## 3 Technologies of Monitoring in Montado
### Precision Agriculture
#### 3.2.3 Pasture Monitoring
Those authors also mention that satellite images with different geometric and spectral characteristics (Landsat 8 and Sentinel-2) have been used in monitoring the Montado ecosystem. However, one of the main limitations in the use of satellite images is the presence of clouds [78; 113] and, in the case of Montado, the existence of trees, which limits the capture of satellite images, under the canopies [43]. To overcome this limitation, using proximal sensors under the canopy of trees is crucial [78]. The information collected through hyperspectral sensors can help agricultural producers to improve productivity, performance, and farm resilience, allowing for more accurate and timely decision making [56]. According to this study, the regular monitoring of the pasture with nearby hyperspectral sensors allows for efficient rotations to be programmed and the supply of supplementary food to be planned only when there is an inadequate level of nutrients in the pasture. On the other hand, this study also mentions that having real-time information about the nutrients in each plot allows for easy adjustment in the number of animals (stocking rate). To support grazing management decisions and to better understand spatial and temporal changes and variability in rangelands, obtaining an accurate estimate of the biomass in these ecosystems is crucial [114]. As mentioned, the traditional methods of cutting and collecting samples of pasture for later bromatological analysis and weighing to obtain the productivity and/or quality of the pasture, despite being quite accurate, become unfeasible because they are expensive in terms of time, human resources, and money. In this regard, and according to [PERSON] and [PERSON] [115], remote sensing techniques can be fundamental since they allow for quantifying and mapping the spatial and temporal variability of the constituent plants of the pastures, being an expeditious and non-destructive method. A vegetation index widely used to estimate pasture productivity and quality is the NDVI (Normalized Difference Vegetation Index), which can be obtained through a proximal sensor or multispectral satellite images. NDVI is related to the amount of chlorophyll in plants [85] and, consequently, to their vegetative vigor ([PERSON], 2007), cited by [81]. It can be calculated from satellite images through the reflectance, by plants, of the emitted radiation [116] or from nearby sensors, such as \"OptRx\" [117]. The NDVI is derived from the reflectance ratio of red to near infrared [85]. [PERSON] et al. [118], in a study where they correlated the NDVI values through the proximal OptRx sensor with the production of the Montado pasture, obtained a relatively low correlation (r\({}^{2}\) = 0.47). Additionally, with the OptRex sensor, [PERSON] et al. [117] obtained high and significant correlations between pasture quality parameters and the NDVI (r\({}^{2}\) = 0.7537 for CP and r\({}^{2}\) = 0.8375 for NDF). The \"OptRx\" sensor measures high NDVI values in places with high DM and GM production, which is directly related to a higher density of photosynthetically active vegetation and is also correlated with the CP content [43]. [PERSON] et al. [81] infer that productivity and NDVI values are higher in places where pasture moisture is high, corresponding to northwest and southwest orientations. On the other hand, according to the same authors, the NDVI values were higher in younger plants and in places with a high percentage of legumes. Still, according to [PERSON] et al. [81], the active proximal sensor \"OptRx\" can identify different botanical species, different development stages, and different productivity zones. On the other hand, [PERSON] et al. [119] used NDVI data from the Sentinel-2 satellite, verifying that the values of that index showed a solid and positive high correlation (r\({}^{2}\) = 82.8) with the values of the percentage of canopy cover in Montado.