indicator beforehand and no distinction between different types of

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.

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(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 (


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