Publikationen
Es wurden 4 Publikationen gefunden
Werner, F.A. & Homeier, J. (2024): Diverging elevational patterns of tree vs. epiphyte species density, beta diversity, and biomass in a tropical dry forest . Plants 13(18), 2555.
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DOI: 10.3390/plants13182555
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Abstract:
Abstract:
There is evidence to suggest that vascular epiphytes experience low competition for resources (light, water, and nutrients) compared to terrestrial plants. We tested the hypothesis that low resource competition may lead to higher nestedness among vascular epiphyte assemblages compared to trees. We studied the species composition and biomass of epiphytes and trees along an elevation gradient in a tropical dry forest in SW Ecuador. Both life-forms were inventoried on 25 plots of 400 m2 across five elevation levels (550–1250 m). Tree species density and total species richness increased with elevation, whereas basal area and biomass did not show significant trends. Epiphyte species density and richness both increased strongly with elevation, in parallel to biomass. Plot-level compositional changes were similarly strong for both life-forms. We attribute elevational increases in the species richness of trees and epiphytes to increasing humidity, i.e., more mesic growth conditions. We attribute the more pronounced elevational increase in epiphyte biomass, species density, and richness—the latter coupled with a higher degree of nestedness—to the greater moisture dependency of epiphytes and relatively low direct competition for resources. Our study provides a first comparison of elevational trends in epiphyte and tree diversity and biomass for a tropical dry forest.
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Keywords: |
biomass |
beta diversity |
species turnover |
Alpha diversity |
competition |
biotic interactions |
González-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 Sensing 11(12), 1-22.
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DOI: 10.3390/rs11121413
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Abstract:
Abstract:
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
ground detection.
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Keywords: |
Ecuador |
mountain rainforest |
UAV |
Biomass |
Drone |
Schmid, M. & Schreiber, K. (2015): Kiefernaufforstungen in den andinen Hochlagen Ecuadors: Waldinventur und Biomasseerhebungen an Einzelbäumen Technische Universität München, master thesis
Jiménez Fadrique, B. (2012): Effects of altitude and topography on liana biomass in southern Ecuadorian montane forests Universidad Internacional Menéndez Pelayo, master thesis