引用方式: Fu Z, Wang JD, Song JL, Zhou HM, Pang Y, Chen BS. Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations.Journal of Applied Remote Sensing, 2011, 5(053530), 10.1117/1.3594171.
| 文献信息 | |
| 标题 | Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations |
| 年份 | 2011 |
| 出版社 | Journal of Applied Remote Sensing |
| 链接 | |
| 语言 | |
| DOI | 10.1117/1.3594171 |
| 摘要 | 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. |
| 此文献未收录PDF(如何提交?) |
Copyright ©right; 2017 - 中国科学院西北生态环境资源研究院 - 兰州数云软件科技有限公司提供技术支持