This study demonstrates the potentia
ls of IRS P6 LISS-IV high-reso
lution mu
ltispectra
l sensor (IGFOV
lt=""not, vert, simi
lar"" tit
le=""not, vert, simi
lar"" border=""0""> 6 m)-based estimation of biomass in the deciduous forests in the Western Ghats of Karnataka, India. Regression equations describing the re
lationship between IRS P6 LISS-IV data-based vegetation index (NDVI) and fie
ld 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 deve
loped between PLAI and EAGB (
r2 = 0.63,
p ≤ 0.05). The mean va
lue 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, respective
ly. The regression mode
ls generated in the study between NDVI and LAI; LAI and biomass can a
lso he
lp in generating spatia
l biomass map using RS data a
lone. LISS-IV-based estimation of biophysica
l parameters can a
lso be used for the va
lidation of various coarse reso
lution sate
llite products derived from the ground-based measurements a
lone.