Brazilian Savanna Height Estimation Using UAV Photogrammetry

Dutra, Andeise Cerqueira; Breunig, Fábio Marcelo; Cassol, Henrique Luís Godinho; Oliveira, Marceli Terra de; Hoffmann, Tânia Beatriz; Arai, Egídio; Duarte, Valdete; Shimabukuro, Yosio Edemir

doi:10.1109/igarss47720.2021.9553339

Cited by 1 publication

(1 citation statement)

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“…Especially in the case of the Cerrado, the use of new remote sensing technologies in the estimation and monitoring of carbon and biomass stocks is still quite scarce [25,26]. Although new studies have been developed in recent years, along with the application of photogrammetry techniques using passive sensors in savannas [27], from the perspective of the Cerrado, these kinds of studies are at a preliminary stage [28,29].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Aboveground Biomass Stock in Tropical Savannas Using Photogrammetric Imaging

Queiroz

d’Oliveira

Rezende

et al. 2023

Drones

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The use of photogrammetry technology for aboveground biomass (AGB) stock estimation in tropical savannas is a challenging task and is still at a preliminary stage. This work aimed to use metrics derived from point clouds, constructed using photogrammetric imaging obtained by an RGB camera on board a remotely piloted aircraft (RPA), to generate a model for estimating AGB stock for the shrubby-woody stratum in savanna areas of Central Brazil (Cerrado). AGB stock was estimated using forest inventory data and an allometric equation. The photogrammetric digital terrain model (DTM) was validated with altimetric field data, demonstrating that the passive sensor can identify topographic variations in sites with discontinuous canopies. The inventory estimated an average AGB of 18.3 (±13.3) Mg ha−1 at the three sampled sites. The AGB model selected was composed of metrics used for height at the 10th and 95th percentile, with an adjusted R2 of 93% and a relative root mean squared error (RMSE) of 16%. AGB distribution maps were generated from the spatialization of the metrics selected for the model, optimizing the visualization and our understanding of the spatial distribution of forest AGB. The study represents a step forward in mapping biomass and carbon stocks in tropical savannas using low-cost remote sensing platforms.

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