Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India

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• 作者：Rangaswamy Madugundu ; Vyjayanthi Nizalapur ; Ch ; ra Shekhar Jha
• 关键词：Deciduous forest ; Leaf area index ; IRS LISS-IV ; Above-ground biomass
• 刊名：International Journal of Applied Earth Observation and Geoinformation
• 出版年：2008
• 出版时间：June 2008
• 年：2008
• 卷：10
• 期：2
• 页码：211-219
• 全文大小：3068 K

文摘

This study demonstrates the potentials of IRS P6 LISS-IV high-resolution multispectral sensor (IGFOV 6 m)-based estimation of biomass in the deciduous forests in the Western Ghats of Karnataka, India. Regression equations describing the relationship between IRS P6 LISS-IV data-based vegetation index (NDVI) and field measured leaf area index (ELAI) and estimated above-ground biomass (EAGB) were derived. Remote sensing (RS) data-based leaf area index (PLAI) image is generated using regression equation based on NDVI and ELAI (r² = 0.68, p ≤ 0.05). RS-based above-ground biomass (PAGB) image was generated based on regression equation developed between PLAI and EAGB (r² = 0.63, p ≤ 0.05). The mean value of estimated above-ground biomass and RS-based above-ground biomass in the study area are 280(±72.5) and 297.6(±55.2) Mg ha⁻¹, respectively. The regression models generated in the study between NDVI and LAI; LAI and biomass can also help in generating spatial biomass map using RS data alone. LISS-IV-based estimation of biophysical parameters can also be used for the validation of various coarse resolution satellite products derived from the ground-based measurements alone.