land data from the earth surface, such as HCMM, Landsat TM/ETM+, AVHRR,
MODIS, ASTER, and TIMS (Quattrochi and Luvall 1999; Weng et al. 2004). LST
modulates the air temperature of the lower layer of urban atmosphere, and is a
primary factor in determining surface radiation and energy exchange, the internal
climate of buildings, and human comfort in the cities (Voogt and Oke 2003).
Important elements such as land cover and land use processes, the global current
and historical carbon cycle, the global nitrogen cycle, management of nutrients in
agricultural systems, and climate variability including interaction with land use are
addressed by Kram and Stehfest (2011) in the integrated modeling of global
environmental change.
The normalized difference vegetation index (NDVI = (ρ NIR − ρ RED )/
(ρ NIR + ρ RED )) is the most commonly used vegetation index. It quantifies the
contrast between red surface reflectance (ρ RED ), which decreases with increasing
chlorophyll content, and near-infrared surface reflectance (ρ NIR ), which increases
with growing leaf area index and crown coverage. Atmospheric noise in the NDVI
caused by clouds, dust, and aerosols is generally considered negatively biased. This
is because additive path radiance causes an increase in red reflectance, while lower
atmospheric transmission reduces near-infrared reflectance (Guyot et al. 1989).
NDVI has been widely used across diverse biomes as a proxy for rainfall (Barbosa
et al. 2006), as a substitute for vegetation growth and health (Schmidt and Karnieli
2000) and in ecological studies (Pettorelli et al. 2005).
4 Vegetation Assessment in Northeast India Using NDVI
In the present study, the IRS P6 (RESOURCESAT-1) AWiFS product is utilized to
quantify NDVI of the region during 2010. The IRS P6 AWiFS-based NDVI of the
Northeast India exhibits that the majority of area (55 %) having higher NDVI
(≥7.0) largely located in eastern Himalayas (Arunachal Pradesh, Nagaland,
Manipur, Mizoram, Tripura) and Meghalaya followed by 33 % (NDVI 0.4–0.7)
located in the adjoining areas of eastern Himalayas (including Assam). In contrast,
only 8 % area is under low positive NDVI (0.1–0.4) mainly located in Assam
(Figs. 2a and 3a).
The Cartosat-I digital elevation model (DEM) of Northeast India exhibits that
the majority of the total area (48 %) exist in 0–500 m elevation spatially found
mainly in Assam, Tripura, western Meghalaya, south Arunachal Pradesh, and
western Mizoram, followed by 500–1000 m (17 %) and 1000–1500 m (12 %),
whereas only 8.5 % area is above 3000 m elevation spatially found only in northern
Arunachal Pradesh and northern Sikkim (Figs. 2b and 3b). Different climatic
zones were classified on the basis of elevation, i.e., tropical (0–800 m), subtropical
(800–1800 m), temperate (1800–2800 m), and alpine (>2800 m) zones. The NDVI
distribution of Northeastern India in four different climatic zones exhibited that
Biodiversity Status and Climate Change Scenario in Northeast India
113
MODIS, ASTER, and TIMS (Quattrochi and Luvall 1999; Weng et al. 2004). LST
modulates the air temperature of the lower layer of urban atmosphere, and is a
primary factor in determining surface radiation and energy exchange, the internal
climate of buildings, and human comfort in the cities (Voogt and Oke 2003).
Important elements such as land cover and land use processes, the global current
and historical carbon cycle, the global nitrogen cycle, management of nutrients in
agricultural systems, and climate variability including interaction with land use are
addressed by Kram and Stehfest (2011) in the integrated modeling of global
environmental change.
The normalized difference vegetation index (NDVI = (ρ NIR − ρ RED )/
(ρ NIR + ρ RED )) is the most commonly used vegetation index. It quantifies the
contrast between red surface reflectance (ρ RED ), which decreases with increasing
chlorophyll content, and near-infrared surface reflectance (ρ NIR ), which increases
with growing leaf area index and crown coverage. Atmospheric noise in the NDVI
caused by clouds, dust, and aerosols is generally considered negatively biased. This
is because additive path radiance causes an increase in red reflectance, while lower
atmospheric transmission reduces near-infrared reflectance (Guyot et al. 1989).
NDVI has been widely used across diverse biomes as a proxy for rainfall (Barbosa
et al. 2006), as a substitute for vegetation growth and health (Schmidt and Karnieli
2000) and in ecological studies (Pettorelli et al. 2005).
4 Vegetation Assessment in Northeast India Using NDVI
In the present study, the IRS P6 (RESOURCESAT-1) AWiFS product is utilized to
quantify NDVI of the region during 2010. The IRS P6 AWiFS-based NDVI of the
Northeast India exhibits that the majority of area (55 %) having higher NDVI
(≥7.0) largely located in eastern Himalayas (Arunachal Pradesh, Nagaland,
Manipur, Mizoram, Tripura) and Meghalaya followed by 33 % (NDVI 0.4–0.7)
located in the adjoining areas of eastern Himalayas (including Assam). In contrast,
only 8 % area is under low positive NDVI (0.1–0.4) mainly located in Assam
(Figs. 2a and 3a).
The Cartosat-I digital elevation model (DEM) of Northeast India exhibits that
the majority of the total area (48 %) exist in 0–500 m elevation spatially found
mainly in Assam, Tripura, western Meghalaya, south Arunachal Pradesh, and
western Mizoram, followed by 500–1000 m (17 %) and 1000–1500 m (12 %),
whereas only 8.5 % area is above 3000 m elevation spatially found only in northern
Arunachal Pradesh and northern Sikkim (Figs. 2b and 3b). Different climatic
zones were classified on the basis of elevation, i.e., tropical (0–800 m), subtropical
(800–1800 m), temperate (1800–2800 m), and alpine (>2800 m) zones. The NDVI
distribution of Northeastern India in four different climatic zones exhibited that
Biodiversity Status and Climate Change Scenario in Northeast India
113