@article{DubovykMenzConradetal.2012,
  author    = {Dubovyk, Olena and Menz, Gunter and Conrad, Christopher and Kann, Elena and Machwitz, Miriam and Khamzina, Asia},
  title     = {Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling},
  series = {Environmental Monitoring and Assessment},
  volume    = {185},
  journal   = {Environmental Monitoring and Assessment},
  number    = {6},
  doi       = {10.1007/s10661-012-2904-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-129912},
  pages     = {4775-4790},
  year      = {2012},
  abstract  = {Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000-2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region's area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 \%), land-use intensity (odds = 103 \%), low soil quality (odds = 49 \%), slope (odds = 29 \%), and salinity of the groundwater (odds = 26 \%). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.},
  language  = {en}
}
@article{DietzConradKuenzeretal.2014,
  author    = {Dietz, Andreas J. and Conrad, Christopher and Kuenzer, Claudia and Gesell, Gerhard and Dech, Stefan},
  title     = {Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data},
  series = {Remote Sensing},
  volume    = {6},
  journal   = {Remote Sensing},
  number    = {12},
  issn      = {2072-4292},
  doi       = {10.3390/rs61212752},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-114470},
  pages     = {12752-12775},
  year      = {2014},
  abstract  = {Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of similar to 4 Mio km(2). The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.},
  language  = {en}
}