Comparación de mapas de cobertura del suelo utilizando PlanetScope y Sentinel-2 mediante la clasificación basado en objetos (OBIA) en la zona de recarga hídrica del Cantón Cuenca

Abstract

Land covering go through constant changes due to human activities and climate change, directly affecting important ecosystems such as water recharge areas. To identify this transition, thematic cartography obtained from the official repositories of each country is used. In Ecuador, the current official cartography produced by the Ministry of Agriculture and Livestock (MAG) corresponds to the year 2020, which highlights the need to update this information for future studies. Considering this, the present study generated and compared land cover maps using Sentinel-2 and PlanetScope satellite imagery from 2025 through Object-Based Image Analysis (OBIA) and supervised classifications using the Mean Shift and Support Vector Machine algorithms, with field data used as reference. The maps were compared according to the area of each class to evaluate the reliability between sensors and independently. The results obtained through confusion matrices showed that Sentinel-2 achieved an overall accuracy of 94.14% and a Kappa coefficient of 0.89, while PlanetScope obtained an overall accuracy of 98% and a Kappa coefficient of 0.96. Furthermore, the comparison between the maps showed moderate agreement, with an overall accuracy of 82,62% and a Kappa coefficient of 0,69. Considering these findings, both sensors allow the generation of reliable land cover cartography; however, PlanetScope demonstrated better performance, showing higher values than Sentinel-2, which is associated with its greater spatial resolution. This characteristic makes it possible to better differentiate land cover types and provide a more accurate representation of the terrain.