Land Use and Land Cover Mapping in Fragmented Areas of São Paulo: Application of SITS and LSMM in TCRAs

The state of São Paulo is home to remnants of the Atlantic Forest and Cerrado biomes, both of which face intense anthropogenic pressure and hight fragmentation. In this context, Environmental Recovery Commitment Agreements (TCRAs) serve as essential instruments for restoring degraded areas and monitoring vegetation recovery over time. This study assesses land use and land cover (LULC) classification performance in TCRA sites by integrating Satellite Image Time Series (SITS) with spectral fractions derived from the Linear Spectral Mixture Model (LSMM), utilizing 2025 Sentinel-2A/2B imagery. Data were organized into spatiotemporal cubes within R environment and classified using the Random Forest algorithm. Model performance was assessed using a confusion matrix and accuracy metrics, including User’s Accuracy (UA), Producer’s Accuracy (PA), F1-score, and Intersection over Union (IoU), as well as spatial analysis of agreement and disagreement between predicted maps and reference data. Results demonstrate high classification precision for vegetation classes, specifically pasture (F1 = 0.91) and forest formations (F1 = 0.87). Primary misclassifications occurred between spectrally similar classes, particularly within small fragments and intermediate regeneration stages. Overall, the integration of SITS and LSMM enhanced class separability by incorporating temporal dynamics and mitigating spectral mixing effects, highlighting its potential as an operational tool for environmental restoration monitoring.