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Enumeration of Prairie Wetlands With Landsat and Aircraft Data
Abstract
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.
... In the 1970s when MSS satellite data became publically available, the data were initially limited to a few organizations due to the equipment and trained personnel required to process the data into information. Several early studies using Landsat 1, formerly known as Earth Resources Technology Satellite (ERTS-1), used MSS data and automated mapping approaches to detect open water and classify vegetation in prairie potholes (Work and Gilmer 1976;Best and Moore 1979;Gilmer et al. 1980). The primary objective of those early investigations was to develop remote sensing applications for the management of migratory waterfowl. ...
... Landsat 1 MSS data were used for many of the first space-based wetland mapping studies in the PPR Work et al. 1974;Work and Gilmer 1976;Gilmer et al. 1980). Gilmer et al. (1974) confusion between spectral classes may be improved with multitemporal data that distinguishes vegetation classes based on phenological differences. ...
... In the 1980s, Gilmer et al. (1980) incorporated high-resolution aerial photography with a double sampling approach to improve estimates of small wetlands (as small as 5 m diameter) from Landsat MSS data. Open surface water was delineated using an earlier established thresholding approach (Work and Gilmer 1976). ...
... For example, Work et al. (1974) used Landsat Multispectral Scanner (MSS) data (79 × 79 m spatial resolution) to map wetlands in a portion of the Prairie Pothole Region of North Dakota, and Seevers et al. (1975) manually classified wetlands larger than 4 ha from MSS data in the Sandhills of Nebraska. Gilmer et al. (1980) digitally processed MSS data to extract wetland extent in east central North Dakota, statistically calibrating their results with wetland information 80 TODHUNTER AND RUNDQUIST derived from air photos corresponding to randomly sampled subsets of their study area. Turner and Rundquist (1980) used MSS to map wetlands in parts of nine states in the north-central U.S. for the United States Army Corps of Engineers. ...
... Density slicing requires the user to both interpret the Band 5 histogram and to examine known water pixels to determine an upper threshold brightness value for water. All pixels with brightness values equal to or less than the selected threshold value are classed as water, and all others as non-water, resulting in a binary data set (Work et al., 1974;Work and Gilmer, 1976;Gilmer et al., 1980;Best and Moore, 1981;Lunetta and Barlogh, 1999;Frazier and Page, 2000). A limitation of the density slice approach is that it fails to detect water bodies smaller than the spatial resolution of the satellite sensor (30 × 30 m, or 0.09 ha, in the case of TM). ...
The Devils Lake Basin of North Dakota, an interior drainage basin located within a dry, subhumid environment, has experienced pervasive flood conditions since the 1993 onset of a wet spell of unprecedented magnitude and duration. This unique natural-hazard environment has resulted in flooding from both the expansion of the surface area of the basin's terminal lakes (Devils Lake and Stump Lake) and increases in the number and size of rural wetlands. To assess the relative extent of both terminal lake and rural wetland flooding, we focused on Nelson County, which contains Stump Lake and is representative of other counties in the basin. Remotely sensed data acquired by Landsat Thematic Mapper was used to map open-water extent in 2001, and results were compared to 1992 land-cover data provided by the United States Geological Survey (USGS). Our analysis indicates a 53% increase in the size of Stump Lake and a 426% increase in the area of rural wetland ponds. Stump Lake flooding is spatially restricted and has had limited impact upon the surrounding lakeshore environment. Rural wetland flooding is pervasive and has a deleterious effect upon the region's agricultural economic base.
... Pereira and Itami [60] have indicated that remote sensing data provide useful spatial information and temporal changes over large geographic areas. For instance, remote sensing and GIS have emerged to be important tools in the management and inventory of aquatic macrophyte distributions [1] [11] [13] [16] [17] [29] [38] [91] [92]. These technologies provide resource managers with an efficient method for monitoring plant distributions over large geographic areas. ...
... This ability to make change studies can be used as long as the ecology of the target species is known, and is understood to predict the future distribution of such weeds in invaded tropical waters. Several researchers [11] [13] [29] [38] [91] have documented the theory behind the use of these techniques for monitoring aquatic weeds. ...
A Ab bs st tr ra ac ct t. . This paper reviews applications of remote sensing and geographic information systems (GIS) techniques to the assessment of tropical waters. These applications are discussed in the context of specif-ic management objectives and sensors used. The need to monitor the spreading patterns of weeds in the tropical waters, land-use changes in the areas surrounding them, change detection, disappearance of wet-lands, productivity and nutrient status, in order to establish trends and subsequently develop predictive models to facilitate effective management, is highlighted. GIS capability can be used to link ecological information with the management decisions of these waters. Remote sensing provides useful information in the form of satellite images and aerial photographs that can be integrated and analyzed in a GIS to pro-vide useful spatial information and temporal changes over large geographic areas affecting the structure and function of tropical waters. K Ke ey yw wo or rd ds s: : GIS, remote sensing, sustainable management I In nt tr ro od du uc ct ti io on n Shallow tropical waters play a vital role in many people's lives and contain remark-able communities of plants and animals. Shallow tropical waters are profoundly affect-ed by their locality and by changes taking place on land, even at great distances from them. They are often faced with a number of threats as a result of socio-economic activ-ities, taking place within them and their catchments. Rapid population growth in catch-ment zones has resulted in intensive use of land for farming, deforestation and growth of urban centers. Consequently, there is accelerated runoff leading to increased silt and nutrients discharge into the shallow tropical waters. Currently most tropical lakes are being choked by water hyacinth (Eichhornia cras-sipes), which continues to cause severe hardship and immense economic difficulties to most countries. The pressure on tropical waters from the weed requires intensive research in order to come up with useful suggestions towards their management. It is at this level that availability of automated, real time data becomes imperative. Remote sensing has developed rapidly to address these needs. The advantage of remote sensing as documented by Richards [64] and Sabins [69] is its ability to capture and record land details instantaneously. Its spatial resolution and aerial coverage provide the researcher with a synoptic view of a land surface. Such data derived from remotely sensed images may be stored efficiently and analysed effectively in a GIS (Risser and Treworgy, [67]).
... Missing these small wetlands can have an impact on the accuracy of an inventory, since research has shown that more than 70 percent of wetlands in portions of the PPR are 0.4 ha or smaller (Johnson and Higgins, 1997). Gilmer et al. (1980) developed a statistical method to deal with the small subpixel wetland problem. Working in eastern North Dakota, the researchers extracted surface water area from MSS Band 7 data using a density slice. ...
... They found that the technique yielded results similar to a separate wetland sample count via low-altitude aircraft occurring at the same general times. While their results cannot be applied universally, Gilmer et al. (1980) found that the value of this double-sample procedure is that it takes advantage of the ability of Landsat MSS to gather information cheaply over a large area, while utilizing the capability of aerial photography to detect small wetlands. ...
A decadal-scale wet spell in the closed Devils Lake basin of North Dakota has resulted in increases in the elevation and extent of the basin's terminal lakes— Devils and Stump—as well as increases in the size and number of small prairie pothole ponds. Changes in lake surface area have been studied thoroughly, whereas the fluctuations in pond surface area have been virtually ignored. We use a subpixel classification technique in combination with a Landsat TM and ETM+ Band 5 (middle infrared; 1,550–1,750 nm) density slice to improve estimates of changes in the combined area of ponds in the basin for selected years between 1991 and 2002. The resulting information is a first step toward more accurate assessment of the impact of wetland flooding on the region.
... Many of Canada's peatlands are in remote locations, making the collection of ground data challenging and costly [8,9]. Optical satellites have been used to map wetlands, including peatlands, for many years [10][11][12][13][14]. Several optical satellites have data in the visible, near infrared and shortwave infrared portions of the electromagnetic spectrum. ...
For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (−25 dB and −19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, but the inclusion of SAR variables did increase overall accuracy, indicating that a multi-sensor approach is preferred. There was no significant difference between the RF classifications which included RADARSAT-2 and simulated RCM data. Both medium- and high-resolution compact polarimetry and dual polarimetric RCM data appear to be suitable for mapping landcover within peatlands when combined with optical data and a DEM.
... A GIS based integrated approach can be used for the risk management of natural hazards. Several researchers (Brown, 1978;Gilmer et al., 1980;Berry, 1986;Welch et al., 1988;Jensen et al., 1992) have documented the theory behind the use of these techniques for monitoring aquatic weeds. And other authors (Campell, 1987;Lillesand and Kieffer, 1987;Jensen, 1989; Lo, 1990) have explained eloquently how remote sensing can be used as a tool for natural resource management. ...
Now-a-days the field of Remote Sensing and Geographical Information system (GIS) has become
exciting and desirable with rapidly expanding opportunities and provides vital tools which can be
applied in the various levels leading to decision making toward sustainable socio-economic
development and conservation of natural resources. Remote sensing and GIS technology, and its
applications in various fields, have experienced a successful development in recent decades. In this
paper the most commonly used processing procedures for remotely sensed data in particular image
processing techniques and the application capabilities of GIS technologies are presented.GIS and
remote sensing can play an important part in the rapid planning of various control management
programmes. Further, remote sensing and GIS have been used conjunctively in several studies for
addressing issues related to developmental planning. Remote sensing also provides a sound data base
for generating baseline information on natural resources, a pre-requisite for planning and
implementation, and monitoring of any developmental programme.
Remote Sensing and GIS has proved a powerful tool for the environmental monitoring in many cases.
Satellite remote sensing showed monitoring capability not only at global scale but also at local scale.
... However, wetlands that express changes in surface water extent at fine scales (below 30 mthe resolution of 1 Landsat pixel) and small wetlands, which we define as wetlands smaller than 5 ha, have received considerably less attention (Ryan et al., 2014). This is an issue because in many regions of the world the majority of the landscape is composed of small wetlands (Downing et al., 2014;Gilmer, Work, Colwell, & Rebel, 1980;Halabisky, Moskal, & Hall, 2011). ...
... The multi-temporal combination of May and July imagery produced the highest accuracy (95.9%), although compared to results using the only the July image (84.4%) authors concluded that the increase in accuracy may not be enough to justify the high cost of additional multi-temporal image acquisitions. Interestingly, in this study the earlier season (May) image produced the lower classification accuracy (50.5%), while others found that spring imagery was most optimal for wetland discrimination (Ozesmi and Bauer, 2002;Gilmer et al., 1980). Dingle-Robertson (2014) examine Ontario wetland classification according to the Ontario Wetland Evaluation System across three seasons using WorldView2, Landsat5, and Radarsat2 data and found that high spatial resolution WorldView2 data from spring or summer acquisitions produced the highest accuracies. ...
Mapping wetlands across both natural and human-altered landscapes is important for the management
of these ecosystems. Though they are considered important landscape elements providing both ecological
and socioeconomic benefits, accurate wetland inventories do not exist in many areas. In this study, a
multi-scale geographic object-based image analysis (GEOBIA) approach was employed to segment three
high spatial resolution images acquired over landscapes of varying heterogeneity due to humandisturbance
to determine the robustness of this method to changing scene variability. Multispectral
layers, a digital elevation layer, normalized-difference vegetation index (NDVI) layer, and a first-order
texture layer were used to segment images across three segmentation scales with a focus on accurate
delineation of wetland boundaries and wetland components. Each ancillary input layer contributed to
improving segmentation at different scales. Wetlands were classified using a nearest neighbor approach
across a relatively undisturbed park site and an agricultural site using GeoEye1 imagery, and an urban
site using WorldView2 data. Successful wetland classification was achieved across all study sites with
an accuracy above 80%, though results suggest that overall a higher degree of landscape heterogeneity
may negatively affect both segmentation and classification. The agricultural site suffered from the greatest
amount of over and under segmentation, and lowest map accuracy (kappa: 0.78) which was partially
attributed to confusion among a greater proportion of mixed vegetated classes from both wetlands and
uplands. Accuracy of individual wetland classes based on the Canadian Wetland Classification system
varied between each site, with kappa values ranging from 0.64 for the swamp class and 0.89 for the
marsh class. This research developed a unique approach to mapping wetlands of various degrees of
disturbance using GEOBIA, which can be applied to study other wetlands of similar settings.
... In areas of partial vegetation cover (30-60%), the classes were easily mixed, attributed to the influence of the unvegetated background. Satellite data are often used in conjunction with airborne data for an integrated regional approach to identifying wetlands (Gilmer et al. 1980, Jensen et al. 1986, and Butera 1983. ...
... Remote sensing is a powerful and effective way to monitor vegetation status, growth and bio-physical parameters, that can effectively complement environmental studies based on in situ measurements (Hunter et al., 2010;Jiang et al., 2012) and allows frequent acquisitions for multi temporal studies and reconstruction of historical time series in a cost effective way (Coppin and Bauer, 1994;Munyati, 2000). Remote sensing has been widely used in vegetation monitoring in the last decades (Gilmer et al., 1980;Xie et al., 2008 and bibliography included). Most of the research though has focused on terrestrial vegetation and fewer studies have been carried out on aquatic vegetated ecosystems (e.g. ...
Although spectral vegetation indices (VIs) have been widely used for remote sensing of vegetation in general, such indices have been traditionally targeted at terrestrial, more than aquatic, vegetation. This study introduces two new VIs specifically targeted at aquatic vegetation: NDAVI and WAVI and assesses their performance in capturing information about aquatic vegetation features by comparison with pre-existing indices: NDVI, SAVI and EVI. The assessment methodology is based on: (i) theoretical radiative transfer modeling of vegetation canopy-backgrounds coupling, and (ii) spectral linear mixture simulation based on real-case endmembers. Two study areas, Lake Garda and Lakes of Mantua, in Northern Italy, and a multisensor dataset have been exploited for our study. Our results demonstrate the advantages of the new indices. In particular, NDAVI and WAVI sensitivity scores to LAI and LIDF parameters were generally higher than pre-existing indices’ ones. Radiative transfer modeling and real-case based linear mixture simulation showed a general positive, non-linear correlation of vegetation indices with increasing LAI and vegetation fractional cover (FC), more marked for NDVI and NDAVI. Moreover, NDAVI and WAVI show enhanced capabilities in separating terrestrial from aquatic vegetation response, compared to pre-existing indices, especially of NDVI. The new indices provide good performance in distinguishing aquatic from terrestrial vegetation: NDAVI over low density vegetation (LAI < 0.7–1.0, FC < 40–50%), and WAVI over medium-high density vegetation (LAI > 1.0, FC > 50%). Specific vegetation indices can therefore improve remote sensing applications for aquatic vegetation monitoring.
... Smiatek (1995) said that, while the " true " classifier does not yet exist in the remote sensing world, visual classification of imagery could be very accurate, implying that manual interpretation provides the highest level of accuracy obtainable. Heller and Johnson (1979), Gilmer et al. (1980) and Smiatek (1995used manually interpreted sample data as a source of " correction " for wall-to-wall land-cover information derived from semiautomated algorithms, again implying the high level of accuracy afforded by manual interpretation versus purely algorithm-based approaches. The manual interpretation of imagery and aerial photography is often used to validate results from semiautomated change procedures (Cohen et al., 1998; Hayes and Sader, 2001). ...
The need for comprehensive, accurate information on landcover change has never been greater. While remotely sensed imagery affords the opportunity to provide information on land-cover change over large geographic expanses at a relatively low cost, the characteristics of land-surface change bring into question the suitability of many commonly used methodologies. Algorithm-based methodologies to detect change generally cannot provide the same level of accuracy as the analyses done by human interpreters. Results from the
Land Cover Trends project, a cooperative venture that includes the U.S. Geological Survey, Environmental Protection Agency, and National Aeronautics and Space Administration, have shown that land-cover conversion is a relatively rare event, occurs locally in small patches, varies geographically and temporally, and is spectrally ambiguous. Based on these characteristics of change and the type of information required, manual interpretation was selected as the primary means of detecting change in the Land Cover Trends project.
Mixtures of algorithm-based detection and manual interpretation may often prove to be the most feasible and appropriate design for change-detection applications. Serious examination of the expected characteristics and measurability of change must be considered during the design and implementation phase of any change analysis project.
... For example, in the prairie pothole region of North Dakota between 73% and 88% of wetlands are less than 0.4 hectares. 14 Inventorying and monitoring small wetlands is important as small wetlands are considered valuable ecosystems for maintaining biodiversity, 15 and are particularly vulnerable to climate change 16 and land use conversion. On the other end of the cost spectrum, there are high spatial resolution satellite images, such as IKONOS and Quickbird, that are available, but high costs often limits their use. ...
Wetlands are valuable ecosystems that benefit society. However, throughout history wetlands have been converted to other land uses. For this reason, timely wetland maps are necessary for developing strategies to protect wetland habitat. The goal of this research was to develop a time-efficient, automated, low-cost method to map wetlands in a semi-arid landscape that could be scaled up for use at a county or state level, and could lay the groundwork for expanding to forested areas. Therefore, it was critical that the research project contain two components: accurate automated feature extraction and the use of low-cost imagery. For that reason, we tested the effectiveness of geographic object-based image analysis (GEOBIA) to delineate and classify wetlands using freely available true color aerial photographs provided through the National Agriculture Inventory Program. The GEOBIA method produced an overall accuracy of 89% (khat = 0.81), despite the absence of infrared spectral data. GEOBIA provides the automation that can save significant resources when scaled up while still providing sufficient spatial resolution and accuracy to be useful to state and local resource managers and policymakers. (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3563569]
... Hinson used a hybrid classification approach with Landsat TM images from December to classify Texas coastal wetlands [6] . In the prairie pothole region of North Dakota, Gilmer used aerial photography combining with Landsat MSS data to estimate wetland numbers [7] . Min Wenbin used index of NDVI, NDWI and improved index to identify Ruoergai wetland with TM images [8] . ...
aIn this paper, band-combining operation is applied to extract water body area of three different period remote sensing pictures. Based on the Beijing wetland management classification system of wetlands and wetland patches census data, through the superposition of space under the counter-analysis and recursive speech, build a VBA classification, different stages of the wetlands water are separated automatically in order for the organic connection and unity of data from wetland administration section and remote sensing data. The results show that: (1) Decision tree is a good way to get the whole water information. All the information of water can be extracted by tm2 + tm3> tm4 + tm5, but there are still some mixed information. Towns and clouds can be removed when tm5 and tm7 less than a specific threshold, and shadow of the mountain can also be removed well when tm3-tm4 are greater than a specific threshold. (2) The urban wetlands can be obtained rapidly with the secondary development of VBA functions in GIS. (3) This classification method has high accuracy, the overall classification accuracy is 91.8%, kappa statistics is 0.88, and this method avoid post-processing work, is very time-sensitive.
... A GIS based integrated approach can be used for the risk management of natural hazards (Chen et al., 2003). Several researchers (Brown 1978, Gilmer et al., 1980, Berry 1986, Welch et al., 1988, Jensen et al., 1992) have documented the theory behind the use of these techniques for monitoring aquatic weeds. And other authors (Campell 1987, Lillesand and Kieffer 1987, Jensen 1989, Lo 1990) have explained eloquently how remote sensing can be used as a tool for natural resource management. ...
... Remote sensing is a documented tool for monitoring and analyzing inland aquatic environments (Work and Gilmer, 1976;Gilmer et al., 1980;Rundquist et al., 1987;Bobba et al., 1992;Gitelson et al., 1993;Yacobi et al., 1995). Satellite data allow one to obtain information about the number, spatial distribution, size, productivity, and, in some cases, depth of inland waters. ...
The Landsat-Muitispectral Scanner (MSS) data were used to measure lake area fluctuations (1972–1989) for 130 ground-water dominated lakes in the Western Lakes Region of the Nebraska Sand Hills. In general, the pattern shown in lake area hydrographs was similar to that for in-situ lake elevations. In-situ lake-elevation data verify that remote monitoring of surface-area fluctuations, even at relatively coarse spatial resolution, is not only practical and useful, but also it elucidates the hydrologic characteristics of groundwater-dominated lakes of the Sand Hills. The apparent differences in behavior between lakes in the northern and southern portions of the study area may be related to both their location in the regional ground water system and the substantial local hydrologic complexity.
... Remote sensing and geographic information systems (GIS) have emerged as important tools in the management and inventory of aquatic macrophyte distributions (Brown, 1978; Bogucki et al. 1980; Gilmer et al. 1980; Ader and Johnston, 1982; Bussom et al. 1982; Carter, 1982; Welch, et al. 1988 and Jensen, et al. 1992 ). These technologies provide resource managers with an efficient method for monitoring plant distributions over large geographic areas. ...
A GIS database developed for Lake Marion, South Carolina was utilized to assess existing relationships between aquatic macrophyte distributions and environmental parameters affecting plant growth. The significance of water depth, sedimentation, nitrogen, phosphorus, top dissolved oxygen, bottom dissolved oxygen, percent light and absolute light was tested using GIS overlay techniques and the Chi Square test of independence. Specific levels of the eight parameters found to be spatially related to aquatic vegetation were then utilized to develop a provisional cartographic model describing optimum growth conditions for aquatic macrophytes. Model validation by comparing predicted vegetation with actual vegetation distributions indicated only water depth and sedimentation data layers are necessary for predicting more than 90 percent of emergent and submergent distributions. Resource managers can use this model to identify lake areas that are susceptible to excessive macrophyte growth and require special attention.
... The life history of many animals is tightly linked with temporal and/or spatial vegetation patterns and, over the past 20 years, there has been considerable progress in the development of hardware and data analysis techniques for monitoring vegetation using airborne and spaceborne sensors (Curran 1980(Curran , 1983Gates 1970;Coward et al. 1985;Jackson 1983;Justice et al. 1985;Kalensky and Wilson 1975;Knipling 1970;Kumar and Monteith 1982;MacDonald 1984;Malingreau 1986;Sellers 1985Sellers , 1987Tucker 1978;Tucker et al. 1980Tucker et al. , 1981Tucker et al. , 1985bTucker and Sellers 1986;Woolley 1971). Thus far, there have been very few attempts to use satellite data to examine the large scale spatial and temporal dynamics of animal habitat (Craighead et al. 1982(Craighead et al. , 1988Gilmer et al. 1980, Hielkema et al. 1986Saxon et al. 1983, Tucker et al. 1985aVoss 1986). The objective of this paper is to determine whether satellite based remote sensing is useful for monitoring the large scale spatial and temporal dynamics of potential breeding habitat for an African, colonial, granivorous weaver-bird; the redRed-billed quelea breeding colonies often contain hundreds of thousands of pairs and may cover a few tens of hectares (Ward 1971;Wiens and Dyer 1977). ...
Data derived from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA series of operational, polar orbiting,
meteorological satellites have previously been shown to be quite useful for monitoring vegetation dynamics at scales ranging
from regional (104 km2) to global. In this report, we demonstrate that these same data can be used to monitor potential breeding habitat for a highly
mobile, granivorous African weaver-bird, the red-billed quelea (Quelea quelea). This species is often considered to be an agricultural pest, affecting cereal production throughout sub-Saharan Africa.
The temporal resolution and very large (continental) spatial coverage provided by these data can provide a unique context
within which to examine species distribution and abundance patterns.
... Combining satellite and aerial photographs could improve the monitoring program. For example, Gilmer et al. (1980) investigated the statistical relation between aerial photographs and Landsat and found an approach that took advantage of Landsat's ability to gather information over a large area while using the capability of aerial photographs to detect small wetlands. Zhang et al. (2009) relied on Landsat and aerial photographs for lake size estimates and examined the power law relationships for different hydrological conditions for systems of thousands of pothole lakes. ...
... Remote sensing and geographic information systems (GIS) have emerged as important tools in the management and inventory of aquatic macrophyte distributions (Brown, 1978;Bogucki et al. 1980;Gilmer et al. 1980; Ader and Johnston, 1982;Bussom et al. 1982;Carter, 1982;Welch, et al. 1988 and 1991; Jensen, et al. 1992). These technologies provide resource managers with an efficient method for monitoring plant distributions over large geographic areas. ...
A GIS database developed for Lake Marion, South Carolina was utilized to assess existing relationships be- tween aquatic macrophyte distributions and environmental parameters affecting plant growth. The sig- nificance of water depth, sedimentation, nitrogen, phosphorus, top dissolved oxygen, bottom dissolved oxy- gen, percent light and absolute light was tested using GIS overlay techniques and the Chi Square test of in- dependence. Specific levels of the eight parameters found to be spatially related to aquatic vegetation were then utilized to develop a provisional cartographic model describing optimum growth conditions for aquatic macrophytes. Model validation by comparing predicted vegetation with actual vegetation distributions indi- cated only water depth and sedimentation data layers are necessary for predicting more than 90 percent of emergent and submergent distributions. Resource managers can use this model to identify lake areas that are susceptible to excessive macrophyte growth and require special attention.
The combined use of GIS and Remote Sensing has widespread tremendously over the last few decades. We have studied the concepts of GIS and Remote Sensing in our previous chapters. It’s very necessary to understand the practical importance of these geospatial technologies rather than having only theoretical knowledge. In this chapter, it’s attempted to present before you all the burning fields which are being controlled and monitored by these technologies. After finishing this chapter, we will be capable of understanding the following GIS and Remote Sensing applications.
Geographic Information Systems in Microlevel Planning
Geographic Information Systems in Water Resource Management
Geographic Information Systems in Sustainable Development
Geographic Information Systems in Agricultural and Natural Resource Management
Geographic Information Systems in Sustainable Tourism Development
Geographic Information Systems in Disaster Management.
KeywordsRemote SensingGeographic information systemsMulti-sectoral sustainable developmentDisaster management
Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of Lemna minor in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. The study focuses on a selected section of the Lis River (Portugal), a small river that is often affected by water pollution. The study approach uses spatiotemporal multispectral data from the Sentinel-2 satellite and from 2021 and investigates the potential of remote sensing-based vegetation and water indices (Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), Green Red Vegetation Index (GRVI), Normalized Difference Water Index (NDWI)) for detecting duckweeds’ infestation and its severity. The NDAVI was identified as the vegetation index (VI) that better depicted the presence of duckweeds in the surface of the water course; however, results obtained for the other VIs are also encouraging, with NDVI showing a response that is very similar to NDAVI. Results are promising regarding the ability of remote sensing products to provide insight into the behavior of Lemna minor and to identify problematic sections along small watercourses.
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.
With the relatively recent widespread availability of operational fine spatial resolution imagery from satellites, there is more opportunity to conduct spatial sampling with combinations of spatial resolution data. This method is generally called regression estimation. Regression estimation consists of first using a coarser spatial resolution sensor over a large area. For selected samples, a second delineation is done with a finer spatial resolution system which is presumed to be more accurate. A statistical comparison of the two estimates for the selected sample areas is made and if a suitable relationship exists, a correction factor can be determined and applied to the full study area. This manuscript reviews the regression estimation process and provides a case study. The case study used the technique to determine the extent of the winter rice crop in Bangladesh with Landsat data and statistics provided for local administrative units by agricultural agents.
Portions of the Savannah River floodplain swamp were evaluated for vegetation change using high resolution (5·6 m) aircraft multispectral scanner (MSS) data. Image distortion from aircraft movement prevented precise image-to-image registration in some areas. However, when small scenes were used (200–250 ha), a first-order linear transformation provided registration accuracies of less than or equal to one pixel. A larger area was registered using a piecewise linear method. Five major wetland classes were identified and evaluated for change. Phenological differences and the variable distribution of vegetation limited wetland type discrimination. Using unsupervised methods and ground-collected vegetation data, overall classification accuracies ranged from 84 per cent to 87 per cent for each scene. Results suggest that high-resolution aircraft MSS data can be precisely registered, if small areas are used, and that wetland vegetation change can be accurately detected and monitored.
Within the Wetlands Research Program, a work unit under the Stewardship and Management Task Area, Techniques for Characterizing Changes to Wetlands, is investigating the application of remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.... Change detection, Wetland assessment, Remote sensing, Wetlands.
Homogenous stands of Typha, Phragmites, and Scirpus, and heterogenous stands of Typha‐Phragmites and Typha‐Scirpus were scanned by means of a hyperspectral field radiometer at four times during the 1998 growing season. The purpose was to characterize the multidate vegetation spectra, to test the statistical significance of differences among species in various spectral regions and at various growth stages, and to determine the growth stage(s) of maximum separability. Spectra did not vary qualitatively but did vary quantitatively. Spectral responses for both individual species and mixed groups were significantly different from one another in both the visible and NIR regions. The results show that the best stage to distinguish species is flowering.
An airphoto survey carried out at the La Grande hydroelectrical complex (James Bay, Quebec) revealed numerous signs of degradation of patterned fens, with a decrease in terrestrial vegetation and increase in ponds, a process known as aqualysis. The principal goal of this study is to provide information on the present and past (the last 50 years) state of patterned peatlands, associated with the pattern of the aquatic and terrestrial compartments, and to evaluate their changing cover using remote sensing techniques. In this paper, we present a semi-automated, object-based method for QuickBird panchromatic image classification. The method emphasizes contextual information. We have also integrated texture images calculated in advance as supplementary data layers in the process of segmentation and classification. The validation of QuickBird image classification shows that the proposed method can delineate peatlands with 95% producer's accuracy and 88% user's accuracy. The overall classification accuracy in the peatlands is 81%. The same robust technique was applied to the aerial photographs taken in 1957. The classification of QuickBird images and aerial photographs was used to assess the structural development of patterned peatlands in the La Grande 3 sector over the last 50 years. The analysis shows an increase in aquatic areas for only two out of seven studied peatlands. To confirm our hypothesis of active aqualysis, additional image analysis is required from other areas of the La Grande hydroelectrical complex where the aqualysis process is more pronounced.
Kenya is largely dependent on its biological resources for its social and economic development and therefore is in urgent need of comprehensive, quality information on the distribution, status and utilization of its biodiversity for conservation planning and management. This paper discusses potential applications and current limitations of GIS for mapping and modeling biodiversity in Kenya. Seven functions of GIS that are important for biodiversity modelling are database structure, ground surveys, spatial and statistical analysis, remote sensing integration, terrain analysis, data integration and data visualisation. Terrain analysis can be used to identify micro, meso and macroterrain indices. Data integration can be used to determine the environmental characteristics of known habitats of species. Data visualisation uses maps, graphs and statistics to process and facilitate understanding of the enormous amount of data that can be derived from the analysis of a species’ habitat. GIS software is being underutilised in Kenya for biodiversity mapping because of poor sharing and storing of data, and lack of co-ordination of environmental information management.
To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.
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.
A statistical procedure to assess level II continental resources using Landsat MSS digital data is presented. The statistical procedure involves a two-stage cluster sample within a stratified random sample. The utility of this procedure is assessed by using it to estimate the areal extent of the conifer and hardwood resources of the continental United States. National estimates of conifer and hardwood derived using this sampling procedure were within 3% of U.S. Forest Service figures. According to the Landsat-based study, 11% of the country is conifer forest, and 12% is hardwood. The corresponding U.S.F.S. figures are 13 and 15%, respectively. Comparison of the MSS classification products and airphotos showed that the conifer cover class was correctly identified 74% of the time and hardwood 80% of the time. The average classification accuracy countrywide for the four cover types considered (conifer, hardwood, water, and “other”) is 74%, the overall accuracy is 85%. The statistical procedure provides a method of incorporating Landsat MSS digital data as a second stage for level II continental resource assessment. Alternate data sources, e.g., satellite and aircraft photographic imagery, may also be used in conjunction with this statistical model.
Nature Conservancy. Moskal will describe a project she is working on with the Nature Conservancy. The objective is to develop a remote sensing-based algorithm to automate the extraction of arid wetlands. The project is based in Eastern Washington.
An integrated resource inventory program involving Ducks Unlimited (Canada), Purdue University (LARS), Interdisciplinary Systems Limited (Canada) and the Canada Centre for Remote Sensing is described. The objective is to test the feasibility and operational capability of applying over a wide area, Landsat data classification techniques in order to identify, map and measure the areal extent and relative quality of waterfowl breeding habitat, primarily in the Prairie Pothole Region of Canada.Ducks Unlimited (Canada) is a private, nonprofit conservation organization dedicated to preservation of wetlands in Canada. The Prairie Pothole Region of the southern prairie provinces is the focus of our efforts. Because of its abundance of wetlands, its well documented importance to breeding waterfowl, and because habitat there is endangered by outright loss or at least significant degradation. An up to date and comprehensive inventory of wetlands is required to assist DU in the planning and budgeting phase of our reconnaissance and development programs.The program is designed to provide maps at scales of 1:250,000 and 1:50,000 on which all wetlands over 25 ac (10 ha) in size are located. Maps are to be registered to and capable of directly overlaying existing NTS mapsheets. Tabular output will include a table of wetland statistics which will locate individual wetlands (UTM reference), and provide an area estimate. For wetlands 25 ac or greater in size, acreages of various wetland zones will be indicated as well as an estimate of shoreline distance. Summary statistics will be provided for each mapsheet which provide a breakdown of wetlands according to size classes, providing number in each class, total acreage and acreages of the various zones.
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