa statistics ranging from 86% to 98.1% and from 0.789 to 0.960, respectively. Based on the SVM-RBF model’s outperformance of four other algorithms, we identified spatial distributions of wetland in the study area and associated change trends. We found that 45.71 km2 of wetland area was lost over the past 3.7 decades (January 1984–December 2020), or 81.82% of wetland area coverage. In this paper, we explore how factors such as county economic growth (GDP), humidity, and temperature variations are tightly linked with wetland change.
... Inicialmente, se definieron 15 clases potenciales, sin embargo, al analizar la separabilidad de coberturas se redujeron a 13 clases (Tabla 1) que se pudieron identificar/clasificar mediante la utilización de imágenes Landsat ETM+ (2010) y OLI (2015 y 2017). Numerosos investigadores, entre ellos Lillesand et al. (1998), Lunetta y Balogh (1999), Oettera et al. (2001), Wolter et al. (1995), y Yuan et al. (2005) han demostrado la importancia de las imágenes multitemporales para la clasificación de la cubierta terrestre. ...
El mapeo y la evaluación de la cobertura del suelo es una de las áreas centrales de la percepción remota. El cambio en la cobertura del suelo es una variable importante del cambio global que afecta a sistemas ecológicos con un impacto en el medio ambiente, asociado con el cambio climático. A pesar del papel de información sobre la cobertura del suelo en el monitoreo y comprensión del medio ambiente, todavía carecemos de conocimiento sobre cobertura del suelo y su dinámica, especialmente en las zonas rurales de Honduras. El objetivo del estudio fue analizar la dinámica de cambio de cobertura del suelo en 65 microcuencas del corredor seco hondureño. Imágenes derivadas de los sensores ETM+ y OLI de los satélites Landsat, para los años 2010, 2015 y 2017 fueron clasificadas en 13 categorías. La cobertura de pastos y cultivos fue la predominante durante los tres años analizados (23%, 28% y 33%), mostrando un avance en el tiempo en detrimento de la cobertura boscosa. La pérdida de bosque para el período 2010-2015 fue de 15% y de 12% para el período 2015-2017. Se utilizó el modelo GEOMOD2 para hacer una proyección de la deforestación al 2020, y se obtuvo que la cobertura boscosa para el área de estudio representará un 47% (siendo de un 58% para el 2010). Finalmente se realizó un análisis de la incidencia de incendios forestales, obteniéndose que las microcuencas más vulnerables son las ubicadas en las mancomunidades MAMSURPAZ y MANCOSOL, siendo el pino denso el más afectado.
... The identification of the subsets to be used can be categorized into two basic approaches. The analysis is based either on calculating the eigenvalues (and eigenvectors) (Li et al., 2014) or separability analysis (Lunetta & Balogh, 1999). Separability analysis deals with calculating the statistical distance between the spectral classes. ...
Land cover classification is one of the important applications of the satellite images. The accuracy of the classification process depends on the feature selection. In multispectral satellite images, the separability of the features depends on the band combinations used. This work demonstrates the change in the accuracy of the classifiers with different band combinations and different distance measures used for analysing the separability. Landsat-8 images have been classified into four land cover classes using the Maximum Likelihood (ML) Classifier, Minimum Distance (MD) Classifier and the Spectral Angle (SA) mapper. The spectral separability between each of the land cover classes is analysed for the band combinations of 3-2, 4-3-2, 3-2-1, 7-6-5-4, 7-6-5-4-3, using the Jeffries-Matusita Distance measure, the Euclidean Distance and the spectral angle measure. It is shown that maximum separability and hence the optimal accuracy of 85.81% is obtained with a SA mapper using the spectral angle measure on a three-band combination of 4-3-2. An accuracy of 80.12% is achieved with a ML classifier using Jeffries-Matusita distance measure with a band combination of 4-3-2. Lastly, the MD classifier gives an accuracy of 76.56% using the Euclidean distance measure with a band combination of 4-3-2.
... Further, RS data helps to deal with the problem of terrain inaccessibility and provide top of canopy vegetation phenology of wetland (Nghiem et al., 2017;Tong et al., 2019). Many satellite missions, including optical and radar, offer the long time series and still ongoing multi-temporal data with global coverage for land cover monitoring and wetland classification and change detection efforts (Baker et al., 2006;Chen et al., 2014;Lunetta and Balogh, 1999;Nghiem et al., 2017). Recent review insights the available data types for using wetland covers (Nghiem et al., 2017). ...
Wetlands are important for their peat reservoir, dynamic land cover and natural resources, ecological and hydrological regimes, fossil fuels reservoir, and crucial carbon storage. The global wetlands are decreasing since 1800 due to climatic phenomena and human activities. Wetland mapping with satellite data is not new but an
ongoing challenge due to its precision relies on data quality and data classification schemes. The accuracy of such mapping is emerging due to the gradual establishment of satellite data and subsequent data modeling technologies, i.e., big data modeling with machine learning (ML) algorithms. Our study introduced a simple, scalable,
and robust wetland classification by applying unsupervised (K-means cluster – KMC) and supervised (Support vector machine classification – SVMc) ML algorithms. We used Landsat optical data to model normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), as the primary inputs for KMC. Later KMC data were supervised by SVMc with training data from 20 field observations. The accuracy tests insights that both optical indices have considerably less error and all SVMc models have about 99% accuracy. The 1 to 1 validation insights that our wetland classification presented more detail wetland cover areas than reference data. The test case SVMc showed that Class 1 and 2 are optimally fitting, Class 3 and 4 are overfitting, and Class 5 is underfitting. Furthermore, the sensitivity analysis insight that all SVMc models are optimally fitting
and SVMc is more sensitive to NDVI than NDWI. From SVMc models we can see that Selisoo bog in Estonia lost a considerable amount of wetland covers including water bodies, and more large forest covers are taking wetland areas, i.e., mixed forest and coniferous trees. Our methodological approach insights a simple and robust wetland classification based on advanced unsupervised and supervised ML algorithms despite some unavoidable limitation.
... [11] [28] [30][31] Thepopulation data for year 1996, 2006 and 2016 are 18089, 20825, 23931 respectively, based on the Population and Housing Census of Bhutan (PHCB 2005). The population figures are computed based on the 1.4% population growth annually based on the 2005 population where the population of Phuentsholing was 20537 in 2005. ...
The rapid phase of urbanization and infrastructure development in Bhutan has been observed recently. This leads to causing of decrease in vegetation cover and growth in urban sprawl undergoing rapid land use/land cover change (LULC). This paper attempts to analyze the temporal and spatial patterns of LULC change and detects the urbanization processes of Phuentsholing city over a period of three decades (1996-2016) using multi temporal remotely sensed data. For this, the satellite images of Landsat 5, 7 and 8 were used to assess the changes of vegetation cover, built form and water bodies. This study has found that urban built area was increased from 6.7% in 1996 to 17% in 2016 and similarly vegetation cover was declined from 48.4% in 1996 to 49.9% in 2016. This urban expansion causes loss of vegetation cover that hinders the country’s regulation of retaining 60% forest according to The Constitution of the Kingdom of Bhutan. These finding can provide city planners and decision makers with information about the past and current spatial dynamics of LULC change to investigate, plan and monitor the urban development and management of Phuentsholing municipality.
... Thus, the value that each pond received for the water permanence value was the number of the 9 dates that it was inundated with water. This approach is accurate at detecting inundated wetlands (>87%; Muller et al. 1998, Lunetta andBalogh 1999), and expected to be more accurate for the coastal ponds in our study where emergent vegetation is minimal (Grenier et al. 2008). ...
The Laguna Madres of Texas, USA, and Tamaulipas, Mexico, are the most important wintering areas for redheads (Aythya americana) as most of the continental population winters in these lagoons. Redheads forage in the saline waters of the Laguna Madre and make daily flights to coastal freshwater ponds on the adjacent mainland to drink. The abundance and spatial distribution of coastal ponds varies depending on precipitation and can influence the foraging pressure on adjacent seagrass meadows. We conducted weekly aerial surveys to monitor coastal pond use by wintering redheads from mid‐October through mid‐March along the entire length of the Laguna Madre of Texas, during 2000–2003 and in 2012–2014. We developed 3 parameters to provide a measure of biological value of each coastal pond to redheads: amount of foraging habitat within 10 km of each pond, water permanence of the pond, and the potential to distribute redheads if inundated. During 101 aerial surveys across 5 years of study, we identified 140 coastal ponds that were used by redheads. We developed a prioritization scheme to identify wetlands that remain inundated in all years and targeted them for conservation protection. We identified those coastal ponds that, if enhanced through increasing their water permanence, would provide additional drinking sites during dry years and help distribute redheads on more foraging habitat, thereby reducing potential overgrazing on seagrass meadows. We identified 3,624 ha of foraging habitat (21.5% of all foraging habitat) in the lower Laguna Madre that had no coastal ponds within a 10‐km radius and, thus, was proximal to potential areas for coastal pond creation. Our results provide guidance for resource managers to protect, enhance, or create coastal ponds to reduce foraging pressure on seagrass meadows in the Laguna Madre and help sustain future populations of wintering redheads.
... While industrial agriculture is widespread across Orange Walk, traditional agriculture, including milpa agriculture and horse-drawn plowing, still occurs today, but much less so than in the past. Using fire and letting fields lie fallow in the milpa technique maintains soil fertility through time [41]. In other areas, the land is cultivated for longer periods of time with horse-drawn plowing and organic or inorganic fertilizer inputs. ...
Changes in land-use and land-cover, including both agricultural expansion and the establishment of protected areas, have altered the landscape pattern and extent of forest and wetland cover in the tropics. In Central America, land-use and land-cover change is also threatening the cultural resources of the region’s ancient Maya heritage since many ancient sites have been degraded by burning, deforestation, and plowing. In this study of Orange Walk District of northern Belize, from the 1980s to the present, we used multitemporal Landsat data with a random forest classifier to reveal trends in land-use and land-cover change and the increasing loss of forest and wetlands. We develop a random forest classifier that is time-generalized to map land-use and land-cover across the entire Landsat record, including Landsat 4, 5, 7, and 8, with a single algorithm. Including multiyear and seasonal composites was important for obtaining cloud-free coverage and distinguishing between different land-use and land-cover types. Early deforestation (1984–1987) was in small patches scattered across the landscape and likely driven by small scale agriculture such as milpa and smaller area tractor and horse-drawn plowing. The establishment of protected areas in the late 1980s and early 1990s allowed for forest regrowth in these areas, while wetland losses were high at 15%. The transition to industrial agriculture in the 2000s, however, drove a 43.6% expansion of agriculture and a 7.5% loss of forest and a 28.2% loss of wetlands during the ~15 years. Protected areas initiated in the 1980s led to a nearly 100 km2 decrease in agriculture from 1984–1987 to 1999–2001, and they became essential refugia for habitat and maintaining ecosystem services.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
This book presents the most relevant basaltic plateau exposures in the provinces of Neuquén (northern Patagonia) and Santa Cruz (southern Patagonia), and analyzes their geomorphological and morphometric characteristics. The existence of wetland ecosystems near the volcanic plateaus is quantified, thus providing indexes that describe the quantitative relationships between these landscape features. These indexes also make it possible to estimate the development of these wetlands in non-surveyed areas, opening the door for studying remote, isolated areas by means of remote sensing images. In turn, the book proposes a numerical classification system for this type of landscape that summarizes the main geomorphological and hydrological characteristics.
... For instance, estimated the distinguish of wetlands with the bands 2 to 5 of the single date and Landsat 5 images with multi temporal. The overall accuracy was 69% of the single date image compared with 88% from the two date images with an impotent increase in the Kappa test statistics [11]. Maxwell et al. [12] introduced an automated technique for classification of four land cover types using only the bands 2 and 4 from Landsat MSS with 92.2% overall accuracy. ...
Abstract. On a large scale, the land cover classification has been investigated throughout the world in remote sensing for different kinds of applications such as
water resources, agricultural, environmental, as well as ecological and hydrological applications. In order enhance accuracy of the classification results, Landsat and multispectral bands are used to study the numerous classification methods. Remote
sensing thermal data provides valuable information in order to examine the effectiveness of applying the thermal bands to extract useful land cover thematic maps. In this research, Landsat-8 satellite data captured by Operational Land Imager (OLI) and the Thermal Infrared (TIRS) Sensors, with using remotely sensing data and Geographic Information System (GIS) analysis with using ground truth data collect from fieldwork in same time of imagery capturing by using infrared thermometer camera. In 2018, single date Landsat-8 image of the study area in Iraq was captured in winter. This image is used to estimate Land Surface Temperature (LST) by split window algorithm and performing Land Cover (LC) classification after image noise removal by using supervised classification algorithms Support Vector Machine (SVM) with multi-spectral and thermal bands combinations to find out which one has more accuracy. Result shows the effective and efficiency of the proposed method compared by traditional classification methods. The overall accuracy and Kappa coefficient are 94.25%, 64.43% and 0.93, 0.63, respectively.
... Previous studies have noted the benefits to wetland mapping by using multi-temporal observations from multispectral optical sensors (Lunetta and Balogh, 1999;Zhang et al., 2017), SAR sensors (Banks et al., 2019;Brisco et al., 2011;Mahdavi et al., 2017;Martinez and Le Toan, 2007), or a combination of both (Bourgeau-Chavez et al., 2016; Corcoran et al., 2013;Töyrä et al., 2001). Earlier studies utilizing remotely sensed time series data over wetlands were often limited to relatively smaller spatial extents due to lack of data and burdensome data preprocessing needs, including co-registration, cross calibration, and quality screening. ...
Wetlands are recognized for their importance to a range of ecosystem goods and services; however, detailed information on wetland presence, type, extent, and persistence is challenging to attain over large areas and/or long time periods due to the spatial complexity and temporal dynamism of wetlands. In this study we explored the potential for within-year time series of C-band Synthetic Aperture Radar (SAR) observations from the free and open Sentinel-1 data archive to improve discrimination of treed and non-treed wetlands and non-wetlands in a boreal forest environment. Through a set of 3843 classification experiments for the year 2017, we tested the influence of three factors on classification accuracy: (i) input features (two backscatter coefficients in VV and VH polarization (VV and VH) and four quantitative measures derived from the Stokes vector); (ii) the temporal form of features (i.e. using all within-year observations versus generalized measures such as monthly/seasonal means or annualized statistics); and (iii) missing observations in Sentinel-1 time series due to varying observation availability across space. Among the tested features, we found the greatest utility in VV and VH. Directly using all within-year observations yielded higher accuracy than using generalized temporal forms. Moreover, the temporal form of the features had a greater impact on classification accuracy than the features themselves. The highest overall accuracy (0.860 ± 0.002) was achieved using VV and VH from all within-year observations. The majority of class confusion occurred between treed wetlands and non-wetlands. We found no significant reduction in the overall accuracy by simulated missing observations in time series when using all within-year observations. With the increasing availability of free and open data from the Sentinel-1 archive, new opportunities are emerging to readily integrate within-year time series into large-area land cover mapping, particularly if analysis-ready SAR data products further reduce preprocessing requirements for end users.
... Landsat TM and SPOT images have been widely used to study wetlands, (Jensen et al., 1984;Lunetta and Balogh, 1999;Harvey andHill, 2001 andDwivedi et al., 2004). More recently, ASTER, ERS, AVHRR and Hyperspecteral data have been extensively used especially in wetland delineation, (Roshier and Rumbachs, 2004;Becker et al., 2005;Li and Chen, 2005;Judd et al., 2007 andRenzong andLiliang, 2008). ...
Wetlands represent a very small area of the earth's land surface. These ecosystems are ecologically and economically valuable. However, they continue to be among the world's most threatened ecosystems. Successful conservation of these areas relies on accurate mapping and monitoring. Therefore, the current work suggested a protocol for mapping the wetlands along the Mediterranean Sea Coast of Egypt using remote sensing and GIS techniques. El-Bardawil and El-Burullus regions were selected as the study areas for the current work. The proposed protocol included a sequence of image pre-processing and processing steps. The pre-processing steps included linear stretch, noise reduction, relative sun angle correction, resolution merge, mosaicking and finally subsetting of the images for only the study area. The image processing included the principal component analysis (PCA) and image classification. Two principal component analyses were applied namely; a general PCA of all the bands of both ASTER and ETM+ data and a specific PCA of only the shortwave and the thermal infrared bands. The first and second layers of both PCAs were stacked into one image for each data type and used in the image classification. Accordingly, the overall mapping accuracy of the ETM+ data ranged from 82.7 to 90.7%, while it ranged from 86.3 to 92.2 % in the case of ASTER data.
... In the case of optical data, study in Lunetta and Balogh (1999) proposed the use of SITS with Landsat 5 TM imagery for wetlands identification, as one of temporary surface water area. They use a GIS rule-based classification model in an attempt to improve the classification accuracies of the wetland classes. ...
Les eaux de surface sont des ressources importantes pour la biosphère et l'anthroposphère. Elles favorisent la préservation des habitats, le développement de la biodiversité et le maintien des services écosystémiques en contrôlant le cycle des nutriments et le carbone à l’échelle mondiale. Elles sont essentielles à la vie quotidienne de l’homme, notamment pour l'irrigation, la consommation d’eau potable, la production hydro-électrique, etc. Par ailleurs, lors des inondations, elles peuvent présenter des dangers pour l'homme, les habitations et les infrastructures. La surveillance des changements dynamiques des eaux de surface a donc un rôle primordial pour guider les choix des gestionnaires dans le processus d’aide à la décision. L’imagerie satellitaire constitue une source de données adaptée permettant de fournir des informations sur les eaux de surface. De nos jours, la télédétection satellitaire a connu une révolution avec le lancement des satellites Sentinel-1 (Radar) et Sentinel-2 (Optique) qui disposent d’une haute fréquence de revisite et d’une résolution spatiale moyenne à élevée. Ces données peuvent fournir des séries temporelles essentielles pour apporter davantage d'informations afin d'améliorer la capacité d'observation des eaux de surface. L’exploitation de telles données massives et multi-sources pose des défis en termes d’extraction de connaissances et de processus de traitement d’images car les chaines de traitement doivent être le plus automatiques possibles. Dans ce contexte, l'objectif de ce travail de thèse est de proposer de nouvelles approches permettant de cartographier l’extension spatiales des eaux de surface et des inondations, en explorant l'utilisation unique et combinée des données Sentinel-1 et Sentinel-2.
... Optical remote sensing (RS) can provide the quantitative, multi-scale information necessary to assess vegetation condition and the risk and impact of weed invasions across landscapes [20,43,51,[56][57][58][59][60]. It can provide accurate, cost-effective, continuous and contiguous spatial information on the distribution of vegetation and habitats over extensive and often inaccessible wetland landscapes [40,[61][62][63][64][65][66][67][68][69][70]. Indeed, RS is often the only source of information readily available to characterise habitats, and monitor and detect weeds or environmental change in such areas [71,72]. ...
African para grass (Urochloa mutica) is an invasive weed that has become prevalent across many important freshwater wetlands of the world. In northern Australia, including the World Heritage landscape of Kakadu National Park (KNP), its dense cover can displace ecologically, genetically and culturally significant species, such as the Australian native rice (Oryza spp.). In regions under management for biodiversity conservation para grass is often beyond eradication. However, its targeted control is also necessary to manage and preserve site-specific wetland values. This requires an understanding of para grass spread-patterns and its potential impacts on valuable native vegetation. We apply a multi-scale approach to examine the spatial dynamics and impact of para grass cover across a 181 km 2 floodplain of KNP. First, we measure the overall displacement of different native vegetation communities across the floodplain from 1986 to 2006. Using high spatial resolution satellite imagery in conjunction with historical aerial-photo mapping, we then measure finer-scale, inter-annual, changes between successive dry seasons from 1990 to 2010 (for a 48 km 2 focus area); Para grass presence-absence maps from satellite imagery (2002 to 2010) were produced with an object-based machine-learning approach (stochastic gradient boosting). Changes, over time, in mapped para grass areas were then related to maps of depth-habitat and inter-annual fire histories. Para grass invasion and establishment patterns varied greatly in time and space. Wild rice communities were the most frequently invaded, but the establishment and persistence of para grass fluctuated greatly between years, even within previously invaded communities. However, these different patterns were also shown to vary with different depth-habitat and recent fire history. These dynamics have not been previously documented and this understanding presents opportunities for intensive para grass management in areas of high conservation value, such as those occupied by wild rice.
... Suitable pairs were obtained for the years 1989-1990, 1999-2000, 2006, and 2015. As shown by Lunetta and Balogh (1999), who selected leaf-on and leaf-off Landsat TM images to identify wetland habitats in Maryland and Delaware, this is a useful approach in environments that undergo strong seasonal contrasts. Adam et al. (2010) emphasized that mapping areas containing wetlands was particularly difficult because of their short ecotones (sharp demarcations between vegetation units) and because differences in spectral responses are subtle owing to variability in the spectral signatures of soils and vegetation -which themselves depend on hydrological regime and atmospheric vapour content. ...
This work maps and interprets the evolution of the urban footprint of Phnom Penh from 1973 to 2015 and reviews its main socio-ecological impacts. The quantified patterns of urban growth and land-use change are based on the processing and analysis of Landsat satellite images (MSS, TM, and OLI sensors) and are enhanced by observation- and interview-based information obtained in the field. The growth of Phnom Penh is shown to have encroached initially on the fertile agricultural lands of the Mekong River floodplain, but since 2006 the city has
been sprawling predominantly over natural lakes and wetlands that until then were functional components of the urban mosaic and underpinned the livelihoods of its population. Urban land areas increased from 3000 ha in 1973 to 4000 ha in 1990, subsequently soaring to 25,000 ha in 2015, i.e. an average annual increase of 850–1000 ha. The discussion of these changes focuses on (i) changing livelihoods on the urban fringe; (ii) the numerous, and sometimes large, real-estate projects that have sprung up around the city centre on former wetland areas and are funded by strongly imbricated capital interests between the nation’s elites and foreign investors; (iii) a comparison of the urban growth figures with other Asian cities; and (iv) current land policies in Cambodia. The results call for urgent policymaking to address the diseconomies that arise from the impending loss of ecosystem services; from the accentuation of flood hazards caused by the rapid, ongoing suppression of wetland habitats at the delta head of the eighth largest river in the world; and from the unequal distribution of the costs and benefits of urban growth among the metropolitan population.
... Suitable pairs were obtained for the years 1989-1990, 1999-2000, 2006, and 2015. As shown by Lunetta and Balogh (1999), who selected leaf-on and leaf-off Landsat TM images to identify wetland habitats in Maryland and Delaware, this is a useful approach in environments that undergo strong seasonal contrasts. Adam et al. (2010) emphasized that mapping areas containing wetlands was particularly difficult because of their short ecotones (sharp demarcations between vegetation units) and because differences in spectral responses are subtle owing to variability in the spectral signatures of soils and vegetation -which themselves depend on hydrological regime and atmospheric vapour content. ...
... To increase the separability between land-cover categories, information on variations in the phenological state of vegetal cover can be added by incorporating of multi-seasonal images. Several studies have shown that a combination of multi-seasonal images facilitates discrimination between certain land covers (Lunetta and Balogh 1999;Oetter et al. 2001;Wolter et al. 1995;Yuan et al. 2005). In summer images, shrublands can be confused with bare soils and outcrops. ...
... Wetland detectionby satellite imagery is well known (Carlson and Azofeifa, 1999;Lunetta & Balogh, 1999;Ozesmiand Bauer, 2002;Hayder, 2013;Hayder 2018). Wetlands can be effectively estimated froman aerial photos and/or satellite imagies (Dahl, 2006). ...
Al-Razaza Lakeis one of the biggest lake in Iraq, and it is considered as a sources of wealth of fish, and flood water retention. The lake suffered from dehydration extent in during the last three decades. In this study; we propose a method to monitor and detect the changes of AL-Razaza Lake in the course of the time between1992–2018usingtime series of Landsat satellite images. In this study different stages of processing and analyzing, noise removal were performed. In doing so, the applicability of different satellite derived indices including normalized difference water index and normalized difference vegetation index were investigated for the extraction of Lake surface water. An unsupervised (K-Means) classification was applied. The results showed that AL-Razaza Lake has been changed rapidly. Two noticeable results show the rapidly decreasing in the Lake area using NDWIs and NDVIs by81.17%and 79.69% with area about 1187.40km2and 1189.24 km2 respectively. Unfortunately all the dehydration extended areas were replaced by soil, threat the biodiversity and wildlife in this Lake, and left the Lake suffering more for near future.
... Also, Esam et al (2012) reported that Landsat TM imagery provided good accuracyfor quantifying land cover changes, and very useful information for natural resources management of the West Tahta Region, Sohage Governorate, Upper Egypt, but it still lacks the spatial resolution to map all important cover classes, especially in small areas, where the class area covered less than the pixel size in the TM image [3]. Finally, it might be worth noting that usual costs increase roughly in proportion to increases in mapping resolution [6]. In study area, the Landsat TM images were classified using two methods: unsupervised and supervised. ...
ABSTRACT
Muthurajawela wetland is a coastal wetland system of high biodiversity and ecological
significance. At present, this Muthurajawela wetland is being rapidly degraded by inadequately
planned development activities and other detrimental activities related to growing human
population pressure. As over a time, there will be change in vegetation area. Therefore, an
effective method should be used to re-evaluate the change in area. Remote sensing technology is
the most effective method and is used in this study. Three Landsat (TM) satellite images (1992,
2001 and 2015) were taken for comparison. The results showed that Multi-temporal Landsat
images with the average resolution have the ability to assess the vegetation coverage changes
with guaranteed results as, we have established a six-vegetation cover layer classification map
with an overall accuracy of 84.66% and a kappa coefficient of 0.81. The total natural land area of
Muthurajawela wetland was 6,232 ha in 2015. Of which, 492.95 ha was marsh, 232.94 ha was
grass, 281.62 ha was water and 5,225.27 ha was of forest land; The area of mangroves forest in
1992 increased by 317.66 ha compared to in 2001 and decreased by 300.42 ha in 2001 compared
with in 2015, increasing only 17.24ha in 1992 compared with in 2015.
KEYWORDS:Landsat, Remote sensing,Muthurajawela wetland, Sri Lanka.
... Suitable pairs were obtained for the years 1989-1990, 1999-2000, 2006, and 2015. As shown by Lunetta and Balogh (1999), who selected leaf-on and leaf-off Landsat TM images to identify wetland habitats in Maryland and Delaware, this is a useful approach in environments that undergo strong seasonal contrasts. Adam et al. (2010) emphasized that mapping areas containing wetlands was particularly difficult because of their short ecotones (sharp demarcations between vegetation units) and because differences in spectral responses are subtle owing to variability in the spectral signatures of soils and vegetation -which themselves depend on hydrological regime and atmospheric vapour content. ...
This work maps and interprets the evolution of the urban footprint of Phnom Penh from 1973 to 2015 and reviews its main socio-ecological impacts. The quantified patterns of urban growth and land-use change are based on the processing and analysis of Landsat satellite images (MSS, TM, and OLI sensors) and are enhanced by observation- and interview-based information obtained in the field. The growth of Phnom Penh is shown to have encroached initially on the fertile agricultural lands of the Mekong River floodplain, but since 2006 the city has been sprawling predominantly over natural lakes and wetlands that until then were functional components of the urban mosaic and underpinned the livelihoods of its population. Urban land areas increased from 3000 ha in 1973 to 4000 ha in 1990, subsequently soaring to 25,000 ha in 2015, i.e. an average annual increase of 850–1000 ha. The discussion of these changes focuses on (i) changing livelihoods on the urban fringe; (ii) the numerous, and sometimes large, real-estate projects that have sprung up around the city centre on former wetland areas and are funded by strongly imbricated capital interests between the nation’s elites and foreign investors; (iii) a comparison of the urban growth figures with other Asian cities; and (iv) current land policies in Cambodia. The results call for urgent policymaking to address the diseconomies that arise from the impending loss of ecosystem services; from the accentuation of flood hazards caused by the rapid, ongoing suppression of wetland habitats at the delta head of the eighth largest river in the world; and from the unequal distribution of the costs and benefits of urban growth among the metropolitan population.
... Given the interest in capturing not only wetland area, but also change in wetland status and extent over time, multi-temporal investigations are increasingly of interest. The use of time series satellite imagery has been demonstrated [17,19,20]. Dingle Robertson et al. [19] showed for two wetland complexes in Ontario, Canada, over a 26-year period using Landsat-5 Thematic Mapper data, an ability to capture a range of wetland dynamics as well as inference of the drivers of change. ...
Wetlands are important globally for supplying clean water and unique habitat, and for storing vast amounts of carbon and nutrients. The geographic extent and state of wetlands vary over time and represent a dynamic land condition rather than a permanent land cover state. Herein, we combined a time series of land cover maps derived from Landsat data at 30-m resolution to inform on spatial and temporal changes to non-treed and treed wetland extents over Canada's forested ecosystems (>650 million ha) from 1984 to 2016. Overall, for the period, 1984 to 2016, we found the extent of wetlands (non-treed and treed combined) in Canada's forested ecosystems to be stable, with some regional variability, often resulting from offsetting decreases and increases within a given ecozone. Notwithstanding difficulties in using optical satellite data for mapping a land condition, by accumulating wetland evidence via earth observations consistently through multiple decades, our results capture the trends in wetland cover over a previously unmapped, national extent at a level of spatial detail and temporal reach suitable for further focused interpretations of wetlands and drivers and projections of wetland dynamics.
... Also, Esam et al (2012) reported that Landsat TM imagery provided good accuracyfor quantifying land cover changes, and very useful information for natural resources management of the West Tahta Region, Sohage Governorate, Upper Egypt, but it still lacks the spatial resolution to map all important cover classes, especially in small areas, where the class area covered less than the pixel size in the TM image [3]. Finally, it might be worth noting that usual costs increase roughly in proportion to increases in mapping resolution [6]. In study area, the Landsat TM images were classified using two methods: unsupervised and supervised. ...
ABSTRACT
Muthurajawela wetland is a coastal wetland system of high biodiversity and ecological significance. At present, this Muthurajawela wetland is being rapidly degraded by inadequately planned development activities and other detrimental activities related to growing human population pressure. As over a time, there will be change in vegetation area. Therefore, an effective method should be used to re-evaluate the change in area. Remote sensing technology is the most effective method and is used in this study. Three Landsat (TM) satellite images (1992, 2001 and 2015) were taken for comparison. The results showed that Multi-temporal Landsat images with the average resolution have the ability to assess the vegetation coverage changes with guaranteed results as, we have established a six-vegetation cover layer classification map with an overall accuracy of 84.66% and a kappa coefficient of 0.81. The total natural land area of Muthurajawela wetland was 6,232 ha in 2015. Of which, 492.95 ha was marsh, 232.94 ha was grass, 281.62 ha was water and 5,225.27 ha was of forest land; The area of mangroves forest in 1992 increased by 317.66 ha compared to in 2001 and decreased by 300.42 ha in 2001 compared with in 2015, increasing only 17.24ha in 1992 compared with in 2015.
KEYWORDS: Landsat, Remote sensing, Muthurajawela wetland, Sri Lanka.
... Study area Data ( WorldView-2 (2 m) WorldView-2 spectral bands and spatial resolution improve wetland-mapping accuracy over similar high-resolution satellite and aerial imagery Lunetta and Balogh (1999) Maryland and Delaware ...
... Most of the studies dealing with the detection of inundated areas use medium-resolution optical imagery such as those acquired by the Landsat series sensors [28], which is characterized by a fairly high spatial resolution (30 m for sensors working since 1982). This is sufficient for mapping water bodies with few or seasonal variations in their extent like rivers [29][30][31], coast lines [32][33][34], lakes [35], small water bodies [36] or natural stable wetlands [37][38][39][40]. However, their 16-day revisiting time limits their usefulness for detecting rapid flooding events or fast water dynamics: this is the case for example of disastrous inundations or agronomic flooding, the persistence of which can be restricted to only a few days [41]. ...
The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of submerged paddies strictly depends on crop management practices: in this framework, the recent diffusion of rice seeding in dry conditions has led to a reduction of flooded surfaces during spring and could have contributed to the observed decline of the populations of some waterbird species that exploit rice fields as foraging habitat. In order to test the existence and magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period, MODIS remotely-sensed data were used to estimate the extent of the average flooded surface and the proportion of flooded rice fields in the years 2000–2016 during the nesting period of waterbirds. A general reduction of flooded rice fields during the rice-sowing season was observed, averaging − 0.86 ± 0.20 % per year (p-value < 0.01). Overall, the loss in submerged surface area during the sowing season reached 44 % of the original extent in 2016, with a peak of 78 % in the sub-districts to the east of the Ticino River. Results highlight the usefulness of remote sensing data and techniques to map and monitor water dynamics within rice cropping systems. These techniques could be of key importance to analyze the effects at the regional scale of the recent increase of dry-seeded rice cultivations on watershed recharge and water runoff and to interpret the decline of breeding waterbirds via a loss of foraging habitat.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
Taking into consideration the different situations observed in the field, this chapter proposes a model of evolution of the Patagonian volcanic landscape developed from the outcrop of basaltic flows. The different geomorphological processes that act upon the evolution of these landscapes are exposed, particularly fluvial erosion and mass movement processes, and the factors that contribute to the modification or interruption of the evolutionary sequence proposed. The term “landscape of lobes and hummocks” is proposed for the final evolutionary stage of these landscapes. The rate of relative elevation of the basaltic mesetas is also estimated.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
This chapter highlights the importance of those landscapes formed by volcanic ‘escoriales’ in the context of the extensive Patagonian region which is located east of the Andean Cordillera, as well as their associated ‘mallín’ ecosystems. Their more important role is the water supply within a drier, surrounding environment, both for rural dwellers as well as the survival and development of the biota. Likewise, these landscapes provide scenic resources and they create sources of information to understand the geological and paleoenvironmental history of southernmost South America. The ‘mallines’ also possess high productivity of biomass and fodder, which are highly appreciated by the rural inhabitants and thus, used as cattle and sheep grazing places. Nevertheless, their condition of azonal ecosystems provides them high brittleness and many of them show different degrees of degradation, which display alterations of the hydrological conditions of soil and vegetation and soil which sooner or later derive in the erosion of the more endangered sites. The hydro-geological model described in this book, perhaps could be used as a terrestrial analogue for the process of water infiltration in the polar regions and volcanic landscapes of Mars. In these Martian environments, water ice melts and permeates along vertical and oblique fractures in volcanic rocks, to reappear again at the base of the extremely dry volcanic outcrops, like it happens on Earth.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
This chapter analyzes the spatial heterogeneity of the “mallines” ecosystems, by means of case studies. This variability is related to water availability, which is associated to the slopes, topography, and seasonal changes of the water table, factors which define the distribution and permanence of water resources within the wetlands. The adaptation of the soil conditions and the biota to water availability outlines the internal configuration of wet meadows, which is exposed in different environmental units. The recognition of these units is a very important tool for planning and it identifies the working scales that must be adopted for management and conservation of these natural resources.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
The present chapter offers the studies accomplished in the “mallines” or wet meadows associated with basaltic plateaus in the provinces of Neuquén and Santa Cruz in different working scales, and which are later exposed in the following chapters. Likewise, the remote sensing techniques used for the identification and cartography of these wetlands are described, which encompass a relevant methodological tool to perform their inventory that has never been completed yet in this region.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
This chapter presents the problems discussed in this book and the methodology used for their study, based upon a wide use of remote sensing techniques. The spatial relationships established between the two major and typical elements of the Patagonian landscapes, the basaltic plateaus and the wet meadows, known in this region as “escoriales” and “mallines”, respectively, are analyzed. The characteristics of both landscape components are described and a classification system is proposed, based upon a six-digit system which synthetizes the geological, geomorphological, and hydrological characteristics of each “escorial”. The full structure of the book, organized in 10 chapters, is herein presented.
... The first experiences were conducted by means of vertical aerial photographs Tiner 1990;Lyon and Greene 1992;Rutchey and Vilchek 1999) and airborne video (Thomasson et al. 1994), widening later into the field of multispectral satellite imagery, with techniques of visual analysis and digital processing. There are vast quantities of papers concerning wetland surveying, using diverse spatial systems, among which LANDSAT, MODIS, and SPOT are the mostly used sensors with medium spatial resolution (Cowardin and Myers 1974;Tiner 1996;Lunetta and Balogh 1999;Baker et al. 2006;Hu et al. 2015;Ghosh 2016). ...
This chapter analyzes the qualitative attributes of the “escoriales” surveyed in the provinces of Neuquén and Santa Cruz, particularly those related to their geological and geomorphological features that are the more relevant aspects of the volcanic landscapes. The information synthesizes the observations performed on the 452 “escoriales” included in the corresponding inventory of the provinces of Neuquén and Santa Cruz, Argentina Patagonia. Plains, “mesetas” and cones with geomorphological features pertaining to volcanic processes have been identified, as well as those modeled by exogenic processes.
... A supervised classification run on ERDAS Imagine, using a false colour composite image made up of Landsat spectral bands 7, 4 and 2, was used to build wetland training sites which are hydrophytic vegetation, hydric soils, and open water. This was necessary as the major wetland indicator is saturation of land surfaces by water (Lunetta and Balogh, 1999). In the tropics, the three are easily noticeable during the dry season as the vegetation remains green and the seasonally flooded area is visible by its hydric soils. ...
Wetlands can only be well managed if their spatial location and extent are accurately documented, which presents a problem as wetland type and morphology are highly variable. Current efforts to delineate wetland extent are varied, resulting in a host of inconsistent and incomparable inventories. This study, done in the Witbank Dam Catchment in Mpumalanga Province of South Africa, explores a remote-sensing technique to delineate wetland extent and assesses the seasonal variations of the inundated area. The objective was to monitor the spatio-temporal changes of wetlands over time through remote sensing and GIS for effective wetland management. Multispectral satellite images, together with a digital elevation model (DEM), were used to delineate wetland extent. The seasonal variations of the inundated area were assessed through an analysis of monthly water indices derived from the normalised difference water index (NDWI). Landsat images and DEM were used to delineate wetland extent and MODIS images were used to assess seasonal variation of the inundated area. A time-series trend analysis on the delineated wetlands shows a declining tendency from 2000 to 2015, which could worsen in the coming few years if no remedial action is taken. Wetland area declined by 19% in the study area over the period under review. An analysis of NDWI indices on the wetland area showed that wetland inundated area is highly variable, exhibiting an increasing variability over time. An overlay of wetland area on cultivated land showed that 21% of the wetland area is subjected to cultivation which is a major contributing factor to wetland degradation.
The Geospatial Information System (GIS) and remote sensing being very modern and effective environmental assessment tools provide major sources of spatial information about the Earth surface’s cover and constitution by using different sensors in order to capture sufficient and relevant information of the different components of the Earth. The scientists across the world have been using these techniques on standardizing them for monitoring the environmental changes based on spatial information in tune with the requirements of a particular region in well-timed and cost-effective manner. These techniques have been applied successfully for monitoring wetlands by acquiring spatial information in a timely manner during the last five decades. Remote sensing for the assessment of environmental status involves the techniques for the measurement, of any spectral object of the Earth’s surface and atmosphere with distinct features by some special instruments carried by satellites or aircraft. All the generated data and information are used to infer the environmental status with proper quantified interpretations. The successful application of remote sensing techniques to understand several ecological states of wetlands has become emerged as a popular means to study environment and ecology. The eco-dynamics of wetlands are determined by the seasonal oscillation of physico-chemical parameters of the water bodies along with the areal dynamicity. Multispectral satellite data and various band algebra could provide valuable synoptic data on the dynamics of wetlands. This chapter presents basics of the uses of GIS and remote sensing technologies for wetland mapping and monitoring. Few case studies are being highlighted to depict the scopes of applicability of medium-resolution digital satellite data for mapping of different types of wetlands along with surface hydrologic flow pathways. Remote sensing-satellite imageries have been used successfully to delineate the different environmental characteristics of the wetlands in respect of their extent, geomorphology and ecological features. Quantification of major water quality parameters and valuation of ecosystem service of wetland ecosystem in different time scales not only substantiated the ground truth information but also opened up new vistas in classifying different water bodies constituting the EKW into different categories based on their sizes, like small, medium, large, etc., by evaluating the trend and extent of shrinkages of wetlands. In such context, it can be concluded that simultaneous recording of biodiversity and physico-chemical parameters through ground truth study along with application of GIS and remote sensing to record the ecological changing patterns can justify the need of undertaking basic of holistic approach towards integrated eco-management of the sensitive, vulnerable and fragile wetland ecosystems.
Conventional soil mapping in montane environments is often a difficult and laborious task, given access difficulties, the topography of the environment, and the time required to conduct the field investigation. Utilising a remote-sensing tool, such as the Arc Soil Inference Engine (ArcSIE), to map soils in these locations can add valuable information for land management. The ArcSIE tool was utilized in the Cathedral Peak research catchments, in KwaZulu-Natal, South Africa, with the aim of creating an understanding of the hydropedological behaviour of the soils of three research catchments. A rule-based approach was first undertaken, followed by a case-based validation. A fuzzy membership map of each soil group was produced which integrated all inputs. The overall Kappa coefficient for CP-III is 0.57, for CP-VI is 0.59, and for CP-IX is 0.74. The hydropedological soil group maps achieved an appropriate representation of the complex nature of the soil–landscape relationship, with changes between one soil group and the next being gradual and continuous. Accuracies and inaccuracies within the fuzzy membership maps can be quantified, allowing for a confidence rating in the use of these maps. These maps can therefore be used in further applications in water and land management for the area.KeywordsDigital soil mappingRemote sensingHydropedologyAfromontane catchmentsSoil science
Located at an interface between water and land, wetlands are complex ecosystems, which are important for the maintenance of biodiversity. The objective of this work was to evaluate five different optical satellite imagery with different spatial resolution (original: RapidEye – 5 m; Sentinel-2B – 10 m; Landsat 8–30 m; resampled: RapidEye – 30 m; Sentinel-2B – 30 m) in order to obtain the classification of Land Use/Land Cover (LULC) types of a coastal ecosystem, including its wetlands, in the Environmental Protection Area of Guaraqueçaba, PR, Brazil. For this, a supervised classification was made using the algorithm of maximum likelihood to obtain the LULC of each satellite image, using a mosaic of high-resolution spatial imagery from DigitalGlobe as ground-truth verification. A SRTM relief image was used to check wetlands classification against their location in flat and low altitude areas. Analyzing the spatial resolutions of the studied imagery, no significant differences were observed between RapidEye and Sentinel-2B imagery in obtaining the wetland areas, differently of the Landsat 8 image, which showed a classification accuracy a little lower than the others, which may be related to its lower spatial resolution. Using multivariate statistics, three groups were formed with two of them very close to each other (RapidEye and Sentinel-2B), corroborating the conclusion obtained by the kappa index and the overall accuracy. Analyzing the slope and altitude classes, all imagery classified wetlands into flat and low-altitude regions. It is recommended that the Sentinel-2B imagery are used by governments and research agencies for wetland-related policies and research, since this image is distributed free of charge and has a temporal resolution of only 5 days, against payed access of RapidEye.
The Prairie Pothole Region (PPR) of North America is an extremely important habitat for
a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described.
A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing
systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.
Wetlands are among the most bio-diverse and highest productivity ecosystems on earth, making their monitoring a high priority to conservation, protection and management interests. Although visual interpretation of satellite images is generally precise for monitoring wetlands, recent works have emphasized computerized classification methods because of the reduction in analyst time. However, it is difficult to automatically identify wetland solely based on spectral characteristics due to the complexity of wetland ecosystems. The ability to extract wetland information rapidly and accurately is the basis and the key to wetland mapping at a large scale. Here we propose an operational method to map China wetlands based on Landsat TM data and ancillary data. On the basis of theoretical analysis of wetland automatic classification, we developed a revised multi-layer wetland classification scheme and a rule-based classification model. In the latter, supervised classification (SVM and decision tree) and unsupervised classification (ISODATA) methods were tested. Four Landsat TM images, representing various wetland eco-regions in China (i.e. the Sanjiang Plain in the northeast China, the North China Plain, the Zoige Plateau in the southwest China and the Pearl River Estuary in southeast China), were automatically classified. The overall classification accuracies were 86.57%, 96.00%, 84.51% and 88.30%, respectively, which we considered to be satisfactory accuracy. Our results indicate that issues such as the resolution of geographic data and the understanding of wetland samples should be carefully addressed in the future.
Stubbs, Q.; Yeo, I.-Y.; Lang, M.; Townshend, J.; Sun, L.; Prestegaard, K., and Jantz, C., 2020. Assessment of wetland change on the Delmarva Peninsula from 1984 to 2010. Journal of Coastal Research, 36(3), 575–589. Coconut Creek (Florida), ISSN 0749-0208.
The decline in wetland extent and condition emphasizes the need for sound wetland restoration and conservation policies, which require baseline information on wetland status, change, and change drivers. Multiple wetland maps are available, but they can be quite inconsistent bcause of different input and generation techniques, dates, and objectives. Moderate-resolution (30 m²) regional land-cover data sets (LCDs) were analyzed to (1) quantify historical wetland changes on the Delmarva Peninsula at multiple spatial scales between 1984 and 2010, (2) identify differences in the spatial area of wetland change and discuss the source of and implications for these differences, and (3) investigate the extent to which drivers of wetland change can be identified using existing LCDs. The following regional LCDs were considered: the National Oceanic and Atmospheric Administration Coastal Change Analysis Program (C-CAP), the U.S. Geological Survey (USGS) Chesapeake Bay Land Cover Data Series (CBLCD), and the USGS National Land Cover Database. The C-CAP and CBLCD had the highest spatial agreement at 97%, and an average of 76% spatial agreement with the U.S. Fish and Wildlife Service National Wetland Inventory. The highest percentages of net wetland loss occurred between 1992 and 2001, whereas net wetland gain occurred between 2001 and 2010. Wetlands were predominantly converted (e.g., lost) to croplands/grass/shrubs (67%) and water (11%), which could be linked to drivers such as agriculture and sea-level rise.
Coastal areas are natural systems with high ecological and economic value. Among other natural systems, deltas are the ecosystems with the most dynamic and complex relations. This study comprises of Göksu Delta which is located in the Mediterranean region of Turkey. Göksu Delta have one of the most important international wetlands that provide reproduction, nutrition and accommodation facilities for delicate, rare and in danger of extinction of many species. As a result of the hydrologic interventions and several human activities in Göksu Delta, coastal systems of Göksu Delta have been changed drastically. Remotely sensed data, which have obtained from several satellite systems, have proven to be an effective tool for monitoring coastal changes using digital or analog techniques. Thus, in recent years, most mapping and monitoring of coastal areas has been dependent on the use of remotely sensed data, because of the spatially comprehensive view provided by such data. The aim of this is to examine coastal changes in Göksu Delta using principle component analysis. The study demonstrates that this method can reduce errors in change detection using multitemporal images and provide a very useful way in monitoring rapid coastal changes. Coastal progression was detected in the west and southeast. On the other hand, there is a severe regression in the coastal line at the eastern side of the delta.
In recent years, the importance of wetlands better understood and included among the priority areas for conservation in Turkey and the world. A significant number of researchers have examined wetlands due to their various valuable aspects such as diverse vegetation, feeding and shelter areas for many species of birds and positive effects on water quality. Remote sensing data are becoming an important source of information for assessment of wetland health and quality. Traditional methods are time consuming, expensive and labor intensive. In this study, the spectral properties of some wetland plants (Phragmites, Typha, Scirpus and Juncus) found in wetlands located on the Göksu delta were examined in the scope of The Scientific and Technological Research Council of Turkey funded project (110Y295). Handheld spectroradiometer was used to determine the spectral properties of selected wetland plants. Reflectance measurements were conducted on May 28, July 16, September 18 and November 16 and on March 12 (2013). According to results, typical vegetation spectral curves were obtained for all species. All resembled typical upland vegetation curves, with higher reflectance values at wavelength of 700 nanometers (near infrared) as compared to visible wavelengths (400-700 nm) and a green peak around 560 nm.
We combine soil science and economics to provide an integrated framework of Valley-Bottom Wetland (VBW) selection criteria for surface water-quality preservation at the headwater watershed scale when there is no reliable method for quantifying water-quality benefits. We focus on a French agricultural landscape and more particularly on wet meadows. We implement a method of VBW identification based on topographic indices which use threshold values that are calibrated using field measurements. This identification criterion allows us to accurately identify both downstream and upstream VBWs. We then look for criteria with which to prioritize the VBWs identified for conservation purposes. We concentrate on budget-constrained cases and provide a procedure for estimating the cost of conservation for the VBWs. We finally show that it is very important (i) to simultaneously consider hydrogeomorphic and cost criteria for the selection in a budget constrained context and (ii) to go beyond the mean per hectare cost of conservation.
Wetlands are important resources which provide multi-faceted benefits to the environment, ecologically as well as economically. Thus, there is a need for proper wetland monitoring aided through mapping and inventorying. The advent of remote sensing platforms present unique opportunities for large scale and multi-temporal mapping of wetlands. In the Indian context, the National Wetland Inventory and Assessment (NWIA-I) carried out in 2006-07 extensively provides information about the spatial extent of different wetland types. Wetland monitoring through conventional visual methods is tedious and time consuming, rendering large scale monitoring as infeasible. In this article we present an algorithm using index based Gaussian mixture model for automatic delineation of wetland boundaries in India with the aid of ancillary data derived from NWIA-I database. Furthermore, we employ multi temporal optical data from Indian Remote Sensing Satellite (IRS) LISS-III sensor for automatic delineation of water spread boundary along with pre and post monsoon aquatic vegetation extent. We find good correlation between the visual inspected boundary and boundary derived using proposed algorithm. Also, we observe a good correlation between the area derived from visual and automatic method. As an illustration we present results for Gujarat and Goa, thus encompassing all wetland classes.
Models to predict the magnitude of agricultural nonpoint-source pollution in streams have been developed to meet a growing demand for management information. The Agricultural Nonpoint Source (AGNPS) model requires 20 parameters to calculate potential nonpoint-source pollution for a watershed. Landsat thematic mapper data, SPOT multispectral data, and SPOT panchromatic data were tested to determine their ability to provide selected inputs to AGNPS. Each data set was classified using supervised and unsupervised methods, and the accuracy of each classification was evaluated using contingency tables and the kappa statistic. -from Authors
The paper addresses elements of a cost-estimating approach for photogrammetric mapping and related aerial photography and control surveying services. The background for the estimating approach is presented, as are the details related to the elements incorporated in the algorithm to estimate costs. Available details for unit costs and unit hours of effort to complete tasks are provided. The paper also provides supporting information and documentation, including a set of parameters that should be specified in thc contract or be known or assumed to help in the development of a cost estimate using the algorithm described here. The set of parameters includes estimates of the time involved in stereomodel setup and the time involved in stereocompilation for several conditions of topography and planimetric detail. Also included are current photogrammetric mapping, surveying, and aerial photography services' contracting costs, and unit efforts of various federal, state, and private companies as obtained during the execution of this project.
The manuscripts contained in the report describe what the Federal government is doing to map the Nation's coastal wetlands. Various aspects of a series of Federally funded programs are described, including the purpose and intent of the programs, technologies used, the type of data and other mapping products generated, and how the information is used. The report summarizes the major programs and present the Habitat Loss and Modification Working Group's conclusions and recommendations for actions that could be taken to improve the effectiveness of Federal activities.
An investigation was undertaken to compare satellite image classification techniques to delineate forest wetlands in Maine. Four classification techniques were compared, including a GIS rule-based model. Accuracy assessments of the four methods on two study sites, Orono and Acadia, revealed very similar results. Overall accuracy for four super groups (forest wetland, other wetland, forest upland, other upland) ranged from 72% to 81% at Orono and 74% to 82% at Acadia. Pairwise significance tests indicated that the GIS model was significantly better than unsupervised classification at both study sites, and significantly better than tasseled cap (Acadia) in classifying the four super groups. Although Kappa coefficients were slightly higher for the GIS model compared to hybrid classification, there was no significant difference between the two methods at either study site. Forest wetland user's and producer's accuracy was in the 80% range for the highest accuracy achieved either by the GIS model or hybrid classification. Hydric soils, National Wetland Inventory data, and slope percentage were the most important variables in the GIS model. From this study, it appears that a combination of hybrid and GIS rule-based classification methods are the most promising for further investigations of forest wetland delineation.
This paper reviews the necessary considerations and available techniques for assessing the accuracy of remotely sensed data. Included in this review are the classification system, the sampling scheme, the sample size, spatial autocorrelation, and the assessment techniques. All analysis is based on the use of an error matrix or contingency table. Example matrices and results of the analysis are presented. Future trends including the need for assessment of other spatial data are also discussed.