Estimation of Leaf Area Index Using IRS Satellite Images

Document Type : Research Article

Authors

• A Faridhosseini
• A Astaraei
• S. H Sanaeinejad
• P Mirhoseini Moosavi

10.22067/gsc.v10i3.17810

Abstract

Estimation of vegetation cover attributes, such as the Leaf Area Index (LAI), is an important step in identifying the amount of water use for some plants. The goal of this study is to investigate the feasibility of using IRS LISS-III data to retrieve LAI. To get a LAI retrieval model based on reflectance and vegetation index, detailed field data were collected in the study area of eastern Iran. In this study, atmospheric corrected IRS LISS-III imagery was used to calculate Normalized Difference Vegetation Index (NDVI). Data of 50 samples of LAI were measured by Sun Scan System – SS1 in the study area. In situ measurements of LAI were related to widely use spectral vegetation indices (NDVI). The best model through analyzing the results was LAI = 19.305×NDVI+5.514 using the method of linear-regression analysis. The results showed that the correlation coefficient R2 was 0.534 and RMSE was 0.67. Thereby, suggesting that, when using remote sensing NDVI for LAI estimation, not only is the choice of NDVI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using multi- spectral imagery for large-scale mapping of vegetation biophysical variables.

Keywords

• Remote sensing
• Normalized Difference Vegetation Index
• Leaf Area Index
• Plant architecture
• soil backgrounds

20.1001.1.20081472.1391.10.3.13.6

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