248
identified the northern Rarh region and upper moribund delta region to be highly
sensitive to droughts. She has emphasized that the western-degraded plateau and its
adjacent regions which encompass the districts of Purulia, Bankura, Purba, and
Paschim Medinipur are the most vulnerable. According to Dash et al. (2019) and
Cornish et al. (2015) the western part of West Bengal as one of the worst droughtprone areas of India. Datta and Das 2018 have identified in their research work
significant decline in monsoon rain in sub-Himalayan West Bengal and have forecasted its adverse impact on western parts of West Bengal. Hence an urgent need for
monitoring agricultural drought based on remotely sensed images is required, especially to reduce crop failures.
Different literatures show that accurate prediction of drought and estimation of
losses were not possible due to the lack of meteorological information, costly data,
cumbersome, and lengthy data processing techniques, whereas remote sensing has
proved to be a better alternative (Brown et al. 2008; Gu et al. 2007; Palmer 1968;
Dutta et al. 2015; Kogan 1995; Rhee et al. 2010). Various band ratio techniques have
been applied on the different remotely sensed images in order to compute drought
indicators. Among them, the normalized difference vegetation index (NDVI) developed by Rouse et al. (1974) has been widely used for drought monitoring (Peters
et al. 2002). Kogan (1995) developed the vegetation condition index (VCI) by linearly scaling NDVI values from 0 to 1 for each pixel to separate weather-related
components from ecosystem components. In addition to VCI, thermal band-based
temperature condition index (TCI) was developed to provide additional information
on land surface temperature to distinguish vegetation stress caused by drought
events from other factors (Kogan 1995). VCI and rainfall anomaly index (RAI) have
demonstrated its usefulness in establishing a relation between agricultural drought
and meteorological drought (Patel and Yadav 2015). Literatures (Quiring and
Ganseh 2010; Dutta et al. 2011, 2015) have proved the efficiency of VCI, RAI, and
YAI and thus has been adopted for this study.
The present study endeavors to identify the spatiotemporal variation of agricultural drought using various remote sensing and GIS techniques and uses different
indices to monitor the agricultural drought at regional scale on the western districts
of West Bengal.
14.2 Study Area
The study area is situated in the western part of West Bengal. The location extent
spreads between 21.94° to 22.60° North latitude and 85.75° to 87.78° East longitude
(Fig. 14.1). This area covers the largest part of the state and consists of 4 districts,
10,000 villages, and 4 major towns (Census 2011). The study area is a part of Chota
Nagpur Plateau. There are many monadnocks, scattered especially in Bankura and
Purulia districts. The southern part of the region is flat, occupied by East and West
Medinipur districts near Bay of Bengal. Slope of this area is toward East as can be
seen in Figure 14.2a. Greater parts of study area are covered by laterite and alluvium
M. Dey et al.
identified the northern Rarh region and upper moribund delta region to be highly
sensitive to droughts. She has emphasized that the western-degraded plateau and its
adjacent regions which encompass the districts of Purulia, Bankura, Purba, and
Paschim Medinipur are the most vulnerable. According to Dash et al. (2019) and
Cornish et al. (2015) the western part of West Bengal as one of the worst droughtprone areas of India. Datta and Das 2018 have identified in their research work
significant decline in monsoon rain in sub-Himalayan West Bengal and have forecasted its adverse impact on western parts of West Bengal. Hence an urgent need for
monitoring agricultural drought based on remotely sensed images is required, especially to reduce crop failures.
Different literatures show that accurate prediction of drought and estimation of
losses were not possible due to the lack of meteorological information, costly data,
cumbersome, and lengthy data processing techniques, whereas remote sensing has
proved to be a better alternative (Brown et al. 2008; Gu et al. 2007; Palmer 1968;
Dutta et al. 2015; Kogan 1995; Rhee et al. 2010). Various band ratio techniques have
been applied on the different remotely sensed images in order to compute drought
indicators. Among them, the normalized difference vegetation index (NDVI) developed by Rouse et al. (1974) has been widely used for drought monitoring (Peters
et al. 2002). Kogan (1995) developed the vegetation condition index (VCI) by linearly scaling NDVI values from 0 to 1 for each pixel to separate weather-related
components from ecosystem components. In addition to VCI, thermal band-based
temperature condition index (TCI) was developed to provide additional information
on land surface temperature to distinguish vegetation stress caused by drought
events from other factors (Kogan 1995). VCI and rainfall anomaly index (RAI) have
demonstrated its usefulness in establishing a relation between agricultural drought
and meteorological drought (Patel and Yadav 2015). Literatures (Quiring and
Ganseh 2010; Dutta et al. 2011, 2015) have proved the efficiency of VCI, RAI, and
YAI and thus has been adopted for this study.
The present study endeavors to identify the spatiotemporal variation of agricultural drought using various remote sensing and GIS techniques and uses different
indices to monitor the agricultural drought at regional scale on the western districts
of West Bengal.
14.2 Study Area
The study area is situated in the western part of West Bengal. The location extent
spreads between 21.94° to 22.60° North latitude and 85.75° to 87.78° East longitude
(Fig. 14.1). This area covers the largest part of the state and consists of 4 districts,
10,000 villages, and 4 major towns (Census 2011). The study area is a part of Chota
Nagpur Plateau. There are many monadnocks, scattered especially in Bankura and
Purulia districts. The southern part of the region is flat, occupied by East and West
Medinipur districts near Bay of Bengal. Slope of this area is toward East as can be
seen in Figure 14.2a. Greater parts of study area are covered by laterite and alluvium
M. Dey et al.