A recent paper published by Forestry and featuring Dr. Hooman Latifi from Dept. of Remote Sensing presents novel results of estimating most relevant forest inventory attributes from very high resolution stereoscopic satellite imagery. The paper couples a systematic review of the state-of-the-art in photogrammetry-basad forest attribute estimation, a case study in southwestern Germany and an expert survey on the potenaitls and pitfalls of remote sensing-assisted forest inventory, in which internationally renowned peers from all over the world took part.
Area-based predictions of tree species, aboveground biomass and tree density based on WorldView-2 stereo data
The modeling/classification results were comparable to earlier studies in the same test site, obtained with more expensive airborne acquisitions. All in all, the study concludes that the simpler acquisition, reasonable price and the comparably easy data format and handling of VHRSI compared with other sensor types justifies further research on the application of these data for supporting operational forest inventories. The fulltext version of the paper together with the supplementary material can be found here.
Fassnacht, F.E., Mangold, D., Schäfer, J., Immitzer, M., Kattenborn, T., Koch, B and Latifi, H. 2017. Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications? Forestry, DOI: 10.1093/forestry/cpx014
A new review paper featuring Dr. Hooman Latifi as the second author has been recently published by Remote Sensing of Environment. The paper is a product of more than 1 year intensive teamwork of a group of well-known international scholars, and focuses on a meta analysis of reviewed studies on tree species classification by means of remotely sensed data. The authors finally recommend that future research should focus rather on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important -why they do not work in other cases. This might require more complex field acquisitions than those typically used in the reviewed studies.
The paper also recommends reducing the number of purely data-driven studies and algorithm benchmarking studies as these studies are of limited value, especially if the experimental design is limited, e.g. the tree population is not representative and only a few sensors or acquisition settings are simultaneously investigated.
World map displaying where the 116 selected studies have been conducted
Further information on this review paper can be found here.
Fassnacht, F.E., Latifi, H., Ghosh, A., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L., Straub, C. 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment 186: 64-87.
In this M.Sc thesis, a set of multi-seasonal satellite imagery from RapidEye will be applied to develop algorithms and improve the existing ones in tree species mapping across a portion of mixed forest stands in northern Black Forests in the state of Baden-Württemberg in Germany. The focus of the research will be on small private forest patches, for which conducting regular forest inventories has always been a major challenge. For most private forests (i.e. more than 30% of the forest area in Baden-Württemberg) there is a shortage of accurate and up-to-date forest inventory data.
To this aim, a set of multi seasonal RapidEye satellite imagery with a spatial resolution of about 5m are obtained from the Black Bridge company. The data cover 4 seasonally different times of the year, including March 2014, May 2013, July 2014, September 2013, and October 2012. Based on the fact that the tree species show phonological (and in turn spectral) differences during the year, these comprehensive dataset will be used here to map different tree species and other important forest characteristics, such as needle fall, storm effects or effects caused by bark beetles.
Both the results of the single tree-based aerial photo interpretation as well as those from a small area in which all pine trees have been mapped will be used as reference data. In addition, validation data can also be achieved from a number of aerial photo-interpreted stands dominated by Scots pine. The data for the neighboring public forests can also be accessed via the local forest enterprises.
The work will be carried out in close cooperation between University of Würzburg and the LandConsult company (Dr Markus Weidenbach). A Visit to the study area in the northern Black Forest is supported by the LandConsult company.
Moreover, Dr. Piotr Tompalski from the University of British Columbia (Canada) will give advices as an external supervisor.
For questions on this topic, please contact Dr. Hooman Latifi at the Dept. of remote sensing of the University of Würzburg (firstname.lastname@example.org). A more comprehensive description of this topic can be found at following address: