Abstract
Zhalong wetland is a globally important breeding habitat for many rare migratory bird species. Prompted by the high demand for temporal and spatial information about the wetland’s hydrological regimes and landscape patterns, eight time series Radarsat-2 images were utilized to detect the flooding characteristics of the Zhalong wetland. Subsequently, a random forest model was built to discriminate wetlands from other land cover types, combining with optical, radar, and hydrological regime data derived from multitemporal synthetic aperture radar (SAR) images. The results showed that hydrological regimes variables, including flooding extent and flooding frequency, derived from multitemporal SAR images, improve the land cover classification accuracy in the natural wetlands distribution area. The permutation importance scores derived from the random forest classifier indicate that normalized difference vegetation index (NDVI) calculated from optical imagery and the flooding frequency derived from multitemporal SAR imagery were found to be the most important variables for land cover mapping. Accuracy testing indicate that the addition of hydrological regime features effectively depressed the omission error rates (from 52.14% to 2.88%) of marsh and the commission error (from 77.34% to 51.27%) of meadow, thereby improving the overall classification accuracy (from 76.49% to 91.73%). The hydrological regimes and land cover monitoring in the typical wetlands are important for eco-hydrological modeling, biodiversity conservation, and regional ecology and water security.
Highlights
Wetlands are considered an integral part of the global ecosystem as they provide functions of preventing/reducing the severity of floods, feeding ground water, regulating hydrological cycle, and filtering contaminants and sediments from runoff to rivers, streams, and ground water [1,2]
This study has explored the possibility of the wetland seasonal hydrological regimes monitoring using a series of polarimetric synthetic aperture radar (SAR) data, and developed a semi-automated method exploiting optical, radar, and hydrological regimes data to discriminate wetlands from the other land cover types
It was found that the multitemporal polarimetric backscattering coefficients observations can reveal the seasonal fluctuation of the flooding extent
Summary
Wetlands are considered an integral part of the global ecosystem as they provide functions of preventing/reducing the severity of floods, feeding ground water, regulating hydrological cycle, and filtering contaminants and sediments from runoff to rivers, streams, and ground water [1,2]. Many of the world’s most important wetlands in developing countries are likely to be viewed for their water resources in terms of untapped economic potential, while the ecological benefits and requirements of wetlands are often ignored [5]. This situation is exacerbated due to the lack of basic information on the extent and flooding situations of wetlands [6]. To better quantify the role of wetlands in providing ecosystem services and effectively conserve and manage wetland resources, it is essential to have up-to-date information on wetlands’ distribution and their hydrological conditions [7]. Accurate wetlands information is difficult to obtain in situ, due to their dynamic hydrological characteristics, extensive areas, and remote locations
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