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

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. Th...

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Detalles Bibliográficos
Autores principales: Ma, Lingling, Li, Chuanrong, Tang, Bohui, Tang, Lingli, Bi, Yuyin, Zhou, Beiyan, Li, Zhao-Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714664/
https://www.ncbi.nlm.nih.gov/pubmed/27879907
http://dx.doi.org/10.3390/s8063767
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author Ma, Lingling
Li, Chuanrong
Tang, Bohui
Tang, Lingli
Bi, Yuyin
Zhou, Beiyan
Li, Zhao-Liang
author_facet Ma, Lingling
Li, Chuanrong
Tang, Bohui
Tang, Lingli
Bi, Yuyin
Zhou, Beiyan
Li, Zhao-Liang
author_sort Ma, Lingling
collection PubMed
description 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.
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spelling pubmed-37146642013-07-18 Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data Ma, Lingling Li, Chuanrong Tang, Bohui Tang, Lingli Bi, Yuyin Zhou, Beiyan Li, Zhao-Liang Sensors (Basel) Article 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. Molecular Diversity Preservation International (MDPI) 2008-06-06 /pmc/articles/PMC3714664/ /pubmed/27879907 http://dx.doi.org/10.3390/s8063767 Text en © 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Ma, Lingling
Li, Chuanrong
Tang, Bohui
Tang, Lingli
Bi, Yuyin
Zhou, Beiyan
Li, Zhao-Liang
Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
title Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
title_full Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
title_fullStr Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
title_full_unstemmed Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
title_short Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
title_sort impact of spatial lai heterogeneity on estimate of directional gap fraction from spot-satellite data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714664/
https://www.ncbi.nlm.nih.gov/pubmed/27879907
http://dx.doi.org/10.3390/s8063767
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