%0 Journal Article
%T Remote sensing estimation of natural forest biomass based on an artificial neural network.<br>基于人工神经网络的天然林生物量遥感估测
%A WANG Li-hai
%A XING Yan-qiu
%A <br>王立海
%A 邢艳秋
%J 应用生态学报
%D 2008
%I 
%X Based on Landsat TM images aod with the natural forest area of Wangqing in Jilin Province as a case,a nonlinear RS(remote sensing) modeling system of forest biomass was built by using a back-propogation artificial neural network(B-P ANN).In addition to RS data,the factors representing terrain conditions,such as elevation,slope,aspect and site type,were also included as independent variables in the modeling system.The standard B-P ANN was made more robust by reducing the size of input data and by improving the training algorithms,thereby leading to faster convergence speed and stronger capabilities of self-study and self-adaptation.The simulation results showed that the robust B-P ANN was able to utilize previous knowledge of data sets,and to automatically determine reasonable models.Model predictions of forest biomass were successful,with the mean relative errors and the mean absolute of relative errors for needle-leaved,broad-leaved,and mixed forests being-1.47%,2.38% and 3.56%,and 6.33%,8.46% and 8.91%,respectively.A forest biomass distribution map was derived,and the overall accuracy of the map was 88.04%.
%K natural forest
%K biomass
%K remote sensing
%K estimation
%K model
%K artificial neural network(ANN)<br>天然林
%K 生物量
%K 遥感
%K 估测
%K 模型
%K 人工神经网络
%K 工神经网络
%K 天然林
%K 生物量
%K 遥感估测
%K artificial
%K neural
%K network
%K based
%K biomass
%K natural
%K forest
%K estimation
%K sensing
%K 总体精度
%K 分布图
%K 定量
%K 研究
%K 系统生成
%K 效果
%K 预估
%K 平均相对误差
%K 误差分
%K 针阔混交林
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=42FEDDFE54F8BCD9D5FD1926ABDD7722&aid=DD25B49EE814525EEAB53003BD93E98B&yid=67289AFF6305E306&vid=2A8D03AD8076A2E3&iid=0B39A22176CE99FB&sid=4D7D059FFBF006B9&eid=4FE459D71E3BF8EB&journal_id=1001-9332&journal_name=应用生态学报&referenced_num=1&reference_num=27