We present a novel approach for the prediction of forest growing stock volume based on explanatory variables from unmanned aerial vehicle (UAV) image photogrammetry without relying on the availability of a digital terrain model. This DTM-independent approach was developed to avoid the need for a detailed DTM, which is instead required in traditional photogrammetry to obtain relative heights above the terrain. The method, following an Area Based Approach (ABA), was tested in a boreal forest on a flat area in Norway and in a temperate mixed forest in a mountain steep terrain in Italy, on the basis of aerial images acquired with a SenseFly eBee Ag fixed-wing UAV. The plot level predictive performance of the models based on the DTM-independent metrics were evaluated against the results based on two more traditional approaches based on: (i) metrics from UAV photogrammetric data normalized using a DTM from airborne laser scanning (ALS), and (ii) metrics from ALS data. Percent root mean square error of predictions against measured values (RMSE%) was used for quantifying the performance of the different tests. Results revealed that the DTM-independent approach produced comparable results with both the traditional photogrammetric and ALS methods (the RMSE% ranged between 15.9% and 16.7% in Italy, and between 16.3% and 17.9% in Norway). Our results demonstrated that UAV photogrammetry can be used effectively for predicting forest growing stock volume even when high-resolution DTMs are not available, hence increasing the potentiality of UAVs in forest monitoring and inventory.
A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data / Giannetti, Francesca*; Chirici, Gherardo; Gobakken, Terje; Næsset, Erik; Travaglini, Davide; Puliti, Stefano. - In: REMOTE SENSING OF ENVIRONMENT. - ISSN 0034-4257. - ELETTRONICO. - 213:(2018), pp. 195-205. [10.1016/j.rse.2018.05.016]
A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data
Giannetti, Francesca
;Chirici, Gherardo;Travaglini, Davide;
2018
Abstract
We present a novel approach for the prediction of forest growing stock volume based on explanatory variables from unmanned aerial vehicle (UAV) image photogrammetry without relying on the availability of a digital terrain model. This DTM-independent approach was developed to avoid the need for a detailed DTM, which is instead required in traditional photogrammetry to obtain relative heights above the terrain. The method, following an Area Based Approach (ABA), was tested in a boreal forest on a flat area in Norway and in a temperate mixed forest in a mountain steep terrain in Italy, on the basis of aerial images acquired with a SenseFly eBee Ag fixed-wing UAV. The plot level predictive performance of the models based on the DTM-independent metrics were evaluated against the results based on two more traditional approaches based on: (i) metrics from UAV photogrammetric data normalized using a DTM from airborne laser scanning (ALS), and (ii) metrics from ALS data. Percent root mean square error of predictions against measured values (RMSE%) was used for quantifying the performance of the different tests. Results revealed that the DTM-independent approach produced comparable results with both the traditional photogrammetric and ALS methods (the RMSE% ranged between 15.9% and 16.7% in Italy, and between 16.3% and 17.9% in Norway). Our results demonstrated that UAV photogrammetry can be used effectively for predicting forest growing stock volume even when high-resolution DTMs are not available, hence increasing the potentiality of UAVs in forest monitoring and inventory.File | Dimensione | Formato | |
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