Remote sensing and geographic information systems are tools that in the last decade have been widely used in the management of natural resources, however, they have presented deficiencies for precision livestock studies due to the quality of spatial resolutions, spectral and temporal. Faced with this limitation, microsensors appear as an alternative in Unmanned Aerial Systems (UAS) that allow obtaining orthophotographs with better resolutions. Considering these advantages, a study was developed to determine the best flight height in the detection and identification of the tolar and bofedal plant communities of the dry puna. For the study, RGB and NDVI photographs were collected with ZENMUSE X3 DJI RGB-NDVI sensors in UAS with flight heights of 25, 50, 75 and 100 m. In the field, tola plants and DIMU cushions were counted in quadrants of 10 m x 10 m (100 m2). The preparation of orthophoto...

Remote sensing with large-scale satellite images for precision studies in grasslands has spatial and spectral resolution limitations. Against this, using spectral signs and vegetation indices obtained with microsensors transported by unmanned aerial vehicles (UAV) constitutes a more accurate alternative for biomass estimation. In the fieldwork, images were acquired with microsensors, and fixed transects of 100 m were used where vegetation samples were collected. The photographs acquired with the UAV were processed in Pix 4D, Arc Gis, and algorithms elaborated in R programming language. The biomass estimation was carried out with Multiple Linear Regression, Vector Support Machine, and Random (Forest Random) models. The Random model showed a Kappa coefficient of 0.94 in the training set and 0.901 in the test set (R2 = 0.482). The Random Forest model predicted 3 g/pixel of MV for Puna grass...