open position: Research Associate – Local Representative of the Regional Research Network ‘Water in Central Asia’

open position: Research Associate – Local Representative of the Regional Research Network ‘Water in Central Asia’

 

 

Name of position: 1 Research Associate – Local Representative of the Regional Research Network ‘Water in Central Asia’

Location of the position: Almaty, Kazakhstan

Deadline for applications: March 1, 2017

Employer: CAWa and German Kazakh University in Almaty

 

The regional research network ‘Water in Central Asia’ (CAWa) with project partners in Germany and Central Asia is looking for a highly self-motivated, independently working, communicative, and team-oriented fulltime (100%) research associate, starting from April 2017 until April 2018 with possibility for prolongation. Main tasks of the successful candidate lie in the active representation of the entire CAWa project in Central Asia with the periodic notification of findings and significant contribution to disseminating and presenting the project to decision makers and scientific and governmental organizations/institutions. Support in capacity building is also expected from the candidate. The successful candidate willconduct organizational, advisory, and scientific tasks in an international, ambitious and committed team. We offer to work in a strong international network with numerous organizations in research and practice. The successful candidate will need to be able to familiarize himself or herself fast with the project contents and processes, goals, methods, and findings. In case of mutual interest of the project partners and the successful candidate, a further career at the GKU is possible.

 

Our project

The CAWa project has started in June 2008 and has since then been funded as scientific-technical component of the German Water Initiative for Central Asia (“Berlin Process”) by the German Federal Foreign Office. Funding is continued for the third project phase (2015-2017). Overall goals for the third project phase of CAWa are: (i) informed decision making in the water and land management by data transparency, (ii) support of regional and trans-sectoral cooperation and communication, and (iii) strengthening of technical and methodological competences of researchers and specialists in water management organizations, at universities and governmental institutions. The international partners involved in the project are the Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences (Germany), the Department of Remote Sensing at the University of Wuerzburg (Germany), the German-Kazakh University and its UNESCO Chair on Water Management in Central Asia (Almaty, Kazakhstan), the SIC ICWC (Scientific Information Center of the Interstate Commission for Water Coordination of Central Asia, Tashkent, Uzbekistan), KRASS (Khorezm Rural Advisory Support Service, Urgench, Uzbekistan), CAREC (Regional Environmental Centre for Central Asia), Central-Asian Institute for Applied Geosciences (CAIAG), and the Central Asia’s Hydrometeorological (Hydromet) Services. Methods applied in the CAWa project focus on satellite-based earth observation in the context of water and land management. Particular focus lies on the utilization of optical remote sensing data for the monitoring of irrigated land use and yield estimation (WUEMoCA tool), snow cover and glacier are as well as on the use of geodetic information for monitoring lake’s and reservoir’s water levels and snow depths.

 

Requested fields of research and main tasks

  • Active communication and design of implementation in cooperation with all project partners, including self-organized short research visits to the partner institutions, and organization of workshops and team meetings,
  • Active representation of the CAWa project and all related work packages in Central Asia with the periodic notification and significant contribution to disseminating and presenting the project contents and processes, methods and goals of developed information tools (MODSNOW, WUEMoCA) to decision makers and scientific and governmental organizations and institutions at conferences and workshops,
  • Communication and exchange of feedback and raised questions and challenges on the use of tools to the project partners in Germany and recommendations for improvement,
  • Preparation of scientific project proposals and realization of own scientific research
  • Development and implementation organization of Remote Sensing and GIS courses and teaching activities for water managers to imparting methods and supporting data processing for analyzing and visualizing geo-information,
  • Active support in planning and realizing the annual CAWa Summer School with international participants from Central Asian countries and Afghanistan,
  • Development of teaching materials with focus on earth observation (optical remote sensing, laser and radar altimetry) and GIS and support in teaching MSc and BSc students at the GKU in the context of the CAWa project,
  • Support in supervision of MSc and BSc students at the GKU in the context of the project,
  • Support in project cooperation with the German Cooperation GIZ in the field of transboundary water management in Central Asia.

 

Expected skills and competences

  • High-quality graduation in Geography or related fields,
  • Extended basic knowledge in Earth Observation and Geographic Information Systems and strong willingness to increase knowledge and expertise,
  • Sound knowledge in geostatistical analyses and programming skills,
  • Excellent English skills (both written and spoken) are mandatory, good Russian skills are advantageous,
  • Advanced knowledge of the region and organizational structures are advantageous,
  • Very good ability to represent the entire research team effectively internally and externally, including at conferences and meetings in the region,
  • Professional and respectful behavior and attitude in a collaborative environment,
  • Experience in international research, project management, and project coordination is advantageous,
  • Experience in working in a team-oriented, international and collaborative environment,
  • Teaching experiences are advantaegous.

 

Contract’s conditions

The successful candidate will receive a position for 1 year with possibility for prolongation.Successful candidate will be placed at the German-Kazakh University (GKU) in Almaty, Kazakhstan,with access to office infrastructure. Salary dependent on qualifications and experience and according to local conditions.

Please submit your application (in English) including the reference number 2017/CAWa project representative and containing a cover letter, motivation letter (1 page maximum), curriculum vitae with list of publications (if present) and working/project history, copies of diplomas, certificates or references from employers, by email to Dr. Barbara Janusz-Pawletta (German-Kazakh University, janusz-pawletta@dku.kz) until March 1, 2017.

For further information on the project, candidates are encouraged to consult the project’s website https://www.cawa-project.net.

 

 

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

The Department of Remote Sensing recently applied for a time series of RapidEye scenes to support a PhD thesis by Christian Bauer (Dep. of Remote Sensing, Würzburg) in the context of the project LaVaCCA. The proposal has recently been accepted, and the PhD work of Christian Bauer will be supported by a time series of RapidEye scenes acquired over two irrigated landscapes, Khorezm and Elliqkala, in Uzbekistan.

New publication on decision fusion

New publication on decision fusion

Decision_fusion_loewOur new publication in ISPRS is accepted: “Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data”.

This study addressed the classification of multi-temporal satellite data from RapidEye by considering different classifier algorithms and decision fusion. Four non-parametric classifier algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), were applied to map crop types in various irrigated landscapes in Central Asia. A novel decision fusion strategy to combine the outputs of the classifiers was proposed. This approach is based on randomly selecting subsets of the input data-set and aggregating the probabilistic outputs of the base classifiers with another meta-classifier. During the decision fusion, the reliability of each base classifier algorithm was considered to exclude less reliable inputs at the class-basis. The spatial and temporal transferability of the classifiers was evaluated using data sets from four different agricultural landscapes with different spatial extents and from different years. A detailed accuracy assessment showed that none of the stand-alone classifiers was the single best performing. Despite the very good performance of the base classifiers, there was still up to 50% disagreement in the maps produce by the two single best classifiers, RF and SVM. The proposed fusion strategy, however, increased overall accuracies up to 6%. In addition, it was less sensitive to reduced training set sizes and produced more realistic land use maps with less speckle. The proposed fusion approach was better transferable to data sets from other years, i.e. resulted in higher accuracies for the investigated classes. The fusion approach is computationally efficient and appears well suited for mapping diverse crop categories based on sensors with a similar high repetition rate and spatial resolution like RapidEye, for instance the upcoming Sentinel-2 mission.

 

LaVaCCA Team inspecting agricultural landscape in Khorezm

LaVaCCA Team inspecting agricultural landscape in Khorezm

LaVaCCA_Project_Logo“This field has been abandoned approximately 30 years ago” – such statements can be heard quite often when one talks with farmers in Khorezm, one of the largest irrigated agricultural landscapes in Central Asia. In this region, vast irrigation systems were installed during Soviet times, but after breakdown in the 1990s many fields were finally abandoned for many reasons (e.g. soil salinization, insufficient water supply). However, the spatial extent and timing of cropland abandonment in many regions in Central Asia remain unknown.

The LaVaCCA project aims at shedding more light on these issues, which could allow land managers to make better informed decisions about their land, or to propose alternative land uses such as pasture or afforestation on formerly abandoned fields – once these will have been identified. Staffers from different institutions in Central Asia (SIC-ICWC, KRASS, Al´Farabi University) recently visited the test sites in Khorezm together, where important information is collected during the project runtime (2015-2017) for calibration and validation of diverse remote sensing based methodologies (e.g. yield modelling, crop classification, salinity mapping).

Präsentation1

LaVaCCA project partners inspecting abandoned croplands in Khorezm, Uzbekistan (Foto: Dr. Fabian Löw)

Three PhD students from Usbekistan, Kazakhstan, and Germany investigate the recent and past status of agricultural areas in the lowlands of Central Asia, including Khorezm. They seek to back-trace the past development in order to better understand the various pathways that led to the widespread cropland abdandonment, which was already observed (see recent publication on this issue). Their overall aim is to make use of remote sensing for identifying abandoned fields and explaining their spatio-temporal pattern in order to gain more insight into the drivers of cropland abandonment in Central Asia.

Second R training in Central Asia (LaVaCCA)

Second R training in Central Asia (LaVaCCA)

LaVaCCA_Project_LogoFrom June 29th to July 2rd, experts from the Department of Remote Sensing, Würzburg University, met their colleagues of the LaVaCCA project from KRASS, SIC-ICWC, and Al´Farabi University at the office of KRASS in Urgench, Uzbekistan. During this meeting, the second R-course was done in the scope of the capacity building of the LaVaCCA Project. This course aimed at fostering the methodological skills of the participants in using R for geospatial data analysis, with a focus on agricultural monitoring. Participants have already gained first analytical skills in using R during the first training in Almaty, Kazakhstan in February 2015.

 

A new concept for this course was applied, in which the participants selected and suggested the course agenda, according to their specific project requirements. The course was then set up accordingly and held by Dr. Fabian Löw and Christian Bauer from Würzburg University.

LaVaCCA_R_Training_Urgench

Participants of the second R-course in KRASS office, Urgench City, Uzbekistan (Foto: Dr. Fabian Löw)

Three major topics were focused on:

  • Working with raster data: satellite image pre-processing in R (atmospherically calibration, stacking, sub-setting of large data sets, calculation of vegetation indices for assessing vegetation dynamics and within-field assessments of vegetation growth)
  • Supervised image classification for crop type mapping, based on multi-temporal Landsat images and Random Forest algorithm (crop type distribution and acreage), accuracy assessments (confusion matrices)
  • Regression analysis in R
IMG_0183

Participants vising selected sampling sites near Urgench City, Uzbekistan (Foto: Dr. Fabian Löw)

After three intensive days of training, the participants togehther explored the subject of the LaVaCCA program “on-ground”: a field survey was organized and performed around Urgench city, where staffers from SIC-ICWC and KRASS maintain several test sites on cotton, wheat, and rice fields, which are the major crops in this region. On these selected fields, SIC-ICWC staffers assess biophysical parameters (fPAR, biomass), which will later be incorporated into yield models. The R course enables them to analyze important agricultural parameter (crop yield, crop acreage) with one of the most advanced programming languages, R. Staffers from KRASS also maintain soil salinity sampling test sites in this region, and the course enables them to analyse their field measurements, for example to perform regression analysis or supervised land use classifications, based on satellite images.