Author: Fabian Loew

New publication on the impact of scale in supervised image classification

A new paper published recently in PFG presents our ongoing research on the scale issue in the context of supervised image classification for crop monitoring. It extends a conceptional framework for the definition of pixel size requirements in land use classification, which we prevsiously published in Remote Sensing. It adds an analysis of the thematic scale, i.e. the selction of a proper class legend and its interaction with the spatial scale (pixel size). A full text of this paper can be found at:

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PhD thesis of Fabian Löw awarded by GGW

The dissertation thesis of Fabian Löw was awarded with a prize by the Geographical Society in Würzburg (GGW). In his thesis, Fabian Löw worked on land use classification at the example of irrigated landscapes in Central Asia. His researach focusses on the impact of spatial scale on classification accuracy in crop mapping (see publications in Remote Sensing and PFG) and classification uncertainty (see publications from 2013 and 2015 in ISPRS).       The prize was awarded to Fabian Löw on 08-December 2015 by Prof. Heiko Paeth and Dr. Konrad Schliephake. The laudation was held by Prof. Christopher...

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Visiting MSc student from Tajikistan

Mustakim Akhmedov from Tajikistan is recently visiting the Department of Remote Sensing in Würzburg. He is doing his MSc on Integrated Water Resource Management at the Kazakh-German University (DKU, see link) in Almaty, Kazakhstan, under initiative of German Ministry of Foreign Affairs to support Central Asian region to improve water resource management and cooperation on transboundary river basins. He arrived in Wurzburg on 8th of April 2015 to learn GIS technology and remote sensing and will work there until end of May 2015. The objective of his visit is to foster his skills in GIS and remote sensing for further applying it in the practice in his home country Tajikistan. His specific topic of research is social aspects of water management in rural areas of Tajikistan. He plans to analyze water distribution and water demand in rural areas of Zerafshan Valley of Tajikistan, and identify irrigation efficiency. He is closely cooperating and supporting the ongoing resrach at the Department of Remote Sensing, Würzburg in the context of the CAWa (see link) and LaVaCCA (see link) projects, which both deal with land and water ressources in Central...

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PhD position on land use modelling (application closed)

The Department of Remote Sensing invited applications for a PhD position starting from January, 1st 2015 for a period of 3 years. The successful candidate will conduct her/his PhD in the project “Assessing Land Value Changes and Developing a Discussion-Support-Tool for Improved Land Use Planning in the Irrigated Lowlands of Central Asia” funded by the Volkswagen Foundation. Focus will be on analysis of spatial and socio economic drivers of land use change in the downstream regions in Central Asia (Uzbekistan and Kazakhstan). LaVaCCA Project Description: Immense losses of land productivity have been observed on eight million hectares of irrigated agricultural land in Central Asia (CA) during the past decades. Especially the irrigated lowlands of the Amu Darya and Syr Darya Rivers are affected by land degradation (LD) problems. One major shortcoming in the attempt to combat LD is the generally lack of spatially explicit data, especially after the breakdown of the Soviet Union. The proposed research addresses the identification of hotspots of decreasing land production (e.g. crop yield) and gaining knowledge about the drivers of change in land production and LD by analysing socio-economic and ecological indicators. A strong methodological focus is set on remote sensing, geographical information systems (GIS), indicator systems, and land use modelling. The generated information will be bundled in cooperation with the project partners and presented as a tool of discussion support for politicians and...

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New article on scales and land use classification published

Our new article about the definition of the spatial resolution requirements for crop identification using optical remote sensing has been published in a special issue on Remote Sensing in Food Production and Food Security. Abstract: The past decades have seen an increasing demand for operational monitoring of crop conditions and food production at local to global scales. To properly use satellite Earth observation for such agricultural monitoring, high temporal revisit frequency over vast geographic areas is necessary. However, this often limits the spatial resolution that can be used. The challenge of discriminating pixels that correspond to a particular crop type, a prerequisite for crop specific agricultural monitoring, remains daunting when the signal encoded in pixels stems from several land uses (mixed pixels), e.g., over heterogeneous landscapes where individual fields are often smaller than individual pixels. The question of determining the optimal pixel sizes for an application such as crop identification is therefore naturally inclined towards finding the coarsest acceptable pixel sizes, so as to potentially benefit from what instruments with coarser pixels can offer. To answer this question, this study builds upon and extends a conceptual framework to quantitatively define pixel size requirements for crop identification via image classification. This tool can be modulated using different parameterizations to explore trade-offs between pixel size and pixel purity when addressing the question of crop identification. Results over contrasting landscapes in Central...

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