## 3 Results
### Approaches for Pasture Quantification
#### 3.4.2 Botanical Composition  
Some studies have pursued the key objective of finding a suitable instrument to discriminate vegetation canopy. Multi and hyper-spectral sensors \(\leq\) 30 m (Tables 5 and 6) are the most deployed to discriminate vegetation canopies with either image-based [33; 52; 199; 200] or object-based image analysis (OBIA) [62; 163; 169; 172; 184]. Classifiers derived from hyperspectral sensors are more accurate than multispectral instruments [177; 200], while multisource instruments are more accurate than single sensors [113; 169; 177]. [PERSON] et al. [177] established a higher spectral accuracy of 92% from HySpiri than from Landsat 8 (75%), Sentinel 2 (82%), and Venus (83%). Likewise, the study from [113] concludes that IKONOS, QuickBird, and Worldview sensors with finer spatiotemporal resolutions are more sensitive to discriminating grassland from shrubs and trees than Landsat imagery. Studies that used ML algorithms as classifiers (mainly MLC, RF, SVM, and k-Nearest Neighbour (KNN) algorithms) [62; 172; 199; 200] improved their classification accuracy more than traditional methods. For example, the accuracy reached 98% and the Kappa coefficient \(\geq\) 90% when ancillary data were added using SVM and RF as classifiers [62]. OBIA enables the mapping of vegetation/species classes and integrations of geometric, textural, and spatial data (i.e., ancillary data) in addition to the primary spectral information to improve accuracy [62; 200]. Hence, ML with OBIA can capture the environmental and management variables more accurately than pixel-based algorithms. More studies have used supervised than non-supervised classification to use novel sampling techniques to build spectral signatures from field areas of interest. Studies have used botanical sampling protocols [139], dominant pasture species [138; 161], the percentage of pasture species [33], and the height of pasture species [7; 133] to build spectral features. The authors used phenological stages and early growing seasons to improve accuracy using single imagery [49; 86; 131; 201; 202]. [PERSON] and [PERSON] [133] reported a better classification accuracy from a multi-temporal study than a single-date one. Mapping species in the early season reduces canopy complexity and provides insight into their phenology. The available studies on the combination of optical and radar sensors to discriminate pastures show that the spectral features derived from Sentinel-2 outperformed the backscatter and dual-polarised features of Sentinel-1 [139] when subjected to similar ML models (SVM and RF). However, merging Sentinel-2 and Sentinel-1 produced a higher accuracy (i.e., RF = 93% and SVM = 89%). Like optical sensors, Sentinel-1 data have been used to discriminate between C3, C4, and mixed C3/C4 pastures, using RF to achieve 68% accuracy [138]. The accuracy level increased to 73% on those including textural features (i.e., leaf area, plant height, size, and orientation) derived from Grey Level Co-occurrence Metrics analysis (GLCM). A study used UAS-LiDAR with a 3 cm accuracy level and a maximum 100 m measurement to detect shrub encroachment and classify 2000 habitat types with 75% accuracy, using RF as a classifier [181].