snow-depth-mapping
1.0
Snow Depth Mapping
Mauro Marty, Yves Bühler, Christian Ginzler
Mauro Marty
Snow Depth Mapping
2019
Birmensdorf, Switzerland
EnviDat
1.0
tiff,pdf
DOI
doi:10.16904/envidat.62
https://www.envidat.ch/dataset/snow-depth-mapping
TECHNICAL CONTACT
Mauro
Marty
mauro.marty@wsl.ch
EARTH SCIENCE
CLIMATE INDICATORS
ATMOSPHERIC/OCEAN INDICATORS
environment
AERIAL IMAGERY
AVALANCHE
DSM
IMAGE MATCHING
SNOW DEPTH
SNOW HYDROLOGY
SNOW WATER EQUIVALENT
Not provided
Not provided
Not provided
2019-01-01
COMPLETE
CARTESIAN
CARTESIAN
9.836883544921875
46.763340811937375
46.68384982559895
46.8428317982758
9.707107543945312
9.966659545898438
9.818344116210938
46.78550188115742
9.766159057617188
46.75728441911433
9.707107543945312
46.78550188115742
9.774398803710938
46.8428317982758
9.871902465820312
46.81182484123776
9.966659545898438
46.71116410315922
9.906234741210938
46.68384982559895
9.836883544921875
46.763340811937375
Not provided
Public access to the data
Usage constraintes defined by the license "ODbL with Database Contents License (DbCL)", see https://opendefinition.org/licenses/odc-odbl
English
Remote Sensing
DISTRIBUTOR
WSL
Swiss Federal Institute for Forest, Snow and Landscape Research WSL
https://www.wsl.ch
DATA CENTER CONTACT
EnviDat
envidat@wsl.ch
https://www.envidat.ch/envidat_thumbnail.png
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData). For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth. The work is published in: Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015
https://www.envidat.ch/dataset/snow-depth-mapping
gcmd_dif
VERSION 10.2
2019-01-30T09:47:20.374264
2019-11-03T16:46:11.138802
2019-01-30T09:47:20.374264
2019-11-03T16:46:11.138802
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