Dipanwita Dutta

Monitoring the extent of desertification processes in western Rajasthan (India) using geo-information science

Desertification is considered as a major worldwide environmental problem mainly caused by the climate changes and human activities during the last decades. Areas affected by desertification processes are gradually losing their level of biological quality and productivity. Among the different indicators of desertification, degradation of vegetation cover and increasing amount of bare soil have been popularly used by researchers. In India, desertification is one of the major sluggish hazards which is found in northwestern part of this country, mainly in the state of Rajasthan. The infringement of the Thar Desert has become a serious problem in the adjoining districts of Bikaner, Churu, and Nagaur. In this study, a linear spectral unmixing (LSU) method has been used for end-member fraction estimation primarily to differentiate the sand percentage and vegetation cover percentage. This linear spectral unmixing model is a widely used technique in remote sensing to estimate the fractions of several individual surface components present in an image pixel and the pure reflectance spectrum of a component which is called end-member. The LSU technique is able to monitor desertification process in terms of fractional changes in bare soil (sand) and vegetation covers. These two land features are the most crucial indicator of desertification and their long-term changes can produce expected result in identification of desertification process in an area. The long-term multispectral satellite data such as Landsat Thematic Mapper (TM) (1990, 1995, and 1999) and Enhanced Thematic Mapper (ETM+) (2003, 2009) have been used in this study. The time series analysis of fractional images of vegetation cover and bare soil has been employed for monitoring desertification processes over a long period. After analyzing the changes, some distinct patches of vegetation depletion coupled with increasing bare soil fraction were identified within the region that clearly indicates the ongoing process of desertification over there.

Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices

The detection and monitoring of drought-related vegetation stress over a large spatial area have become possible with the use of satellite-based remote sensing indices, namely, vegetation condition index (VCI) and temperature condition index (TCI). In particular, the water (precipitation)-related moisture stress during drought may be determined using the VCI, while the temperature-related stress using the TCI. An attempt is made here to investigate and demonstrate the importance of these indices over India during the contrasting monsoon years, 2009, 2010, and 2013, termed as meteorological drought, wet, and normal monsoon years, respectively. The overall health of the vegetation during these years is compared using the vegetation health index (VHI). The advantage of VHI over the VCI and TCI is also shown. An assessment of drought over India is then made using the combined information of VCI, TCI, and VHI. The occurrence of vegetative drought over Rajasthan, Gujrat, and Andhra Pradesh is confirmed using drought assessment index, which shows very low value (well below 40) during 2009 over these regions. The area-averaged time series indices as well as spatial maps over the state of Uttar Pradesh show higher thermal stress and poor vegetation health during 2009 as compared to 2010 and 2013. The standardized precipitation index (SPI) and standardized water-level index (SWI) are used to validate the results obtained using the remote sensing indices.

Long-Term Trend of Vegetation in Bundelkhand Region (India): An Assessment Through SPOT-VGT NDVI Datasets

Vegetation cover is an important natural resource of the terrestrial ecosystem, and it has significant role in preserving the ecological balance in an area. Analyzing the dynamic pattern of vegetation cover and its trend can be a key to explain any unusual condition of the environment. Bundelkhand, located at the central part of India, has experienced recurrent drought events in last decade, and considering the devastating effects of drought in that region, the present study aims to explore the long-term trend of vegetation using geo-spatial technology. The remote sensing-based SPOT-VGT NDVI data were used to identify the changes in vegetation with time. The normalized difference vegetation index (NDVI) has proven to be a very powerful indicator of global vegetation productivity. In this study, we used linear regression model for evaluating the long-term trend of vegetation considering NDVI as dependable and time as independent variable. Our results showed that there is a varying pattern of vegetation trend and its response to rainfall.

Assessing Pattern of Spatio-temporal Change in NCT of Delhi and its Peri-urban Areas using Geospatial Techniques

Big Cities like Mumbai, Kolkata and Delhi, etc., are expanding very fast mainly due to changing socio-economic activities which in turn put pressure on land and natural environment of the cities. Rapid development of cities without proper planning and ecological concern has been a great challenge to the urban planners as well the policy makers to manage a livable environment for city dwellers. Development of new urban areas and expansion of existing cities is inevitable as it’s an essential part of sustainable economy but uncontrolled and haphazard urban growth may raise serious problems related to environmental pollution, changes in urban micro climate, loss of biodiversity and ecological balance, human and traffic congestion, etc. Actual information on spatial distribution of different land use and land cover has multi-dimensional utility in planning and management of the land resources which is perceived as a key factor in the process of development of an area. However, optimal use of land resource requires quantitative information on spatial distribution as well as spatio-temporal changes of various land use and land cover in an area. In this context remote sensing data and GIS techniques are well accepted and established tool for assessing the land dynamics. For this paper landsat data of 1977, 2003 and 2014 were used to assess the spatio-temporal change over NCT of Delhi and its per-urban areas within a buffer of 15 km from the outer boundary of NCT of Delhi. In order to identify the urban growth and associated land use land cover changes, change detection analysis was carried out. The study reveals that areas under different land use and land cover has changed during 1977–2003 and the level of change recorded maximum 14.5% increase in low-density built-up and 8.79% high-density built-up areas but sparse vegetation recorded 12.20% decrease in the NCT of Delhi. On the other hand there is just little change, i.e. an increase of about 9.09% in the low-density built-up and almost little change in the rest of the classes in the peri-urban areas. Furthermore the result shows that during 2003–2014 there is large scale change, i.e. 19.63% in high-density built-up area has been recorded at the cost of 8.4% sparse vegetation and 4.4% agricultural in the NCT of Delhi. In the peri-urban areas there is decrease of agricultural land of about 13% during last decades.

A geo-spatial study on spatio-temporal growth of brackish water aquaculture along the coastal areas of West Bengal (India)

The present study seeks to identify the landuse changes occurring due to haphazard growth of brackish water fisheries along the coastal areas of West Bengal through remote sensing and GIS techniques. High resolution multi-temporal Google Earth images were used for detecting spatio-temporal changes of two blocks of Contai sub-division located in Midnapore district of West Bengal. Also, the area was surveyed with GPS and the digitized maps were verified using the information collected from the aquaculture farm owners. It is evident from change detection analysis that a significant amount of area under agricultural land has been converted into aquaculture farm and also a large number of pre-existing ponds have been converted into brackish water fisheries. Remote sensing derived statistical information on inland fisheries of Contai II and III blocks reveals that area under brackish water fisheries has increased about 2950 acres within the period 2006 to 2011.