Study on land cover classification in Xinjiang based on GEE
This paper is based on Landsat-8 OLI data provided by the Google Earth Engine(GEE)cloud platform,utilizing NDVI,EVI,NDWI,DEM and SLOPE as ancillary data.By utilizing of RF,CART,and SVM classification algorithms,the research classifies land cover types in Xinjiang,to provide technical support for the intelligent and rapid extraction of long-term land cover data products.The creation of land cover maps in recent years serves as a reference for the management of land resources and the formulation of eco-logical environment protection strategies in Xinjiang.The experimental results indicate:1)Under the same data source and training sample conditions,the RF algorithm outperforms the CART algorithm,and the CART algorithm outperforms the SVM algorithm.Regarding to the classification accuracy of land cover in Xinjiang,the RF classification algorithm achieves the highest overall accuracy at 96.6%,with a Kappa coefficient of 0.95.The SVM classification algorithm exhibits the lowest overall accuracy at 84.2%,with a Kappa coefficient of 0.81.2)Since 2016,NDVI in Xinjiang has shown a decreasing trend primarily due to the degradation of a sub-stantial amount of grassland in the Junggar Basin.The paper suggests that advancing ecological protection and restoration efforts re-quires preventing grassland degradation and strengthening the management of land desertification.
land cover classificationremote sensingmachine learning
国家科技期刊平台
NSTL
万方数据
