Modeling the influence of changes in the edaphic environment on the ecosystem valuation of the zone of influence of the Ozogoche and Atillo lake systems in Ecuador
Ecosystem valuation (EV) of soil resources is essential for understanding changes in environmental services in monetary terms. A lack of this information, which includes economic indices, hinders the optimal management of natural resources. This study evaluated the influence of changes in the edaphi...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/404399 |
| Acesso em linha: | https://hdl.handle.net/2117/404399 https://dx.doi.org/10.3390/app14062249 |
| Access Level: | acceso abierto |
| Palavra-chave: | Remote sensing Land use Ecosystem valuation Opportunity costs Sustainability Land-use Ozogoche Lagoon Atillo Lagoon Teledetecció Sòl, Ús del Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Medi ambient |
| Resumo: | Ecosystem valuation (EV) of soil resources is essential for understanding changes in environmental services in monetary terms. A lack of this information, which includes economic indices, hinders the optimal management of natural resources. This study evaluated the influence of changes in the edaphic ecosystem on the EV of the zone of influence of the Ozogoche and Atillo lake systems in Ecuador. The classification was carried out through spectral indices and support vector machines (SVMs), and the EV was determined through opportunity costs including environmental service provisioning and indirect use. The land use and EV classification methods were performed efficiently; the degradation trend was constant. The Modified Water Difference Index was the most efficient in the extraction of water bodies, with an accuracy of 91%. The SVMs algorithm, in recognizing coverage in general, had an overall accuracy of 85%. The adjustment made to the SVMs algorithm to improve the selection of hyperparameters was effective; a robust architecture of the algorithm in terms of automation was achieved. Between 2000 and 2020, moorland, water and wetland degraded by 19%, 2% and 3.4%, respectively. In 2000, the EV as a function of avoided CO2 content was USD 8.00 × 106; in 2010 and 2020, it was USD 6.00 × 106 and USD 5.00 × 106, respectively. |
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