Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

Abstract

Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.

Landscape metrics as predictors of hydrologic connectivity between Coastal Plain forested wetlands and streams

“…Recent technological improvements in the quality and spatial resolution of remote sensing and GIS products provide increasing opportunities to accurately model hydrological patterns as a function of GIS‐based variables. High‐resolution (≤5 m) satellite imagery is currently available upon request (Vanderhoof, Alexander, & Todd, ; Vanderhoof, Alexander, & Todd, ) and will become available at near‐daily recurrence intervals in the near future (Tiner, Lang, & Kleman, ). The coverage of high‐resolution data is increasing as well.…”

“…High-resolution (≤5 m) satellite imagery is currently available upon request (Vanderhoof, Alexander, & Todd, 2016;Vanderhoof, Alexander, & Todd, 2017) and will become available at near-daily recurrence intervals in the near future (Tiner, Lang, & Kleman, 2015). The coverage of high-resolution data is increasing as well.…”

Modeling Connectivity of Non‐floodplain Wetlands: Insights, Approaches, and Recommendations

“…; Vanderhoof et al. ), distributed water‐level sensor networks (e.g., McLaughlin and Cohen ; Epting et al. ), a suite of environmental tracer data (e.g., Fossey and Rousseau ; Thorslund et al.…”

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

“…To our knowledge, little work has been done to delineate potential flow paths between wetlands and stream networks and use flow paths to characterize hydrologic connectivity in the PPR. In addition, previous remote-sensing-based work on the hydrology of prairie wetlands mainly focused on mapping wetland inundation areas (e.g., Huang et al, 2014;Vanderhoof et al, 2017) or wetland depressions (e.g., McCauley and Anteau, 2014;Wu and Lane, 2016); few studies have treated wetlands and catchments as integrated hydrological units. Therefore, there is a call for treating prairie wetlands and catchments as highly integrated hydrological units because the existence of prairie wetlands depends on lateral inputs of runoff water from their catchments in addition to direct precipitation (Hayashi et al, 2016).…”