农业工程学报Issue(2)：139-145,7.DOI:10.3969/j.issn.1002-6819.2014.02.018
漓江上游山区水源林叶面积指数变化的遥感监测
Remote monitoring of leaf area index changes for water source forest over mountain areas in upper reaches of Lijiang River
摘要
Abstract
Leaf area index (LAI) is a crucial vegetation structural parameter that has an influence on a forest ecosystem. In order to monitor the LAI change of the water source forest over the complex mountain areas in the upper reaches of Lijiang River, the ground LAI measurements were made by using the TRAC instrument in broadleaf, coniferous, and bamboo forests during September and October 2009. Then five spectral vegetation indices, NDVI, SR, RSR, SAVI, and EVI, were calculated from TM remote sensing data, and also elevation, slope gradient, and slope aspect were obtained from DEM data. RBF neural network models were established and trained by using the different combination of vegetation index as inputs, and the ground LAI measurements as the output. After the correlation coefficients of linear regression equations and the root mean square errors between estimated LAI and measured LAI were compared, the optimum combination of a multi vegetation index with the highest correlation coefficient and the lowest error was obtained for each of the broadleaf, coniferous, and bamboo species. As the neural network model was extended to complex mountain areas by adding terrain factors to input units, it was used to estimate LAI from six TM/ETM images during 1989 to 2009. Results showed that a neural network could successfully solve the problems that the coefficients of the non-linear regression equation between LAI and multi vegetation index are difficult to calculate and the regression equation can not include terrain factors. The accuracy of LAI estimation from the optimum model added terrain factors was improved as compared to the ground LAI measurements. LAI change in the forests results from the shrinkage of the mature broadleaf forest and the increase of the young economical forests. In the eleven years of 1989-2000, the area percentage of forest with an LAI value more than 6.0 sharply decreased from 78.8% to 44.1%, and the area percentage of forest with an LAI range from 1.0 to 6.0 enormously increased from 20.8% to 55.4%. During 2000-2009, although the area percentage of forest with an LAI value more than 6.0 gradually recovered to 74.5%with the growth of young forest and the fast growing of bamboo forest, it didn’t approach the area percentage in 1989. Meanwhile, the area percentage of forest with an LAI range from 1.0 to 6.0 gradually dropped to 25.1%. The results provide a reference for the ecological assessment of water source forests in the upper reaches of Lijiang river.关键词
遥感/神经网络/模型/地形/监测/森林/叶面积指数Key words
remote sensing/neural networks/models/land form/monitoring/forest/leaf area index分类
农业科技引用本文复制引用
王修信,付洁,王培娟,朱启疆,汤谷云,孙涛,罗涟玲..漓江上游山区水源林叶面积指数变化的遥感监测[J].农业工程学报,2014,(2):139-145,7.基金项目
国家自然科学基金资助项目（41061040）；国家自然科学基金资助项目 （）
