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用CASI遥感数据估计横跨美国俄勒冈州针叶林叶面积指数(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
1993年01期
Page:
41-48
Column:
研究论文
publishdate:
1900-01-01

Article Info:/Info

Title:
ESTIMATION OF CONIFEROUS FOREST LEAF AREA INDEX ALONG THE OREGON TRANSECT USING CASI DATA
Article ID:
1000-2006(1993)01-0041-08
Author(s):
Pu Ruiliang Gong Peng John R. Miller(Nanjing Forestry University) (The University of Calgary Canada)
York University, Canada
Keywords:
Leaf area index (LAI) CASI imagery Correlation analysis Prediction
Classification number :
-
DOI:
10.3969/j.jssn.1000-2006.1993.01.007
Document Code:
A
Abstract:
The potentials of the compact airborne spectrographic imager(CASI) have been studied for coniferous forest LAI estimation by using three types of modelling techniques: univariate regression, multiple regression and vegetation index (VI) based LAI estimation. Four study sites have been selected along a forest transect in Oregon, USA. LAI measurements were colected from these study sites. CASI data of two imaging modes: spatial and spectral modes had been calibrated and corrected. The LAI measurements and the corrected CASI data were then used to study their relationships. Results indicate that the two imaging modes CASI data have similar effectiveness for LAI estimation. The multiple regression method resulted in higher accuracies of predicted LAI as compared to the univariate regression and VI based LAI estimation methods. The use of normalized difference vegetation index (NDVI) produced better LAI estimation than the use of other forms of variates for both univariate regression and VI based LAI estimation methods. For the univariate regression, a non-linear hypebola relationship between the LAI and the NDVI was the most apprepriate for LAI estimation. In this study, the VI based LAI estimation method has proved to be simple to use and very effective.

References

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Last Update: 1900-01-01