dc.contributor.advisor | Kleinn, Christoph Prof. Dr. | de |
dc.contributor.author | Vega-Araya, Mauricio | de |
dc.date.accessioned | 2012-11-23T15:47:42Z | de |
dc.date.accessioned | 2013-01-18T11:00:59Z | de |
dc.date.available | 2013-01-30T23:50:10Z | de |
dc.date.issued | 2012-11-23 | de |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-000D-F05E-A | de |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-2359 | |
dc.description.abstract | Die Zielsetzung dieser Forschungsarbeit
war die Beurteilung zweier sich noch in der Entwicklung befindenden
Technologien der Fernerkundung, hyperspektrale und Synthetic
Aperture Radar (SAR) Sensoren, im Rahmen einer untersuchenden
Datenanalyse von Landbedeckungen im Süden Costa Ricas. Die
hyperspektralen Daten enthalten Informationen in vielen schmalen
spektralen Bändern im optischen Bereich, die Aufschluss über die
biochemischen und strukturellen Eigenschaften von Vegetation geben
können. Dagegen können SAR-Sensoren, als aktive Systeme, Wolken
durchdringen und stellen somit ein viel versprechendes Werkzeug für
das Monitoring von | de |
dc.format.mimetype | application/pdf | de |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | de |
dc.title | Applications of Hyper-spectral and Radar Remote Sensing analysis: a case study of forest landscapes in Costa Rica | de |
dc.type | doctoralThesis | de |
dc.title.translated | Anwendungen und Untersuchungen der Hyperspektralen und Radar- Fernerkundung: eine Fallstudie für Waldlandschaften in Costa Rica | de |
dc.contributor.referee | Kuntz, Steffen Prof. Dr. | de |
dc.date.examination | 2012-11-12 | de |
dc.subject.dnb | 550 Geowissenschaften | de |
dc.subject.gok | YA 000 | de |
dc.description.abstracteng | The main objective this research was to
assess two types of emerging remote sensing technology,
hyper-spectral and SAR sensors, for an exploratory data analysis of
land covers in the south of Costa Rica. Hyper-spectral data contain
information in several narrow spectral bands in the optical domain,
which give information on the biochemical and structural properties
of vegetation, while the SAR data, as an active system, can
penetrate the clouds making it a promising tool for ecosystem
monitoring. The main hypothesis was that these two datasets would
permit greater understanding of the spectral confusion between
different land covers. From the hyper-spectral point of view, this
knowledge could help to select and derive spectral signatures which
serve as training data sets in supervised species classification in
the optical domain. In the microwave domain, fusion and derived
bands increase the separability and permit greater
forest/non-forest classification accuracy in non-flat terrains. The
hyper-spectral information is based on two information sources. The
first comes from two scenes of the space-borne Earth Observing-1
mission with the Hyperion sensor and two scenes of the airborne
hyper-spectral sensor HyMap. The second hyper-spectral data source
was acquired from the field-based hyper-spectral clip-prove system.
Furthermore, the microwave information corresponds to the
TerraSAR-X HH and VV polarized images. Working in different land
covers including Gmelina arborea plantations in the south of Costa
Rica, individual Regions of Interest were manually digitized with
reference to high spatial resolution aerial photographs datasets.
Principal components of hyper-spectral space and airborne data were
derived to perform a classification using two different approaches
of Hierarchical Cluster Analysis. Spectra from field-based
hyper-spectral clip-prove data was acquired from Gmelina arborea
leaves in three plantations of 6, 8 and 18 years. Other reference
spectra of land covers were also measured. With seven TerraSAR-X
polarized images, principal component analysis as fusion technique
and derived bands ratios were generated in order to evaluate the
availability of reducing the speckle noise in non flat terrains to
classify forest in the south of Costa Rica. The highest scene based
spectra variability was in the Near Infra-Red portion of the
electromagnetic spectrum. Hierarchical Cluster Analysis applied to
the hyper-spectral scenes showed that cluster solutions of the PCs
spectra from the two sensors present different separability
solutions. The clusters solutions were subject to systematic
differences; only one scene of EO-1 Hyperion and one of HyMap PCs
spectra did not present spectral confusion among Gmelina arborea,
palm oil and the forest. That indicates that the same sensor under
different conditions will give different spectra and different
cluster results. These results suggested that hyper-spectral
imagery need not to be acquired at a very high spatial resolution
to provide adequate discrimination of land covers. Furthermore,
spectra collection and analysis are needed to acquire time series
spectral signatures. The best Hierarchical Cluster Analysis
classification was with the Approximately Unbiased p-values which
permit the identification of clusters that exist at a predefined
level of significance. Canopy phenology, a property related to the
different acquisition times and atmospheric conditions, was
important in clustering land covers. Regarding the field based
spectra, there was spectral confusion in the majority of 18 years
of leaves of Gmelina arborea and mangrove. Also, 6 spectra of this
age were not clustered at all. There was spectral confusion between
the spectra of Gmelina arborea leaves of 6, 8 and 18 years.
However, the reflectance of field based spectrometers should be
interpreted with caution. Sampling is a key factor as well as a
challenge in leaf spectral analysis. Hyper-spectral and Synthetic
Aperture Radar data was useful for land cover discrimination, and
it did provide an unprecedented potential to classify forest and
non-forest in tropical environments and avoid spectral confusion
with highly related land covers. However, all the associated
variability of acquisition parameters has be to taken into account
in order to provide acceptable levels of accuracy. | de |
dc.contributor.coReferee | Knohl, Alexander Prof. Dr. | de |
dc.subject.topic | Forest Sciences and Forest Ecology | de |
dc.subject.ger | Fernerkundung | de |
dc.subject.ger | SAR-Sensoren | de |
dc.subject.ger | Hyperspektralen | de |
dc.subject.ger | Waldlandschaften | de |
dc.subject.ger | Costa Rica | de |
dc.subject.eng | Remote Sensing | de |
dc.subject.eng | Synthetic Aperture Radar | de |
dc.subject.eng | Hyper-spectral | de |
dc.subject.eng | forest landscapes | de |
dc.subject.eng | Costa Rica | de |
dc.subject.bk | 38 Geowissenschaften | de |
dc.identifier.urn | urn:nbn:de:gbv:7-webdoc-3807-2 | de |
dc.identifier.purl | webdoc-3807 | de |
dc.affiliation.institute | Fakultät für Forstwissenschaften und Waldökologie | de |
dc.identifier.ppn | 733962629 | de |