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Spatiotemporal ecosystem health assessment comparison under the pressure-state-response framework
M.S. Boori 1, K. Choudhary 1,2, R. Paringer 1,3, A. Kupriyanov 1,3

Scientific Research Laboratory of Automated Syatem of Scientific Research (SRL-35),
Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34;
Department of Land Surveying and Geo-informatics, Smart Cities Research Institute
The Hong Kong Polytechnic University, Kowloon, Hong Kong, China;
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 4276 kB

DOI: 10.18287/2412-6179-CO-1067

Pages: 634-642.

Full text of article: English language.

A spatiotemporal ecosystem health (EH) assessment study is necessary for sustainable development and proper management of natural resources. At present higher rate of human-socio-economic activities, industrialization, and misuse of land are major factors for ecosystem degradation. Therefore this research work used remote sensing (RS) and geographical information system (GIS) technology, under pressure-state-response (PSR) framework with analytic hierarchy process (AHP) weight method based on 29 indicators were analyzed for spatiotemporal EH assessment in Tatarstan and Samara states in Russia from 2010 to 2020. Results indicate continuous degradation of EH in Tatarstan state while in Samara state first decreased and later on an improved ecosystem health condition. This is one of the most innovative analyses work for real-time accurate ecosystem health assessment, mapping, and monitoring as well as protect fragile eco-environment with sustainable development, proper policy-making, and management at any scale and region.

spatiotemporal ecosystem health, PSR, remote sensing & GIS, AHP, indicators.

Boori MS, Choudhary K, Paringer R, Kupriyanov A. Spatiotemporal ecosystem health assessment comparison under the pressure-state-response framework. Computer Optics 2022; 46(4): 634-642. DOI: 10.18287/2412-6179-CO-1067.

The research was supported by the Ministry of Science and Higher Education of the Russian Federation (Grant # 0777-2020-0017) and partially funded by RFBR, project number # 19-29-01135.


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