## 3 Data and Methods
### Data Collection
#### 3.1.2 Ground Truth Data  
We used vegetation survey data collected from field campaigns, botanical collections, and vegetation inventories carried out by the team of researchers of the Plant Ecology Laboratory at the University of Mato Grosso State [2; 12; 24; 27; 31; 65] as reference data to guide the creation of the training dataset for the U-net model. The survey data comprised 112 forest, 46 Cerradao, and 79 Savanna plots, of which 59 were located within the study region but still used in model training since they were located in vegetation types that were also found within the CAT (Figure 4a). The earliest of these plots was delineated in 1996, followed by periodic remeasurements. The collected information includes geographic coordinates, vegetation type, dominant species, tree diameter, soil type, etc. However, not all plots were remeasured every year, so we visually inspected annual high-resolution imagery from Google Earth between 1996 and 2023 to ensure that only plots exhibiting continuous stable vegetation cover throughout this period were considered. Since the areas of most surveyed plots are relatively small (\(\sim\)1 hectare) and their number is insufficient for direct use as training samples, we leveraged these plots to guide the manual creation and labeling of the training dataset for the classification model via visual interpretation.  
Specifically, we produced 50 classified sample images, each measuring 256 \(\times\) 256 pixels (Figure 4b). To enable reliable visual interpretation and labeling of vegetation types, sample areas were chosen near vegetation survey plots and in locations with clear remote sensing imagery. Land-cover classes within each sample image were manually delineated as vector polygons using reference data from vegetation plots, high-resolution imagery from Google Earth Pro, and multi-temporal Sentinel-2 and PlanetScope images. Each polygon corresponded to a specific land-cover type and was drawn to match clear visual boundaries observed in the imagery. These vector labels were then rasterized at a spatial resolution of 4.77 m--matching that of PlanetScope imagery--so that every pixel in the sample images was assigned a land-cover class based on the underlying polygon. To address data imbalance, particularly for underrepresented classes such as Cerradao, 19 of the 50 sample images were selected from outside the study area. These external sampleswere carefully chosen to include only land-cover types that were also commonly found within the study region.  
In order to expand the training dataset, we applied data augmentation by extracting 128 \(\times\) 128-pixel patches from the original 256 \(\times\) 256-pixel sample images, using a 64-pixel offset. These patches were then mirrored vertically and horizontally, resulting in a total of 1350 training patches (128 \(\times\) 128 pixels), which served as the direct input to the model. To ensure spatial independence between training and validation, 1080 patches derived from 40 labeled images were used exclusively for training, while the remaining 270 patches were extracted from 10 completely separate sample images for validation. This strategy follows widely adopted practices in deep learning-based land cover classification, where overlapping patches and mirroring are commonly used to increase sample quantity and spatial variability [62; 81]. All validation patches were drawn from within the study area. The total number of pixels per land cover class in the final training and validation sets is summarized in Table 1. All processing steps were performed in Jupyter Notebook (v6.4.5) using the rasterio package [82].