Examinando por Materia "Forest fragmentation"

Mostrando 1 - 2 de 2

Forest structure and fragmentation dynamics in cacao-producing landscapes of Amazonas, Peru, revealed by multi-temporal land-use change and spaceborne LiDAR
(Springer Science+Business Media, LLC, part of Springer Nature, 2026-05-27) Cotrina Sanchez, Alexander; Barboza, Elgar; Veneros, Jaris; Huaman Pilco, Angel Fernando; García, Ligia; Guzman Valqui, Betty Karina; Oliva, Manuel; Rojas Briceño, Nilton B.; Torresani, Michele
The ongoing loss and degradation of tropical forests poses a significant threat to biodiversity, carbon storage, and ecosystem services throughout the Amazon Basin. Agroforestry systems such as cacao cultivation can help balance production and conservation, yet integrated analyses combining spatial and structural forest data remain limited. This study integrates multi-temporal land-use/land-cover (LULC) data, fragmentation metrics, and canopy indicators from the Global Ecosystem Dynamics Investigation (GEDI) mission to assess forest transformation across two contrasting cacao-producing landscapes in the Amazonas region of Peru. LULC dynamics (1985–2020) were derived from the 30m Global Land Cover Change Dataset (GLC_FCS30D), with 2020 used as a baseline consistent with the European Union Deforestation Regulation (EUDR). The 2020 forest/non-forest map was compared with the 10m Global Forest Cover 2020 product to quantify fragmentation across multiple grid sizes. GEDI L2A and L2B data provided structural metrics, including relative height (RH25–RH98), plant area index (PAI), foliage height diversity (FHD), and canopy cover, which were linked to fragmentation indicators. In the indigenous territories of Condorcanqui, cacao landscapes maintained stable forest cover, while rural areas in Bagua and Utcubamba showed greater forest loss and landscape modification. Fine-scale (10m) data revealed localised zones of conservation and degradation, particularly in lowland cacao areas. Taller, more structurally complex canopies were associated with less fragmented forests, whereas shorter and more heterogeneous structures reflected long-term disturbance. Integrating spaceborne LiDAR with multi-scale fragmentation metrics provides robust indicators of forest integrity, supporting sustainable cacao agroforestry management and conservation plannin.
Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation
(Elsevier, 2024-07-28) 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.
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.