Next Article in Journal
The Effect of Sea Surface Slicks on the Doppler Spectrum Width of a Backscattered Microwave Signal
Next Article in Special Issue
A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest
Previous Article in Journal
Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index
Previous Article in Special Issue
A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage
Sensors 2008, 8(6), 3767-3779; doi:10.3390/s8063767
Article

Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data

1,2
, 3
, 2,4
, 3
, 5,* , 6
 and 4
Received: 31 January 2008; in revised form: 29 May 2008 / Accepted: 30 May 2008 / Published: 6 June 2008
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
View Full-Text   |   Download PDF [156 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: Directional gap probability or gap fraction is a basic parameter in the optical remote sensing modeling. Although some approaches have been proposed to estimate this gap probability from remotely sensed measurements, few efforts have been made to investigate the scaling effects of this parameter. This paper analyzes the scaling effect through aggregating the high-resolution directional gap probability (pixel size of 20 meters) estimated from leaf area index (LAI) images of VALERI database by means of Beer's law and introduces an extension of clumping index, Ĉ, to compensate the scaling bias. The results show that the scaling effect depends on both the surface heterogeneity and the nonlinearity degree of the retrieved function. Analytical expressions for the scaling bias of gap probability and Ĉ are established in function of the variance of LAI and the mean value of LAI in a coarse pixel. With the VALERI dataset, the study in this paper shows that relative scaling bias of gap probability increases with decreasing spatial resolution for most of land cover types. Large relative biases are found for most of crops sites and a mixed forest site due to their relative large variance of LAI, while very small biases occur over grassland and shrubs sites. As for Ĉ, it varies slowly in the pure forest, grassland and shrubs sites, while more significantly in crops and mixed forest.
Keywords: directional gap probability; scaling bias; leaf area index; clumping index directional gap probability; scaling bias; leaf area index; clumping index
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Ma, L.; Li, C.; Tang, B.; Tang, L.; Bi, Y.; Zhou, B.; Li, Z.-L. Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data. Sensors 2008, 8, 3767-3779.

AMA Style

Ma L, Li C, Tang B, Tang L, Bi Y, Zhou B, Li Z-L. Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data. Sensors. 2008; 8(6):3767-3779.

Chicago/Turabian Style

Ma, Lingling; Li, Chuanrong; Tang, Bohui; Tang, Lingli; Bi, Yuyin; Zhou, Beiyan; Li, Zhao-Liang. 2008. "Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data." Sensors 8, no. 6: 3767-3779.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert