Abstract
In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation.
Highlights
Wetlands in high-latitude areas are well-known as an irreplaceable part of the cold region ecosystems because they regulate climate, replenish ground water, store carbon and maintain seasonal frozen soil mainly due to the heat insulation and water storage characteristics of the peat layer [1]
The abundances of various land cover classes were obtained from the HJ-1A/B hyperspectral images based on the proposed SCLS-LSMM and traditional FCLS-LSMM
Most Grassy meadow concentrated on the northeast of the Wuyuer River, which associated with areas of high relief
Summary
Wetlands in high-latitude areas are well-known as an irreplaceable part of the cold region ecosystems because they regulate climate, replenish ground water, store carbon and maintain seasonal frozen soil mainly due to the heat insulation and water storage characteristics of the peat layer [1]. Wetlands in cold regions provide a unique habitat for many endangered wetland species, such as Grus japonensis and Grus monacha [2]. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report and Millennium Ecosystem Assessment Report indicate that the wetlands in high-latitude cold regions are fragile and unstable because they are nutrient-limited and sensitive to climate changes and human disturbances [3]. Identifying the spatial distribution and heterogeneous pattern of wetland vegetation communities in cold regions is very important to evaluate the impacts of climate change and permafrost degradation on the wetland ecosystem health and safety [4]. Multi-spectral sensors (Landsat multispectral scanner (MSS), Landsat-5 thematic mapper (TM), Landsat-7 enhanced thematic
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