Examinando por Materia "Modelamiento espacial"

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Agro-environmental vulnerability and ecosystem sustainability in Peruvian family farming: integrating survey data, spatial modeling, and remote sensing
(MDPI (Multidisciplinary Digital Publishing Institute), 2026-01-30) Pizarro, Samuel; Ccopi Trucios, Dennis; Otoya Barrenechea, José; Romero Vasquez, Juan; Tolentino Soriano, María; Cotrina Sanchez, Alexander; Barboza, Elgar
Subsistence family farming in Peru is increasingly constrained by ecosystem degradation, climate variability, and limited access to productive services, particularly where environmental exposure is high. This study develops an Agro-productive and Territorial Vulnerability Index (IVAPT) to evaluate environmental, ecosystem, and socioeconomic vulnerability of subsistence agriculture at the district level nationwide. The index integrates district-level agricultural survey data (ENA-2024) with multi-temporal MODIS NDVI series (2000–2024) and comprehensive climatic, topographic, land-cover, and accessibility indicators, processed through multivariate statistics. Three objective weighting schemes (ENTROPY, CRITIC, PCA) construct thematic sub-indices of Environmental Exposure (EnvExp), Ecosystem Condition (EcoCond), and Socioeconomic Capacity (SocioCap). Results show more than half of Peru's 1552 districts fall within moderate to very high vulnerability, with highest concentration in the Amazon region (Loreto, Ucayali, Madre de Dios), Andean-Amazonian transitions, and highland districts (Huancavelica, Apurímac, Ayacucho, Puno) where biophysical constraints, ecosystem pressure, and socioeconomic isolation converge. Dimensional spatial complementarity EnvExp peaking on coast, EcoCond in Amazon, SocioCap in Andes demonstrates effective vulnerability reduction requires dimension-specific interventions. Despite divergent weighting schemes, spatial patterns remained consistent, validating identified hotspots. IVAPT provides a reproducible framework supporting evidence-based territorial planning and targeted investments in water infrastructure, ecosystem restoration, and climate adaptation.
Mapping and future prediction of land use and land cover dynamics using Google Earth Engine and an artificial neural network model in the Jucusbamba River Basin, Amazonas (NW-Peru)
(ALÖKI Kft, 2026-01-30) Zabaleta Santisteban, J.A.; Cachay Reynaga, R.; Rojas Briceño, N.B.; Silva López, J.O.; Medina Medina, A.J.; Tuesta Trauco, K.M.; Rivera Fernandez, A.S.; Sánchez Vega, J.A.; Silva Melendez, T.B.; Grandez Alberca, M.A.; Salas López, R.; Oliva Cruz, M.; Gómez Fernández, Darwin; Barboza, E.
Monitoring land use and land cover (LULC) changes is essential due to its close relationship with ecological processes, land-use planning, and environmental sustainability. In the Jucusbamba River sub-basin (Amazonas, Peru), knowledge of spatial transitions and cover change dynamics remains limited. This study analyzed LULC changes from 1992 to 2022 using Landsat and Sentinel satellite imagery classified with the Random Forest algorithm on the Google Earth Engine (GEE) platform. Additionally, future scenarios for 2037 and 2052 were simulated using the MOLUSCE plugin along with Artificial Neural Networks (ANN). Five main land cover classes were the followings: urban areas, pasture and cropland mosaics, forests, grasslands, and secondary shrub/herbaceous vegetation. Between 1992 and 2022, pasture/cropland mosaics increased by 24.36% and urban areas by 0.76%, while forests and secondary vegetation decreased by 8.89% and 16.25%, respectively. Projections to 2052 indicate further expansion of agricultural (4.74%) and urban (0.07%) land use, along with additional losses in forest cover (-1.47%) and secondary vegetation (-3.35%). The classification achieved an overall accuracy of 89.8% and a Kappa coefficient of 0.86. These findings provide a robust foundation for evidence-based decision-making in land management, ecological zoning, and natural resource conservation within the Andean-Amazonian region.