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: LiDAR-based simulation of tree-and stand development after bark beetle disturbances

New publication: LiDAR-based simulation of tree-and stand development after bark beetle disturbances

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.

Bibliography:

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

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 SPOT stereo and multispectral data granted

New SPOT stereo and multispectral data granted

Via an official proposal submitted to the ESA,  multispectral and stereo panchromatic SPOT 6/7 and Pleiades scenes were achieved over a portion of dry deciduous western Hymalayan region of India. The data delivery include three  multispectral  SPOT 6, two multispectral SPOT 7, two multispectral Pleiades, two stereo panchromatic SPOT 6 and one stereo panchromatic SPOT 7 scenes. The total value of the granted scenes amounts to ca. 3100 EUR.

Stereo Pair Images and Multi-temporal multi spectral data of SPOT 6/7 (1.5 m spatial resolution)

Stereo Pair Images and Multi-temporal multi spectral data of SPOT 6/7 (1.5 m spatial resolution)

The data has been granted and is currently being aplied under the project running title “Object Based approaches for remote sensing-assisted assessment of forest biodiversity focusing on plant species diversity and forest structural parameters”. The project serves as a part of the PhD work of Siddhartha Khare (Indian Institute of Technology Roorkee) supervised by Prof. S.K. Ghosh and Dr. Hooman Latifi from University of Würzburg.

 

MSc thesis handed in on Analysis of Airborne LiDAR Data for Deriving Terrain and Surface Models

MSc thesis handed in on Analysis of Airborne LiDAR Data for Deriving Terrain and Surface Models

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)

DTMs Extracted using denser point cloud LiDAR data (leaf-on condition) using mirror points (left side) and without using mirror (right side)