@article{AtaeeMaghsoudiLatifietal.2019,
  author    = {Ataee, Mohammad Sadegh and Maghsoudi, Yasser and Latifi, Hooman and Fadaie, Farhad},
  title     = {Improving estimation accuracy of growing stock by multi-frequency SAR and multi-spectral data over Iran's heterogeneously-structured broadleaf Hyrcanian forests},
  series = {Forests},
  volume    = {10},
  journal   = {Forests},
  number    = {8},
  issn      = {1999-4907},
  doi       = {10.3390/f10080641},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197212},
  year      = {2019},
  abstract  = {Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.},
  language  = {en}
}
@article{MayrKuenzerGessneretal.2019,
  author    = {Mayr, Stefan and Kuenzer, Claudia and Gessner, Ursula and Klein, Igor and Rutzinger, Martin},
  title     = {Validation of earth observation time-series: a review for large-area and temporally dense land surface products},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {22},
  issn      = {2072-4292},
  doi       = {10.3390/rs11222616},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193202},
  year      = {2019},
  abstract  = {Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.},
  language  = {en}
}
@article{AbdullahiWesselHuberetal.2019,
  author    = {Abdullahi, Sahra and Wessel, Birgit and Huber, Martin and Wendleder, Anna and Roth, Achim and Kuenzer, Claudia},
  title     = {Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs11242903},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193902},
  year      = {2019},
  abstract  = {Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68\% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.},
  language  = {en}
}
@article{TrappeKneisel2019,
  author    = {Trappe, Julian and Kneisel, Christof},
  title     = {Geophysical and sedimentological investigations of Peatlands for the assessment of lithology and subsurface water pathways},
  series = {Geosciences},
  volume    = {9},
  journal   = {Geosciences},
  number    = {3},
  doi       = {10.3390/geosciences9030118},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-201699},
  pages     = {118},
  year      = {2019},
  abstract  = {Peatlands located on slopes (herein called slope bogs) are typical landscape units in the Hunsrueck, a low mountain range in Southwestern Germany. The pathways of the water feeding the slope bogs have not yet been documented and analyzed. The identification of the different mechanisms allowing these peatlands to originate and survive requires a better understanding of the subsurface lithology and hydrogeology. Hence, we applied a multi-method approach to two case study sites in order to characterize the subsurface lithology and to image the variable spatio-temporal hydrological conditions. The combination of Electrical Resistivity Tomography (ERT) and an ERT-Monitoring and Ground Penetrating Radar (GPR), in conjunction with direct methods and data (borehole drilling and meteorological data), allowed us to gain deeper insights into the subsurface characteristics and dynamics of the peatlands and their catchment area. The precipitation influences the hydrology of the peatlands as well as the interflow in the subsurface. Especially, the geoelectrical monitoring data, in combination with the precipitation and temperature data, indicate that there are several forces driving the hydrology and hydrogeology of the peatlands. While the water content of the uppermost layers changes with the weather conditions, the bottom layer seems to be more stable and changes to a lesser extent. At the selected case study sites, small differences in subsurface properties can have a huge impact on the subsurface hydrogeology and the water paths. Based on the collected data, conceptual models have been deduced for the two case study sites.},
  language  = {en}
}
@phdthesis{Wohlfart2018,
  author    = {Wohlfart, Christian},
  title     = {The Yellow River Basin in Transition - Multi-faceted Land Cover Change Analysis in the Yellow River Basin in the Context of Global Change Using Multi-sensor Remote Sensing Imagery},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-163724},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {As a cradle of ancient Chinese civilization, the Yellow River Basin has a very long human-environment interrelationship, where early anthropogenic activities re- sulted in large scale landscape modifications. Today, the impact of this relationship has intensified further as the basin plays a vital role for China's continued economic development. It is one of the most densely-populated, fastest growing, and most dynamic regions of China with abundant natural and environmental resources providing a livelihood for almost 190 million people. Triggered by fundamental economic reforms, the basin has witnessed a spectacular economic boom during the last decades and can be considered as an exemplary blueprint region for contemporary dynamic Global Change processes occurring throughout the country, which is currently transitioning from an agrarian-dominated economy into a modern urbanized society. However, this resourcesdemanding growth has led to profound land use changes with adverse effects on the Yellow River social-ecological systems, where complex challenges arise threatening a long-term sustainable development. Consistent and continuous remote sensing-based monitoring of recent and past land cover and land use change is a fundamental requirement to mitigate the adverse impacts of Global Change processes. Nowadays, technical advancement and the multitude of available satellite sensors, in combination with the opening of data archives, allow the creation of new research perspectives in regional land cover applications over heterogeneous landscapes at large spatial scales. Despite the urgent need to better understand the prevailing dynamics and underlying factors influencing the current processes, detailed regional specific land cover data and change information are surprisingly absent for this region. In view of the noted research gaps and contemporary developments, three major objectives are defined in this thesis. First (i), the current and most pressing social-ecological challenges are elaborated and policy and management instruments towards more sustainability are discussed. Second (ii), this thesis provides new and improved insights on the current land cover state and dynamics of the entire Yellow River Basin. Finally (iii), the most dominant processes related to mining, agriculture, forest, and urban dynamics are determined on finer spatial and temporal scales. The complex and manifold problems and challenges that result from long-term abuse of the water and land resources in the basin have been underpinned by policy choices, cultural attitude, and institutions that have evolved over centuries in China. The tremendous economic growth that has been mainly achieved by extracting water and exploiting land resources in a rigorous, but unsustainable manner, might not only offset the economic benefits, but could also foster social unrest. Since the early emergence of the first Chinese dynasties, flooding was considered historically as a primary issue in river management and major achievements have been made to tame the wild nature of the Yellow River. Whereas flooding is therefore largely now under control, new environmental and social problems have evolved, including soil and water pollution, ecological degradation, biodiversity decline, and food security, all being further aggravated by anthropogenic climate change. To resolve the contemporary and complex challenges, many individual environmental laws and regulations have been enacted by various Chinese ministries. However, these policies often pursue different, often contradictory goals, are too general to tackle specific problems and are usually implemented by a strong top-down approach. Recently, more flexible economic and market-based incentives (pricing, tradable permits, investments) have been successfully adopted, which are specifically tailored to the respective needs, shifting now away from the pure command and regulating instruments. One way towards a more holistic and integrated river basin management could be the establishment of a common platform (e.g. a Geographical Information System) for data handling and sharing, possibly operated by the Yellow River Basin Conservancy Commission (YRCC), where available spatial data, statistical information and in-situ measures are coalesced, on which sustainable decision-making could be based. So far, the collected data is hardly accessible, fragmented, inconsistent, or outdated. The first step to address the absence and lack of consistent and spatially up-to-date information for the entire basin capturing the heterogeneous landscape conditions was taken up in this thesis. Land cover characteristics and dynamics were derived from the last decade for the years 2003 and 2013, based on optical medium-resolution hightemporal MODIS Normalized Differenced Vegetation Index (NDVI) time series at 250 m. To minimize the inherent influence of atmospheric and geometric interferences found in raw high temporal data, the applied adaptive Savitzky-Golay filter successfully smoothed the time series and substantially reduced noise. Based on the smoothed time series data, a large variety of intra-annual phenology metrics as well as spectral and multispectral annual statistics were derived, which served as input variables for random forest (RF) classifiers. High quality reference data sets were derived from very high resolution imagery for each year independently of which 70 \% trained the RF models. The accuracy assessments for all regionally specific defined thematic classes were based on the remaining 30 \% reference data split and yielded overall accuracies of 87 \% and 84 \% for 2003 and 2013, respectively. The first regional adapted Yellow River Land Cover Products (YRB LC) depict the detail spatial extent and distribution of the current land cover status and dynamics. The novel products overall differentiate overall 18 land cover and use classes, including classes of natural vegetation (terrestrial and aquatic), cultivated classes, mosaic classes, non-vegetated, and artificial classes, which are not presented in previous land cover studies so far. Building on this, an extended multi-faceted land cover analysis on the most prominent land cover change types at finer spatial and temporal scales provides a better and more detailed picture of the Yellow River Basin dynamics. Precise spatio-temporal products about mining, agriculture, forest, and urban areas were examined from long-trem Landsat satellite time series monitored at annual scales to capture the rapid rate of change in four selected focus regions. All archived Landsat images between 2000 and 2015 were used to derive spatially continuous spectral-temporal, multi-spectral, and textural metrics. For each thematic region and year RF models were built, trained and tested based on a stablepixels reference data set. The automated adaptive signature (AASG) algorithm identifies those pixels that did not change between the investigated time periods to generate a mono-temporal reference stable-pixels data set to keep manual sampling requirements to a minimum level. Derived results gained high accuracies ranging from 88 \% to 98 \%. Throughout the basin, afforestation on the Central Loess Plateau and urban sprawl are identified as most prominent drivers of land cover change, whereas agricultural land remained stable, only showing local small-scale dynamics. Mining operations started in 2004 on the Qinghai-Tibet Plateau, which resulted in a substantial loss of pristine alpine meadows and wetlands. In this thesis, a novel and unique regional specific view of current and past land cover characteristics in a complex and heterogeneous landscape was presented by using a multi-source remote sensing approach. The delineated products hold great potential for various model and management applications. They could serve as valuable components for effective and sustainable land and water management to adapt and mitigate the predicted consequences of Global Change processes.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Knauer2018,
  author    = {Knauer, Kim},
  title     = {Vegetation Dynamics in West Africa - Spatio-temporal Data Fusion for the Monitoring of Agricultural Expansion},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164776},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {West Africa is one of the fastest growing regions in the world with annual population growth rates of more than three percent for several countries. Since the 1950s, West Africa experienced a fivefold increase of inhabitants, from 71 to 353 million people in 2015 and it is expected that the region's population will continue to grow to almost 800 million people by the year 2050. This strong trend has and will have serious consequences for food security since agricultural productivity is still on a comparatively low level in most countries of West Africa. In order to compensate for this low productivity, an expansion of agricultural areas is rapidly progressing. The mapping and monitoring of agricultural areas in West Africa is a difficult task even on the basis of remote sensing. The small scale extensive farming practices with a low level of agricultural inputs and mechanization make the delineation of cultivated land from other land cover and land use (LULC) types highly challenging. In addition, the frequent cloud coverage in the region considerably decreases the availability of earth observation datasets. For the accurate mapping of agricultural area in West Africa, high temporal as well as spatial resolution is necessary to delineate the small-sized fields and to obtain data from periods where different LULC types are distinguishable. However, such consistent time series are currently not available for West Africa. Thus, a spatio-temporal data fusion framework was developed in this thesis for the generation of high spatial and temporal resolution time series. Data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) enjoyed increasing popularity during recent years but they have hardly been used for the application on larger scales. In order to make it applicable for this purpose and to increase the input data availability, especially in cloud-prone areas such as West Africa, the ESTARFM framework was developed in this thesis introducing several enhancements. An automatic filling of cloud gaps was included in the framework in order to use even partly cloud-covered Landsat images for the fusion without producing gaps on the output images. In addition, the ESTARFM algorithm was improved to automatically account for regional differences in the heterogeneity of the study region. Further improvements comprise the automation of the time series generation as well as the significant acceleration of the processing speed through parallelization. The performance of the developed ESTARFM framework was tested by fusing an 8-day NDVI time series from Landsat and MODIS data for a focus area of 98,000 km² in the border region between Burkina Faso and Ghana. The results of this test show the capability of the ESTARFM framework to accurately produce high temporal resolution time series while maintaining the spatial detail, even in such a heterogeneous and cloud-prone region. The successfully tested framework was subsequently applied to generate consistent time series as the basis for the mapping of agricultural area in Burkina Faso for the years 2001, 2007, and 2014. In a first step, high temporal (8-day) and high spatial (30 m) resolution NDVI time series for the entire country and the three years were derived with the ESTARFM framework. More than 500 Landsat scenes and 3000 MODIS scenes were automatically processed for this purpose. From the fused ESTARFM NDVI time series, phenological metrics were extracted and together with the single time steps of NDVI served as input for the delineation of rainfed agricultural areas, irrigated agricultural areas and plantations. The classification was conducted with the random forest algorithm at a 30 m spatial resolution for entire Burkina Faso and the three years 2001, 2007, and 2014. For the training and validation of the classifier, a randomly sampled reference dataset was generated from Google Earth images based on expert knowledge of the region. The overall classification accuracies of 92\% (2001), 91\% (2007), and 91\% (2014) indicate the well-functioning of the developed methodology. The resulting maps show an expansion of agricultural area of 91\% from about 61,000 km² in 2001 to 116,900 km² in 2014. While rainfed agricultural areas account for the major part of this increase, irrigated areas and plantations also spread considerably. Especially the expansion of irrigation systems and plantation area can be explained by the promotion through various national and international development projects. The increase of agricultural areas goes in line with the rapid population growth in most of Burkina Faso's provinces which still had available land resources for an expansion of agricultural area. An analysis of the development of agricultural areas in the vicinity of protected areas highlighted the increased human pressure on these reserves. The protection of the remnant habitats for flora and fauna while at the same time improving food security for a rapidly growing population, are the major challenges for the region in the future. The developed ESTARFM framework showed great potential beyond its utilization for the mapping of agricultural area. Other large-scale research that requires a sufficiently high temporal and spatial resolution such as the monitoring of land degradation or the investigation of land surface phenology could greatly benefit from the application of this framework.},
  subject      = {Fernerkundung},
  language  = {en}
}
@article{LandmannSchrammColditzetal.2010,
  author    = {Landmann, Tobias and Schramm, Matthias and Colditz, Rene R. and Dietz, Andreas and Dech, Stefan},
  title     = {Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68628},
  year      = {2010},
  abstract  = {Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km2 large study area in western Burkina Faso and southern Mali (West Africa). A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class "Wetland") is 9,350 km2, corresponding to 4.3\% of the total study area size. The classes "No wetland"/"Wetland" could be separated with very high certainty; the overall agreement (KHAT) was 84.2\% (0.67) and 97.9\% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments.},
  subject      = {Geologie},
  language  = {en}
}
@phdthesis{Cord2012,
  author    = {Cord, Anna},
  title     = {Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-71021},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called 'biodiversity crisis', which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called 'megadiverse' nations cover less than 10\% of the earth's land surface but support nearly 70\% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico's large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Duenkeloh2011,
  author    = {D{\"u}nkeloh, Armin},
  title     = {Water Balance Dynamics of Cyprus - Actual State and Impacts of Climate Change},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-75165},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {A completely revised and enhanced version of the water balance model MODBIL of the regional water balance dynamics of Cyprus was developed for this study. The model is based on a physical, process-oriented, spatially distributed concept and is applied for the calculation of all important water balance components of the island for the time period of 1961-2004. The calibrated results are statistically analysed and visualised for the whole island area, and evaluated with respect to the renewability of natural water resources. Climate variability and changes of the past decades are analysed with regard to their influence on water balances. A further part of the study focusses on the simulation of impacts of potential climate change. The water balances are simulated under changing climatic conditions on the base of theoretical precipitation, temperature and relative humidity changes and the revealed impacts on the water balances and renewable resources are discussed. Furthermore, a first principal water balance scenario is developed for the assessment of the regional hydrological changes expected for Cyprus by the end of the 21st century. The scenarios are based on recently calculated climate change assessments for this part of the Mediterranean, under an assumed further increase of greenhouse gasses in the atmosphere.},
  subject      = {Wasserhaushalt},
  language  = {en}
}
@article{ConradFritschZeidleretal.2010,
  author    = {Conrad, Christopher and Fritsch, Sebastian and Zeidler, Julian and R{\"u}cker, Gerd and Dech, Stefan},
  title     = {Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68630},
  year      = {2010},
  abstract  = {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.},
  subject      = {Geologie},
  language  = {en}
}