Mapeo de la vulnerabilidad a la degradación de pastizales mediante AHP-GIS & RPAS en la microcuenca Pomacochas - Perú

In Peru,rangeland monitoring is increasingly essential to support farmers and strengthen new public policies for sustainable management at the watershed level. In this research, we sought to map the vulnerability to grassland degradation in the Pomacochas micro-watershed, Amazonas -Peru. For this, criteria were used (NDVI, precipitation, SOM, soil texture, soil pH and slope). Also, based on expert consultation and the Analytical Hierarchy Process (AHP), the importance of the criteria was weighed. Then, the land suitability map was generated to assess rangeland vulnerability by weighted superimposition of the criteria maps. NDVI was the most important criterion, while land slope was the least important. AHP and GIS based modeling shows that about 4012.08 km2(62.98%) of the total study area is in the category "slightly vulnerable" (C3) to grassland degradation. The results were also validated by means of four validation plots using images from a Remotely Piloted Aircraft (RPA). The study will provide support for decision making in the management of grasslands in micro-watershedsEn Perú, realizar un monitoreo de pastizales es cada vez más esencial para apoyar a los productores agropecuarios y fortalecer nuevas políticas públicas enmarcadas a un manejo sostenible a nivel de cuencas hidrográficas. En esta investigación se buscó mapear la vulnerabilidad a la degradación de pastizales en la microcuenca de Pomacochas, Amazonas − Perú. Para ello, se utilizaron criterios (NDVI, precipitación, MOS, textura del suelo, pH y pendiente). También, basado en consulta a expertos y el Proceso de Jerarquía Analítica (AHP), se sopeso la importancia de los criterios. Luego, se generó el mapa de aptitud del territorio para evaluar la vulnerabilidad de pastizales mediante superposición ponderada de los mapas de criterios. NDVI fue el criterio más importante, mientras que, la pendiente del terreno fue el menos importante. El modelado basado en AHP y SIG muestra que alrededor de 4012.08 km2 (62.98%) del área total de estudio se encuentran en la categoría “ligeramente vulnerable” (C3) a la degradación de pastizales. Asimismo, se validó los resultados mediante cuatro parcelas de validación empleando imágenes de un Aeronave Piloteada Remotamente (RPA). El estudio brindará apoyo para la toma de decisiones en torno al manejo de los pastizales en microcuencas

Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods

Amazonas is a mountain region in Peru with high cloud cover, so using optical data in the analysis of surface changes of water bodies (such as the Burlan and Pomacochas lakes in Peru) is difficult, on the other hand, SAR images are suitable for the extraction of water bodies and delineation of contours. Therefore, in this research, to determine the surface changes of Burlan and Pomacochas lakes, we used Sentinel-1 A/B products to analyse the dynamics from 2014 to 2020, in addition to evaluating the procedure we performed a photogrammetric flight and compared the shapes and geometric attributes from each lake. For this, in Google Earth Engine (GEE), we processed 517 SAR images for each lake using the following algorithms: a classification and regression tree (CART), Random Forest (RF) and support vector machine (SVM).) 2021-02-10, then; the same value was validated by comparing the area and perimeter values obtained from a photogrammetric flight, and the classification of a SAR image of the same date. During the first months of the year, there were slight increases in the area and perimeter of each lake, influenced by the increase in rainfall in the area. CART and Random Forest obtained better results for image classification, and for regression analysis, Support Vector Regression (SVR) and Random Forest Regression (RFR) were a better fit to the data (higher R2), for Burlan and Pomacochas lakes, respectively. The shape of the lakes obtained by classification was similar to that of the photogrammetric flight. For 2021-02-10, for Burlan Lake, all 3 classifiers had area values between 42.48 and 43.53, RFR 44.47 and RPAS 45.63 hectares. For Pomacohas Lake, the 3 classifiers had area values between 414.23 and 434.89, SVR 411.89 and RPAS 429.09 hectares. Ultimately, we seek to provide a rapid methodology to classify SAR images into two categories and thus obtain the shape of water bodies and analyze their changes over short periods. A methodological scheme is also provided to perform a regression analysis in GC using five methods that can be replicated in different thematic areas

Land Suitability for Cocoa Cultivation in Peru: AHP and MaxEnt Modeling in a GIS Environment

Peru is one of the world’s leading exporters of cocoa beans, which directly impacts the household economy of millions of small farmers. Currently, the expansion and modernization of the cocoa-growing area require the zoning of the territory with suitable biophysical and infrastructural conditions to facilitate optimizing productivity factors. Therefore, we analyzed land suitability for cocoa (Theobroma cacao L.) production on the Peruvian mainland as a support measure for sustainable agriculture. To this end, the climatological, edaphological, orographic, and socioeconomic criteria determining sustainable cocoa cultivation were identified and mapped. Three modeling approaches (Analytic Hierarchy Process—AHP, Maximum Entropy—MaxEnt, and AHP—MaxEnt combined) were further used to hierarchize the importance of the criteria and to model the potential territory for sustainable cocoa cultivation. In all three modeling approaches, climatological criteria stood out among the five most important criteria. Elevation (orographic criteria) is also featured in this group. On the other hand, San Martin and Amazonas emerged as the five regions with the largest area ‘Highly suitable’ for cocoa cultivation in all three modeling approaches, followed by Loreto, Ucayali, Madre de Dios, Cusco, Junín, and Puno, which alternated according to modeling approach. From most to least restrictive, the AHP, MaxEnt, and AHP–MaxEnt modeling approaches indicate that 1.5%, 5.3%, and 23.0% of the Peruvian territory is ‘Highly suitable’ for cocoa cultivation, respectively

Accuracy Assessment of Direct Georeferencing for Photogrammetric Applications Based on UAS-GNSS for High Andean Urban Environments

Unmanned Aircraft Systems (UAS) are used in a variety of applications with the aim of mapping detailed surfaces from the air. Despite the high level of map automation achieved today, there are still challenges in the accuracy of georeferencing that can limit both the speed and the efficiency in mapping urban areas. However, the integration of topographic grade Global Navigation Satellite System (GNSS) receivers on UAS has improved this phase, leading to a reach of up to a centimeter-level accuracy. It is therefore necessary to adopt direct georeferencing (DG), real-time kinematic positioning (RTK)/post-processed kinematic (PPK) approaches in order to largely automate the photogrammetric flow. This work analyses the positional accuracy using Ground Control Points (GCP) and the repeatability and reproducibility of photogrammetric products (Digital Surface Model and ortho-mosaic) of a commercial multi-rotor system equipped with a GNSS receiver in an urban environment with a DG approach. It was demonstrated that DG is a viable solution for mapping urban areas. Indeed, PPK with at least 1 GCP considerably improves the RMSE (x: 0.039 m, y: 0.012 m, and z: 0.034 m), allowing for a reliable 1:500 scale urban mapping in less time when compared to conventional topographic surveys

Evaluation of pasture degradation through vegetation indices of the main livestock micro-watersheds in the Amazon region (NW Peru)

Pastures provide different ecosystem services, such as carbon dioxide fixation, are indicators of climate change and prevent soil erosion. However, anthropogenic activities degrade and decrease soil diversity. Therefore, the objective was to evaluate pasture degradation using vegetation indices. For this purpose, the floristic composition was evaluated by means of linear transects (50 m) to determine the percentage of area covered by forages and weeds; the Normalized Vegetation Index (NDVI) and the Pasture Vegetation Cover (PVC) assessment methodology were used to determine pasture degradation, and the chemical properties of the soils were characterized. The degradation map showed that the range ''S4-Strongly degraded (CVP, <40%) for the Pomacochas micro-watershed is 2.60% (93.43 ha); however, in Ventilla, 0.40% (8.95 ha) was obtained. An equation was generated from a multiple linear regression model and a principal component analysis (PCA) of the main regressor variables. The results were obtained for both micro-watersheds with an R2 greater than 60% determined by CVP + Bands 3, 4, 5, 6, 8 and pH. In addition, the PCA showed that at acidic pH, the percentage of degradation increased, reaching the level of advanced degradation in both micro-watersheds under study, and when the pH values were close to neutrality, they indicated optimal zones for pasture production. This study provides a new source of information regarding the degradation of high Andean grasslands. It is also the basis for further research in the agricultural sector