Share Email Print

Journal of Applied Remote Sensing

Applicability of spectral and spatial information from IKONOS-2 imagery in retrieving leaf area index of forests in the urban area of Nanjing, China
Author(s): Zhujun Gu; Weimin Ju; Yibo Liu; Dengqiu Li; Weiliang Fan
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Remote sensing is currently an indispensable tool for retrieving the leaf area index (LAI) of forests. However, the applicability of remote sensing in retrieving LAI of forests in urban areas has not been thoroughly investigated. The ability of spectral and spatial information from IKONOS-2 imagery to retrieve LAI of forests was studied through analyzing the correlations of four commonly used vegetation indices (VIs) and four texture measures (TEXs) with LAI measured at different types of plots in the urban area of Nanjing, China and comparing the ability of models based on these parameters to estimate LAI of forests. The results show that VIs and TEXs calculated from the high-resolution remote sensing data are both applicable in retrieving LAI of forests in urban areas. The relative advantages of VIs and TEXs are related to the density and spatial regularity of forests. TEX exceeds VI for regularly planted low broad-leaf forests with low density owing to the deterioration of the linkage of VIs with canopy LAI caused by strong soil noise. For forests with moderate and high density, VI exceeds TEX in the retrieval of LAI. As to natural broad-leaf forests with high density and spatial complexity, combining VI and TEX can improve the accuracy of the retrieved LAI by 8.9% to 27.0%. VIs and TEXs are exclusive in retrieving LAI due to the intrinsic linkages of these parameters. The atmospherically resistant vegetation index over-perform other VIs in retrieving LAI of forests owing to its ability to constrain atmospheric disturbance on remote sensing data, which is serious and exhibits great spatial variability in the study area.

Paper Details

Date Published: 20 September 2012
PDF: 14 pages
J. Appl. Remote Sens. 6(1) 063556 doi: 10.1117/1.JRS.6.063556
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Zhujun Gu, Nanjing Univ. (China)
Weimin Ju, Nanjing Univ. (China)
Yibo Liu, Nanjing Univ. (China)
Dengqiu Li, Nanjing Univ. (China)
Weiliang Fan, Nanjing Univ. (China)

© SPIE. Terms of Use
Back to Top