New RapidEye datasets granted from RESA platform of DLR/Black Bridge

New RapidEye datasets granted from RESA platform of DLR/Black Bridge

The Dept. of Remote Sensing recently applied for a time series of 12 RapidEye scenes to support a PhD thesis by Siddhartha Khare (Indian Institute of Technology Roorkee, India). The proposal is now accepted, and th PhD work will be supported by a dense time series of RapidEye scenes acquired in 2013 over dry forests in Lesser Himalayan region of India.

The PhD thesis which will use these processed datasets is entitled  “Object Based approaches for remote sensing-assisted assessment of forest biodiversity focusing on invasive species”, supervised and advised by Prof. S.K. Ghosh (IIT Roorkee) and Dr. Hooman Latifi (University of Würzburg). Additional multisource datasets to be used within the work include time series of Landsat 8 data and Cartosat 2-derived terrain model.

RESA already published this project in RESA project map 2015 which can be found online HERE. The project area is flagged under No. 120 (project number 184) in the map.

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New RapidEye datasets granted from RESA platform of DLR/Black Bridge

New RapidEye datasets granted from RESA platform of DLR/Black Bridge

The Dept. of Remote Sensing recently applied for a time series of 7 RapidEye scenes to support a PhD thesis by M.Sc Omid Karami (Sari University of Agricultural and Natural Resources Sciences, Iran). The proposal is now accepted, and th PhD work will be supported by a time series of RapidEye scenes acquired between 07.2009 and 09.2014 over a semi-Meditteranean forested site in Lorestan province in Western Iran.

The PhD thesis which will use these processed datasets is entitled  “Monitoring and modelling of Zagros forest oak decline using high resolution satellite data”, supervised and advised by Dr. Asghar Fallah (Sari University of Agricultural and Natural Resources Sciences), Dr. Shaban Shataee (University of Gorgan) and Dr. Hooman Latifi (University of Würzburg). Additional multisource datasets to be used within the work include very high resolution data from Quickbird and World View-2.

RESA already published this project in RESA project map 2015 which can be found online HERE. The project area is flagged under No. 104 (project number 168) in the map.

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The RapidEye Science Archive (RESA) supported by DLR (BMWi) and Black Bridge AG supports scientific projects with German participation through the provision of free image data of the RapidEye satellite constellation. The projects are given the opportunity on an extensive image archive to get current and archived satellite image data to ensure optimal support for individual research projects.
Topic for M.Sc thesis: Application of multi-seasonal RapidEye satellite imagery for inventory of private and municipal forests in the northern Black Forest

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.

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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 a number of aerial photo-interpreted stands dominated by Scots pine. The data for the neighboring public forests can also be accessed via the local forest enterprises.

The work will be carried out in close cooperation between University of Würzburg and the LandConsult company (Dr Markus Weidenbach). A Visit to the study area in the northern Black Forest is supported by the LandConsult company.

Moreover, Dr. Piotr Tompalski from the University of British Columbia (Canada) will give advices as an external supervisor.

For questions on this topic, please contact Dr. Hooman Latifi at the Dept. of remote sensing of the University of Würzburg (hooman.latifi@uni-wuerzburg.de).  A more comprehensive description of this topic can be found at following address:

http://www.geographie.uni-wuerzburg.de/fileadmin/04140500/Dokumente/Stellenausschreibungen/Abschlussarbeiten/masterarbeit1_13022015_EN.pdf.