Gonzales-Jaramillo, V.; Fries, A.; Zeilinger, J.; Homeier, J.; Paladines, J. & Bendix, J. (2018): <b>Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data</b>. <i>Remote Sensing</i> <b>10</b>, .
Resource Description
Title:
Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data
FOR816dw ID:
1708
Publication Date:
2018-04-24
License and Usage Rights:
Resource Owner(s):
Individual:
Victor Gonzales-Jaramillo
Contact:
email:
webmaster <at> tropicalmountainforest.org
Individual:
Andreas Fries
Contact:
email:
andy_fries <at> gmx.de
Loja
Ecuador
Individual:
Joerg Zeilinger
Contact:
email:
jzeilinger <at> staff.uni-marburg.de
Ecuador
Individual:
Jürgen Homeier
Contact:
email:
jhomeie <at> gwdg.de
Faculty of Resource Management
University of Applied Sciences and Arts (HAWK)
37077 Göttingen
Germany
Individual:
Jhoana Paladines
Contact:
email:
webmaster <at> tropicalmountainforest.org
Individual:
Jörg Bendix
Contact:
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
Abstract:
A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF)<br/>
is still complicated, due to fast-changing climate and topographic conditions, which modifies the<br/>
forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly<br/>
detectable by small field plots, especially in natural TMF due to the high tree diversity and the<br/>
inaccessibility of remote areas. Therefore, the present approach used remotely sensed data from a<br/>
Light Detection and Ranging (LiDAR) sensor in combination with field measurements to estimate<br/>
AGB accurately for a catchment in the Andes of south-eastern Ecuador. From the LiDAR data,<br/>
information about horizontal and vertical structure of the TMF could be derived and the vegetation at<br/>
tree level classified, differentiated between the prevailing forest types (ravine forest, ridge forest and<br/>
Elfin Forest). Furthermore, topographical variables (Topographic Position Index, TPI; Morphometric<br/>
Protection Index, MPI) were calculated by means of the high-resolution LiDAR data to analyse the<br/>
AGB distribution within the catchment. The field measurements included different tree parameters<br/>
of the species present in the plots, which were used to determine the local mean Wood Density<br/>
(WD) as well as the specific height-diameter relationship to calculate AGB, applying regional scale<br/>
modelling at tree level. The results confirmed that field plot measurements alone cannot capture<br/>
completely the forest structure in TMF but in combination with high resolution LiDAR data, applying<br/>
a classification at tree level, the AGB amount (Mg ha??1) and its distribution in the entire catchment<br/>
could be estimated adequately (model accuracy at tree level: R2 > 0.91). It was found that the AGB<br/>
distribution is strongly related to ridges and depressions (TPI) and to the protection of the site (MPI),<br/>
because high AGB was also detected at higher elevations (up to 196.6 Mg ha??1, above 2700 m), if the<br/>
site is situated in depressions (ravine forest) and protected by the surrounding terrain. In general,<br/>
highest AGB is stored in the protected ravine TMF parts, also at higher elevations, which could only<br/>
be detected by means of the remote sensed data in high resolution, because most of these areas are<br/>
inaccessible. Other vegetation units, present in the study catchment (pasture and subpáramo) do not<br/>
contain large AGB stocks, which underlines the importance of intact natural forest stands.
Keywords:
| LiDAR | AGB estimation |
Literature type specific fields:
ARTICLE
Journal:
Remote Sensing
Volume:
10
Page Range:
Metadata Provider:
Individual:
Jörg Bendix
Contact:
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
