## 6 Conclusions  
Fast crisis response after a natural or man-made disaster is crucial to support rescue, humanitarian and reconstruction operations in the affected area. Rapid damage mapping provides important information about the affected area, including the grade and type of damage. Satellite remote sensing plays a key role in rapid damage assessment due to its quick response and fast monitoring of large areas at low costs. Optical sensors rely on a cloud free sky to provide useful imagery, while synthetic aperture radar (SAR) sensors are almost completely weather independent. Therefore, SAR sensors are in general able to provide useful imagery within a much shorter time frame than optical sensors. A comprehensive review on damage assessment techniques using multi-temporal SAR procedures, such as the interferometric coherence and SAR intensity correlation (Sections 3.1-3.4) was presented. Comparison of pre- and post-disaster SAR amplitude images for damage assessment is in most cases not very useful, as after a disaster the SAR amplitude both increases (e.g., due to debris) and decreases (e.g., due to floods, clearing activities, _etc._). Contrary to this, both interferometric coherence, as well as the SAR intensity correlation, show a decrease of their values with increasing damage level, as the intensity as well as the phase of the complex SAR backscatter changes due to damage on the ground. Coherence is more sensitive to smaller changes on the ground than the SAR intensity correlation. Therefore, the former is better suited to detect lower damage levels as well as to better differentiate between slightly damaged and still intact buildings. On the other hand, the intensity correlation is more sensitive to larger changes caused by stronger damages. Moreover, the intensity correlation procedure still provides useful information at spatial baselines, which are too large for a useful coherence application (see also Sections 2.1 and 3.2). Unchanged (_i.e._, undamaged) areas show higher coherence values over a longer time period at longer SAR wavelengths such as L-band compared to the shorter C or X-band, which also leads to a better coherence contrast between damaged and undamaged areas and therefore to a better differentiation of both. Challenges for future research on multi-temporal SAR based damage assessment methods can be divided into two main categories:  
1. The requirement of fast, easy-to-use, worldwide applicable damage assessment procedures with high accuracy. 2. The availability of at least two pre-disaster SAR images acquired shortly before the event at the same imaging geometry as the post-event SAR acquisition (see Figure 3). Regarding challenge (I), a possible solution to increase the accuracy of the damage assessment is the usage of other data additional to pre- and post-event SAR data such as high spatial resolution optical imagery and GIS data (see Section 3.4). For instance by fusing SAR and optical imagery the accuracy of the damage assessment methods was improved from about 41%-60% (based on only SAR intensity correlation and/or coherence) up to _ca._ 77%-88% (Table 5). GIS data (_i.e._, administrative information, city parcel boundaries, _etc._) allows the focus of the damage assessment to be directed to the most important areas of interest, _i.e._, densely build-up urban areas, and to assess the damage at pre-defined meaningful boundaries. Therefore, for each important area of interest exposed to natural hazards, a geodatabase containing useful GIS data (e.g., boundaries of city parcels and critical infrastructure such as hospitals or shelters) may be set up and continuously updated (_cf._ Section 4). The most crucial and also difficult step in damage assessment is the detection of the damage and quantifying its severity. Several methods have been reported in the literature for change detection, _i.e._, detection of the damage.