摘要
基于Google Earth Engine云平台和2014—2017年Landsat OLI影像序列,根据其在时间域上的光谱特征,结合植被指数特征、地形和温度特征,采用随机森林分类算法,开展香格里拉森林类型分类研究。结果表明:不同森林类型的生长轨迹有明显差异,4种森林类型在冬季的植被指数差异最明显;时间序列影像数据能够提供不同森林类型的物候差异特征,弥补单一日期影像难以区分不同森林类型的困难;研究区森林/非森林覆盖的总体精度为97.17%,Kappa系数为0.943,森林类型分类的总体精度87.78%,Kappa系数为0.80。基于Landsat时间序列的方法能够提供一个精度较高的森林分类产品,可为基于森林类型制图的应用提供帮助。
Based on the Google Earth Engine platform and Landsat OLI time-series data from 2014 to 2017,combined with vegetation index,topography and temperature related features,the forest type classification of Shangri-La was studied by using random forest classification classifier.The findings indicates that the different types have obvious phenological characteristics difference in various seasons,among which 4 forest types were the most in winter.Therefore,the time-series data can provide the phenological difference characteristics for classification,thus benefitting the classification of forest types.Accuracy assessment of forest/non forest classification indicates that the overall accuracy and Kappa coefficient are 97.17%and 0.943,respectively.And the forest type classification’s overall accuracy and Kappa coefficient are 87.78%and 0.80,respectively.The method can provide a forest classification product with high precision and is helpful for mapping forest types.
作者
李若楠
欧光龙
代沁伶
徐伟恒
王雷光
Li Ruonan;Ou Guanglong;Dai Qinling;Xu Weiheng;Wang Leiguang(College of Forestry,Southwest Forestry University,Kunming Yunnan 650233,China;Art and Design College,Southwest Forestry University,Kunming Yunnan 650233,China;Institute of Big Data and Artificial Intelligence,Southwest Forestry University,Kunming Yunnan 650233,China;Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data,Southwest Forestry University,Kunming Yunnan 650233,China)
出处
《西南林业大学学报（自然科学）》
CAS
北大核心
2020年第5期115-125,共11页
Journal of Southwest Forestry University：Natural Sciences
基金
国家自然科学基金项目(31860182,41771375,31860240,41961053,41571372)资助
云南省中青年学术和技术带头人后备人才项目(2018HB026)资助。
