## 5 Discussion
The results demonstrate that the combination of fused PlanetScope and Sentinel-2 optical imagery, together with SAR, significantly outperforms single-source models,particularly for identifying the Amazon forest and Cerradao, and concurs with several previous findings that also report improved classification outcomes using multi-source data fusion [52; 111; 112]. High-resolution imagery (e.g., PlanetScope) reveals finer surface details, allowing precise delineation of vegetation patches [113]. Although Sentinel-2 has lower spatial resolution than PlanetScope, it provides richer spectral information, capturing vegetation surface reflectance across different wavelengths [114; 115]. SAR data offers structural information and surface roughness characteristics, aiding in distinguishing vegetation types and artificial structures [116; 117]. In this study, data fusion effectively combines PlanetScope's high spatial resolution with Sentinel-2's spectral richness and Sentinel-1's SAR data, enhancing vegetation classification in ecologically complex regions. Agricultural expansion and increased human activities have significantly degraded vegetation in the CAT region [34]. Our results indicate that natural vegetation covers 50.3% of the region, with the Amazon forest and Savanna occupying similar areas, while Cerradao represents only 8.4%. Agricultural lands are widespread, particularly fragmenting the southern Cerrado into small patches (Figure 7). This landscape shift highlights the long-term impacts of intensive agriculture on regional ecological functions. These activities disrupt ecological balance by reducing soil organic matter and impairing soil respiration [118; 119] while contributing to the erosion and spatial-temporal decline of plant species [16; 17; 65]. Unlike the Amazon, the Cerrado region lacks effective vegetation protection policies, exacerbating vegetation loss and fragmentation [120; 121; 122]. We also observed that the natural vegetation landscape in the CAT region exhibits pronounced spatial heterogeneity. The Amazon forest, concentrated in the north, features high connectivity and coverage, offering substantial carbon storage and biodiversity support. In contrast, the Savanna is primarily located in the south, forming isolated high-density regions with poor connectivity, while Cerradao, though scattered, is present throughout the region. These patterns reflect dynamic ecological processes and underscore the profound influence of human activities on natural landscapes [34; 120]. As ecotone vegetation between tropical forests and savannas, Cerradao lacks sufficient protection policies [28; 29], leading to higher deforestation rates compared to the Amazon [123; 124]. This trend not only physically degrades vegetation but also reduces ecosystem services. The decline of Cerradao diminishes regional carbon storage, increasing greenhouse gas emissions and exacerbating climate change [65; 125]. Given Cerradao's prevalence in infertile soils [30; 34], its loss further degrades soil structure and fertility, intensifying soil erosion. A lack of comprehensive reference datasets has long posed a significant challenge for land cover classification studies in tropical regions [126; 127]. The diversity of vegetation types, combined with complex spatial structures and distributions, complicates the acquisition of accurate ground truth data due to logistical challenges, high time requirements, and costs. Although traditional deep learning methods have been widely applied to land cover classification and shown to outperform conventional classifiers in distinguishing complex surface types [128; 129; 130], their accuracy typically depends on large training datasets, which presents additional challenges for land cover classification in the CAT region. In this study, the U-net model effectively handled multi-scale information, even with limited and imbalanced samples [62; 131].