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Article

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

1
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India
2
DST-Mahamana Centre for Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India
3
Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara 390002, India
4
Department of Geography, Harokopio University of Athens, El. Venizelou 70, Kallithea, 17671 Athens, Greece
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 530; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090530
Received: 10 July 2020 / Revised: 25 August 2020 / Accepted: 29 August 2020 / Published: 2 September 2020
(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)
Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction. View Full-Text
Keywords: forest degradation; fragmentation statistics; Land cover prediction; remote sensing; CA-Markov; FRAGSTATS forest degradation; fragmentation statistics; Land cover prediction; remote sensing; CA-Markov; FRAGSTATS
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MDPI and ACS Style

Malhi, R.K.M.; Anand, A.; Srivastava, P.K.; Kiran, G.S.; P. Petropoulos, G.; Chalkias, C. An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas. ISPRS Int. J. Geo-Inf. 2020, 9, 530. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090530

AMA Style

Malhi RKM, Anand A, Srivastava PK, Kiran GS, P. Petropoulos G, Chalkias C. An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas. ISPRS International Journal of Geo-Information. 2020; 9(9):530. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090530

Chicago/Turabian Style

Malhi, Ramandeep K.M., Akash Anand, Prashant K. Srivastava, G. S. Kiran, George P. Petropoulos, and Christos Chalkias. 2020. "An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas" ISPRS International Journal of Geo-Information 9, no. 9: 530. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090530

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