Application is now open for those interested in participating in the training course funded by the European Facility for Airborne Research (EUFAR) through EU’s 7th Framework Programme will be held at the Bavarian Forest National Park and DLR from 3th to 14th of July 2017. In this training course, the special skills required for processing the new generation of airborne hyperspectral, thermal, and LiDAR data for retrieving essential biodiversity variables in forest ecosystems will be presented. The course features Dr. Hooman Latifi from the Dept. of Remote Sensing of the University of Würzburg.
The ground data collection that will be performed during the first week of the training course at the Bavarian Forest National Park aims to provide the participants (PhD students, post-docs and university lecturers) with knowhow on tools (field spectroscopy, thermal spectrometry and terrestrial LiDAR) and measurement techniques to collect different vegetation variables. In addition, an airborne campaign with a NERC Twin Otter for the concurrent acquisitions of hyperspectral imaging data in visible, near-infrared, shortwave-infrared and longwave-infrared (thermal) wavelengths as well as LiDAR data (with full wave form component) will be organised during the training course if the weather conditions allow.
Data acquired during the training course as well as archived data will be processed and analysed in the hands-on sessions with the support of experienced users of airborne facilities and form the basis for the final scientific report. RS4forestEBV data will also be made available after the training course via the EUFAR website, accessible to all EUFAR registered members.
Furthermore, during the second week, participants will be able to attend certain sessions of the 2nd International Conference on Airborne Research for the Environment (ICARE) that will be held simultaneously on the DLR premises from 10 -13 July 2017.
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
In a newly-published paper featuring Steven Hill and Hooman Latifi from Dept. of Remote Sensing, very high resolution remote sensing (laser scanner data and aerial orthophotos) were used in a full remote sensing-based framework to study post-disturbance tree and stand development, particularly in its early seral stages.
Future stand development on test sites 1–5 simulated for a period of 80 years. A) Number of trees (N) per test site. B) Basal area (BA) of trees per hectare. C) Mean tree height (MH). D) Tree height variation (MAD = mean absolute deviation).
The first step involved extraction of single trees and their allometric attributes form LiDAR-based canopy height models, after which the extracted tree locations were additionally validated by a sample based scheme implemented on aerial photos. The single tree based forest growth simulator SILVA ver. 2.2 was then used to simulate the stand development during a 80 year simulation period. In addition, landscape and spatial point pattern metrics were calculated to assess the structural heterogeneity. The results approve that natural regeneration of post disturbed forest stands reveal structural heterogeneity even at the early-seral stages. Furthermore, the study showed that the structural heterogeneity might already be determined in the early successional stages. following the bark beetle disturbances. This study open up interesting horizons in how remote sensing data and methods can be combined with spatial statistics to investigate early-phase forest dynamics in natural stands.
Further information on the published material can be found here.
Hill, S., Latifi, H., Heurich, M., Müller, J. 2017. Individual-tree- and stand-based development following natural disturbance in a heterogeneously structured forest: a LiDAR-based approach. Ecological Informatics 38, 12-25. DOI: dx.doi.org/10.1016/j.ecoinf.2016.12.004
A M.Sc thesis was written by Bastian Schumann under the supervision of Dr. Hooman Latifi and Prof. Christopher Conrad that focused on a LiDAR-based approach to combine structural metrics and forest habitat informaiton for causal and predictive models of understory canopy cover. The data base used consisted of a bi-temporal LiDAR dataset as well as two field datasets and two habitat maps. The entire data were initially edited, revealing that a bi-temporal treatment is only possible for understory layers. The statistical models used for modelling canopy cover density included random forest, logistic models and zero-and-one inflated beta regression.
The results revealed the most relevant LiDAR metrics which contribute to explain the canopy cover density. Furthermore it indicates that the habitat types have a significant influence on canopy cover density. In addition, it was shown that with the use of a denser point cloud a higher performance can be achieved in almost every vertical stand layer.
Wall-to-wall predictions of understory canopy cover usign high density point cloud, habitat types and a logistic model
A M.Sc thesis by Raja Ram Aryal at the University of Applied Sciences Stuttgart was recently written under the supervision of Dr. Hooman Latifi and Prof. Michael Hahn. The thesis focused on a comparative study on the variations of an adaptive TIN ground filtering algorithm to extract DTM from discrete LiDAR point cloud captured in leaf-off and full wave LiDAR point cloud collected in leaf-on conditions. In addition Analysis of Variance (ANOVA) type II was used to assess the influential factors that are related to DTM random error. The Accuracy assessment of extracted DTMs was done at local and landscape levels in heterogeneous forest stands of Bavarian Forest National Park. The DTM generated using mirror points in leaf-off returned less RMSE (0.844 m) than in leaf-on (0.988 m) conditions. Furthermore RMSE values of 0.916 m (leaf-off) and 1.078 m (leaf-on) were observed the local level analysis when no mirror points were used. However, RMSE value of ca. 0.5 m was observed at the landscape level, with leaf-off DTM showing slightly higher error than leaf-on DTM. The DTM error increased with increasing slope. Deciduous habitat was found to significantly influence DTM error in both leaf-off and leaf-on conditions. Interaction effects were mainly observed between slope and forest habitat type.
DTMs Extracted using denser point cloud LiDAR data (leaf-on condition) using mirror points (left side) and without using mirror (right side)