Unmanned aerial systems and passive remote sensors to classify microecosystems of high Andean grasslands

Abstract

Pastures are the fodder base for camelid and sheep production in the southern Peruvian Andes, where 80% of alpacas and 15 % of sheep live, which requires better land management and grazing programs through the classification of microecosystems. The objective of this study was to classify the microecosystems based on the grasslands of the Kayra Agronomic Center in the Region of Cusco using unmanned aerial vehicles and remote sensors. To do this, traditional evaluation and estimation methods such as modified Parker and quadrat sampling, were combined with biomass classification and estimation methods supported by multispectral images. This was done using 5 m RapidEye satellite images, and multispectral orthophotographs acquired with a Micasense sensor transported by a Matrix 300 RTK Drone with 10 cm pixels. Processing was performed by Pix 4D version 4.7.5 photogrammetry software, and ENVI and ArcGIS 10.3 image processing software. An algorithm designed in the R programming language was used to estimate the biomass. The results show three life zones, three climatic zones, four ecosystems, and four plant communities with eleven dominant species. The condition of the grasslands evaluated was regular with a tendency to poor and a carrying capacity of 0.3 UV.ha-1.year-1; 0.83 UO.ha-1.year-1 and 1.11UA.ha-1.year-1. The use of remote sensors made it possible to classify grasslands quickly and efficiently.