Keywords
vegetal cover
supervised classification
CobCal
grassland
cattle
biomass
annual evaluation
productivity
vegetables modelo de predicción
cobertura vegetal
clasificación supervisada
CobCal
pastizales
ganado
biomasa
evaluación anual
productividad
vegetales
How to Cite
Abstract
The acquisition of high-resolution images by drones and their subsequent processing provides valuable information on biophysical variables of grassland vegetation. The objective of this study was to generate a prediction model of the annual productivity dynamics of a semi-arid grassland in northern Mexico through the estimated vegetation cover in digital photographs obtained by drone. A permanent sampling system was designed in the La Cieneguilla cattle ranch. The study variables measured were the direct cut biomass production as the dependent variable and the vegetation cover estimated in digital images acquired by drone as the independent variable. The number of samples collected for both variables was 640 during the year 2020. With 50% of the data, the prediction model was generated and with the other 50% of the data, the validation model, the adjusted regression models were the form Y = β0 + β1X + β2X2 with adjusted R2 = 71.64% and 69.90% respectively. This proposed methodology offers a non-destructive and accurate means for annual monitoring and evaluation of grasslands in dry areas.
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