New publication: Further progress in model-based estimation of forest understorey by LiDAR data

New publication: Further progress in model-based estimation of forest understorey by LiDAR data

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.

Bibliography:

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

 

New Publication: Canopy structure-corrected retrieval of foliar nitrogen by hyperspectral data

New Publication: Canopy structure-corrected retrieval of foliar nitrogen by hyperspectral data

A new review paper has been recently published by International Journal of Applied Earth Observation and Geoinformation. The paper is amongst the outputs of a PhD thesis by Zhihui Wang from the University of Twente, and focuses on retrieval of forest canopy foliar nitrogen from hyperspectral imagery by additionally correcting for canopy structure effects. Te main research question arose from the fact that the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen, which can be corrected for by using the canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF).

 

Directional area scattering factor (DASF) calculated based on spectral invariant theory for broadleaf, needle leaf, and mixed forest

The results of the research conducted across the Bavarian Forest National Park confirm that %N can be retrieved using the scattering coefficient aftercorrecting for canopy structural effect. With the aid of  upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling ofecosystem processes as well as ecosystem-climate feedbacks.

Further information on this paper can be found here.

Source:

Wang, Z., Skidmore, A. K., Wang, T., Darvishzadeh, R., Heiden, U., Heurich, M., Latifi, H., Hearne, J. 2016. Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects. International Journal of Applied Earth Observation and Geoinformation 54(2017): 84-94.

 

New publication: Estimating over- and understorey canopy density by LiDAR data

New publication: Estimating over- and understorey canopy density by LiDAR data

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

LASmoons license granted for a M.Sc candidate

LASmoons license granted for a M.Sc candidate

Recently, the M.Sc student Bastian Schumann applied for a 3-month sull license of LAStools to be applied in the course of his M.Sc thesis “Explaining over- and understory canopy coved by leaf-off and leaf-on LiDAR data”. LAStools consists of highly efficient, batch-scriptable, multicore command line tools to classify, tile, convert, filter, raster, triangulate, contour, clip, and polygonize LiDAR data. By submitting an appllication supported by a proposal,  limited number of underfunded academics who have no budget for a full academic LAStools license qualify for a few LASmoons of full LAStools functionality. These complimentary licenses typically last three full months.

Example of LiDAR flight strips over the entire Bavarian Forest National Park

Example of LiDAR flight strips over the entire Bavarian Forest National Park

Supervised by Dr. Hooman Latifi from Dept. of remote sensing in Würzburg, Bastian Schumann will extract statistical and density metrics from two sets of leaf-off and leaf-on LiDAR data to make an accurate and unbiased estimation of canopy density in tree, shrub and herbal layers within the Bavarian Forest National Park. LAStools will be used here to initially process the raw point cloud data and create DTMs, DSMs and CHMs and to derive LiDAR metrics from normalized LiDAR points. The full version of LAStools is needed to assure timely processing of the vast amount of raw data.The results of this study will be used as a benchmark to compare with those previously achieved by Latifi et al. (2015) using leaf-on data across the same study area.

Further informaiton on this project can be found in the LASmoons blog which will also cover further updates on the opurtcomes of this work.