## 3 Results  
### Monitoring of Land Use and Occupation  
Thematic land use and occupation maps (Figure 3) were generated through classification using the kNN algorithm. The classes analyzed were water, urban area, bare soil, and vegetation, allowing the identification of landscape changes between 2018 and 2023. In Figure 3, there is a noticeable increase in built-up areas proportional to a reduction in vegetated areas, especially in regions near natural water resources, indicating accelerated loss of riparian forests. On the other hand, bare soil areas did not show significant visible changes. The \"urban area\" class includes roofs of structures such as houses, warehouses, sports courts (with ceramic or metallic coverings), and paved roads like asphalt or cobblestone streets. The cartographic conventions for linear features such as roads were incorporated into the maps using the photointerpretation method on PlanetScope images, complemented by data from the Brazilian National Department of Transport Infrastructure (DNIT) ([[https://servicos.dnit.gov.br/vgeo/](https://servicos.dnit.gov.br/vgeo/)]([https://servicos.dnit.gov.br/vgeo/](https://servicos.dnit.gov.br/vgeo/)), accessed on 4 April 2024). This approach allowed for the identification of roads in the study area with high precision, considering the 3 m spatial resolution of the analyzed images. The analysis revealed that several roads are closely related to mining areas, indicating a possible correlation between the expansion of road infrastructure and increased productive activities. This methodology revealed a significant expansion of the road network during the analyzed period, contributing directly to the growth of the built-up area identified in the maps. The inclusion of these features highlights the essential role of roads and other constructions in shaping the landscape, thereby explaining the changes observed in the urban class from 2018 to 2023. By comparing the classifications from 2018 and 2023, a reduction of approximately 2.10% in vegetation over the five years was identified, as shown in Figure 4.  
Figure 3: Thematic land use and occupation maps for 2018 and 2023: (**a**) classified image for 2018 and (**b**) classified image for 2023, generated using the kNN algorithm. The maps highlight four classes: water, urban area, bare soil, and vegetation. The comparison reveals a noticeable increase in built-up areas and a proportional reduction in vegetated areas, particularly near natural water resources, indicating accelerated loss of riparian forests. Bare soil areas remained largely unchanged over the five-year period. Data extraction enabled the quantification of land use and occupation classes, as shown in Figure 5.  
In 2023, vegetation cover decreased by 9.68 km\({}^{2}\), which is equivalent to 3.28% compared to the vegetation area in 2018 and 2.11% of the total area of 459.33 km\({}^{2}\). Rainfall over  
Figure 4: Thematic land use map highlighting deforested areas between 2018 and 2023. This map emphasizes the reduction of approximately 2.10% in vegetation during the five-year period, particularly in regions adjacent to water resources, reflecting the extent of deforestation. Figure 5: Quantification of land use and occupation classes (in km\({}^{2}\)) for 2018 and 2023. The graph highlights significant changes in land use over the five-year period, including a 9.68 km\({}^{2}\) (3.28%) decrease in vegetation cover, a 0.44 km\({}^{2}\) (22.80%) reduction in water areas, and a 20.31 km\({}^{2}\) (11.52%) decrease in bare soil. Conversely, built-up areas increased by 30.43 km\({}^{2}\) (142.53%), reflecting urban expansion. The percentages are relative to the total study area of 459.33 km\({}^{2}\). the period studied fell considerably, which may have influenced changes in the Caatinga vegetation. This decrease was particularly noticeable in October 2023, when there was no precipitation.