is subject to errors when used as a potential biomass predictor. The seasonal
changes in leaf canopy induces changes in NDVI. The grasses and shrubs with
good amount of greenness can simultaneously affect NDVI values.
(Cammarano, Fitzgerald, Casa, & Basso, 2014) have used a suite of spectral indices to estimate
the N content in plant leaves.Even though the paper mentions the use of
vegetation indices for plant characterisation but it has not dealt with the
assessment of biophysical parameters through vegetation indices.
(Anaya, Chuvieco, & Palacios-Orueta, 2009) attempted to increase the details of biomass estimation
at regional scales by using MODIS products and field measurements in Columbia.
They classified the area into grassland, primary forests and secondary forests.
They used different MODIS products for different types of vegetation. However,
such estimation presupposed the classification of forests beforehand and no
distinction between different types of forests was made.
(Devagiri et al., 2013) used Remote sensing
and GIS based
approach for estimation
of above ground
biomass (AGB) and
carbon pool at
regional scale in
south western part
of Karnataka. He integrated field measured biomass with spectral responses of different bands
and indices of
MODIS 250 m
spatial resolution. Based
on relative forest
area within the
MODIS pixel, area
weighted biomass was
estimated for each
site using ground
measured plot biomass. They used the spectral modeling to estimate the AGB and
vegetation carbon pool and prepared a map to understand the geospatial
distribution in the region. However this study has been done on a relatively
less undulating area when compared with Sikkim.
(Ramachandran, Jayakumar, Haroon,
Bhaskaran, & Arockiasamy, 2007) conducted a study on
estimation of carbon stock in natural forests using geospatial technology in the Eastern Ghats of Tamil Nadu,
India. Expert classification technique was followed to prepare the forest cover
density map of the study area. Normalized Difference Vegetation Index (NDVI)
was prepared and recoded into four classes based on the density, viz. very
dense (>70%), dense (40–70%), open (10–40%) and degraded (