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  - Forkuor, Gerald
A1  - Conrad, Christopher
A1  - Thiel, Michael
A1  - Ullmann, Tobias
A1  - Zoungrana, Evence
T1  - Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa
N2  - Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.
KW  - random forest
KW  - crop mapping
KW  - agriculture
KW  - West Africa
KW  - RapidEye
KW  - TerraSAR-X
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-113070
ER  -