Monitoring of critical water and vegetation anomalies of sub-Saharan West-African Wetlands

Moser, Linda; Voigt, Stefan; Schöepfer, Elisabeth

doi:10.1109/igarss.2014.6947322

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Irrigated cultivation around the lake has increased particularly throughout the past five years. This has been detected from remote sensing data by previous works of Moser et al [1,54]. Irrigation activities result in vegetable gardening, mainly green beans that are also internationally exported, tomatoes, and onion.…”

Section: Study Area-lac Bammentioning

confidence: 98%

Monitoring of the Lac Bam Wetland Extent Using Dual-Polarized X-Band SAR Data

et al. 2016

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Wetlands in semi-arid Africa are vital as water resource for local inhabitants and for biodiversity, but they are prone to strong seasonal fluctuations. Lac Bam is the largest natural freshwater lake in Burkina Faso, its water is mixed with patches of floating or flooded vegetation, and very turbid and sediment-rich. These characteristics as well as the usual cloud cover during the rainy season can limit the suitability of optical remote sensing data for monitoring purposes. This study demonstrates the applicability of weather-independent dual-polarimetric Synthetic Aperture Radar (SAR) data for the analysis of spatio-temporal wetland dynamics. A TerraSAR-X repeat-pass time series of dual-co-polarized HH-VV StripMap data-with intervals of 11 days, covering two years (2013)(2014)(2015) from the rainy to the dry season-was processed to normalized Kennaugh elements and classified mono-temporally and multi-temporally. Land cover time series and seasonal duration maps were generated for the following four classes: open water, flooded/floating vegetation, irrigated cultivation, and land (non-wetland). The added value of dual-polarimetric SAR data is demonstrated by significantly higher multitemporal classification accuracies, where the overall accuracy (88.5%) exceeds the classification accuracy using single-polarimetric SAR intensity data (82.2%). For relevant change classes involving flooded vegetation and irrigated fields dual-polarimetric data (accuracies: 75%-97%) are favored to single-polarimetric data (42%-87%). This study contributes to a better understanding of the dynamics of semi-arid African wetlands in terms of water areas including water with flooded vegetation, and the location and timing of irrigated cultivations.

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Section: Study Area-lac Bammentioning

confidence: 98%

Monitoring of the Lac Bam Wetland Extent Using Dual-Polarized X-Band SAR Data

et al. 2016

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“…Furthermore, classic thematic classification methods have been used, such as the maximum likelihood method used to map surface water in a wetland [21,22], the NIR single-band method, a pixel-based method, the object-based segmentation band [23] and the Automated Water Extraction Index (AWEI) [24]. Furthermore, several researchers applied single-band thresholds, such as the NIR band based on the Advanced Very High Resolution Radiometer (AVHRR) and MODIS sensors, to map seasonal inland waters in Central Asia [25] and to estimate the water surface of Sub-Saharan West-African wetlands [26], respectively.…”

mentioning

confidence: 99%

“…Some methods applied to wetland monitoring combined the NIR band to estimate the water pixels and the NDVI to study wetland vegetation [26], as well as the spectral analytical process and Band 5 of Thematic Mapper [27]. The objectives of our study were to: (1) investigate the applicability of some of the previously-tested methods to estimate the water pixels in our study area and to compare them with the genetic algorithm approach; (2) develop a systematic methodology to monitor the changes in the water cover in several temporary lakes located in the Biosphere Reserve of La Mancha Húmeda (Spain) using Landsat 7-ETM+ imagery; and (3) investigate the interplay between precipitation, evaporation and the subsequent changes of water-covered area in these lakes to unveil the seasonal effect.…”

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confidence: 99%

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

et al. 2016

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Abstract:The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

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“…This becomes especially relevant under the current climate change scenario. For years, efforts have focused on finding an approach to estimate the occurrence of water pixels at different spatial and temporal scales [7][8][9][10][11][12]. For instance, images of satellite sensors such as Landsat, QuickBird, or SPOT family have been commonly used, given their suitable spatial resolution for the analysis of medium to small size water bodies [7,8,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Although several methods to estimate water pixels from remote sensing sensors exist, such as single-band thresholds [10,11,19,20], image transformation [17,[21][22][23][24][25] or thematic classifications [26][27][28], water indices are widely used, since they are considered as less restrictive and more reproducible, especially for applications at large or even on a global scale [29][30][31]. Water indices are based on the capability of the water spectrum response in the near and middle infrared bands to identify flooded areas.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula

et al. 2021

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This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.

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Monitoring of critical water and vegetation anomalies of sub-Saharan West-African Wetlands

Cited by 6 publications

References 10 publications

Monitoring of the Lac Bam Wetland Extent Using Dual-Polarized X-Band SAR Data

Monitoring of the Lac Bam Wetland Extent Using Dual-Polarized X-Band SAR Data

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula

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