Predicting Forest Cover and Density in Part of Porhat Forest Division, Jharkhand, India Using Geospatial Technology and Markov Chain
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BIOSCIENCES BIOTECHNOLOGY RESEARCH ASIA, September 2017. Vol. 14(3), p. 961-976 Predicting Forest Cover and Density in Part of Porhat Forest Division, Jharkhand, India using Geospatial Technology and Markov Chain Firoz Ahmad* and Laxmi Goparaju Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India. http://dx.doi.org/10.13005/bbra/2530 (Received: 22 August 2017; accepted: 05 September 2017) The increasing population has posed a threat to the existence of the forests, which provide many services to us. Of late, they seem to be degraded, deforested and converted into other land use classes. In such situation, it becomes necessary to monitor and analyze the changes such that in future protection measures are enforced suitably. Geospatial technology, which is a combination of satellite remote sensing data, GIS and GPS offers better prospective in analyzing the changes in natural resources over various spatial scales and spectral resolutions. The present study aims to study both qualitatively and quantitatively, analyzing and predicting the changes in forest cover by generating forest cover classification map, area statistics, transition matrix in part of Saranda forest of West Singbhum district of the state of Jharkhand, India using remote sensing and GIS. The study evaluates the magnitude, rate and dynamics of change in the spatial extent of the forest between 1975 and 2015 using multi-temporal datasets (Landsat MSS 1975, ETM+ 1999 and OLI/TIRS 2015. The analysis revealed that the dense forests periodically are showing a decreasing trend which constitutes approximately 50%, 33% and 27% of the study area in 1975, 1999 and 2015 respectively. Finally using Markov chain analysis (MCA) forest cover area statistics was predicted for the year 2031. This analysis would help to have a holistic view of the future scenario of forests which would guide the policy makers and managers. Strict policy implementation to safeguard the forests against various anthropogenic pressures and community involvement is necessary to prevent further destruction of forests. Keywords: Forest cover; Geographic Information System; Markov; Remote Sensing; Saranda. Forest cover loss aggravates the loss of of forest per year). The tropical forests of the genetic resources, biodiversity and wildlife habitat. world accounted for a loss of 5.2 million hectares It affects the utilization of the environmental goods between the time period 2000-2010 ( Brambilla et and services provided by the ecosystems for the al., 2010; FAO, 2012). The most vulnerable of all mankind, changes the biogeochemical cycles the ecosystems are the tropical forests. Many tribal (Turner 2005) and gives way to the invasive and and native populations depend on the forests for alien species (Cairns et al., 2000). The loss further their livelihood. It is observed that about one-third increases the cases of flooding and encourages the of the world’s population depends on the forests for degradation of soil. The world has lost a significant their sustenance more importantly in less developed area of forest cover since 1990s (8.3 million hectare countries. The dependency on wood as a fuel is still prevalent despite new alternatives (FAO, 2014). In recent years the ever increasing * To whom all correspondence should be addressed. E-mail: [email protected] population has put pressure on the forests which has resulted in the changing patterns of the land 962 AHMAD & GOPARAJU, Biosci., Biotech. Res. Asia, Vol. 14(3), 961-976(2017) use to suffice the demands globally (Yu & Ng sensing data becomes a major source for studies of 2007). In the past few decades, a decline in the urban areas , landscapes and natural environments forest areas had sought attention because of the (Peled & Gilichinsky 2013; Ye et al. 2011; ; Ye & environmental impact observed at local, regional Fang 2011; Bhatta et al. 2010; ; Ahmad & Goparaju and global scales (Cabrera & Vilatta 2013; Chen et 2016). Further, when coupled with GIS modelling al. 2013; Lambin & Meyfroidt 2011). Thus, forests such as Markov Chain model, it provides data for have reduced in size and significant changes in monitoring the spatial development to increase the the patterns of the natural landscape have led to understanding of the current trends of development. the global climate change (Leuzinger et al. 2011; A better understanding of forest cover in various Watson et al. 2000; Abdalla et al. 2013; Dale 1997). proportions and changes taking place over a The conversion of fringes of forest into agriculture, period of time is achieved for proper planning and illegal logging and indiscriminate cutting and development. grazing contributes to the fragmentation of the Remote sensing satellite images of forests and degradation of the landscape (Kabba different resolutions were extensively used to & Li 2011). The quantification of the changes estimate accurately not only deforestation and over a period of time is important to comprehend reforestation but also degradation and regeneration the forest cover patterns and the relationship at various scales (Celine et al 2013). It is a reliable with the environmental factors necessary for the data source and gives up to date information on development (Pal & Mather 2004). area, change in area, growing stock, wood and non- India is one of the biodiversity rich wood products, carbon, protected areas, biological countries where the forests cover is about 701,673 diversity and contribution of forests to the nation’s sq km, or 21.34% of the country (ISFR, 2015). The economy, which helps in formulation policies forest productivity in Indian forests is roughly one and programmes to achieve sustainable forestry tenth of its potential which is due to the growing (Ridder 2007). Using Markov chain analysis, future biotic pressure and inadequate resources for prediction scenarios have been studied in various scientific forest management (FAO Report 1997). natural resources for e.g Oludare et al. (2015) in Geospatial technology (including remote forest reserve assessment, ( Mahmoud & Alazba, sensing, GIS and GPS) has the potential and 2015) in hydrological responses and (Moghadam provides the capacity to acquire suitable satellite & Helbich, 2013; Ahmad et al. 2017) in urban data, analyse large datasets in GIS and interpret growth analysis. Akingbogun et al. 2012 analyzed the outcome about various natural resources the forest degradation and utilized Markov chain information at various spatial scales in less time analysis for prediction and monitoring. and in cost effective manner (Lillisand and Kiefer Further, to understand the various ,2000). The availability of Landsat satellite data historical and current driving factors which bring from USGS (free download) provides images since about the change in the forest cover and landscape, 1972 has given many opportunities for researchers many simulation models have been developed to to study the changes taking place on the Earth’s study the changes both spatially and dynamically, surface. It has coverage worldwide with spatial which would further assist in the management resolution of 30m. Many studies have utilized the strategies(Brown et al., 2004; Sturtevant et al., multi temporal satellite data and change detection 2007). Various statistical approaches like logistic techniques to analyse the forest dynamics (Tang et regression and static modeling have been applied al., 2005; Coppin et al., 2004). to study the influence of environmental and spatial Satellite remote sensing images provide variable in the distribution of forest and other updated information and comprehensive views and vegetated areas (Guisan and Zimmermann, 2000). use different parts of the electromagnetic spectrum Oludare et al (2015) analyzed Landsat TM to record characteristics of various features on imageries of 1984 and 2000 and OLI/TIRS of 2014 ground (Antwi et al. 2014; Fonji & Taff 2014). to identify, classify, assess and interpret changes in The repetivity, speed and variety of data types the Gambari Forest Reserve, Nigeria for the time are of great value to study the natural resources periods 1984, 2000 and 2014 and also predicted (Mabwoga & Thukral 2014). Thus, satellite remote LULC for the year 2044 using Markov analysis. AHMAD & GOPARAJU, Biosci., Biotech. Res. Asia, Vol. 14(3), 961-976(2017) 963 The analysis indicates that the thick forest and globalforestwatch.com ) has also reported tree loss plantation of LULC class were fast declining and in this area. various anthropogenic activities were responsible The forests of West Singhbhum are such as illegal felling of trees for timber, fuelwood pristine and some of the areas have been untouched and farming. Khoi & Muriyama (2010) evaluated mostly the forests at high altitudes. Increasing Landsat imageries of time periods 1993, 2000 anthropogenic pressure has put the forests under and 2007 for Tam Dao National park Region, stress because of which they are degrading and Vietnam and also predicted LULC for the year at places conversion to other land uses has led to 2014, 2021 using Markov analysis. The primary deforestation. In such scenario it is a prerequisite forest class was showing a decreasing trend due that good database is available at spatial and to forest clearance, which indicates that intensified temporal domain so that future policy decisions protection measures are required to prevent further can benefit from it. loss of the primary forest. Giriraj et al. 2008 The objective of the present work is 1) utilized MSS of Landsat