Strategies for detecting land-use change on the River Tea SCI ecological corridor via satellite images.

Autor: García-Ontiyuelo M; Universidade de Vigo, Hydro-Forestry Geomodeling Research Group, School of Forestry Engineering, 36005 Pontevedra, Spain. Electronic address: mario.garcia.ontiyuelo@uvigo.es., Acuña-Alonso C; CINTECX, Universidade de Vigo, Applied Geotechnologies Group, Vigo 36310, Spain; Centro de Investigação e Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, Ap 1013, 5001-801 Vila Real, Portugal. Electronic address: carolina.alonso@uvigo.es., Vasilakos C; University of the Aegean, Department of Geography, University Hill, 81100 Mytilene, Greece. Electronic address: chvas@aegean.gr., Álvarez X; Universidade de Vigo, Hydro-Forestry Geomodeling Research Group, School of Forestry Engineering, 36005 Pontevedra, Spain. Electronic address: xaalvarez@uvigo.es.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Nov 19; Vol. 957, pp. 177507. Date of Electronic Publication: 2024 Nov 19.
DOI: 10.1016/j.scitotenv.2024.177507
Abstrakt: Land-cover change is often accompanied by land-use change, altering ecosystem services, species habitats and contributing to climate change. The general goal of this study is to analyse different sources of information to quantify land use and land cover change (LULCC) in the River Tea SCI (Galicia, NW Spain) in 2015-2023. The study area has multiple coverages with very low variability between them and with a great deal of fragmentation in the territory, which makes it difficult to obtain high accuracy levels. Land cover was classified using object-based image analysis (OBIA) Classification and Artificial Neural Network (ANN) methodologies based on images from the Sentinel-2 and Planet Labs (RapidEye and PlanetScope) multispectral satellite platforms. In addition, simulations for 2031 were carried out using different techniques based on post-classification. The highest accuracy obtained in this study is 80 %, for the Planet Labs data and the OBIA methodology. Using the same methodology and Sentinel-2 data the best accuracy obtained is around 70 %, with the Planet Labs data and the ANN developed the accuracy is around 55 %. Thus, the data used and the methodology followed influence the accuracy levels obtained in the classifications. The choice of what data and methodology are to be used depends on the goals pursued and on the characteristics of the study area. Finally, it is concluded that the geospatial data available are very useful for detecting and quantifying changes in land cover. It is confirmed that the methodology used contributes to territorial planning and sustainable forest management, facilitating future decisions and action plans in the governance of the region.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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