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dc.contributor.author
Lang, Nico
dc.contributor.supervisor
Schindler, Konrad
dc.contributor.supervisor
Wegner, Jan Dirk
dc.contributor.supervisor
Jetz, Walter
dc.contributor.supervisor
Le Saux, Bertrand
dc.date.accessioned
2022-06-28T07:10:07Z
dc.date.available
2022-06-27T18:42:49Z
dc.date.available
2022-06-28T07:10:07Z
dc.date.issued
2022
dc.identifier.isbn
978-3-03837-013-0
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/554994
dc.identifier.doi
10.3929/ethz-b-000554994
dc.description.abstract
Mapping vegetation properties globally is crucial to understand the role of terrestrial ecosystems in the global carbon cycle. Spatially explicit, high-resolution data are needed to manage terrestrial ecosystems so that climate change can be mitigated and biodiversity loss prevented. Since no current single data source can provide such data with global coverage and high spatial resolution, new solutions must be found. This thesis aims to develop novel data-driven tools based on state-of-the-art deep learning to advance the mapping of vegetation properties, in particular canopy height, at global scale. Two ongoing space missions, namely the Copernicus Sentinel-2 mission and NASA’s GEDI LIDAR mission, deliver publicly available data that form the basis of the methods presented in this thesis. While GEDI is a key climate mission that provides sparse vegetation structure measurements at global scale (between 51.6° N & S), Sentinel-2 delivers dense optical images with global coverage, but cannot directly measure vertical vegetation structure. The presented work is a holistic approach based on gradually extended methods towards the large-scale fusion of Sentinel-2 and GEDI for the global mapping of canopy top height with high spatial resolution. Furthermore, since transparency of the modelling limitations is critical to build trust and to inform downstream applications about the reliability of the estimates, probabilistic deep learning techniques are integrated to quantify the predictive uncertainty. In a first step, a novel approach based on deep convolutional neural networks (CNNs) was developed to estimate dense canopy height maps from Sentinel-2 optical images by training with local dense reference data from airborne measurement campaigns (LIDAR and photogrammetry) in Gabon and Switzerland. By exploiting textural image features, the model achieved low error, even for canopies up to 50 m height. However, its applicability is limited to regions represented by the available training data. The launch of the spaceborne GEDI full waveform LIDAR in December 2018 promised to provide sparse reference data of vegetation structure measurements at global scale. Since interpreting on-orbit GEDI LIDAR waveforms proved to be a difficult task due to unknown noise in the data, a novel probabilistic deep learning approach was developed to retrieve canopy top height globally and quantify the predictive uncertainty from GEDI. Given these footprint-level estimates, the Sentinel-2 based canopy height mapping approach could be extended to be trained with sparse supervision. After demonstrating that this approach allows to estimate canopy top height suitable to map indicative high carbon stocks in tropical Southeast Asia, a global probabilistic model was developed to retrieve canopy top height anywhere on Earth. Ultimately, the first global, wall-to-wall canopy top height map at 10 m ground sampling distance was computed for the year 2020.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Vegetation height
en_US
dc.subject
Canopy height
en_US
dc.subject
Global mapping
en_US
dc.subject
High Carbon Stock Approach
en_US
dc.subject
Remote Sensing
en_US
dc.subject
Machine Learning
en_US
dc.subject
Deep learning
en_US
dc.subject
Computer Vision
en_US
dc.subject
image interpretation
en_US
dc.subject
Probabilistic deep learning
en_US
dc.subject
Convolutional neural network (CNN)
en_US
dc.subject
Deep ensembles
en_US
dc.subject
Uncertainty estimation
en_US
dc.subject
Sentinel-2
en_US
dc.subject
satellite data
en_US
dc.subject
GEDI
en_US
dc.subject
LIDAR
en_US
dc.subject
Carbon conservation
en_US
dc.subject
Forest conservation
en_US
dc.subject
Carbon stock
en_US
dc.subject
Biomass
en_US
dc.title
Mapping Vegetation Height — Probabilistic Deep Learning for Global Remote Sensing
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2022-06-28
ethz.size
163 p.
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
en_US
ethz.code.ddc
DDC - DDC::5 - Science::550 - Earth sciences
en_US
ethz.identifier.diss
28465
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
en_US
ethz.date.deposited
2022-06-27T18:43:12Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.identifier.internal
IGP Mitteilungen Nr. 130
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-06-28T07:10:14Z
ethz.rosetta.lastUpdated
2022-06-28T07:10:14Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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