ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 173-180, 2016
https://doi.org/10.5194/isprs-annals-III-7-173-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
ASSESSMENT OF CROPPING SYSTEM DIVERSITY IN THE FERGANA VALLEY THROUGH IMAGE FUSION OF LANDSAT 8 AND SENTINEL-1
D. Dimov, J. Kuhn, and C. Conrad Department of Remote Sensing, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany
Keywords: Image Fusion, Crop mapping, Synthetic Aperture Radar, Image classification, Sentinel-1 Abstract. In the transitioning agricultural societies of the world, food security is an essential element of livelihood and economic development with the agricultural sector very often being the major employment factor and income source. Rapid population growth, urbanization, pollution, desertification, soil degradation and climate change pose a variety of threats to a sustainable agricultural development and can be expressed as agricultural vulnerability components. Diverse cropping patterns may help to adapt the agricultural systems to those hazards in terms of increasing the potential yield and resilience to water scarcity. Thus, the quantification of crop diversity using indices like the Simpson Index of Diversity (SID) e.g. through freely available remote sensing data becomes a very important issue. This however requires accurate land use classifications. In this study, the focus is set on the cropping system diversity of garden plots, summer crop fields and orchard plots which are the prevalent agricultural systems in the test area of the Fergana Valley in Uzbekistan. In order to improve the accuracy of land use classification algorithms with low or medium resolution data, a novel processing chain through the hitherto unique fusion of optical and SAR data from the Landsat 8 and Sentinel-1 platforms is proposed. The combination of both sensors is intended to enhance the object´s textural and spectral signature rather than just to enhance the spatial context through pansharpening. It could be concluded that the Ehlers fusion algorithm gave the most suitable results. Based on the derived image fusion different object-based image classification algorithms such as SVM, Naïve Bayesian and Random Forest were evaluated whereby the latter one achieved the highest classification accuracy. Subsequently, the SID was applied to measure the diversification of the three main cropping systems.
Conference paper (PDF, 1475 KB)


Citation: Dimov, D., Kuhn, J., and Conrad, C.: ASSESSMENT OF CROPPING SYSTEM DIVERSITY IN THE FERGANA VALLEY THROUGH IMAGE FUSION OF LANDSAT 8 AND SENTINEL-1, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 173-180, https://doi.org/10.5194/isprs-annals-III-7-173-2016, 2016.

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