Author: Hooman Latifi

University of Würzburg is a member of initiative “Data Pool of Bavarian Forest”

The Dept. of remote sensing of the University of Würzburg will contribute to the recently-initiated platform of “Data Pool of Bavarian Forest”. The Bavarian Forest National Park (BFNP) is the first national park in Germany and one of the unique natural landscapes in central Europe. Natural events such as windthrow, insect and fungal attack together with the forest ecosystem natural dynamics continuously change the shape of the forest. These dynamics can be monitored over various temporal durations using field and remotely sensed data. In a recently-started initiative of the Earth Observation Center of German Aerospace Center (EOC-DLR) and the BFNP administration now coordinate the systematic exchange of data and  methods in a formal cooperation, in which a number of other European research institutions including Univesity of Würzburg also contribute. Within this formal agreement, the Šumava  National Park (Czech Republic), BFNP, EOC-DLR, University of Würzburg, University of Twente (ITC Netherlands), Munich University of Applied Sciences and Technical University of Munich (TUM) have agreed to strengthen their collaboration in exchanging remote sensing data and methods for research purposes within the BFNP.  In April 2015, the corresponding cooperation agreement was signed by all the partners. The EOC-DLR will contribute with hyperspectral data from HySpex sensor, TerraSAR-X RADAR data and Spot 5 multispectral data. The BFNP will provide an edvanced access to its comprehensive archive of aerial imagery, GIS layers and field-based data for research...

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First announcement of the IBS-DR/DVFFA Biometry workshop 2015

First announcement of the joint IBS-DR/DVFFA Biometry workshop 2015   The working groups “Ecology and Environment“, “Bayes Methods” and “Spatial Statistics” of the German Region of the International Biometric Society and the Forest Biometrics unit of the German Association of Forest Research Stations (DVFFA) announce a joint biometry workshop to be held in the University of Würzburg-Germany from 7th to 9th of October 2015. The workshop will focus on all relevant topics in spatial statistics and inference, with a main focus on environmental and ecological applications of statistical methods. Along with the presentations held by the interested participants from various disciplines, he workshop will be enriched by a statistical tutorial held by:   Dr. Lauri Mahtätalo Associate Professor of the Applied Statistics Universities of Eastern Finnland and Helsinki on “Mixed-Effects Models in theory and practice”   Mixed-effects models are tools to analyze grouped datasets where the groups constitute a random sample from a population of groups. The aim of the workshop is to provide a space for presentations and discussions of recent developments in spatial modeling (with particular focus on mixed effects models) and their applications in answering environmental and ecological research questions. In addition to this tutorial, presentations on the following topics are particularly welcome:   Various constellations for Mixed-Effects models, including Linear Mixed-Effects models Linear Mixed-Effects models for multilevel datasets Multivariate Mixed-Effects models Nonlinear Mixed-Effects models Other...

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PhD position: forest structure (application closed)

PhD: Monitoring the structural parameters of forest habitats by multi-source/multi-temporal remote sensing data   The Department of Remote Sensing at the institute of Geography and Geology invites applications for a PhD position starting from May, 20th 2015 for a period of 3 years. The successful candidate will conduct her/his PhD with a multidisciplinary focus on remote sensing and spatial statistics.   Project Description: The structure of a natural forest landscape is characterized through elements such as the amount of foliage, canopy cover of woody plant species, structural properties of vegetation (i.e. diameter, basal area, vegetation height, aboveground biomass and amount of woody debris). Many of these (and other) factors related to the dynamics of a natural forest ecosystem (e.g. landscape structure, growing stock of forest stands, amount of coarse dead woody material and the vertical distribution of landscape elements) can presumably interact with natural disturbance agents such as biological infestations. Landscape structure can be assessed using remote sensing in a spatially and temporally continuous way. This project is part of a bigger framework on early detec-tion strategies for forest natural disturbance agents. In this part of the project, the derivations from various possible remote sensing sources (airborne and terrestrial LiDAR, UAV and possibly RADAR interferometry data) will be used to form a multi-temporal set of 3D information, which will further be applied to model the actual as well as...

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New Paper: Stratified aboveground forest biomass estimation by remote sensing data

A new paper published recently by Int. J. Appl. Earth Obs. Geoinf. presents the results of a systhematic survey on the effects of post-stratification of sampling units on the quality of remote sensing-assisted biomass models. This is somwhat controversial to the status quo in the literature, which mostly suggests that estimates can be improved by building species- or strata-specific biomass models.   We analyzed the impact of stratifying forest data into three classes (broadleaved, coniferous and mixed forest). We compared predictive accuracy a) between the strata b) to a case without stratification for a set of pre-selected predictors from airborne LiDAR and hyperspectral data. The achieved RMSE and r2 diagnostic values were analyzed in a factorial design to rank the relative importance of each factor. Selected models were used for wall-to-wall mapping of biomass estimates and their associated uncertainty. The results revealed marginal advantages for the strata-specific prediction models over the unstratified ones, which were more obvious on the wall-to-wall mapped area-based predictions. Yet, further tests are necessary to establish the generality of these results. Input data type and statistical prediction method are concluded to remain the two most crucial factors for the quality of remote sensing-assisted biomass models. A full text of this paper can be found at:

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Topic for M.Sc thesis: Application of multi-seasonal RapidEye satellite imagery for inventory of private and municipal forests in the northern Black Forest

In this M.Sc thesis, a set of multi-seasonal satellite imagery from RapidEye will be applied to develop algorithms and improve the existing ones in tree species mapping across a portion of mixed forest stands in northern Black Forests in the state of Baden-Württemberg in Germany. The focus of the research will be on small private forest patches, for which conducting regular forest inventories has always been a major challenge. For most private forests (i.e. more than 30% of the forest area in Baden-Württemberg) there is a shortage of accurate and up-to-date forest inventory data. To this aim, a set of multi seasonal RapidEye satellite imagery with a spatial resolution of about 5m are obtained from the Black Bridge company. The data cover 4 seasonally different times of the year, including March 2014, May 2013, July 2014, September 2013, and October 2012. Based on the fact that the tree species show phonological (and in turn spectral) differences during the year, these comprehensive dataset will be used here to map different tree species and other important forest characteristics, such as needle fall, storm effects or effects caused by bark beetles. Both the results of the single tree-based aerial photo interpretation as well as those from a small area in which all pine trees have been mapped will be used as reference data. In addition, validation data can also be achieved from...

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the Remote Sensing Department
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