Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation
Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection and management, sustainable development, and climate change. One of the main challenges in image classification is the intricate selection of parameters, as the optimal c...
| Main Authors: | , , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | Inglés |
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Elsevier
2024
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| Online Access: | https://hdl.handle.net/20.500.12955/2577 https://doi.org/10.1016/j.ecoinf.2024.102738 |
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| author | Gómez Fernández, Darwin Salas López, Rolando Zabaleta Santisteban, Jhon Antony Medina Medina, Angel J. Goñas Goñas, Malluri Silva López, Jhonsy O. Oliva Cruz, Manuel Rojas Briceño, Nilton B. |
| author_browse | Goñas Goñas, Malluri Gómez Fernández, Darwin Medina Medina, Angel J. Oliva Cruz, Manuel Rojas Briceño, Nilton B. Salas López, Rolando Silva López, Jhonsy O. Zabaleta Santisteban, Jhon Antony |
| author_facet | Gómez Fernández, Darwin Salas López, Rolando Zabaleta Santisteban, Jhon Antony Medina Medina, Angel J. Goñas Goñas, Malluri Silva López, Jhonsy O. Oliva Cruz, Manuel Rojas Briceño, Nilton B. |
| author_sort | Gómez Fernández, Darwin |
| collection | Repositorio INIA |
| description | Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection and management, sustainable development, and climate change. One of the main challenges in image classification is the intricate selection of parameters, as the optimal combination significantly affects the accuracy and reliability of the final results. This research aimed to analyze landscape change and fragmentation in northwestern Peru. We utilized accurate land cover and land use (LULC) maps derived from Landsat imagery using Google Earth Engine (GEE) and ArcGIS software. For this, we identified the best dataset based on its highest overall accuracy, and kappa index; then we performed an analysis of variance (ANOVA) to assess the differences in accuracies among the datasets, finally, we obtained the LULC and fragmentation maps and analyzed them. We generated 31 datasets resulting from the combination of spectral bands, indices of vegetation, water, soil and clusters. Our analysis revealed that dataset 19, incorporating spectral bands along with water and soil indices, emerged as the optimal choice. Regarding the number of trees utilized in classification, we determined that using between 10 and 400 decision trees in Random Forest classification doesn't significantly affect overall accuracy or the Kappa index, but we observed a slight cumulative increase in accuracy metrics when using 100 decision trees. Additionally, between 1989 and 2023, the categories Artificial surfaces, Agricultural areas, and Scrub/ Herbaceous vegetation exhibit a positive rate of change, while the categories Forest and Open spaces with little or no vegetation display a decreasing trend. Consequently, the areas of patches and perforated have expanded in terms of area units, contributing to a reduction in forested areas (Core 3) due to fragmentation. As a result, forested areas smaller than 500 acres (Core 1 and 2) have increased. Finally, our research provides a methodological framework for image classification and assessment of landscape change and fragmentation, crucial information for decision makers in a current agricultural zone of northwestern Peru. |
| format | info:eu-repo/semantics/article |
| id | INIA2577 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INIA25772024-11-29T04:19:15Z Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation Gómez Fernández, Darwin Salas López, Rolando Zabaleta Santisteban, Jhon Antony Medina Medina, Angel J. Goñas Goñas, Malluri Silva López, Jhonsy O. Oliva Cruz, Manuel Rojas Briceño, Nilton B. Fragmentation LULC Changes Classification Random Forest Amazon Forest https://purl.org/pe-repo/ocde/ford#1.06.13 Habitat fragmentation Fragmentacion de los hábitats Land use Utilización de la tierra Land cover Cobertura de suelos Machine learning Aprendizaje automático Amazonia Forest fragmentation Fragmentación de los bosques Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection and management, sustainable development, and climate change. One of the main challenges in image classification is the intricate selection of parameters, as the optimal combination significantly affects the accuracy and reliability of the final results. This research aimed to analyze landscape change and fragmentation in northwestern Peru. We utilized accurate land cover and land use (LULC) maps derived from Landsat imagery using Google Earth Engine (GEE) and ArcGIS software. For this, we identified the best dataset based on its highest overall accuracy, and kappa index; then we performed an analysis of variance (ANOVA) to assess the differences in accuracies among the datasets, finally, we obtained the LULC and fragmentation maps and analyzed them. We generated 31 datasets resulting from the combination of spectral bands, indices of vegetation, water, soil and clusters. Our analysis revealed that dataset 19, incorporating spectral bands along with water and soil indices, emerged as the optimal choice. Regarding the number of trees utilized in classification, we determined that using between 10 and 400 decision trees in Random Forest classification doesn't significantly affect overall accuracy or the Kappa index, but we observed a slight cumulative increase in accuracy metrics when using 100 decision trees. Additionally, between 1989 and 2023, the categories Artificial surfaces, Agricultural areas, and Scrub/ Herbaceous vegetation exhibit a positive rate of change, while the categories Forest and Open spaces with little or no vegetation display a decreasing trend. Consequently, the areas of patches and perforated have expanded in terms of area units, contributing to a reduction in forested areas (Core 3) due to fragmentation. As a result, forested areas smaller than 500 acres (Core 1 and 2) have increased. Finally, our research provides a methodological framework for image classification and assessment of landscape change and fragmentation, crucial information for decision makers in a current agricultural zone of northwestern Peru. 2024-09-30T18:26:13Z 2024-09-30T18:26:13Z 2024-07-28 info:eu-repo/semantics/article Gómez-Fernández, D.; Salas-López, R.; Zabaleta-Santisteban, J.A.; Medina-Medina, A.J.; Goñas-Goñas, M.; Silva-López, J.O.; Oliva-Cruz, M.; & Rojas-Briceño, N.B. (2024). Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation. Ecological Informatics, 82(2024), 102738. doi: 10.1016/j.ecoinf.2024.102738 1878-0512 https://hdl.handle.net/20.500.12955/2577 https://doi.org/10.1016/j.ecoinf.2024.102738 eng urn:issn:1878-0512 Ecological Informatics info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc/4.0/ application/pdf application/pdf Elsevier NL Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Fragmentation LULC Changes Classification Random Forest Amazon Forest https://purl.org/pe-repo/ocde/ford#1.06.13 Habitat fragmentation Fragmentacion de los hábitats Land use Utilización de la tierra Land cover Cobertura de suelos Machine learning Aprendizaje automático Amazonia Forest fragmentation Fragmentación de los bosques Gómez Fernández, Darwin Salas López, Rolando Zabaleta Santisteban, Jhon Antony Medina Medina, Angel J. Goñas Goñas, Malluri Silva López, Jhonsy O. Oliva Cruz, Manuel Rojas Briceño, Nilton B. Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation |
| title | Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation |
| title_full | Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation |
| title_fullStr | Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation |
| title_full_unstemmed | Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation |
| title_short | Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation |
| title_sort | landsat images and gis techniques as key tools for historical analysis of landscape change and fragmentation |
| topic | Fragmentation LULC Changes Classification Random Forest Amazon Forest https://purl.org/pe-repo/ocde/ford#1.06.13 Habitat fragmentation Fragmentacion de los hábitats Land use Utilización de la tierra Land cover Cobertura de suelos Machine learning Aprendizaje automático Amazonia Forest fragmentation Fragmentación de los bosques |
| url | https://hdl.handle.net/20.500.12955/2577 https://doi.org/10.1016/j.ecoinf.2024.102738 |
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