In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.
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Tropical dry forests consist of deciduous broad-leaved trees located in tropical and subtropical areas. These forests thrive in regions characterized by a prolonged period of drought, rainy and humid conditions with annual precipitation of 400-1700 mm. The drought occurs during the time of limited evapotranspiration, which permits the growth of deciduous and evergreen vegetation (Hayden et al. 2010). The tropical forests are referred to as one of the species-rich terrestrial ecosystems and are known to generate a wide variety of natural resources for sustainable livelihood of local communities as well as provide habitat to some of the endangered species of the world out of an estimated 1 million sq. Km of tropical dry forests of the world, about half of it is found in South America. India and Southeast Asia, Australia, the Caribbean, Central America, and two parallel belts in Africa are some other regions where these forests flourish (Hayden et al. 2010). The trees which alter their leaves every year are considered as deciduous. The existence of Sal trees is widespread in a dry deciduous forest. Sal grows in a wide range of soil type but prefers slightly acidic to neutral (pH 5.1 – 6.8) sandy loam textured soil. The growth of Sal also depends on alluvial and lateritic soil. The maximum temperature recorded in this type of forest is 45˚C. The height of trees can reach 15 to 16 m and have a girth of 1.5 m in its’ favorable localities (Volume 2, Second Working Plan, Puruliya District).
India constitutes 38.2% of tropical dry forest cover characterized by some dominant species like Shorea robusta, Anogeissus latifolia, Lagerstroemia parviflora, Terminalia tomentosa, Hardwickia binata, Boswellia serrata, Buchanania lanzan, Acacia catechu, etc. (Champion and Seth, 1968). Once covering about 52% of the total forests of the world, tropical dry forests are now considered as one of the most vulnerable forest areas of the world (Singh and Singh 1991). Apart from the eminent consequence of climate change, other anthropogenic factors also act as threats for these types of forest. Degradation of forests in the form of felling, encroachment, grazing, and forest fire is usually accompanied by loss of primary productivity, reduction in biodiversity and extinction of species (Kumar et al. 2000, 2001, 2002; Yadav & Yadav 2008). In the future, to secure sustainable flow of the ecosystem services and for proper management of this critical ecosystem, present as well as the future scenario needs to be studied well. At present, the Joint Forest Management Committee plays a vital role in the Forest Department to protect the forest. In the year 1988, Arabari of Midnapore district was envisaged as the procreator of Joint Forest Management. There are 776 JFMC under the forest of Puruliya. It protects 737.93 sq km of forest area (Annual Administrative Report 2017-2018, Department of Forest, West Bengal).
Various empirical studies have recognized the positive relationship that often exists between habitat area and species richness (Brooks et al. 1997, Pimm and Raven 2000). Using multiple sources of satellite data, DeFries et al. (2005) examined the range of existing forest habitat and loss throughout the moist and dry tropical forest of the world over the last two decades. There are a wide variety of modelling approaches to predict landscape patterns and changes (Cheong et al.2012; Liu et al.2009; Pontius et al.2005; Brown et al.2004). Markov models are the earliest of the fitted models, which are simple to create with minimal data requirements (Tattoni et al.2011; Cabral et al.2009; Brown et al.2004]. To evaluate the impact of the National Forest Policy of 1988 on the forest cover, Adhikary and Southwort (2012) selected Bannerghatta National Park (BNP) and its buffer as their study area. Their main objectives were (1) to create e a landscape of no policy intervention by predicting the forest cover of 1999 and 2007 based on a CA Markov model; (2) to compare the observed and predicted separate land covers and changing courses. By integrating remote sensing GIS techniques, counterfactual approach, and Cellular Automata-Markov modeling approach, Mondal and Southworth (2010), estimated the significance of certain conservation intervention in a human-dominated tropical forests of Central India. Their study area witnessed several management strategies since its declaration as a protected area in the mid-1970s.