Wall-to-wall Tree Type Mapping from Countrywide Remote Sensing Surveys

Precise and regularly updated information on the state, change and distribution of tree types (broadleaved and coniferous tree) is essential for forest related studies. Forest inventories provide a precise statistical estimation of forest composition for large areas, but spatially explicit information beyond inventory domains is missing. Thus, maps of broadleaved and coniferous trees are an important source of information for assessing woodland resources and an additional valuable output of National Forest Inventories (NFI). The main objective of this project is to provide up-to-date high-resolution wall-to-wall tree type maps, that consist of the two classes broadleaved and coniferous, and that are continuously optimized for entire Switzerland. The first data set consists of ADS80 aerial imagery. The follow-up product is currently being optimized using Sentinel satellite data.

Project details

Project duration

2016 - 2019

Project lead

Dr. Lars Waser

Remote Sensing

Land Change Science

lars.waser(at)wslto make life hard for spam bots.ch

+41 44 739 22 92+41 44 739 22 92

Birmensdorf

Deputy

Christian Ginzler

Project staff

Financing

FURTHER INFORMATION

Publications

Waser, L.T.; Ginzler, C.; Rehush, N., 2017: Wall-to-wall tree type mapping from countrywide airborne remote sensing surveys. Remote Sensing, 9, 8: 766 (24 pp.). doi: 10.3390/rs9080766

Waser, L.T., 2017: Baumartenerkennung mit Luftbildern. Neueste Auswerteverfahren für digitale Luftbilder ermöglichen Baumartenkarten sehr großer Waldgebiete. LWF aktuell, 24, 115: 19-21.

Ginzler, C.; Waser, L.T., 2017: Entwicklungen im Bereich der Fernerkundung für forstliche Anwendungen. Schweizerische Zeitschrift für Forstwesen, 168, 3: 118-126. doi: 10.3188/szf.2017.0118

Fassnacht, F.E.; Latifi, H.; Stereńczak, K.; Modzelewska, A.; Lefsky, M.; Waser, L.T.; Straub, C.; Ghosh, A., 2016: Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186: 64-87. doi: 10.1016/j.rse.2016.08.013

Waser, L.T.; Straub, C., 2015: Baumartenerkennung mit optischen Fernerkundungsdaten – Stand und Perspektiven. In: Wallner, A.; Seitz, R. (eds), 2015: Der gepixelte Wald – Reloaded. Der gepixelte Wald – Reloaded, Freising-Weihenstephan, March 17-18, 2015. 61-71.

Waser, L.T.; Küchler, M.; Jütte, K.; Stampfer, T., 2014: Evaluating the potential of WorldView-2 data to classify tree species and different levels of ash mortality. Remote Sensing, 6, 5: 4515-4545. doi: 10.3390/rs6054515

Waser, L.T., 2013: Stand und Perspektiven einer landesweiten Baumartenklassifikation mit digitalen Luftbildern. Schweizerische Zeitschrift für Forstwesen, 164, 4: 95-103. doi: 10.3188/szf.2013.0095

Engler, R.; Waser, L.T.; Zimmermann, N.E.; Schaub, M.; Berdos, S.; Ginzler, C.; Psomas, A., 2013: Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution. Forest Ecology and Management, 310: 64-73. doi: 10.1016/j.foreco.2013.07.059

Waser, L.T.; Ginzler, C.; Kuechler, M.; Baltsavias, E.; Hurni, L., 2011: Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sensing of Environment, 115, 1: 76-85. doi: 10.1016/j.rse.2010.08.006

Waser, L.T.; Eisenbeiss, H.; Kuechler, M.; Baltsavias, E., 2008: Potential and limits of airborne remote sensing data for extraction of fragctional canopy cover and forest stands and detection of tree species. In: 2008: 1405-1412.