A Collection of SAR Methodologies for Monitoring Wetlands

White, Lori; Brisco, Brian; Dabboor, Mohammed; Schmitt, Andreas; Pratt, Andrew

doi:10.3390/rs70607615

Cited by 183 publications

(143 citation statements)

References 88 publications

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“…Due to the high spatial and temporal variations of flooded areas, validation of flood maps over relatively wide areas derived from SAR imagery remains challenging. Higher accuracies are observed in other studies with validations based either on limited visual ground observations [31] or by comparison with multispectral satellite observations [15]. Both methods have limitations in terms of spatial coverage and temporal monitoring.…”

Section: Grassland Floods (Intra-field Scale)mentioning

confidence: 74%

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Cazals¹,

Rapinel

Frison³

et al. 2016

Remote Sensing

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Abstract:In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

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Section: Grassland Floods (Intra-field Scale)mentioning

confidence: 74%

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Cazals¹,

Rapinel

Frison³

et al. 2016

Remote Sensing

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“…We found that within each subdivision of the lake, the differences between image pairs (LST and radar) appeared to be random, with no systematic pattern within or across the subdivisions as a function of time. There is a substantial publication record (e.g., [22][23][24][25])) for the use of radar data for mapping wetlands including flooded vegetation. Wilusz et al (2017) [22] found that "low resolution C-band SAR imagery shows promise for long term study of Sudd wetland flood dynamics."…”

Section: Resultsmentioning

confidence: 99%

“…Radar has the advantage over LST of a relatively extensive history of use for mapping flooded vegetation. According to [24], "SAR (Synthetic Aperture Radar) has many characteristics that make it ideal for mapping and monitoring water and wetlands over time." According to [26], "What is evident throughout the recent literature is that multidimensional radar data sets are attaining an accepted role in operational situations needing information on wetland presence, extent and conditions."…”

Section: Resultsmentioning

confidence: 99%

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

et al. 2018

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Lake Chad, located in the middle of the African Sahel belt, underwent dramatic decreases in the 1970s and 1980s leaving less than ten percent of its 1960s surface water extent as open water. In this paper, we present an extended record (dry seasons 1988-2016) of the total surface water area of the lake (including both open water and flooded vegetation) derived using Land Surface Temperature (LST) data (dry seasons [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016] from the NASA Terra MODIS sensor and EUMETSAT Meteosat-based LST measurements (dry seasons 1988-2001) from an earlier study. We also examine the total surface water area for Lake Chad using radar data (dry seasons 2015-2016) from the ESA Sentinel-1a mission. For the limited number of radar data sets available to us (18 data sets), we find on average a close match between the estimates from these data and the corresponding estimates from LST, though we find spatial differences in the estimates using the two types of data. We use these spatial differences to adjust the record (dry seasons 2000-2016) from MODIS LST. Then we use the adjusted record to remove the bias of the existing LST record (dry seasons 1988-2001) derived from Meteosat measurements and combine the two records. From this composite, extended record, we plot the total surface water area of the lake for the dry seasons of 1988-1989 through 2016-2017. We find for the dry seasons of 1988-1989 to 2016-2017 that the maximum total surface water area of the lake was approximately 16,800 sq. km (February and May, 2000), the minimum total surface water area of the lake was approximately 6400 sq. km (November, 1990), and the average was approximately 12,700 sq. km. Further, we find the total surface water area of the lake to be highly variable during this period, with an average rate of increase of approximately 143 km 2 per year.

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“…Nowadays, they also provide cultural services to their visitors, such as aesthetic, recreation and a wilderness feeling. These are some of the reasons why many wetlands have become protected areas [1]. Wetlands face…”

Section: Introductionmentioning

confidence: 99%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

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Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.

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A Collection of SAR Methodologies for Monitoring Wetlands

Cited by 183 publications

References 88 publications

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

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