Estimating biomass in grasslands through traditional methods and the use of drones in the State of Chihuahua, Mexico

Abstract

Objective: To evaluate three biomass estimation methods (Unmanned Aerial Vehicle (UAV or drone), ceptometer, and canopy height), comparing them to the quadrant method in an arborescent tufted grassland in the state of Chihuahua.Methodology: The study was conducted in Teseachi, Namiquipa, in october 2020. We located thirty random points. The first biomass estimation method used was UAV. Once the drone f lights were completed, the quadrant was placed and the coordinates were determined. We carried out nine readings using a ceptometer and obtained an average. Subsequently, we measured the average canopy height. Finally, all forage within the quadrant was cut at ground level and packed for laboratory analysis. The Agisoft Metashape software was used to process the SfM of the aerial images, using nine sampling points, applying the NGBDI vegetation index, and calculating the average pixels of a 33 m moving window. A simple linear regression model was used to analyze the data with the R Project software, version 4.0.3.Results: The simple linear regression model showed an R2 of 0.62 (p0.01), 0.55 (p0.001), and 0.48 (p0.001), for UAV, ceptometer, and canopy height, respectively.Study Limitations: There were no limitations for this report.Conclusions: Data obtained with UAVs can generate predictive biomass maps with acceptable accuracy levels. The ceptometer leaf area index is a reliable method to estimate forage yield. However, using the canopy height method is not advisable to estimate forage yield, since its correlation is weak

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