Ms. Taisser H.H. Deafalla Profile

Taisser H.H. Deafalla

at Technische Univ Dresden

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Author

Publications (3)

Proceedings Article | 23 October 2014 Paper

The application of remote sensing for climate change adaptation in Sahel region

Taisser H. Deafalla, Elmar Csaplovics, Mustafa El-Abbas

Proceedings Volume 9245, 92451R (2014) https://doi.org/10.1117/12.2067460

KEYWORDS: Climate change, Remote sensing, Climatology, Ecosystems, Satellites, Soil science, Image analysis, Environmental sensing, Image classification, Earth observing sensors

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Proceedings Article | 24 October 2013 Paper

Forest structure estimates with GEOBIA and multi-scale optical sensors

Mustafa El Abbas, E. Csaplovics, Taisser H. Deafalla

Proceedings Volume 8893, 88930U (2013) https://doi.org/10.1117/12.2029243

KEYWORDS: Data modeling, Image segmentation, Image analysis, Statistical modeling, Optical sensors, Remote sensing, Feature extraction, Statistical analysis, Digital imaging, Data analysis

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Proceedings Article | 24 October 2013 Paper

Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers

Mustafa El-Abbas, Elmar Csaplovics, Taisser Deafalla

Proceedings Volume 8893, 88930O (2013) https://doi.org/10.1117/12.2029252

KEYWORDS: Agriculture, Image segmentation, Spatial analysis, Remote sensing, Integrated optics, Satellites, Landsat, Image analysis, Classification systems, Earth observing sensors

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Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

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