Assessing satellite image classification for the Cerrado biome with the integration of terrain data: a comparative analysis of machine learning algorithms

• Published in: Boletim de Ciências Geodésicas Vol. 31; pp. 1 - 26
• Main Authors: Holanda, Carlos Eduardo Fernandes, Macedo, Diego Rodrigues, Nóbrega, Rodrigo Affonso Albuquerque
• Format: Journal Article
• Language: English
• Published: Curitiba Universidade Federal do Paraná, Centro Politécnico 01.01.2025
• Subjects: Algorithms; Artificial intelligence; Biodiversity; Classification; Decision trees; Deforestation; Ecosystems; Heterogeneity; Image classification; Land cover; Land use; Machine learning; Remote sensing; Satellite imagery; Satellites; Support vector machines; Surface water; Terrain; Topography; Watershed management; Brazil
• ISSN: 1413-4853; 1982-2170; 1982-2170
• DOI: 10.1590/s1982-21702025000100002

The present paper addresses the relevance of incorporating terrain data for analyzing satellite images in mapping land use and land cover in the Cerrado biome. Assuming that terrain influences the dynamics of landscape changes, the present investigation evaluates three machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) in a watershed with significant topographic heterogeneity. The present study evaluated variations in image classification using Sentinel-2 satellite data. It also included a composite analysis blending Sentinel-2 data and information derived from the Shuttle Radar Topography Mission (SRTM). The results indicate that SVM exhibited the best performance, both with and without terrain data. Although DT demonstrated satisfactory results, the performance was inferior to SVM. However, DTÂ s significantly shorter processing time presents an advantage in scenarios involving large territorial extents or computational constraints. Conversely, RF had a processing time similar to DT but recorded the lowest statistical indices among the three algorithms. Additionally, including the data cube containing elevation data and its derivatives yielded improved land use and land cover classification results for all evaluated algorithms compared to images without terrain data. This demonstrates the robustness of the process and the significant improvement in the quality of the final product.