Study on Water-blocking Vegetation Extraction within River Management Areas Based on ICESat-2 and Sentinel-2
This study addresses the issues of heavy workload,time consumption,and difficulty in field survey of water-blocking vege-tation within river management areas.Utilizing the ATL03 and ATL08 products from the ICESat-2 satellite ATLAS,vegetation heights were screened and calculated through correlation analysis of laser points.Combined with spectral variables of Sentinel-2,spatially continuous vegetation height inversion models were constructed using BP neural network and random forest regression algorithm to rap-idly extract water-blocking vegetation within river management areas.Experimental results indicate that the random forest regression model is superior to the BP neural network model in terms of stability and accuracy,effectively extracting water-blocking vegetation within river management areas.This method can improve the efficiency and accuracy of surveying water-blocking vegetation in river management,providing scientific decision-making support for river governance and management.
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