Examinando por Materia "Google earth engine"

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Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
(Elsevier, 2024-12-12) Pizarro Carcausto, Samuel Edwin; Pricope , Narcisa G.; Vera Vilchez, Jesús Emilio; Cruz Luis, Juancarlos Alejandro; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi; Verástegui Martínez, Patricia
The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R² values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
Modeling the current and future habitat suitability of Neltuma pallida in the dry forest of northern Peru under climate change scenarios to 2100
(John Wiley & Sons Inc., 2024-08-27) Barboza Castillo, Elgar; Bravo Morales, Nino; Cotrina Sanchez, Alexander; Salazar Coronel, Wilian; Gálvez Paucar, David; Gonzales, Jhony; Saravia Navarro, David; Valqui Valqui, Lamberto; Cárdenas Rengifo, Gloria Patricia; Ocaña Reyes, Jimmy Alcides; Cruz Luis, Juancarlos; Arbizu Berrocal, Carlos Irvin
The development of anthropic activities and climate change effects impact worldwide species' ecosystems and habitats. Habitats' adequate prediction can be an important tool to assess current and future trends. In addition, it allows strategies development for their conservation. The Neltuma pallida of the forest region in northern Peru, although very significant, has experienced a decline in recent years. The objective of this research is to evaluate the current and future distribution and conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 variables (bioclimatic, topographic, and soil) were processed and selected to obtain the current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, and MaxEnt. The area under the curve values fell within the range of 0.93–0.95, demonstrating a strong predictive capability for both present and future potential habitats. The findings indicated that the likely range of habitats for N. pallida was shaped by factors such as the average temperature of wettest quarter, maximum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of policies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserving and recovering with an ecological restoration approach.
Territorial zoning as a strategy for sustainable natural resource management in Cajamarca, Northwestern Peru
(Elsevier B.V., 2025-09-25) Gómez Fernández, Darwin; Atalaya Marin, Nilton; Arce Inga, Marielita; Tineo Flores, Daniel; Fernandez Jibaja, Jorge Antonio; Taboada Mitma, Víctor Hugo; Cabrera Hoyos, Héctor Antonio; Cruz Luis, Juancarlos Alejandro; Goñas Goñas, Malluri
Generating agricultural suitability analyses that are objective, consistent, and accessible through digital platforms remains a technical and methodological challenge, creating an information gap for certain stakeholders. To address this issue, we assessed the territorial suitability of the Cajamarca region for coffee and cocoa cultivation using 18 subcriteria grouped into climatic, edaphological, topographic, and socioeconomic categories. To reduce subjectivity and improve consistency in variable comparisons, we applied multicriteria evaluation techniques, including the analytical hierarchy process (AHP) and Shannon entropy method. On the basis of the resulting weights, suitability models were generated using two approaches: one based on threshold reclassification and another using continuous suitability functions. Both approaches were validated using 3886 presence points for coffee and 671 for cocoa. The continuous approach demonstrated a greater ability to capture internal variability and spatial transitions, with greater dispersion and significant differences between classes. The most influential subcriteria for coffee were annual mean temperature, soil texture, elevation, and land use/land cover (LULC); for cocoa, they were annual mean temperature, soil pH, elevation, and LULC. In key districts, up to 59.8 % of the territory was classified as highly suitable, highlighting localized production potential. Finally, the results were integrated into the Suitability Watch Cajamarca application, developed in the Google Earth Engine, enabling interactive inspection of spatial suitability. This tool aims to support evidence-based agricultural planning and is intended for national scaling to other strategic crops.