## 3 Results
### Approaches for Pasture Quantification
#### 3.4.4 Pasture Degradation  
The focus is to correlate key anthropogenic activities (i.e., grazing management) that predispose grasslands to decline with remote sensing products as proxies by relating them to biophysical variables (i.e., fPAR, AGB, fCOVER, ANPP). A significant number of studies have used Landsat and MODIS land surface reflectance products rather than finer satellite imagery to express the productivity of grasslands (fPAR) [191; 209] (fCOVER) [137; 167; 189; 190; 203],  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **Methods** & **Sensor** & **Accuracy** & **Reference** \\ \hline SVM + PCA & Pixel-based & Sentinel-2 & 80\% (overall) & [133] \\ RF & Pixel-based & Sentinel-2, Sentinel-1, & 86\% (overall) & [7] \\  & & ALOS & & \\ SVM + RF & Object-image based & Landsat & & \\ Kernel + SVM & & & Non-linear performed better & \\  & & & (0.55 \(\leq\) R2 CV \(\leq\) 0.78; & [142] \\  & & & 6.68\% \(\leq\) nRMSECV \(\leq\) 26.47\%) & \\ Decision tree & Object-based & IKONOS & 83\% & [172] \\ SVM & & & & \\ Decision tree & & SPOT & & \\ SVM & linear regression/classification & Landsat & & \\ K-NN & & SPOT & Kappa index= 0.82 & \\ Maximum Likelihood & Object-image based & Landsat, SPOT & Landsat = 60.1\%, SPOT = 65.5\%, & [31] \\ Classifier (MLC) & Hierarchical clustering & Landsat & & [203] \\ Multivariate & Pixel-based & Sentinel-1A, Sentinel-2 & 76\%, 62\%, 75\% & [138] \\ RF & SVM, KNN & Pixel-based & Sentinel-1, Sentinel-2 & KNN 0.89, RF 0.96, SVM 0.96 & [139] \\ Multivariate & Where several & & & [204] \\  & treatments are needed & & & [204] \\ Fuzzy/KNN & Pixel-based & HyMap & 98\% and 64\% & [52] \\ \hline \end{tabular}
\end{table}
Table 6: Summary of the main classification algorithms for pasture composition used in the studies.  
(AGB, ANPP) [105; 109; 129; 130; 141; 144; 174; 186; 210; 211], and ecosystems beyond the scope of biomass production. NDVI is the most used proxy for estimating biophysical variables. For example, stocking rate data were compared with yearly AVHRR-NDVI and rainfall data to account for overgrazing on rangelands [212]. Soil-based indices are used after NDVI to understand non-vegetation in mapping landscapes [83; 144; 213]. [PERSON] et al. [144] used the soil-adjusted total vegetation index (SATVI) from red and SWIR bands to map fCOVER, while [213] estimated the pasture productivity decline from grazing intensity and fire regime in a semi-arid environment using the derived soil tillage index (STI) from MODIS. The study of [213] showed that SWIR calculated from STI (B6 and B7) was more accurate (R\({}^{2}\) = 67%) in mapping drier vegetation compared to NDVI and other indices. LAI was found to be more accurate in estimating fPAR than NDVI [191]. Pasture degradation indicators (grazing intensity/pressure) are often correlated with environmental variables (soil and survey data, meteorological, GPS) to understand the drivers, and are also used with (VIs) as predictor variables [2; 20; 141; 105; 129; 130; 140]. Studies have adopted mapping the land cover and land use (LULC) [83; 121; 199] to show the spatial and temporal variability of an area of interest.