林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。

Assessment of different methods for estimating forest leaf area index from remote sensing data

Leaf area index (LAI) is a crucial vegetation structural parameter that has influence on the energy and carbon dioxide exchanges within and over forest canopies. The applications of remote sensing data provide the possibility of the relationship between LAI and vegetation index. In order to improve the estimates of forest leaf area index with remote sensing method, the ground LAI measurements were made by using the TRAC(Tracing Radiation and Architecture of Canopies) instrument in the urban forests of Beijing, and several spectral vegetation indices such as NDVI, SR, RSR and SAVI were calculated from Landsat Thematic Mapper (TM) image. With the establishment of the statistical models dependent on single vegetation index alone and the improved BP (back-propagation) neural networks with multi vegetation index combination, the best-fit method between ground measured LAI and vegetation indices with the highest accuracy of LAI estimation was obtained and used to estimate LAI from TM image in the urban area of Beijing. Results show that the accuracy of LAI estimated by using non-linear statistical model is higher than that estimated by using linear statistical model based on single vegetation index. The neural network with NDVI, RSR and SAVI as inputs outperforms the other methods in estimating LAI with the highest R² (coefficient of determination) value of 0.827 and the lowest RMSE (root mean square error) value of 0.189. The neural network does not need to determine many coefficients and is applicable for estimating forest leaf area index in urban areas using remote sensing data.