摘要
基于LandsatTM遥感图像,以吉林省汪清天然林区为例,应用B-P神经网络建立了森林生物量非线性遥感模型系统.除采用遥感数据外,该系统还引入了地形因子(海拔、坡度、坡向、立地类型等)作为模型自变量.通过压缩输入数据和增强网络训练学习算法等措施,对标准B-P神经网络进行了增强.模型仿真结果表明:增强型B-P神经网络具有收敛速度快和自学习、自适应功能强的特点,能最大限度地利用样本集的先验知识,自动提取合理的模型,模型预测结果能真实合理地反映实际情况.针叶林、阔叶林和针阔混交林的生物量遥感模型系统仿真结果的平均相对误差分别为-1.47%、2.38%和3.56%,平均相对误差绝对值分别为6.33%、8.46%和8.91%,预估效果较理想.应用该模型系统生成了研究区的森林生物量定量分布图,其总体精度为88.04%.
Based on Landsat TM images and 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%.
出处
《应用生态学报》
CAS
CSCD
北大核心
2008年第2期261-266,共6页
Chinese Journal of Applied Ecology
基金
国家科技支撑项目(2006BAD03A0805)
东北林业大学校立基金资助项目(XJ04022)
关键词
天然林
生物量
遥感
估测
模型
人工神经网络
natural forest
biomass
remote sensing
estimation
model
artificial neural network （ANN）.
作者简介
通讯作者王立海，男，1960年生，博士，教授，博士生导师．主要从事森林经营管理方面的研究，发表论文70余篇．E—mail： lihaiwang@yahoo．com
