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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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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