Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India.

Autor: Marcinko CLJ; School of Engineering, University of Southampton, Southampton, SO17 1BJ, UK. Electronic address: cljm1g08@soton.ac.uk., Samanta S; School of Oceanographic Studies, Jadavpur University, 188, Raja S.C. Mallik Road, Jadavpur, Kolkata, 700032, India. Electronic address: sourav.samanta@gmail.com., Basu O; School of Oceanographic Studies, Jadavpur University, 188, Raja S.C. Mallik Road, Jadavpur, Kolkata, 700032, India., Harfoot A; School of Geography and Environmental Sciences, University of Southampton, Southampton, SO17 1BJ, UK. Electronic address: ajph@soton.ac.uk., Hornby DD; School of Geography and Environmental Sciences, University of Southampton, Southampton, SO17 1BJ, UK. Electronic address: ddh@geodata.soton.ac.uk., Hutton CW; School of Geography and Environmental Sciences, University of Southampton, Southampton, SO17 1BJ, UK. Electronic address: ch9@soton.ac.uk., Pal S; School of Oceanographic Studies, Jadavpur University, 188, Raja S.C. Mallik Road, Jadavpur, Kolkata, 700032, India., Watmough GR; School of Geosciences, Institute of Geography, University of Edinburgh, Drummond Street, Edinburgh, Scotland, United Kingdom; Global Academy of Agriculture and Food Security, University of Edinburgh, Easter Bush Campus, Edinburgh, Scotland, United Kingdom. Electronic address: gary.watmough@ed.ac.uk.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2022 Jul 01; Vol. 313, pp. 114950. Date of Electronic Publication: 2022 Apr 01.
DOI: 10.1016/j.jenvman.2022.114950
Abstrakt: There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (<∼30%), large areas of agricultural land (>∼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region.
(Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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