Identifying human elephant conflict hotspots through satellite remote sensing and GIS to support conflict mitigation
Article
Article Title | Identifying human elephant conflict hotspots through satellite remote sensing and GIS to support conflict mitigation |
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ERA Journal ID | 211288 |
Article Category | Article |
Authors | Gunawansa, Thakshila D., Perera, Kithsiri, Apan, Armando and Hettiarachchi, Nandita K. |
Journal Title | Remote Sensing Applications: Society and Environment |
Journal Citation | 35 |
Number of Pages | 17 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 2352-9385 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.rsase.2024.101261 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2352938524001253 |
Abstract | Human-elephant conflict (HEC) is a significant issue in Sri Lanka and many parts of the world where elephants and humans coexist. To address HEC, this study integrates remote sensing and GIS analysis, focusing on monitoring changes in greenery. The study prepared the latest land cover and land use (LCLU) maps with Sentinel-2 satellite data, correlating them with reported HEC incidents reported in 2021 and 2022 to identify HEC hotspots in two forest-dominated regions of Southeast Sri Lanka. High-resolution sentinel-2 satellite imagery were used to detect areas of human activities and elephant habitats in proximity to each other. Random Forest (RF) and Support Vector Machine (SVM) classification methods were used for LCLU classification. The overall accuracy of the classification was 97.31 and 94.62, and kappa was 0.95 and 0.90 for RF and SVM, respectively. Multi-temporal normalised difference vegetation index (NDVI) analysis provided insights into vegetation health and coverage, offering a clear picture of greenery changes. Monthly changes in vegetation cover readings were quantified using NDVI values derived from MODIS data, identifying suitable regions for elephants to forage frequently. Furthermore, Kernel density estimation identified high-density areas for reported incidents of human and elephant deaths. This process involved assigning weight to conflict incidents within a 5 km radius, considering the proximity to the forest, and evaluating greenery changes using NDVI values, revealing varying levels of HEC risk, ranging from very high to low. The LCLU map, created |
Keywords | Human-elephant conflict, Remote sensing, GIS analysis, Land cover and land use classification, MODIS data, Random forest, Support vector machine |
Related Output | |
Is part of | Demarcating High-Risk Zones of Human-Elephant Conflict in Sri Lanka Utilizing GIS and a Satellite Data Fusion Approach |
Article Publishing Charge (APC) Funding | School/Centre |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Surveying and Built Environment |
Uva Wellassa University of Sri Lanka, Sri Lanka | |
University of Ruhuna, Sri Lanka |
https://research.usq.edu.au/item/z7vq1/identifying-human-elephant-conflict-hotspots-through-satellite-remote-sensing-and-gis-to-support-conflict-mitigation
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