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A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data
- Source :
- Remote Sensing of Environment. 213:195-205
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 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.
- Subjects :
- Forest inventory
010504 meteorology & atmospheric sciences
Mean squared error
Laser scanning
0211 other engineering and technologies
Soil Science
Geology
Terrain
02 engineering and technology
Stock prediction
01 natural sciences
Photogrammetry
Computers in Earth Sciences
Digital elevation model
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Subjects
Details
- ISSN :
- 00344257
- Volume :
- 213
- Database :
- OpenAIRE
- Journal :
- Remote Sensing of Environment
- Accession number :
- edsair.doi...........7a47565d74def5221e8a6ad317185004