Gerd Rücker

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

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.

Modeling seasonal actual evapotranspiration with remote sensing and GIS in Khorezm region, Uzbekistan

Seasonal evapotranspiration is an essential measure to model crop growth and hydrological balances particularly for irrigation agriculture in semi-arid environments. Hydrological models traditionally integrate single-spot measurements of meteorological stations to estimate potential evapotranspiration. During the last years, the application of thermal remote sensing data in combination with meteorological data of soil-vegetation-atmosphere models facilitated the estimation of actual evapotranspiration on a large scale. This study employed multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data to apply the Surface Energy Algorithm for Land (SEBAL) model to the heterogeneous environment of the Khorezm region, Uzbekistan. Further meteorological data was used to extrapolate actual evapotranspiration to seasonal actual evapotranspiration. The validation of the modeled actual evapotranspiration showed acceptable accuracy when compared to the limited point-based ground truth data. The integration of a rule-based land use classification with higher spatial resolution revealed the necessity to include sub-pixel knowledge of land use distribution to interpret the modeling results. First evaluations of the water distribution and consumption situation were achieved by interpretation of modeled seasonal actual evapotranspiration with hydrological GIS information.

Beiträge der Satellitenfernerkundung für ein nachhaltiges und grenzüberschreitendes Wassermanagement in Zentralasien

Das Problem des schrumpfenden Aralsees ist hinlanglich bekannt, wesentliche Auswirkungen sind beschrieben und extensiver Bewasserungsfeldbau in Zentralasien wurde als Hauptursache identifiziert. Zur agrarindustriellen Produktion von Baumwolle wurde seit den fruhen 1960er Jahren entlang der beiden grosen Flusse Amudarja und Syrdarja ein extensives Kanalnetzwerk etabliert. Damit sollte der stetig steigende Wasserbedarf der kontinuierlich wachsenden landwirtschaftlichen Nutzflachen in Zentralasien gedeckt werden. Bis Ende der 1980er Jahre wurde dieses System bis an die moglichen Grenzen der Tragfahigkeit erweitert. Nicht angepasster Wasserverbrauch fuhrt zunehmend zu schweren okologischen und okonomischen Problemen in den Bewasserungsgebieten.