# Analysis of a Landscape Intensely Modified by Agriculture in the Tiete-Jacare Watershed, Brazil
## 4 Discussion

Table 7: Land use and land cover using 2017 Sentinel images vs. vegetation using 2017 Landsat images from the Tieté–Jacaré Watershed. By comparing the manual with the automatic classification, we observed that 67,000 ha of the area covered by vegetation was classified as other land use and land cover types. The highest confusion with the vegetation involved the citriculture (31,000 ha), pasture (12,000 ha), and silviculture (5000 ha) classes. On the other hand, the urban and water classes did not present any problems, mainly because their reflectance was found to be different from that of the vegetation. The integration between machine learning and visual classification techniques is the best way to analyse and obtain information about the environmental and anthropic changes in different landscapes and contexts. For future studies, we plan to increase the training and validation samples and include other classes of land use and land cover, as well as other natural ecosystems. This process will be important because the natural landscape in the study area is being rapidly modified and converted. Through our methodology, we can detect the changes faster in order to provide the necessary information to decision-makers to act in the conservation of the natural ecosystems still present in the area. Figure 11 shows the comparison between both classifications for 2017 (automatic and manual). It is possible to see that the automatic classification diverges from the manual in small areas, especially related to the riparian vegetation found near streams. Although the comparison was made visually, it is worth highlighting that the maps were based on two different sensors, resulting in images acquired at different spatial and spectral resolutions. The study conducted by [PERSON] et al. [60] proposes a method to use multi-temporal Landsat Satellite data for determining a water index to constantly monitor the spatial distribution of rice planted in Japan without using complex interactive manual operations [60]. According to the authors, this method has a much more straightforward approach when compared to the semi-automatic or automatic techniques, as it determines the water index based on statistical analyses using manual training data or the difference in histogram distribution information between flooded and non-flooded pixels. [PERSON] et al. [72] used 1 m pixel images from the Digital Globe Satellite and 30 m pixel images from the Landsat Satellite to evaluate the impact of the spatial resolution of a satellite image on land use and land cover classification via the ArcGIS Feature Analyst tool and on total suspended solids (TSS) load estimates from the Soil and Water Assessment Tool (SWAT) for the Camborui watershed in Santa Catarina state, southeastern Brazil. The authors found that the models using 1 m data had better results for predicting both analyses. Moreover, besides higher costs, the high spatial resolution requires additional analysis time. Other critical aspects for enhancing mapping accuracy are the increase in the number of spectral bands and the increase of the radiometric resolution. [PERSON] et al. [73] worked in a comparative study to evaluate the utility of the simulated 12-bit LISS-III sensor against the original 7-bit LISS-III sensor for land use and land cover classification. An increase of 3% was noticed in the overall accuracy when using high radiometric resolution data. The development and variation of machine learning (ML) techniques and visual classification could increase the speed and accuracy for classifying land use and land cover changes in natural and anthropic landscapes. Additionally, such approach could support government agencies' efforts to avoid deforestation and to develop action plans so as to mitigate the impacts of these processes on the landscapes, thus increasing sustainability in the different biomes worldwide [74; 75; 76]. An example is the work conducted by [PERSON] et al.