Extracting damage information of buildings after an earthquake is crucial for emergency rescue and loss assessment. Low-altitude remote sensing by unmanned aerial vehicles (UAVs) for emergency rescue has unique advantages. In this study, we establish a remote sensing information-extraction method that combines ultramicro oblique UAV and infrared thermal imaging technology to automatically detect the structural damage of buildings and cracks in external walls. The method consists of four parts: (1) 3D live-action modeling and building structure analysis based on ultramicro oblique images; (2) extraction of damage information of buildings; (3) detection of cracks in walls based on infrared thermal imaging; and (4) integration of detection systems for information of earthquake-damaged buildings. First, a 3D live-action building model is constructed. A multi-view structure image for segmentation can be obtained based on this method. Second, a method of extracting information on damage to building structures using a 3D live-action building model as the geographic reference is proposed. Damage information of the internal structure of the building can be obtained based on this method. Third, based on analyzing the temperature field distribution on the exterior walls of earthquake-damaged buildings, an automatic method of detecting cracks in the walls by using infrared thermal imaging is proposed. Finally, the damage information detection and assessment system is researched and developed, and the system is integrated. Taking earthquake search-and-rescue simulation as an example, the effectiveness of this method is verified. The damage distribution in the internal structure and external walls of buildings in this area is obtained with an accuracy of 78%.

With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.

Estimates of regional net primary productivity (NPP) are useful in modeling regional and global carbon cycles, especially in karst areas. This work developed a new method to study NPP characteristics and changes in Chongqing, a typical karst area. To estimate NPP accurately, the model which integrated an ecosystem process model (CEVSA) with a light use efficiency model (GLOPEM) called GLOPEM-CEVSA was applied. The fraction of photosynthetically active radiation (fPAR) was derived from remote sensing data inversion based on moderate resolution imaging spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values, which were simulated by the model, matched the observed data well. The values of other relevant NPP models, as well as the MOD17A3 NPP products (NPP MOD17), were compared. In terms of spatial distribution, NPP decreased from northeast to southwest in the Chongqing region. The annual average NPP in the study area was approximately 534 gC/m²a (Std. = 175.53) from 2001 to 2011, with obvious seasonal variation characteristics. The NPP from April to October accounted for 80.1% of the annual NPP, while that from June to August accounted for 43.2%. NPP changed with the fraction of absorbed PAR, and NPP was also significantly correlated to precipitation and temperature at monthly temporal scales, and showed stronger sensitivity to interannual variation in temperature.

Floods are one of the most widespread and severe natural disasters. Then, evaluating the accuracy of multiscale remote sensing for flood monitoring is an essential research topic. In this study, floodwater boundary was obtained for application of the mechanism-based upscaling method and NDWI water extraction method. Floodwater extraction was simulated and its accuracy was analyzed for different spatial extents, spatial resolutions, landscape patterns and water body proportions at eight floodplains in China. The regional accuracy gradually increased with the spatial extent when the spatial resolution was constant and decreased with the spatial resolution when the spatial extent was constant. The water extraction accuracy showed a significant correlation with the water body proportion and water landscape pattern indices. The relative error of the water extraction had a strong positive correlation with the complexity of the patch shape and a strong negative correlation with the patch aggregation and connectivity and the water body proportion. Therefore, it may be concluded that different spatial resolutions of remotely sensed images should be selected according to differences in the water landscape patterns of regions to obtain results with the required water body extraction accuracy for regional flood monitoring in China.