Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations
Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations
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DOI：
10.1117/1.3594171
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发表时间：
2011
影响因子：
1.7
通讯作者：
Zhuo Fu;Jindi Wang;Jinling Song;Hong-min Zhou;Y. Pang;Bai S. Chen
中科院分区：
文献类型：
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作者：
Zhuo Fu;Jindi Wang;Jinling Song;Hong-min Zhou;Y. Pang;Bai S. Chen
提出了一种利用多传感器观测数据由几何光学模型反演森林叶面积指数的新方法。为了提高崎岖地形下林区LAI的估计精度，分别从高空间分辨率的机载光探测与测距(LiDAR)数据和光学遥感数据中提取了树高先验信息和几何-光学互阴影(GOMS)模型中4个场景分量的光谱。研究区域的坡度和坡向是从数字高程模型数据中提取的。将这些提取的参数应用于GOMS模型中的森林冠层结构参数的反演，以改进估计结果。在野外调查中，结合中分辨率成像光谱仪(MODIS)和多角度成像光谱仪(MISR)的多角度遥感观测，采集了针叶林像元的双向反射系数数据集。然后，基于GOMS模型，对森林冠层参数进行了反演。最后，从反演的结构参数中估计每个像素点的森林冠层的叶面积指数，并通过野外测量进行验证。结果表明，将被动多角度和主动遥感相结合，可以提高森林冠层叶面积指数的估计精度。
A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging–spectroradiometer (MODIS) and multiangle-imaging–spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.