González-Jaramillo, V.; Fries, A. & Bendix, J. (2019): <b>AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)</b>. <i>Remote Sensing</i> <b>11</b>(12), 1-22.
Resource Description
Title:
AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)
FOR816dw ID:
1744
Publication Date:
2019-01-01
License and Usage Rights:
Resource Owner(s):
Individual:
Víctor González-Jaramillo
Contact:
email:
webmaster <at> tropicalmountainforest.org
Individual:
Andreas Fries
Contact:
email:
andy_fries <at> gmx.de
Loja
Ecuador
Individual:
Jörg Bendix
Contact:
email:
webmaster <at> tropicalmountainforest.org
Abstract:
The present investigation evaluates the accuracy of estimating above-ground biomass (AGB)<br/>
by means of two dierent sensors installed onboard an unmanned aerial vehicle (UAV) platform<br/>
(DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light<br/>
detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of<br/>
other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a<br/>
multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural<br/>
tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included<br/>
80 ha at lower elevations characterized by a fast-changing topography and dierent vegetation covers.<br/>
From the total area, a core study site of 24 ha was selected for AGB calculation, applying two dierent<br/>
methods. The firstmethod used the RGB images and applied the structure formotion (SfM) process to<br/>
generate point clouds for a subsequent individual tree classification. Per the classification at tree level,<br/>
tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input<br/>
parameters to calculate AGB (Mg ha 1) by means of a specific allometric equation for wet forests.<br/>
The second method used the multispectral images to calculate the normalized dierence vegetation<br/>
index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen<br/>
forests. The obtained results were validated against a previous AGB estimation for the same area<br/>
using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained<br/>
by multispectral drone imagery were less accurate due to the saturation eect in dense tropical forests,<br/>
(ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable<br/>
to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital<br/>
terrain model (DTM) in very high resolution is available because in dense natural forests the terrain<br/>
surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes<br/>
ground detection.
email:
bendix <at> staff.uni-marburg.de
Faculty of Geography
Deutschhausstraße 10
Philipps University of Marburg
Laboratory for Climatology and Remote Sensing
35032 Marburg
Germany
