Search

The sensitivity of synthetic aperture radar (SAR) and interferometric SAR (InSAR) to surface properties, especially changes in height and roughness, combined with an all-weather capability, makes radar remote sensing a potential tool for mapping urban damage caused by earthquakes. With InSAR, surface displacement has been mapped successfully and in detail, but for urban-damage mapping, results have so far been less conclusive. ENVISAT Advanced SAR images of Barn, Iran, that were acquired before and after the 2003 earthquake were used. Between preseismic and coseismic image pairs, coherence decreased with increasing damage levels. However, contrary to previous studies, earthquake damage caused both increases and decreases in amplitude. Therefore, its absolute value, which correlated with damage level, was used. Individually, coherence difference led to better results than absolute amplitude change, although still with limited accuracy. The combination of both resulted in an overall accuracy of 52%. Since vegetation causes decorrelation, a predisaster Advanced Spaceborne Thermal Emission and Reflection Radiometer image was used to mask out vegetated areas, which improved the accuracy by 4%. The ground-truth map showed damage levels per district instead of per pixel. Therefore, the ratio of pixels classified as "damage" and "nondamage" was calculated for each damage class of the map, and a clear relation was found. This shows that the aggregation level of the map partly explains the low-accuracy figures of the pixel-based evaluation. Although improvement was made, InSAR techniques for urban-damage mapping do not yet provide the accuracy levels needed for disaster mitigation. However, substantial improvements can be expected from instruments with higher spatial resolution, such as the recently launched Phased Array type L-band SAR and Terra SAR-X. [ABSTRACT FROM AUTHOR]

The sea-level rise (SLR) resulting from climate change and flooding will threaten residents living in low-lying coastal zones in the coming decades. In this regard, evaluating social vulnerability as a significant component of flood risk reduction is necessary. Indicators, in conjunction with multi-criteria decision-making (MCDM) methods, have been recently employed to quantify social vulnerability. In the present study, an indicator-based approach was developed to assess social vulnerability to SLR and flooding in Bandar Abbas city coastal district, southern Iran. To build a social vulnerability index (SoVI) indicators were firstly categorized in, exposure, sensitivity, and adaptive capacity components. Indicators were then weighted using MCDM methods (i.e., analytical hierarchy process (AHP) and fuzzy AHP models). Subsequently, an additive weighting model was employed to produce social vulnerability maps at the block level, and under different combined flooding scenarios in 2050 and 2100. Results showed that using the fuzzy AHP does not necessarily change the ranks of indicators and components compared to the AHP model. However, the spatial extents of social vulnerability were entirely different for both models due to differences in indicators weight. It confirms that using the AHP model as a simplified method can be acceptable when determining indicators and components weight is required. Although, in the case of identifying the detailed spatial extents of social vulnerability using the fuzzy AHP might be appropriate. For all scenarios, using the AHP and fuzzy AHP model, most districts were classified as medium and low vulnerable, respectively. Moreover, the impact of SLR resulting from climate change was significantly evident in the eastern and western parts, where more districts were recognized as highly and very highly vulnerable in the worst-case scenario (S4-2100). The results of this study provide valuable comparative information that can be employed by decision-makers in coastal planning and risk reduction at local scales. Image 1 • Exposure indicators were developed considering coastal flooding triggered by wave set-up, wind set-up, and tides. • Nine vulnerability indicators were organized in the IPCC framework to build SoVI at the local scale. • Using the AHP and fuzzy AHP models, vulnerability indicators rank remained the same while their weights were different. • SLR had the greatest effect on the western and eastern parts of city specifically in scenario S4-2100. • Some eastern and western parts of the study area identified as the most vulnerable areas. [ABSTRACT FROM AUTHOR]

• Different landslide recording methods including pixel-based, centroid, crown, and toe. • Landslide spatial modeling using Mahalanobis distance and random sampling methods. • Negative balance scenariosproposed for landslide susceptibility assessment. The Ziarat Watershed, located in the south of the Golestan Province, Iran, has witnessed several destructive landslide episodes, prompting a number of researchers to aspire to improve landslide susceptibility modeling (LSM) techniques. We constructed three scenarios focusing on landslide positioning techniques (pixel-based, centroid, crown, and toe), training/test sampling strategies (Mahalanobis distance (MD), and random sampling (RS)), with alternative landslide/non-landslide data balances (1:1, 1:2, and 1:3). The data mining boosted regression trees (BRT) model was used for the landslide susceptibility modeling, using landslide data and 13 landslide controlling factors for the Ziarat Watershed. The performance of the scenarios was assessed using the areas under the success and prediction rate curves (AUSRC and AUPRC). A combination of pixel-based–MD–1:2 showed the highest learning capability and goodness-of-fit with an AUSRC value of 0.87, and the highest predictive power and generalization capacity with an AUPRC value of 0.79. Conversely, centroid-based–1:3–RS, crown-based–1:3–RS, and toe-based–1:3–RS performed less well. Comparatively, the pixel-based, MD, and 1:2 data balance scenarios surpassed their counterparts and outperformed the other models. The results indicated a high spatial differentiation with a significant chi-square value of 4549.46 at 95% confidence level. Moreover, 15.21% of the study area, containing almost 50% of the landslides, was found to have a high susceptibility to landslides. According to the premier scenario (pixel-based–MD–1:2), lithological formation, distance from roads, and NDVI, with respective contributions of 31.4%, 12.9%, and 12%, are the main spatial controlling factors leading to landslide occurrences in the study area. [ABSTRACT FROM AUTHOR]