Download
Cite as:
Gonz&aacute;lez-Jaramillo, V.; Fries, A. &amp; 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:
Individual: Andreas Fries
Contact:
Individual: Jörg Bendix
Contact:
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.
Keywords:
| Ecuador | mountain rainforest | UAV | Biomass | Drone |
Literature type specific fields:
ARTICLE
Journal: Remote Sensing
Volume: 11
Issue: 12
Page Range: 1-22
Metadata Provider:
Individual: Jörg Bendix
Contact:
Online Distribution:
Download File: http://www.lcrs.de/publications.do?citid=1744


Quick search

  • Publications:
  • Datasets: