Informatics Publishing Limited

D. P. Semwal , D. N. Pant , P. S. Roy , Varshanaithani

Abstract

This paper deals with the land use land cover change monitoring over the years of 17 duration between 1983 to 2000 using coincidence matrix between the classified details of Landsat MSS and IRS-IC LISS III data of respective years. Maximum Likelihood classification and post classification comparison techniques were performed for evolving coincidence matrix. The ground observations and the empirical evidences quantified under the study reflected that the ischolar_main cause of changes among land use land cover types had been centered on coal mining activities. The overall rate of change was found as 2.16% per year. This includes 0.1% per year change due to coal mines. The agricultural land was reduced by 26.40km² or 22.35% out of its total area and the land was transformed into coal maines, habitation related to coal mines and the abandoned agricultural land classified as grassland! scrub. Coal mining activities have also caused the loss of tree and bamboo vegetation cover by 54.79 km² or 7.27% out of the total area indicating the rate of loss as 0.6% per year.

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B. G. Marcot , A. Kumar , P. S. Roy , V. B. Sawarkar , A. Gupta , S. N. Sangma

Abstract

In the South Garo Hills District and Nokrek area of Western Meghalaya, statistical analyses suggest very low elephant densities and greatest declines of elephants in areas with >10% bamboo and secondary forest (6-10 years old) and >10% scrub and abandonedjhum fields (old fallow jhum 3-6 years old). Elephant densities are hi'ghest, and declines are the least, in areas with >25% semi-evergreen forest (old secondary forests 15-30+ years old). Data on elephant sign (use) in the field generally support these findings, with selection by elephants (ie., use significantly exceeding availability) for native semi-evergreen forest, and lack of selection (use significantly less than availability) for deciduous forests (including Sal forest, Teak, and Cashew plantations) and for scrub and abandonedjhum fields. To maintain elephant populations in the South Garo Hills District and Nokrek area, we suggest official delineation of 7 elephant habitat corridors that we mapped as having low degree of fragmentation of forest cover and a high proportion of contiguous, semi-evergreen and evergreen forest cover.

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C. Jeganathan , P. S. Roy , M. N. Jha

Abstract

Forests have been the key element in maintaining sustainability of many global phenomena. Human dependency on forests is both necessary and unavoidable and hence degradation of this natural resource is inevitable. The study aims to understand the change dynamics over past few decades in the Shimla district, using remote sensing and GIS based techniques. The tree cover area estimated during 1970s, 80s and 90s were 50.65%, 48.30% and 52.31% respectively. The classified images were analysed for changes and found that 2.35% of net tree cover changed into non-tree cover during 1972 to 1989 but during 1989 to 1999 the trend changed into a net positive one with the increase of tree cover by 4.01%. Transition probabilities of each land cover features were calculated for the three-time periods (72-89,89-99 and 72-99) and then analysed for their statistical significance using Markov chain model. Based on the findings, a non-spatial temporal Markov prediction was made for the year 2009. The predicted forest area in 2009 is 55.49% with the 5% error under Markovian assumption of stationarity.

Keywords

Markov Chain, Shimla, Forest, Transition probability, RSGIS

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D. N. Pant , K. K. Das , P. S. Roy

Abstract

Forest fires have caused significant damage to the forest ecosystem. The devastating fire which ravaged extensive forest area during the Summer of 1995 in the Western and Central Himalaya drew wide attention of the forest managers and environmentalists. It is essential to evolve a strategy to minimize the damage caused by fire. IRS-IB LISS-II multi-spectral remote sensing data of pre-fire and post-fire period (1993 and 1995) pertaining to district Tehri (U.P) have digitally heen analysed. The supervised classification and digital enhancement approach have been used. The geometrically registercd images have been compared to assess the varying degree of forest fire damage. The total area affected under forest fire has been estimated as 20.58% of the total geographical area. The forest fire affected areas are classified as burnt forest, partially burnt forest and partially burnt fallow/ grassland/scrub land. A real extent ofthese are: 3.82,10.72 and 6.05% respectively. The forest areas identified under smoke plumes have been estimated as 2.96% of total geographical area.

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C. Jeganathan , P. S. Roy , M. N. Jha

Abstract

Decision making in land use planning needs understanding about the pattern of changes. The current study aims to analyse and predict the land use and land cover change, with the focus on forests, in Shimla District using Bayesian model. Population growth, agricultural-horticulture demands, tourism growth are putting pressure on the valuable forest ecosystem and natural resources of the district. In this study, land cover maps were prepared for the periods 1970s, 1980s and 1990s using remote sensing data. The actual positive changes (i.e., increase in forest) and negative changes (i.e., decrease in forest) derived from the time-series land cover maps were used as apriori evidence in the Bayesian model to derive the statistical weights for various environmental parameters. The environmental parameters were analysed under 4 major group of factors i.e., topographic, land use, landscape, land-water. The probabilistic contribution (i.e., weight) of each attribute under each map was utilised within the weighted summation model to derive spatial maps of potential positive and negative change. The accuracy of the model was validated using actual change maps. Accuracy of the model was 85% for the positive change and 80% for the negative change. The resultant predicted maps of positive and negative change were overlaid together and potential zones of conservation and afforestation were identified.

Keywords

Land Cover Change, Bayesian Model, Prediction, Environmental Parameters,
Topography, Landscape, Land-water

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Sarnam Singh ¹, P. S. Roy ¹, M. B. Chandrashekhar ¹, D. K. Singh ², Surendra Singh ³, B. P. Uniyal ³, P. K. Joshi ¹

Affiliations
1 Indian Institute of Remote Sensing (NRSA), Department of Space, Dehradun, IN
2 Botanical Survey of lndia, Kolkata, IN
3 Botanical Survey of India, Dehradun, IN

Abstract

Stratified random sampling with probability proportion to the size (PPS) is adopted for analyzing vegetation composition of all types. Vegetation cover type map derived using satellite remote sensing data have been considered as prime input for phytodiversity analysis of forest ecosystem. Geographic Information System (GIS) has been used to derive landscape indices such as fragmentation, porosity, patchiness, patch density, interspersion and juxtaposition, which depict landscape characteristics. Phytodiversity richness map generated for the Shiwalik hills of Punjab state is based on the disturbance index, terrain complexity, species richness, biological value and ecosystem uniqueness. The resultant maps highlight areas that are rich in phytodiversity. Forests of Shiwalik hills of Punjab state are moderately rich in some fragmented pockets. Deciduous forest showed high degree of richness (55.09% and 12.86% in high and very high categories respectively) followed by moist deciduous forest (17.92% in high and 16.19% in very high categories). Deciduous scrub shows least richness (13.96 % in high and 1.61 % in very high categories) as compared to pine forest (16.72% in high and 4.55% in very high categories). Phytosociological data collected from field sampling was analyzed to derive species richness, biodiversity value and importance value of various forest types.

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V. S. Chitale ¹, M. D. Behera ¹, P. S. Roy ²

Affiliations
1 Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology, Kharagpur 721 302, IN
2 University Centre for Earth and Space Sciences, University of Hyderabad, Hyderabad 500 046, IN

Abstract

No Abstract.

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V. S. Chitale ¹, M. D. Behera ¹, P. S. Roy ²

Affiliations
1 Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology, Kharagpur 721 302, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502 324, IN

Abstract

Thirty-six global biodiversity hotspots harbour high concentrations of species and endemism¹. India accommodates in parts four hotspots, viz. the Himalaya (44.37% of global hotspot), Indo-Burma (5.13%), Sundaland (1.28%) and the Western Ghats (64.95%) that exhibit high levels of floral and faunal diversity. Based on data on endemic plants collected, we present hotspot-wise congruence in plant endemism using field sampling data from 1264, 1114, 78 and 1004 plots in the Himalaya, Indo-Burma, Sundaland and the Western Ghats respectively, using nested quadrates of 0.04 ha laid based on stratified random sampling².

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