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Topographic correction-based retrieval of leaf area index in mountain areas

Leaf Area Index (LAI) is a key parameter in vegetation analysis and management, especially for mountain areas. The accurate retrieval of LAI based on remote sensing data is very necessary. In a study at the Dayekou forest center in Heihe watershed of Gansu Province, we determined the LAI based on to... Full description

Journal Title: Journal of Mountain Science 2012, Vol.9(2), pp.166-174
Main Author: Chen, Wei
Other Authors: Cao, Chunxiang
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1672-6316 ; E-ISSN: 1993-0321 ; DOI: 10.1007/s11629-012-2248-2
Link: http://dx.doi.org/10.1007/s11629-012-2248-2
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recordid: springer_jour10.1007/s11629-012-2248-2
title: Topographic correction-based retrieval of leaf area index in mountain areas
format: Article
creator:
  • Chen, Wei
  • Cao, Chunxiang
subjects:
  • SPOT-5 image
  • Vegetation Index
  • Leaf Area Index
  • Topographic correction
  • Mountain areas
ispartof: Journal of Mountain Science, 2012, Vol.9(2), pp.166-174
description: Leaf Area Index (LAI) is a key parameter in vegetation analysis and management, especially for mountain areas. The accurate retrieval of LAI based on remote sensing data is very necessary. In a study at the Dayekou forest center in Heihe watershed of Gansu Province, we determined the LAI based on topographic corrections of a SPOT-5. The large variation in the mountain terrain required preprocessing of the SPOT-5 image, except when orthorectification, radiation calibration and atmospheric correction were used. These required acquisition of surface reflectance and several vegetation indexes and linkage to field measured LAI values. Statistical regression models were used to link LAI and vegetation indexes. The quadratic polynomial model between LAI and SAVI (L=0.35) was determined as the optimal model considering the R and R2 value. A second group of LAI data were reserved to validate the retrieval result. The model was applied to create a distribution map of LAI in the area. Comparison with an uncorrected SPOT-5 image showed that topographic correction is necessary for determination of LAI in mountain areas.
language: eng
source:
identifier: ISSN: 1672-6316 ; E-ISSN: 1993-0321 ; DOI: 10.1007/s11629-012-2248-2
fulltext: fulltext
issn:
  • 1993-0321
  • 19930321
  • 1672-6316
  • 16726316
url: Link


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titleTopographic correction-based retrieval of leaf area index in mountain areas
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subjectSPOT-5 image ; Vegetation Index ; Leaf Area Index ; Topographic correction ; Mountain areas
descriptionLeaf Area Index (LAI) is a key parameter in vegetation analysis and management, especially for mountain areas. The accurate retrieval of LAI based on remote sensing data is very necessary. In a study at the Dayekou forest center in Heihe watershed of Gansu Province, we determined the LAI based on topographic corrections of a SPOT-5. The large variation in the mountain terrain required preprocessing of the SPOT-5 image, except when orthorectification, radiation calibration and atmospheric correction were used. These required acquisition of surface reflectance and several vegetation indexes and linkage to field measured LAI values. Statistical regression models were used to link LAI and vegetation indexes. The quadratic polynomial model between LAI and SAVI (L=0.35) was determined as the optimal model considering the R and R2 value. A second group of LAI data were reserved to validate the retrieval result. The model was applied to create a distribution map of LAI in the area. Comparison with an uncorrected SPOT-5 image showed that topographic correction is necessary for determination of LAI in mountain areas.
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titleTopographic correction-based retrieval of leaf area index in mountain areas
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abstractLeaf Area Index (LAI) is a key parameter in vegetation analysis and management, especially for mountain areas. The accurate retrieval of LAI based on remote sensing data is very necessary. In a study at the Dayekou forest center in Heihe watershed of Gansu Province, we determined the LAI based on topographic corrections of a SPOT-5. The large variation in the mountain terrain required preprocessing of the SPOT-5 image, except when orthorectification, radiation calibration and atmospheric correction were used. These required acquisition of surface reflectance and several vegetation indexes and linkage to field measured LAI values. Statistical regression models were used to link LAI and vegetation indexes. The quadratic polynomial model between LAI and SAVI (L=0.35) was determined as the optimal model considering the R and R2 value. A second group of LAI data were reserved to validate the retrieval result. The model was applied to create a distribution map of LAI in the area. Comparison with an uncorrected SPOT-5 image showed that topographic correction is necessary for determination of LAI in mountain areas.
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doi10.1007/s11629-012-2248-2
pages166-174
date2012-04