FSAUA: A framework for sensitivity analysis and uncertainty assessment in historical and forecasted land use maps

2016 ◽  
Vol 84 ◽  
pp. 70-84 ◽  
Author(s):  
Amin Tayyebi ◽  
Amir Hossein Tayyebi ◽  
Jamal Jokar Arsanjani ◽  
Hossein Shafizadeh Moghadam ◽  
Hichem Omrani
2018 ◽  
Vol 18 (11) ◽  
pp. 3089-3108 ◽  
Author(s):  
Ayse Duha Metin ◽  
Nguyen Viet Dung ◽  
Kai Schröter ◽  
Björn Guse ◽  
Heiko Apel ◽  
...  

Abstract. Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.


2017 ◽  
Author(s):  
Wilian F. Costa ◽  
Michel J. M. Bieleveld ◽  
Raphael G. Chinchilla ◽  
Antonio M. Saraiva

We present a multilayer image segmenter adapted to be used for Precision Agriculture (PA). PA depends strongly on the application of information technologies to divide and group geographic areas based on land use, soil data, metrological data and agricultural resources for planning and implementation of activities to increase output by using optimal strategies for each segment. We implemented a modified Baatz algorithm in the statistical language R and speed sensitive code was implemented in C++. The code will be made publicly available under the GNU Lesser Public License. We show the merit of our approach at the hand of a landscape and discuss the obtained segments generated by our tool.


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