Gonzalez-Jaramillo, V.; Fries, A. & Bendix, J. (2019): AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV). Remote Sensing11(12), 1-22.
The present investigation evaluates the accuracy of estimating above-ground biomass (AGB)
by means of two dierent sensors installed onboard an unmanned aerial vehicle (UAV) platform
(DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light
detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of
other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a
multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural
tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included
80 ha at lower elevations characterized by a fast-changing topography and dierent vegetation covers.
From the total area, a core study site of 24 ha was selected for AGB calculation, applying two dierent
methods. The ﬁrstmethod used the RGB images and applied the structure formotion (SfM) process to
generate point clouds for a subsequent individual tree classiﬁcation. Per the classiﬁcation at tree level,
tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input
parameters to calculate AGB (Mg ha 1) by means of a speciﬁc allometric equation for wet forests.
The second method used the multispectral images to calculate the normalized dierence vegetation
index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen
forests. The obtained results were validated against a previous AGB estimation for the same area
using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained
by multispectral drone imagery were less accurate due to the saturation eect in dense tropical forests,
(ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable
to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital
terrain model (DTM) in very high resolution is available because in dense natural forests the terrain
surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes