Abstract
Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.
Highlights
Wetlands act as one of the most important types of ecosystems and perform many vital functions, including water storage and purification, flood and erosion control, shoreline protection, conservation of biological diversity, and as a habitat for wildlife and fishery resources for human communities [1,2,3].As one of the most important abiotic factors, the wetland inundation extent greatly dominates the function of the wetland ecosystem and its consequent effects on the interactions between the land and atmosphere system [4]
Soil has the lowest accuracy (91.07%) for both producer’s accuracy (PA) and user’s accuracy (UA). It can be partly explained by the fact that the spectral response from the soil has some signal confusion with low grass coverage and shallow water
We compared the ability of three common methods (LSU, artificial neural networks (ANN), and regression tree (RT)) to extract the subpixel inundation percentage (SIP) information with the combination use of the Landsat-8 data and the unmanned aerial vehicle (UAV) image
Summary
Wetlands act as one of the most important types of ecosystems and perform many vital functions, including water storage and purification, flood and erosion control, shoreline protection, conservation of biological diversity, and as a habitat for wildlife and fishery resources for human communities [1,2,3].As one of the most important abiotic factors, the wetland inundation extent greatly dominates the function of the wetland ecosystem and its consequent effects on the interactions between the land and atmosphere system [4]. It is well known that even small inundation regime changes may result in a substantial impact on the ecosystem characteristics and function [5]. To ascertain wetland inundation changes at a regional scale, remote sensing has been proven to be an economical and efficient tool [8,9]. Land cover classification for remote sensing images is one popular way of defining the land surface characteristics of each observed pixel. This approach is usually ineffective or involves a high level of uncertainty due to the assumption that a single land cover type is assigned to each pixel, especially for moderate spatial resolution pixels. To estimate the subpixel land cover proportion from remotely sensed data, different methods have been developed based on the pixel signal from remote sensing observation and the spectral differences between different land surface components [5,10,11,12]
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