Publicaciones
Se encontró/encontraron 2 Publicaciones(s).
Gonzales-Jaramillo, V.; Fries, A.; Zeilinger, J.; Homeier, J.; Paladines, J. & Bendix, J. (2018): Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data. Remote Sensing 10, .
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DOI: 10.3390/rs10050660
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Resumen:
Resumen:
A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF)
is still complicated, due to fast-changing climate and topographic conditions, which modifies the
forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly
detectable by small field plots, especially in natural TMF due to the high tree diversity and the
inaccessibility of remote areas. Therefore, the present approach used remotely sensed data from a
Light Detection and Ranging (LiDAR) sensor in combination with field measurements to estimate
AGB accurately for a catchment in the Andes of south-eastern Ecuador. From the LiDAR data,
information about horizontal and vertical structure of the TMF could be derived and the vegetation at
tree level classified, differentiated between the prevailing forest types (ravine forest, ridge forest and
Elfin Forest). Furthermore, topographical variables (Topographic Position Index, TPI; Morphometric
Protection Index, MPI) were calculated by means of the high-resolution LiDAR data to analyse the
AGB distribution within the catchment. The field measurements included different tree parameters
of the species present in the plots, which were used to determine the local mean Wood Density
(WD) as well as the specific height-diameter relationship to calculate AGB, applying regional scale
modelling at tree level. The results confirmed that field plot measurements alone cannot capture
completely the forest structure in TMF but in combination with high resolution LiDAR data, applying
a classification at tree level, the AGB amount (Mg ha??1) and its distribution in the entire catchment
could be estimated adequately (model accuracy at tree level: R2 > 0.91). It was found that the AGB
distribution is strongly related to ridges and depressions (TPI) and to the protection of the site (MPI),
because high AGB was also detected at higher elevations (up to 196.6 Mg ha??1, above 2700 m), if the
site is situated in depressions (ravine forest) and protected by the surrounding terrain. In general,
highest AGB is stored in the protected ravine TMF parts, also at higher elevations, which could only
be detected by means of the remote sensed data in high resolution, because most of these areas are
inaccessible. Other vegetation units, present in the study catchment (pasture and subpáramo) do not
contain large AGB stocks, which underlines the importance of intact natural forest stands.
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Keywords: |
LiDAR |
AGB estimation |
Wallis, C.; Paulsch, D.; Zeilinger, J.; Silva, B.; Curatola Fernández, G.F.; Brandl, R.; Farwig, N. & Bendix, J. (2016): Contrasting performance of Lidar and optical texture models in predicting avian diversity in a tropical mountain forest. Remote sensing of environment 174, 223-232.
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DOI: 10.1016/j.rse.2015.12.019
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Resumen:
Resumen:
Ecosystems worldwide are threatened by the increasing impact of land use and climate change. To protect their diversity and functionality, spatially explicit monitoring systems are needed. In remote areas, monitoring is difficult and recurrent field surveys are costly. By using Lidar or themore cost-effective and repetitive optical satellite data, remote sensing could provide proxies for habitat structure supporting measures for the conservation of biodiversity. Here we compared the explanatory power of both, airborne Lidar and optical satellite data in modeling the spatial distribution of biodiversity of birds across a complex tropical mountain forest ecosystem in southeastern Ecuador. Weused data fromfield surveys of birds and chose three measures as proxies for different aspects of diversity: (i) Shannon diversity as a measure of ?-diversity that also includes the relative abundance of species, (ii) phylodiversity as a first proxy for functional diversity, and (iii) community composition as a proxy for combined ?- and ?-diversity.We modeled these diversity estimates using partial least-square regression of Lidar and optical texturemetrics separately and compared themodels using a leave-one-out validated R2 and rootmean square error. Bird community informationwas best predicted by both remote sensing datasets, followed by Shannon diversity and phylodiversity. Our findings reveal a high potential of optical texture metrics for predicting Shannon diversity and ameasure of community composition, but not for modeling phylodiversity.
Generalizing from the investigated tropicalmountain ecosystem,we conclude that texture information retrieved frommultispectral data of operational satellite systems could replace costly airborne laser-scanning formodeling certain aspects of biodiversity.
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Keywords: |
forest structure |
LiDAR |
QuickBird |
topographic heterogenity |
bird community |
Birds |