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To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters’ uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively.

The high resolution visible images from space borne or airborne platforms are widely used for earthquake building damage degree or type identification. But the visible images cannot be used for damage detection during night time. Considering the availability of thermal infrared (TIR) images to get land surface information all day long, this study try to study the possibility of using UAV based TIR image to detect earthquake damaged buildings. The study area is Beichuan county earthquake ruins protection site of the ‘5.12’ Wenchuan earthquake in 2008, which located in Qushan Town, Beichuan Qiang Autonomous County, Mianyang city, Sichuan Province, China. And the TIR image are acquired using UAV of DJI M200 version 2 with the payload of ZENMUSE XT2. The study shows that the texture features in TIR images can be used for earthquake building damage degree detection with the accuracy of 73.4% and 71.6%, respectively, for the training and test dataset.

It is well know that for the same intensity areas, the buildings with different structure types can show different vulnerabilities. Thus, building structure type is one the key parameters for rapid estimation of casualties and injuries after earthquake, which is vital for emergency response and rescue. To estimate building structure types, the buildings are firstly extracted based on the spectrum, texture, and height information of UAV visible images. Then, the structure type of individual extracted buildings is classified using convolution neural network. To evaluate the accuracy of the proposed method, the images of Xuyi county, Huai'an City, Jiangsu Province are acquired using a small rotorcraft UAV. The results show that the user accuracy and cartography accuracy are 80.69% and 78.42%, respectively.

The accuracy of traditional spectral based land surface classification method can be decreased for areas with similarity spectral characters for different land cover types. To extract building distributions in those areas, a method based on the height information using unmanned aerial vehicle (UAV) optical remote sensing data were proposed in this study. With Digital Surface Model (DSM) and Digital Elevation Model (DEM) data produced by EasyUAV software using UAV acquired remote sensing images, the differences between DSM and DEM larger than 2.6 m was taken as a threshold for building extraction. It showed that the user accuracy and mapping accuracy of the building extraction method proposed in this study were 88.69% and 97.42%, respectively. To evaluate the performance of the proposed method, the buildings were also extracted based on traditional supervised classification method using Digital Orthophoto Map (DOM) produced by EasyUAV. The results showed that the new method was more accurate with the user accuracy and mapping accuracy of the supervised classification method of 43.23% and 85.30%, respectively.

The atmospheric aerosols are one of the uncertainties that significantly influence the study of energy balance at regional or global scale. However, few studies focus on the aerosols caused by earthquake induced damage of hazard-affected bodies. To study the influences of earthquakes on the loadings of atmospheric aerosols, the AErosol RObotic NETwork (AERONET) sites retrieved aerosol optical depth (AOD) are used in this study. It showed that the AOD became larger after earthquakes for six cases in this study. After approximately one day, the AOD of the six cases tend to close to the normal range. In addition, the other four cases show that the there is no significant increasement of AOD or the AOD tend to become smaller after earthquake.

The contents and key technologies of GIS based city and county earthquake emergency platform are set forth. These contents are quickly determining earthquake information, collecting earthquake disaster information, aiding government emergency rescue decision-making, and earthquake disaster prevention publicity and social actions. According to these contents, GIS based architecture of the platform is designed, and function modules of the platform are also described. Then key technologies of the development of the emergency platform are discussed, in which much emphasis has been placed on multi-scale based emergency database, SOA based emergency workflow and emergency situation map plotting. Finally, using Nanjing geographic information, Nanjing earthquake emergency platform is developed. And this platform gets good result in a real earthquake emergency drill.