Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation

• Autores: 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.
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2577
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2577; https://doi.org/10.1016/j.ecoinf.2024.102738
• Nivel de acceso: acceso abierto
• Materia: 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.