Abstract
The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and the presence of emergent vegetation. This study develops an open-source process within the Google Earth Engine platform for mapping the spatial distribution of wetlands through the integration of Sentinel-1 C-band SAR (synthetic aperture radar) data with high-resolution (10-m) Sentinel-2 bands. We used two machine-learning algorithms (random forest (RF) and support vector machine (SVM)) to identify wetlands across the study area through supervised classification of the multisensor composite. We trained the algorithms with ground truth data provided through field studies and aerial photography. The accuracy was assessed by comparing the predicted and actual wetland and non-wetland classes using statistical coefficients (overall accuracy, Kappa, sensitivity, and specificity). For this purpose, we used four different out-of-sample test subsets, including the same year, next year, small vegetated, and small non-vegetated test sets to evaluate the methods on different spatial and temporal scales. The results were also compared to Landsat-derived JRC surface water products, and the Sentinel-2-derived normalized difference water index (NDWI). The wetlands derived from the RF model (overall accuracy 0.76 to 0.95) yielded favorable results, and outperformed the SVM, NDWI, and JRC products in all four testing subsets. To provide a further characterization of the potholes, the water bodies were stratified based on the presence of emergent vegetation using Sentinel-2-derived NDVI, and, after excluding permanent water bodies, using the JRC surface water product. The algorithm presented in the study is scalable and can be adopted for identifying wetlands in other regions of the world.
Highlights
Wetlands have been identified as valuable resources that provide a variety of ecological and socioeconomic benefits [1], but they are threatened due to human activities, such as agricultural intensification and climate change [2]
We will provide our algorithm in a format that can be freely shared and readily implemented by those with minimal coding and modeling experience, such as conservation managers. We achieved this through the following objectives: (1) we developed an open-source framework to map the spatial variation in wetland surface inundation and vegetation based on Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 high-resolution bands within the Google Earth Engine (GEE) platform; (2) we deployed this algorithm over a portion of Prairie Pothole Region (PPR) in the high priority conservation area of the PPR; (3) we analyzed the accuracy of this algorithm for generating the information needed for setting conservation targets
This study developed developed an anautomated automatedworkflow workflowwithin within platform mapwetland surface water for and by applying the classifier to a combination of ping wetland surface water for 2016 and 2017 by applying the random forest (RF) classifier to a combinaSentinel-1, Sentinel-2 band data, and spectral reflectance indices derived from tion of Sentinel-1, Sentinel-2 band data, and spectral reflectance indices derived from
Summary
Wetlands have been identified as valuable resources that provide a variety of ecological and socioeconomic benefits [1], but they are threatened due to human activities, such as agricultural intensification and climate change [2]. These threats and others make monitoring the spatiotemporal variation of wetlands’ hydrological processes crucial to their effective management. Wetlands resemble surrounding uplands, whereas when inundated, they can have either moist soils or surface water that ranges from centimeters to meters deep.
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