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  • Author: Laxmi Goparaju x
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Abstract

Environmental Impact Assessments are studies conducted to give us an insight into the various impacts caused by an upcoming industry or any developmental activity. It should address various social, economic and environmental issues ensuring that negative impacts are mitigated. In this context, geospatial technology has been used widely in recent times.

The present study wishes to bring forth certain discrepancies detected while analyzing an Environmental Impact Assessment study of a proposed thermal power plant in Mirzapur district of Uttar Pradesh, India. If proper analysis is not done then the impacts are under estimated or over estimated. Thus, a proper understanding of the area under study and various techniques of analyzing satellite remote sensing data is required to achieve successful impact assessments which lead us in the right direction.

Abstract

The availability of remote sensing satellite data at various spatial, temporal and spectral resolutions provides enormous opportunity to map the urban sprawl. When coupled with Geographic Information System (GIS) it is possible to evaluate, analyse and integrate large data. We need to understand and quantify the urban sprawl on spatial and temporal scales which forms a basis for better planning and sustainable management of cities and towns. The city of Ranchi has witnessed unprecedented urban growth after assuming the status of a capital of Jharkhand state, India in 2000. The increasing population has put pressure on the natural resources of the city. The urban growth has been in a haphazard manner at the cost of agricultural lands, forest land and open green spaces such as park, garden and recreational forestry.

The present study analysed the urban sprawl in Ranchi city, using Landsat data from 1976, 2002 and 2015. The study revealed that the annual urban growth rate was 1.76 ha/yr over the period from 1976 to 2002 whereas the annual growth rate was 2 ha/yr over the period from 2002 to 2015. The northern side of the city has witnessed more expansion in 2002 when compared with the growth in 1976. Increase in urban density was seen at the distances of 3, 4,5,6,7 and 8km between 1976 and 2015 and the rate was higher than 25%.The driving factors of the development were infrastructure, educational and business expansion. Thus, spatial analyses of urban sprawl are a prerequisite for curbing the unplanned urban growth and ensure sustainable living.

Abstract

Climate change and its severity play an important role in forest fire regime. Analysing the forest fires events becomes a prerequisite for safeguarding the forest from further damage. We have made an assessment of the long-term forest fire events at the district level in India and identified the forest fire hotspot districts. The spatial seasonal (January to June) district wise pattern and forest fire trend were analysed. In the second part of the study area (central part of India), we have evaluated the forest fire events in grid format with respect to the climatic/weather datasets, and the statistical analysis Cramer V coefficient (CVC) was performed to understand its association/relationship with forest fire events.

The study revealed that Karbi Anglong and North Cachar Hills districts of Assam of India have the highest forest fire percent among all districts equivalent to 3.4 and 3.2% respectively. Dantewada district of Chhattisgarh and Garhchiroli district of Maharashtra of India occupied 3rd and 4th rank with value 3.1 and 3.0% respectively. The grid-based evaluation (local scale) revealed that most of the fire equivalent of 80% was found in the month of March and April. Forest fire frequency of the month of April is spread over 88 % of the grids over the study area. The 11 years average seasonal month-wise (February to June) maximum temperature, wind velocity, relative humidity, and solar radiation were found in the range of (25.9 to 40.6), (1.69 to 2.7), (0.301 to 0.736) and (14.21 to 22.98) respectively. The percentage increase (in the month of March) of maximum temperature, wind velocity, and solar radiation were 36, 39 and 62% respectively, when compared with the preceding month; whereas, a 60% decrease to relative humidity that was observed in the same month is usually the major cause of forest fire events in the month of March onwards.

The evaluation of Cramer V coefficient (CVC) values of rainfall, relative humidity, potential evapotranspiration, maximum temperature, wind velocity, and solar radiation were in decreasing order and in the range of 0.778 to 0.293. The highest value of rainfall (0.778) showed its strongest association with the forest fire events. In the month of June, these areas receive adequate rainfall, which leads to an increase in the soil moisture and a reduction in forest fuel burning capacity by absorbing the moisture and it is a strong reason for less forest fire events during this month. Geospatial technology provides an opportunity to evaluate large datasets over various spatial and temporal scales and help in decision making/formulating various policies.

Abstract

The dynamic changes in the regimes of forest fires are due to the severity of climate and weather factors. The aim of the study was to examine the trend of forest fires and to evaluate their relationship with climate parameters for the state of Telangana in India. The climate and forest fire data were used and uploaded to the GIS platform in a specified vector grid (spacing: 0.3° x 0.3°). The data were evaluated spatially and statistical methods were applied to examine any relationships. The study revealed that there was a 78% incidence of forest fires in the months of February and March. The overall forest fire hotspot analysis (January to June) of grids revealed that the seven highest forest fire grids retain fire events greater than 600 were found in the north east of Warangal, east of Khammam and south east of Mahbubnagar districts. The forest fire analysis significantly followed the month wise pattern in grid format. Ten grids (in count) showed a fire frequency greater than 240 in the month of March and of these, three grids (in count) were found to be common where the forest fire frequency was highest in the preceding month. Rapid seasonal climate/weather changes were observed which significantly enhanced the forest fire events in the month of February onwards. The solar radiation increased to 159% in the month of March when compared with the preceding month whereas the relative humidity decreased to 47% in the same month. Furthermore, the wind velocity was found to be highest (3.5 meter/sec.) in the month of February and precipitation was found to be lowest (2.9 mm) in the same month. The analysis of Cramer V coefficient (CVC) values for wind velocity, maximum temperature, solar radiation, relative humidity and precipitation with respect to fire incidence were found to be in increasing order and were in the range of 0.280 to 0.715. The CVC value for precipitation was found to be highest and equivalent to 0.715 and showed its strongest association/relationship with fire events. The significant increase in precipitation not only enhances the moisture in the soil but also in the dry fuel load lying on the forest floor which greatly reduces the fuel burning capacity of the forest. The predicted (2050) temperature anomalies data (RCP-6) for the month of February and March also showed a significant increase in temperature over those areas where forest fire events are found to be notably high in the present scenario which will certainly impact adversely on the future forest fire regime. Findings from this study have their own significance because such analyses/relationships have never be examined at the state level, therefore, it can help to fulfill the knowledge gap for the scientific community and the state forest department, and support fire prevention and control activities. There is a need to replicate this study in future by taking more climate variables which will certainly give a better understanding of forest fire events and their relationships with various parameters. The satellite remote sensing data and GIS have a strong potential to analyze various thematic datasets and in the visualization of spatial/temporal paradigms and thus significantly support the policy making framework.

Abstract

Geospatial evaluation of various datasets is extremely important because it gives a better comprehension of the past, present and future and can therefore be significantly utilized in effective decision making strategies. This study examined the relationships, using geospatial tools, between various diversified datasets such as land use/land cover (LULC), long term Normalized Difference Vegetation Index (NDVI) based changes, long term forest fire points, poverty percentage, tribal percentage, forest fire hotspots, climate change vulnerability, agricultural vulnerability and future (2030) climate change anomalies (RCP-6) of Jharkhand state, India, for a better understanding and knowledge of its vegetation health, LULC, poverty, tribal population and future climate change impact. The long term NDVI (1982-2006) evaluation revealed negative change trends in seven northwest districts of Jharkhand state, these were: Hazaribag, Ramgarh, Palamu, Lohardaga, Chatra, Garhwa and Latehar. The forests as well as the agriculture of these districts have lost their greenness during this period. The forest fire frequency events were found to be more pronounced in the land use/land cover of “tropical lowland forests, broadleaved, evergreen, <1000 m” category, and were roughly twice the intensity of the “tropical mixed deciduous and dry deciduous forests” category. In the nine districts of Jharkhand it was found that 40 % of the population was living below the poverty line which is around twice the national average. The highest poverty districts, in percentage, were: Garwah (53.93), Palamu (49.24), Latehar (47.99) and Chatra (46.2). The southwest and south of Jharkhand state shows a tribal population density of more than 40%. The climate change vulnerability was found to be highest in the district of Saraikela followed by Pashchim Singhbhum, whereas agricultural vulnerability was found to be highest in the district of Pashchim Singhbhum followed by Saraikela, Garhwa, Simdega, Latehar, Palamu and Lohardaga. The temperature anomalies prediction for the year 2030 shows an increasing trend in temperature with values of 0.8°C to 1°C in the state of Jharkhand. The highest increases were observed in the districts of Pashchim Singhbhum, Simdega and Saraikela. Based on these evaluations we can conclude that a few of the districts of Jharkhand, such as Pashchim Singhbhum, Garhwa, Palamu and Latehar need to be prioritized for development on an urgent basis. The outcomes of this study would certainly guide the policymakers to prepare more robust plans when keeping in mind the future climate change impacts for the prioritization of various districts of Jharkhand which suffer from extreme poverty, diminished livelihood and insignificant agricultural productivity for the betterment of the people of Jharkhand based on their adaptive capacity.

Abstract

The tropical dry deciduous forests of Mirzapur district in Uttar Pradesh (state) in India are facing to severe threat from agricultural expansion, increased urbanisation, infrastructure development and unsustainable use of forest produce. The forests are nowadays fragmented and wildlife habitat is vanishing. Decreasing numbers of wild animal species requires using methods for preventing the loss of biodiversity.

The present study concerns analysis of satellite remote sensing data of Landsat OLI (2013) in conjunction with Geographic Information System (GIS) and support of Geographic Positioning System (GPS) to identify suitable habitat for wild animals. The thematic maps regarding e.g. land use, forest cover type were prepared. The weighted overlay method was used for integration of the thematic layers for identification of potential habitat and corridors for wildlife movement. Most suitable (2.54%), moderately suitable (12.0%) and least suitable (16.20%) areas were delineated and represented in a map. Highly suitable habitat was found at Dadri and Kotwa forest; moderately suitable habitat was found in Darhi Ram forests. Least suitable areas were found in Kotwa and Patewar forests. Such data are the basis to assess the wildlife conservation measures.

Abstract

This study has analysed the Landsat 8 OLI data (December 2016) to delineate the various land use/land cover classes of the area which will be submerged by the proposed Daudhan/Greater Gangau Dam, which is part of the proposed Ken Betwa River Link Project (in the Madhya Pradesh state of India) and also the area likely to be submerged in the Panna Tiger Reserve (PTR). The proposed area of submergence was computed at various full reservoir lengths (FRL), 278 m, 283 m, 288 m, 289 m and 293 m. Similarly the area of submergence for the Panna Tiger Reserve was computed at the mentioned FRLs. It was concluded that a large part of the Panna Tiger Reserve would be submerged and habitat of various animals and plants would be under threat. In comparison with the figures given in the Environmental Impact Assessment certain serious discrepancies and weaknesses were detected and it was felt that they should have been addressed. The results were compared with the EIA – EMP report of the Ken-Betwa link project, Phase 1, prepared by Agricultural Finance Corporation Limited for the National Water Development Agency (Ministry of Water Resources, River Development and Ganga Rejuvenation, Government of India). A proper evaluation of the negative impacts would help when making relevant decisions and appropriate steps to ensure that the loss is kept to a minimum. Safeguarding the biodiversity of forests and wildlife habitats should be the priority as their loss is irreplaceable. Geospatial technology helps in studying the overall spatial view of the proposed submergence area and the visualization gives a clear picture of the likely scenario in the future. It would assist in decision making and mitigation measures.

Abstract

Climate change has very significant impact on livelihoods and food security. The geospatial technology provides a better understanding of various themes related to climate change. This study examined the seasonal (kharif, rabi and zaid) long term (1970-2000) monthly climatic parameters such as precipitation, potential evapotranspiration over the country of India. The seasonal Aridity Index was computed and analyzed with respect to various agro-ecological zones of India. The analysis of long term mean precipitation (mm) during kharif, rabi and zaid season was found to be in the range of (14-7463), (0-914) and (0-1722) respectively. The analyses of the long term mean potential evapotranspiration in all seasons was found notable high in arid/semiarid zones. The Aridity Index during kharif, rabi and zaid seasons was found to be in the range of (0.19-4.27), (0.03-0.73) and (0.01-1.48) respectively. The seasonal Aridity Index in some of the agro-ecological zones of the central India in the arid and semiarid regions was found to be notably low. A concrete plan with synergic approach including integrated watershed management and traditional ecological practices will help to fulfill crop water demand and maintain adequate soil moisture for the present and future crops.

Abstract

Analysing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of the advanced tools that has enormous capacity to evaluate the number of data sets simultaneously and to analyse the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India’s state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship.

The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percentage (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to the total country’s forest fire counts. The various LULC categories which represent the forest show some notable forest fire trends. The category ‘Deciduous Broadleaf Forest’ retain the highest fire frequency equivalent to 38.1% followed by ‘Mixed Forest’ (25.6%), ‘Evergreen Broadleaf Forest’ (16.5%), ‘Deciduous Needle leaf Forest’ (11.5%), ‘Shrub land’ (5.5%), ‘Evergreen Needle leaf Forest’ (1.5%) and ‘Plantations’ (1.2%). Monthly seasonal variation of forest fire events reveal the highest forest fire frequency percentage in the month of ‘March’ (55.4%) followed by ‘April’ (28.2%), ‘February’ (8.1%), ‘May’ (6.7%), ‘June’ (0.9%) and ‘January’ (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern; therefore, future forest fires will be more intensified in large parts of India, whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in the lower Sivalik range of Himalaya. The deciduous forests will further degrade in future.

The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analysed at the country level in view of the future climate scenario. Such analysis gives insight to the policymakers to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario.

Abstract

Geospatial technology has an enormous capacity to analyze large and diversified datasets for evaluating the hidden spatial relationship which provides a better comprehension of the subject and helps significantly in policymaking and planning future strategies.

This study has examined the relationship among diversified remote sensing and GIS datasets such as GHG emission from cropland, rice cultivation area, agro-ecological region, Land use/Land cover (LULC) categories, long-term NDVI (1982−2006) based negative changes, agriculture vulnerability, drought-prone area and future (2021, 2050) climate change anomalies (RCP-6) of India for better understanding and knowledge of the GHG emission scenario, vegetation health, LULC, agriculture vulnerability, and future climate change impact. The LULC analysis revealed that 49.6% (1 628 959 km²) of the geographical area was found to be under category ‘cropland’. The 32.5% of the total cropland areas are used for rice cultivation whereas around 76% of this rice cultivation area is producing high GHG emission (>1000 Mg CO2 e/yr.). LULC categories ‘Cropland’ and ‘Plantation’ show the long-term (1982−2006) negative change equivalent to 19.7 and 70.2% respectively. Similarly, around 56% of LULC categories representing the forest show the long-term negative change whereas the maximum change (139 867 km²) was found in the category of ‘Deciduous Broadleaf Forest’. The 30.6% of the LULC category of ‘cropland’ falls in very high agriculture vulnerable areas whereas 31.7% of the same category falls in the drought-prone area. The significant increase in temperature and abrupt rainfall patterns were observed during Kharif and Rabi seasons in the future. Such variation of climate parameter in the future not only adversely affect the agriculture crop production but also the natural vegetation of India.

The outcomes of the present study would support the policymakers of India to implement the climate-smart agriculture (CSA) and REDD+ on an urgent priority based on a proper evaluation of the socio-economic condition of the poor people. It will certainly help in the reduction of GHG emission, forest amelioration, will bring the resilience in livelihood and mitigate the poverty among the rural communities for the betterment of people.