Global satellite data as proxies for urbanization in flood prone areas

Detta är en Master-uppsats från Uppsala universitet/Institutionen för geovetenskaper

Sammanfattning: Delta regions are typically characterized by their high population density, low elevation, and risk of flooding. Long term planning and preparation is needed to mitigate the adverse effects of floods. Disaster management planning and flood protection measures require information about urbanization patterns, but this information is lacking in many parts of the world. Global satellite data could potentially aid or replace local urbanization data in such data scarce areas. This master thesis assesses the suitability of two global satellite datasets to serve as proxies for urbanization in flood prone areas: the Global Human Settlement (data for 1975, 1990, 2000 and 2014) and stable Nighttime Lights data series (annual data, 1992-2013). The assessment is performed through comparison of spatial-temporal urbanization trends of the global datasets with a previous study performed in the Netherlands using detailed local data. These spatial-temporal trends involve the share or urban area that is situated in flood prone zones and the average inundation depth. Through analysis based on Geographic Information Systems it was found that the Global Human Settlement data series indicates a stable increase in the percentage of urban area in flood prone zones from 31.60% in 1975 to 36.54% in 2014. Potentially, this increase results from the flood protection measures installed between 1954 and 1997. The Nighttime Lights data series shows values of around 36% throughout its time period, with no clear increase or decrease. These values are on average 15-17% higher over the whole time series than the values found with the use of the local data. The Global Human Settlements dataset shows values for the average inundation depth from 1.47m in 1975 to 1.72m in 2014, similar to the local data. The increase could be explained by the fact that only areas with higher inundation depths are available for urbanization. The Nighttime Lights does not show a clear trend with values ranging from 1.52m to 1.70m and large annual variation. Overall, the suitability of the Global Human Settlement dataset is higher than the stable Nighttime Lights dataset for this study area as it shows values more similar to the local data and does not require prerequisite threshold analysis, which is the case for the Nighttime Lights data.

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