基于阈值分割的黑龙江省森林类型遥感识别  

Forest cover classification based on remote sensing threshold consistent with statistics in Heilongjiang Province

作　　者：袁玉娟[1,2] 尹云鹤[1] 戴尔阜[1] 刘荣高[3] 吴绍洪[1] 

机构地区：[1]中国科学院地理科学与资源研究所中国科学院陆地表层格局与模拟重点实验室,北京100101 [2]中国科学院大学,北京100049 [3]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101

出　　处：《地理科学进展》2016年第5期655-663,共9页Progress in Geography

基　　金：国家科技支撑计划项目(2012BAC19B02);国家自然科学基金项目(41571043);国家自然科学基金重点项目(41530749)~~

摘　　要：全球变化背景下,准确获取森林覆盖是监测森林资源动态、实现林业可持续发展的重要基础。为将省级尺度森林资源清查面积资料空间化,以黑龙江省为例,利用1999-2003年该省森林资源清查面积数据,结合2000年500 m分辨率的MODIS数据,构建了基于阈值分割的森林类型遥感识别方法。该方法利用不同地表覆被类型归一化植被指数时间序列的季节分异特征,以森林资源清查面积为标准,设定森林类型的划分阈值,识别了黑龙江省森林类型的空间分布。最后,基于分层随机抽样和精度评价方法,表明森林类型识别结果与地面参考数据具有较高的一致性,总体分类精度为78.1%;特别是季节特征明显的落叶林,精度可达80%以上。本文所构建的方法可将森林清查统计数据进行准确的空间定位,同时结合多期森林资源连续清查资料和遥感信息,可为识别并量化区域生态系统生物量和碳库变化等提供科技支撑。Accurately identifying spatial distribution of forest is critically important for dynamic monitoring and sustainable management of forest resources.In this article,in order to acquire a spatially explicit forest cover classification based on the national forest inventory（NFI） statistics at the provincial scale,we developed an identification method using threshold values based on forest area from NFI statistics in 1999-2003 and the Moderate Resolution Imaging Spectroradiometer（MODIS） surface reflectance data in 2000 with a spatial resolution of 500 m for Heilongjiang Province.Based on the seasonal difference of Normalized Difference Vegetation Index（NDVI） of various forest types,threshold values between different forest types in satellite data were set using the NFI statistical data as criteria.Four forest types were differentiated：evergreen needleleaf,deciduous broadleaf,deciduous needleleaf,and mixed forests.Due to the stratified random sampling method used in this study and reliable threshold identification,the accuracy assessment result shows that the spatial pattern of forest cover classifications is highly consistent with the ground reference map,with an overall classification accuracy of 78.1%.Specifically,the applied method resulted in higher classification accuracy for deciduous forests that have distinct seasonal variations of NDVI（with user accuracy above 80%）.The study provides a practical method for spatially explicit forest coverage estimation,and for quantifying changes in biomass and carbon stock in the ecosystem at the regional scale based on several periods of NFI statistics and remote sensing data.

关 键 词：森林资源清查 遥感 阈值 精度评价 黑龙江省 

分 类 号：S771.8[农业科学—森林工程]