Spatio-Temporal Forest Change Assessment Using Time Series Satellite Data in Palamu District of Jharkhand, India
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RESEARCH ARTICLE
Spatio-Temporal Forest Change Assessment Using Time Series Satellite Data in Palamu District of Jharkhand, India Beependra Singh 1 & C. Jeganathan 2
Received: 26 July 2014 / Accepted: 9 December 2015 # Indian Society of Remote Sensing 2016
Abstract Free availability of time-series satellite data enabled the current study to quantify decadal macrovariations (i.e., trend and percent change) in vegetation vigour in the forested environment of Palamu district based on 11 years (2001 to 2011) of fortnightly data of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI; 250 m). Further, Landsat ETM+ data of year 2001 and 2011 were also used to extract micro-level changes and as a validation reference. The inter-year comparison about standard anomalies revealed an alarming situation about persistent stress in the vegetation, especially after 2008. However, the degradation rate is slow as per anomaly frequency but steadily increasing after 2008. The study estimated that there is a loss of 293 km2 forest which is close to the FSI estimate of 333 km2. The current study provides a quick mechanism to reveal the spatial pattern of temporal changes in the forested region with reliable and reproducible methods.
Keywords Forest change . Anomaly . Time series . MODIS . EVI . Palamu district
* Beependra Singh [email protected] C. Jeganathan [email protected] 1
Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, India
2
Department of Remote Sensing, Birla Institute of Technology (BIT), Mesra, Ranchi 835215, India
Introduction Mapping, quantifying and monitoring changes about the physical characteristics of forest cover is important for the sustainable management of natural forests (Nemani et al. 1996; Vuolo et al. 2011). Forested ecosystems actively regulate biosphere dynamics by means of contributions to hydrological cycle and surface energy balance (Rogan et al. 2002; Huete et al. 2011). The health of a forest could be an important indicator of the ecological conditions of a region (Jaiswal et al. 2002; Tiner 2004). Characterizing spatial patterns of forest and inference about the associated processes assist in policy and management decisions for controlling deforestation (Fuller 2006). About 30 % of Earth’s total land surface area is under forest cover which plays a vital role in atmospheric regulation of CO 2 (Xie et al. 2008; Chaturvedi et al. 2011) and O 2 (Igamberdiev and Lea 2006), soil conservation (Rudel et al. 2005) and at the same time provides habitat for many species and also act as a source of many useful products such as wood and food (Vega et al. 2012). The scientific community of the world is actively participating in developing new methods and technology especially using satellite remote sensing to monitor forest cover changes and to estimate changes in carbon stocks in various parts of the world (Asner 2009). For better understanding the relationships and interactions between human and natural environments, change detecti
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