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

Bibliographic Data

ID22033459
AuthorsRamandeep Kaur M Malhi (0000-0001-6259-7605, Banaras Hindu University), Akash Anand (0000-0003-1086-5676, Banaras Hindu University), Prashant K Srivastava (0000-0002-4155-630X, Banaras Hindu University, corresponding author), G Sandhya Kiran (0000-0003-2610-8442, Maharaja Sayajirao University of Baroda), George P Petropoulos (0000-0003-1442-1423, Harokopio University of Athens), Christos Chalkias (0000-0002-2852-2711, Harokopio University of Athens)
Year2020
Volume9
Issue9
Pages530
Publication date2020-09-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9090530
OpenAlexW3082462884
LanguageEN
Citations received1
References cited60

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

Biodiversity · Ecosystem · Environmental resource management · Forest cover · Forest ecology · Forest fragmentation · Forest management · Geography · Land cover · Land use · Physical geography · Wildlife · Conservation, Biodiversity, and Resource Management · Ecology and Vegetation Dynamics Studies · Environmental Science · Land Use and Ecosystem Services · Ecology · Forestry

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