Examinando por Autor "Salas López, Rolando"

Mostrando 1 - 9 de 9

An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE)
(MDPI, 2024-03-08) Medina Medina, Angel James; Salas López, Rolando; Zabaleta Santisteban, Jhon Antony; Tuesta Trauco, Katerin Meliza; Turpo Cayo, Efrain Yury; Huaman Haro, Nixon; Oliva Cruz, Manuel; Gómez Fernández, Darwin
One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.
Efectividad de áreas de conservación privada comunal en bosques montanos nublados del norte del Perú
(Instituto Pirenaico de Ecología, 2021-11-26) Delgado, Ellen; Mori, Gerson; Barboza Castillo, Elgar; Rojas Briceño, Nilton B.; Torres Guzmán, Cristóbal; Oliva Cruz, Manuel; Chavez Quintana, Segundo G.; Salas López, Rolando; López de la Lama, Rocío; Sevillano Ríos, C. Steven; Sarmiento, Fausto
Las Áreas de Conservación Privada (ACP) son uno de los mecanismos de conservación, gestionadas por ciudadanos privados que más protagonismo han adquirido en los escenarios de conservación local en los últimos años. En este estudio evaluamos la efectividad de cuatro ACP gestionadas por comunidades locales (CC). Se aplicó el Índice de Efectividad Compuesto (IEC) para determinar la efectividad del diseño, la integridad ecológica y la gestión. Los resultados muestran sistemas de gestión con una efectividad media, tres de las cuatro ACP evaluados (Copallín, Huaylla Belén-Colcamar y Tilacancha) reportan un diseño efectivo. Los rangos altitudinales protegidos están entre 2500 y 3500 m.s.n.m., con un índice de representatividad de la superficie promedio de 4,55% con respecto al área conservada en la categoría ACP para el departamento de Amazonas. La evaluación de la integridad ecológica indica que las ACP presentan menor superficie transformada (TS) (0-10%) y mayor TS en sus áreas circundantes, especialmente en el ACP Tilacancha (13,37% de TS en un buffer de 1,5 km). La suma ponderada de los índices individuales resulta en índices de efectividad compuestos de mayor a menor para el ACP Copallín (2,22), Hierba Buena Allpayacku (1,82), Huaylla Belen Colcamar (1,81) y Tilacancha (1,56).
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.
Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru)
(MDPI, 2021-12-10) Calizaya, Elmer; Mejía, Abel; Barboza Castillo, Elgar; Calizaya, Fredy; Corroto, Fernando; Salas López, Rolando; Vásquez Pérez, Héctor Vladimir; Turpo Cayo, Efrain
Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers.
Optimizing landfill site selection using Fuzzy-AHP and GIS for sustainable urban planning
(Salehan Institute of Higher Education, 2024-06-01) Zabaleta Santisteban, Jhon Antony; Salas López, Rolando; Rojas Briceño, Nilton Beltrán; Gómez Fernández, Darwin; Medina Medina, Angel James; Tuesta Trauco, Katerin Meliza; Rivera Fernandez, Abner Shelser; Lévano Crisóstomo, José; Oliva Cruz, Manuel; Silva López, Jhonsy Omar
Careful landfill selection with minimal environmental impact is vital for urban planners. This study aims to identify suitable sites for controlled landfills using Fuzzy-AHP integrated with Remote Sensing and GIS, considering a 20-year projection of population and solid waste generation. Initially, twelve sub-criteria were identified, grouped into environmental, socio-economic, and physical categories, and then weighted using paired comparison matrices involving nine experts. The sub-criteria were rasterized and classified into four suitability levels. The weighted overlay of sub-criteria maps generated a territorial suitability model. Within the Alto Utcubamba Commonwealth (Amazonas, Peru), 0.069%, 41.70%, 66.934%, 0.20%, and 12.4% of the territory are suitable, moderately suitable, less suitable, unsuitable, and restricted, respectively, for landfill establishment. Subsequently, 16 highly suitable sites were selected based on the required area (S4 polygons ≥ 0.505 ha) in line with the projected solid waste generation over 20 years. Of the 16 selected areas, only 15 met the shape index. The model showed high accuracy (AUC = 0.784) during validation. Furthermore, this study provides a comprehensive framework for making decisions about waste management in developing countries, enhancing understanding of key factors in selecting landfill sites. It also offers a deeper insight into global and local factors that determine the suitability of landfill sites.
Optimizing landfill site selection using fuzzy-AHP and GIS for sustainable urban planning
(Salehan Institute of Higher Education, 2024-06-01) Zabaleta Santisteban, Jhon Antony; Salas López, Rolando; Rojas Briceño, Nilton B.; Gómez Fernández, Darwin; Medina Medina, Angel J.; Tuesta Trauco, Katerin M.; Rivera Fernandez, Abner S.; Lévano Crisóstomo, José; Oliva Cruz, Manuel; Silva López, Jhonsy O.
Careful landfill selection with minimal environmental impact is vital for urban planners. This study aims to identify suitable sites for controlled landfills using Fuzzy-AHP integrated with Remote Sensing and GIS, considering a 20-year projection of population and solid waste generation. Initially, twelve sub-criteria were identified, grouped into environmental, socio-economic, and physical categories, and then weighted using paired comparison matrices involving nine experts. The sub-criteria were rasterized and classified into four suitability levels. The weighted overlay of sub-criteria maps generated a territorial suitability model. Within the Alto Utcubamba Commonwealth (Amazonas, Peru), 0.069%, 41.70%, 66.934%, 0.20%, and 12.4% of the territory are suitable, moderately suitable, less suitable, unsuitable, and restricted, respectively, for landfill establishment. Subsequently, 16 highly suitable sites were selected based on the required area (S4 polygons ≥ 0.505 ha) in line with the projected solid waste generation over 20 years. Of the 16 selected areas, only 15 met the shape index. The model showed high accuracy (AUC = 0.784) during validation. Furthermore, this study provides a comprehensive framework for making decisions about waste management in developing countries, enhancing understanding of key factors in selecting landfill sites. It also offers a deeper insight into global and local factors that determine the suitability of landfill sites.
Site Selection for a Network ofWeather Stations Using AHP and Near Analysis in a GIS Environment in Amazonas, NW Peru
(MDPI, 2021-11-28) Rojas Briceño, Nilton B.; Salas López, Rolando; Silva López, Jhonsy Omar; Oliva Cruz, Manuel; Gómez Fernández, Darwin; Terrones Murga, Renzo E.; Iliquin Trigoso, Daniel; Barrena Gurbillón, Miguel; Barboza Castillo, Elgar
Meteorological observations play a major role in land management; thus, it is vital to properly plan the monitoring network of weather stations (WS). This study, therefore, selected ‘highly suitable’ sites with the objective of replanning the WS network in Amazonas, NW Peru. A set of 11 selection criteria for WS sites were identified and mapped in a Geographic Information System, as well as their importance weights were determined using Analytic Hierarchy Process and experts. A map of the suitability of the territory for WS sites was constructed by weighted superimposition of the criteria maps. On this map, the suitability status of the 20 existing WS sites was then assessed and, if necessary, relocated. New ‘highly suitable’ sites were determined by the Near Analysis method using existing WS (some relocated). The territory suitability map for WS showed that 0.3% (108.55 km2) of Amazonas has ‘highly suitable’ characteristics to establish WS. This ‘highly suitable’ territory corresponds to 26,683 polygons (of ≥30 × 30 m each), from which 100 polygons were selected in 11 possible distributions of new WS networks in Amazonas, with different number and distance of new WS in each distribution. The implementation of this methodology will be a useful support tool for WS network planning.
Spatial analysis of environmentally sensitive areas to soil degradation using MEDALUS model and GIS in Amazonas (Peru): an alternative for ecological restoration
(MDPI, 2022-11-10) Meza Mori, Gerson; Torres Guzmán, Cristóbal; Oliva Cruz, Manuel; Salas López, Rolando; Marlo, Gladys; Barboza Castillo, Elgar
Land degradation is a permanent global threat that requires an interdisciplinary approach to addressing solutions in a given territory. This study, therefore, analyses environmentally sensitive areas to land degradation using the Mediterranean Desertification and Land Use (MEDALUS) and Geographic Information System (GIS) method through a multi-criteria approach in the district of Florida (Peru). For the method, we considered the main quality indicators such as: Climate Quality Index (CQI), Soil Quality Index (SQI), Vegetation Quality Index (VQI), and Management Quality Index (MQI). There were also identified groups of parameters for each of the quality indicators analyzed. The results showed that 2.96% of the study area is classified as critical; 48.85% of the surface is classified as fragile; 15.48% of the areas are potentially endangered, and 30.46% are not threatened by degradation processes. Furthermore, SQI, VQI, and MQI induced degradation processes in the area. Based on the results, five restoration proposals were made in the study area: (i) organic manure production, (ii) cultivated and improved pastures and livestock improvement, (iii) native forest restoration, (iv) construction of reservoirs in the top hills and (v) uses of new technologies. The findings and proposals can be a basic support and further improved by decision-makers when implemented in situ to mitigate degradation for a sustainable use of the territory.
Spatiotemporal Dynamics of Grasslands Using Landsat Data in Livestock Micro-Watersheds in Amazonas (NW Peru)
(MDPI, 2022-05-01) Atalaya Marin, Nilton; Barboza Castillo, Elgar; Salas López, Rolando; Vásquez Pérez, Héctor Vladimir; Gómez Fernández, Darwin; Terrones Murga, Renzo E.; Rojas Briceño, Nilton B.; Oliva Cruz, Manuel; Gamarra Torres, Oscar Ándres; Silva López, Jhonsy Omar; Turpo Cayo, Efrain
In Peru, grasslands monitoring is essential to support public policies related to the identification, recovery and management of livestock systems. In this study, therefore, we evaluated the spatial dynamics of grasslands in Pomacochas and Ventilla micro-watersheds (Amazonas, NW Peru). To do this, we used Landsat 5, 7 and 8 images and vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI). The data were processed in Google Earth Engine (GEE) platform for 1990, 2000, 2010 and 2020 through random forest (RF) classification reaching accuracies above 85%. The application of RF in GEE allowed surface mapping of grasslands with pressures higher than 85%. Interestingly, our results reported the increase of grasslands in both Pomacochas (from 2457.03 ha to 3659.37 ha) and Ventilla (from 1932.38 ha to 4056.26 ha) micro-watersheds during 1990–2020. Effectively, this study aims to provide useful information for territorial planning with potential replicability for other cattle-raising regions of the country. It could further be used to improve grassland management and promote semi-extensive livestock farming.