作者:Cao, B (Cao, Biao)[ 1,2 ] ; Du, YM (Du, Yongming)[ 1,2 ] ; Li, J (Li, Jing)[ 1,2 ] ; Li, H (Li, Hua)[ 2,1 ] ; Li, L (Li, Li)[ 1,2 ] ; Zhang, Y (Zhang, Yang)[ 1,2 ] ; Zou, J (Zou, Jie)[ 3 ] ; Liu, QH (Liu, Qinhuo)[ 1,2 ]

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

卷: 12  期: 9  页: 1958-1962

DOI: 10.1109/LGRS.2015.2440438

出版年: SEP 2015

摘要

We compare five slope correction methods developed by Walter et al., Montes et al., Schleppi et al., Espana et al., and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8 degrees, 45 degrees, 57.5 degrees, and 63.2 degrees), and three slope angles (0 degrees, 20 degrees, and 50 degrees) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

通讯作者地址: Du, YM (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

[ 2 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

[ 3 ] Fuzhou Univ, Spatial Informat Res Ctr Fujian Prov, Minist Educ, Key Lab Data Min & Informat Sharing, Fuzhou 350002, Peoples R China