
Remote Sensing
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Head: Christian Ginzler
We develop and apply comprehensive and robust methods to extract and classify natural objects from continuous and discrete raster datasets. Relevant features are acquired to describe changes in landscape and land resources at different levels using image data. Mathematical-statistical methods are adopted for automatic detection and description of image objects. Thus we contribute concepts, methods and data to describe/detect area wide changes and processes in the resources of landscape.
Tasks and main research:
- Development and application of methods to extract natural objects from continuous data.
- Development of methods for a comprehensive description of natural and anthropogenetic boundaries in continuous pattern (e.g. map signatures, vegetation transition, forest borders).
- Development and application of methods to extract 3D-information from remotely sensed data for description of natural structures and changes. The main focus lies on wood and its embedding/interaction within/with the landscape.
- Conception and development of data acquisition based on high resolution remote sensing data.
- Conception, development and maintenance of the software interface in area wide data acquisition using airborne remote sensing data.
- Scientific expert advice and support in the fields of photogrammetry and survey at WSL. Maintenance, enhancements and future development in these specific fields.
- Adequate presentation of scientific results on national level and in noted international journals and at international congresses/workshops/symposia.
About our research:
FURTHER INFORMATION
Publications
Haeler, E.; Bergamini, A.; Blaser, S.; Ginzler, C.; Hindenlang, K.; Keller, C.; Kiebacher, T.; Kormann, U.G.; Scheidegger, C.; Schmidt, R.; Stillhard, J.; Szallies, A.; Pellissier, L.; Lachat, T., 2021: Saproxylic species are linked to the amount and isolation of dead wood across spatial scales in a beech forest. Landscape Ecology, 36: 89-104. doi: 10.1007/s10980-020-01115-4
Kükenbrink, D.; Schneider, F.D.; Schmid, B.; Gastellu-Etchegorry, J.P.; Schaepman, M.E.; Morsdorf, F., 2021: Modelling of three-dimensional, diurnal light extinction in two contrasting forests. Agricultural and Forest Meteorology, 296: 108230 (13 pp.). doi: 10.1016/j.agrformet.2020.108230
Eberhard, L.A.; Sirguey, P.; Miller, A.; Marty, M.; Schindler, K.; Stoffel, A.; Bühler, Y., 2021: Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping. Cryosphere, 15, 1: 69-94. doi: 10.5194/tc-15-69-2021
Pazur, R.; Prishchepov, A.V.; Myachina, K.; Verburg, P.H.; Levykin, S.; Ponkina, E.V.; Kazachokov, G.; Yakovlev, I.; Akhmetov, R.; Rogova, N.; Bürgi, M., 2020: Restoring steppe landscapes: patterns, drivers and implications in Russia’s steppes. Landscape Ecology, doi: 10.1007/s10980-020-01174-7
Ludwig, M.; Runge, C.M.; Friess, N.; Koch, T.L.; Richter, S.; Seyfried, S.; Wraase, L.; Lobo, A.; Sebastià, M.; Reudenbach, C.; Nauss, T., 2020: Quality assessment of photogrammetric methods - a workflow for reproducible UAS orthomosaics. Remote Sensing, 12, 22: 3831 (18 pp.). doi: 10.3390/rs12223831
Malkoc, E.; Waser, L.T., 2020: New opportunities for highly automated countrywide assessment of trees outside forests in Switzerland. In: 2020: EGU2020. EGU general assembly 2020, Göttingen, Germany. 4752 (2 pp.). doi: 10.5194/egusphere-egu2020-4752
Ginzler, C.; Marty, M.; Waser, L.T., 2020: Countrywide surface models from historical panchromatic and true color stereo imagery – a retrospective analysis of forest structures in Switzerland. In: 2020: EGU General Assembly 2020, Göttingen, Germany. 12741 (2 pp.).
Brun, P.; Psomas, A.; Ginzler, C.; Thuiller, W.; Zappa, M.; Zimmermann, N.E., 2020: Large‐scale early‐wilting response of Central European forests to the 2018 extreme drought. Global Change Biology, 26: 7021-7035. doi: 10.1111/gcb.15360
Dehecq, A.; Gardner, A.S.; Alexandrov, O.; McMichael, S.; Hugonnet, R.; Shean, D.; Marty, M., 2020: Automated processing of declassified KH-9 Hexagon satellite images for global elevation change analysis since the 1970s. Frontiers in Earth Science, 8: 566802 (21 pp.). doi: 10.3389/feart.2020.566802
Abegg, M.; Boesch, R.; Schaepman, M.E.; Morsdorf, F., 2020: Impact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests. IEEE Transactions on Geoscience and Remote Sensing, 1-15. doi: 10.1109/TGRS.2020.3037763
Demirbaş Çağlayan, S.; Leloglu, U.M.; Ginzler, C.; Psomas, A.; Zeydanlı, U.S.; Bilgin, C.C.; Waser, L.T., 2020: Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery. Geocarto International, doi: 10.1080/10106049.2020.1783581
Erbach, A.; Weber, D., 2020: Waldmonitoring mit Satelliten: von der Forschung in die Praxis. Bündnerwald, 73, 5: 32-35.
Zweifel, R.; Ginzler, C.; Psomas, A.; Braun, S.; Walthert, L.; Etzold, S., 2020: Baumwasserdefizite erreichten im Sommer 2018 Höchstwerte - war das aus dem All erkennbar?. Schweizerische Zeitschrift für Forstwesen, 172, 5: 302-305.
Boch, S.; Bedolla, A.; Ecker, K.T.; Ginzler, C.; Graf, U.; Küchler, H.; Küchler, M.; Moser, T.; Holderegger, R.; Bergamini, A., 2020: Grünlandqualität verschlechtert sich besonders in hohen Lagen - Ein Früherkennungssystem kann helfen. Anliegen Natur, 42, 2: 111-114.
Pazúr, R.; Lieskovský, J.; Bürgi, M.; Müller, D.; Lieskovský, T.; Zhang, Z.; Prischchepov, A.V., 2020: Abandonment and recultivation of agricultural lands in Slovakia - patterns and determinants from the past to the future. Land, 9, 9: 316 (22 pp.). doi: 10.3390/land9090316
Mazzotti, G.; Essery, R.; Webster, C.; Malle, J.; Jonas, T., 2020: Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. Water Resources Research, 56, 9: e2020WR027572 (25 pp.). doi: 10.1029/2020WR027572
Baltensweiler, A.; Brun, P.; Pranga, J.; Psomas, A.; Zimmermann, N.E.; Ginzler, C., 2020: Räumliche Analyse von Trockenheitssymptomen im Schweizer Wald mit Sentinel-2-Satellitendaten. Schweizerische Zeitschrift für Forstwesen, 171, 5: 298-301. doi: 10.3188/szf.2020.0298
Descombes, P.; Walthert, L.; Baltensweiler, A.; Meuli, R.G.; Karger, D.N.; Ginzler, C.; Zurell, D.; Zimmermann, N.E., 2020: Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography, 43, 10: 1448-1463. doi: 10.1111/ecog.05117
Giuliani, G.; Peduzzi, P.; Chatenoux, B.; Richard, J.; Poussin, C.; Schaepman, M.; Small, D.; Steinmeier, C.; Psomas, A.; Ginzler, C., 2020: The Swiss Data Cube: earth observations for monitoring Switzerland's environment in space and time. In: Nativi, S.; Wang, C.; Landgraf, G.; Liberti, M.A.; Mazzetti, P.; Mohamed-Ghouse, Z.S. (eds), 2020: International Symposium on Digital Earth. 11th international symposium on digital earth, ISDE 2019, Florence. 012021 (2 pp.). doi: 10.1088/1755-1315/509/1/012021
Van 't Veen, H.; Chalmandrier, L.; Sandau, N.; Nobis, M.P.; Descombes, P.; Psomas, A.; Hautier, Y.; Pellissier, L., 2020: A landscape-scale assessment of the relationship between grassland functioning, community diversity, and functional traits. Ecology and Evolution, 10, 18: 9906-9919. doi: 10.1002/ece3.6650
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Staff
Remote Sensing
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