In a recently-published paper in Forestry featuring Hooman Latifi, Steven Hill and Stefan Dech from the Dept. of Remote Sensing, further advancements have been reported in developing unbiased statistical models for area-based estimation of forest understorey layers using LiDAR point cloud information. The study leveraged an original high-density LiDAR point cloud, which was further processed to simulate two lower-density datasets by applying a thining approach. The data were then combnined with three statistical modeling approaches to estimate the proportions of shrub, herb and moss layers in temperate forest stands in southeastern Germany.
Despite the differences between our simulated data and the real-world LiDAR point clouds
of different point densities, the results of this study are thought to mostly reflect how LiDAR and forest habitat data can be combined for deriving ecologically relevant information on temperate forest understorey vegetation layers. This, in turn, increases the applicability of prediction results for overarching aims such as forest and wildlife management.
Further informaiton on the published paper can be retrieved here.
Latifi, H., Hill, S., Schumann, B., Heurich, M., Dech, S. 2017. Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data. Forestry, DOI:10.1093/forestry/cpw066
Our article “Mapping Bushmeat Hunting Pressure in Central Africa” got selected as editor’s choice:
There is no doubt that hunting poses a major threat to the persistence of wildlife throught the tropics. Ziegler and colleagues have done a monumental job of summarizing and analyzing data on the hunting of mammals over the course of almost 20 years in Cameroon, Central African Republic, Democratic Republic of Congo, Equatorial Guinea, Gabon, and Republic of Congo. Coupled with data on environmental variables and anthropogenic pressure, protected areas, and population density they use these data to map hunting pressure across the Congo Basin, and show – among other things – that many protected areas are located in high-risk areas. Their threat map provides a means of identifying areas where hunting is likely to have the greatest impact and to guide large-scale conservation planning initiatives for central Africa. It’s blend of synthesis, innovative analysis, and impact makes it an important study and an easy selection as for the Editor’s Choice. read more….
Editor’s Choice Article for Biotropica 48(3): Stefan Ziegler, John E. Fa, Christian Wohlfart, Bruno Streit, Stefanie Jacob and Martin Wegmann (2016), Mapping Bushmeat Hunting Pressure in Central Africa<http://onlinelibrary.wiley.com/doi/10.1111/btp.12286/abstract>, 48: 405–412.
A new publication recently appeared by Forestry opens some new outlook in leveraging airborne LiDAR derivatives for monitoring the vertical structure of temperate mixed forest stands. Led by Dr. Hooman Latifi from University of Würzburg, the study focuses on associating aerially-mapped habitat characteristics with 3D metrics extracted from fullwave LiDAR data to model canopy density across multiple stand stories.Whereas the
majority of methods applied so far typically concentrate on the structure of the overstorey, the main focus here is on the understorey layers of stands, which are of particular importance for wildlife and forest management applications, especially in protected areas.
LiDAR metrics and information on forest habitat types were combined via regression models to investigate LiDAR metrics that are significantly correlated with vegetation density. The top canopy and the herbal layer showed strong correlations with the applied LiDAR metrics. Moreover, the results suggest that the relationship between LiDAR predictors and vegetation density depends on the forest type. In conclusion, this study highlights the value of the LiDAR metrics for characterizing the structural properties of lower forest layers, with direct and indorect implications for wildlife and forest management.
The published version of this article can be retrieved through the following link.
Latifi, H., Heurich, M., Hartig, F., Müller, J., Krzystek, P., Jehl, H., Dech, S. 2015. Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data. Forestry, DOI.10.1093/forestry/cpv032
As previously announced, the biometrics workshop will take place from 7 to 9 of October 2015 at the Hubland Campus of the University of Würzburg.
The information on Workshop as well as on abstract submission can be addiitonally found in
In order to register and submit the contributions, please send an email until 05 of July 2015 to:
The registration email should contain the following information:
- One MS-Word (.docx) file of the contribution abstract (Max. 300 words and up to 5 keywords)
- Name and Surname of the presenting author
- Affiliation of the presenting author (Institution, City, Country)
- Contact details (Email, Phone number and P.O Box)
- Indication of whether or not a participation certificate is required by the author
The workshop is organized by the working groups “Ecology and Environment”, “Bayesian Methods” and “Spatial Statistics” of the International Biometric Society- German Region together with the section “Forest Biometry and computer science” at the German Association of Forest Research Organizations (DVFFA).
In addition to the presented contributions, Lauri Mehtätalo (Universities of Eastern Finland and Helsinki) will give a tutorial on “Mixed-effects mdoels in theory and practice”.
Best regards and hope to see you in Würzburg in October
The organizing team
The review article lead by Yvonne Walz is published online first. Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions.
We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised.
We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is – in principle – far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from.
Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.
Yvonne Walz, Martin Wegmann, Stefan Dech, Giovanna Raso and Jürg Utzinger (2015) Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook. Parasites & Vector http://www.parasitesandvectors.com/content/8/1/163