Estimación de superficies agrícolas mediante procesamiento en la nube para el altiplano potosino - Hernández-Ramos, José de Jesús; Pimentel-López, José; Amante-Orozco, Alejandro; Osuna-Ceja, Esteban Salvador (Revista Mexicana de Ciencias Agrícolas, 2025)
Optical satellite imagery is a powerful information bank for estimating agricultural areas. This study aimed to estimate agricultural areas in the municipalities of Salinas, Santo Domingo, and Villa de Ramos through cloud processing of satellite images and their comparison with the traditional-INEGI technology. The work was carried out limited to the agricultural area, which totals an area of 190 871 ha, of which 86% are rainfed. The study period was from October 2020 to October 2021. Six classification algorithms were applied; three for traditional-INEGI: minimum distance, maximum likelihood, and spectral angle mapper in QGIS 3.18; and three for cloud processing: classification and regression trees, random forest, and support vector machine with Google Earth Engine. The areas of the main crops (corn, beans, oats, alfalfa, and chili) were estimated for the study area based on 294 field samples. For Sentinel-2 image processing, a cloud-free geomedian was used. The results of the confusion matrices indicated which classifications were more accurate; the values were 89% for classification and regression trees and random forest, 59% for support vector machine, 48% for minimum distance, 43% for maximum likelihood, and 46% for spectral angle mapper. The classification and regression trees and random forest algorithms outperformed the other classifiers evaluated in accuracy, estimating the corn and bean agricultural areas closest to each other (80 131 and 98 138 ha in corn and 60 174 and 60 358 ha in beans) compared to the remaining classifiers.
Keywords: cultivos; google earth engine; QGIS