TY  - JOUR
A1  - Dietz, Andreas J.
A1  - Conrad, Christopher
A1  - Kuenzer, Claudia
A1  - Gesell, Gerhard
A1  - Dech, Stefan
T1  - Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data
JF  - Remote Sensing
N2  - 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.
KW  - AVHRR data
KW  - satellite
KW  - Northern Xinjiang
KW  - cloud
KW  - products
KW  - Central Asia
KW  - climate change
KW  - Amu Darya
KW  - Syr Darya
KW  - Tian Shan
KW  - snow
KW  - snow cover
KW  - snow cover duration
KW  - Pamir
KW  - AVHRR
KW  - MODIS
KW  - algorithm
KW  - validation
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-114470
SN  - 2072-4292
VL  - 6
IS  - 12
ER  - 
TY  - JOUR
A1  - Conrad, Christopher
A1  - Fritsch, Sebastian
A1  - Zeidler, Julian
A1  - Rücker, Gerd
A1  - Dech, Stefan
T1  - Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data
N2  - The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.
KW  - Geologie
KW  - object-based classification
KW  - segmentation
KW  - tasselled cap
KW  - Uzbekistan
KW  - irrigated agriculture
KW  - multi-sensor
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-68630
ER  -