A recently publisher paper featuring Hooman Latifi and Thorsten Dahms from Dept. of Remote Sensing presents novel results on phenological behaviour of the moist deciduous forests Hymalayan foothills in India during 2013–2015 using Landsat 8 time series data. The paper has been published in International Journal of Remote Sensing, and additionally suggests a new vegetation index called the temporal normalized phenology index (TNPI) to quantify the change in trajectories of Landsat 8 OLIderived normalized difference vegetation index (NDVI) during two time steps of the vegetation growth cycle.
Mean NDVI values from April 2014 to June 2015 plotted for study site along with SAL tree
Based on cross-validated statistics the paper concludes that TNPI is a superior alternative for the analysis of temporal phenology cycle between two time steps of maximum and minimum vegetation growth periods. This could, in turn, reduce the requirement of large time-series remote-sensing data sets for studies on long-term vegetation phenology. The paper can be retrieved here.
Khare, S., Ghosh, S.K., Latifi, H., Vijay,S., Dahms,T. 2017. Seasonal-based analysis of vegetation response to environmental variables in the mountainous forests of Western Himalaya using Landsat 8 data. International Journal of Remote Sensing 38(15), 4418-4442.
Following the purchase and operaration of our two low-budget UAVs suring 2016 and 2017, a best practice tutorial of how to use the devices was written by Marius Röder, Steven Hill and Hooman Latifi, and it is now available in two English and German versions. This tutorial assumes no prior knowledge of the reader on handling low-budget Unmanned Aerial Vehicles in ecological and environmental contexts. It initially includes general infos on preperation and constellation of a typical UAV system, followed by instructions on planning and implementation of UAV flights using the available commercial software, importing the acquired imagery, relative orientation, optimization of camera parameters, generation of dense point clouds and finally digital surface modeling of the point clouds. The tutorial eventually includes lessons learned, tipps and tricks on further processing and potential applications of the UAV topographic products.
The tutorial can be retrieved here on Research Gate.
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
The M.Sc thesis by Marius Röder (Hochschule für Technik Stuttgart) was handed in. The thesis was supervised by Dr. Hooman Latifi and Prof. Eberhard Gülch and focuses on monitoring post-disturbed and heterogenuous forest sites by cost-effective methods from Unmaned Aerial Vehicle (UAV) domain. The advantage of normalized digital surface models extracted from UAV dta was initially compared to the products derived from standard aerial photography. Subsequently, the suitability of UAV-based inventory was compared to traditional eld methods . For this purpose, reference and UAV data were compared in terms of quality, quantity and cost eectiveness. In addition, an algorithm for automatic tree detection was compared to the manual detection on the UAV-imagery. The extent to which the results differ for certain forest heterogeneity as well as for single and grouped tree individuals was addressed., followed by a cost and benefit analysis of UAV-based forest inventory compared to traditional field-based methods.
UAV-based point cloud (left) and UAV-based nDSM (right) of an examplified sample plot in Bavarian Forest National Park
the results showed that the UAV inventory can not fully replace the eld methods in terms of quality and quantity due to the general disadvantages of photogrammetric methods in the small-scale forest sites consisting of dense rejuvenation stocks. However, from a purely economic point of view, the advantages over the eld method predominate. Improvements could be achieved by combining field and UAV-methods or a simulteanous use of digital camera and laser scanner mounted on UAV.
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.