Evaluating the addition of radar with optical data for vegetation mapping in a montane region in Sri Lanka  

作　　者：W.D.K.V.NANDASENA Lars BRABYN Silvia SERRAO-NEUMANN 

机构地区：[1]Geography Programme,School of Social Sciences,University of Waikato,Hamilton 3240,New Zealand [2]Department of Geography and Environmental Management,Faculty of Social Sciences and Languages,Sabaragamuwa University of Sri Lanka,Belihuloya 70140,Sri Lanka [3]Environmental Planning Programme,School of Social Sciences,University of Waikato,Hamilton 3240,New Zealand [4]Cities Research Institute,Griffith University,Brisbane 4222,Australia

出　　处：《Journal of Mountain Science》2023年第10期2898-2912,共15页山地科学学报（英文）

摘　　要：The use of freely-available multi-source imagery for mapping vegetation in montane terrain is important for many developing countries that do not have the funding for high-resolution data capture.Radar images are also now freely available and include Sentinel-1 in dual polarisation,and PALSAR-2.These images can penetrate cloud cover and provide the advantage of acquiring data in a cloudy tropical region.This research evaluated whether the addition of radar with optical and topographic data improves classification accuracy in a montane region in Sri Lanka.Six classification experiments were designed based on different combinations of image data to test whether radar data improved land cover classification accuracy compared with optical data alone.Random forest classifier in the Google Earth Engine has been utilised to classify the tropical montane vegetation.The results indicate that radar or optical data alone cannot obtain satisfactory results.However,when combining radar with optical data the overall accuracy increased by approximately 5%,and by an additional 2%when topography data were added.The highest accuracy(92%)was achieved with multiple imagery,and adding the vegetation indices improved the model slightly by 0.3%.In addition,feature importance analysis showed that radar data makes a significant contribution to the classification.These positive outcomes demonstrate that freely-accessible multi-source remotely-sensed data have impressive capability for vegetation mapping,and support the monitoring and managing of forest ecological resources in tropical montane regions.

关 键 词：DEM Google Earth Engine PALSAR Random forest classifier SENTINEL Tropical montane 

分 类 号：P285[天文地球—地图制图学与地理信息工程]