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Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding
Riverine habitats are essential ecotones that bridge aquatic and terrestrial ecosystems, providing multiple ecosystem services. This study analyses the potential use of high-resolution satellite imagery, provided by the WorldView-2 satellite, in order to assess its viability for monitoring riparian...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640423/ https://www.ncbi.nlm.nih.gov/pubmed/36342553 http://dx.doi.org/10.1007/s10661-022-10667-8 |
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author | Rivas-Fandiño, Paula Acuña-Alonso, Carolina Novo, Ana Pacheco, Fernando António Leal Álvarez, Xana |
author_facet | Rivas-Fandiño, Paula Acuña-Alonso, Carolina Novo, Ana Pacheco, Fernando António Leal Álvarez, Xana |
author_sort | Rivas-Fandiño, Paula |
collection | PubMed |
description | Riverine habitats are essential ecotones that bridge aquatic and terrestrial ecosystems, providing multiple ecosystem services. This study analyses the potential use of high-resolution satellite imagery, provided by the WorldView-2 satellite, in order to assess its viability for monitoring riparian ecosystems. It is performed by calculating the riparian strip quality index (RSQI) and calibrating it with the riparian quality index (QBR). The methodology was implemented in the Umia River, which is characterised by elevated anthropogenic pressures (located in the northwest of Spain). The results obtained by the method have a 92% of veracity and a kappa coefficient of 0.88. The average quality value obtained for the RSQI index was 71.57, while the average value for the QBR was 55.88. This difference could be attributed to the fact that the former does not differ between autochthonous and non-autochthonous vegetation. The areas with more accurate mapping corresponded to stretches of vegetation with optimal cover (80–50%), with good connectivity with the adjacent forest ecosystem and few or no presence of invasive plants. The worst-scoring sites had the next characteristics: low connectivity (< 10%), low forest cover (< 10%) and a higher presence of invasive plants. The degradation of vegetation could be explained by the presence of agriculture and deficient land use rationing caused by the type of ownership of the study area. The application of this index through satellite images will facilitate the environmental governance of multiple ecosystems and in special riparian ecosystems, obtaining a quick and objective methodology, easily replicable in other basins. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10661-022-10667-8. |
format | Online Article Text |
id | pubmed-9640423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-96404232022-11-15 Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding Rivas-Fandiño, Paula Acuña-Alonso, Carolina Novo, Ana Pacheco, Fernando António Leal Álvarez, Xana Environ Monit Assess Article Riverine habitats are essential ecotones that bridge aquatic and terrestrial ecosystems, providing multiple ecosystem services. This study analyses the potential use of high-resolution satellite imagery, provided by the WorldView-2 satellite, in order to assess its viability for monitoring riparian ecosystems. It is performed by calculating the riparian strip quality index (RSQI) and calibrating it with the riparian quality index (QBR). The methodology was implemented in the Umia River, which is characterised by elevated anthropogenic pressures (located in the northwest of Spain). The results obtained by the method have a 92% of veracity and a kappa coefficient of 0.88. The average quality value obtained for the RSQI index was 71.57, while the average value for the QBR was 55.88. This difference could be attributed to the fact that the former does not differ between autochthonous and non-autochthonous vegetation. The areas with more accurate mapping corresponded to stretches of vegetation with optimal cover (80–50%), with good connectivity with the adjacent forest ecosystem and few or no presence of invasive plants. The worst-scoring sites had the next characteristics: low connectivity (< 10%), low forest cover (< 10%) and a higher presence of invasive plants. The degradation of vegetation could be explained by the presence of agriculture and deficient land use rationing caused by the type of ownership of the study area. The application of this index through satellite images will facilitate the environmental governance of multiple ecosystems and in special riparian ecosystems, obtaining a quick and objective methodology, easily replicable in other basins. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10661-022-10667-8. Springer International Publishing 2022-11-07 2023 /pmc/articles/PMC9640423/ /pubmed/36342553 http://dx.doi.org/10.1007/s10661-022-10667-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rivas-Fandiño, Paula Acuña-Alonso, Carolina Novo, Ana Pacheco, Fernando António Leal Álvarez, Xana Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
title | Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
title_full | Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
title_fullStr | Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
title_full_unstemmed | Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
title_short | Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
title_sort | assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640423/ https://www.ncbi.nlm.nih.gov/pubmed/36342553 http://dx.doi.org/10.1007/s10661-022-10667-8 |
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