Abstract: A sand dune extraction method based on fusion of positive-negative terrains and light shading includes: obtaining an original digital elevation model of a study region; using a neighborhood analysis method to process the original digital elevation model of the study region, thereby extracting a positive terrain region and a negative terrain region of the study region; using a lighting shading method to perform six directional lighting simulation calculations on each pixel of the original digital elevation model of the study region to obtain a lighting shading map, and extracting a lighting region and a shadow region of the study region from the lighting shading map; using an embedding method to fuse the positive terrain region and the negative terrain region with the lighting region and the shadow region. The sand dune extraction method is a method for remote sensing monitoring and governance of desertification regions.

Abstract: A method and system for evaluating casualty caused by landslide chain disaster induced by heavy rain are provided. The method includes: obtaining historical landslide data, candidate influence factors and personnel traffic distribution data of an observation site; dividing the historical landslide data into a model construction sample group, a model verification sample group, and a model test sample group; obtaining an occurrence probability of landslide chain disaster of each spatial position in the observation site; determining a start-up landslide source region of the landslide chain disaster according to the model verification sample group and the occurrence probability; determining an affected region of the landslide chain disaster according to the start-up landslide source region; determining a personnel number exposed to the landslide chain disaster; and setting an event division threshold, and evaluating the number of casualties according to the event division threshold and the personnel number.

Abstract: A high-temperature disaster forecast method based on a directed graph neural network is provided, and the method includes the following steps: S1, performing standardization processing on meteorological elements respectively to scale the meteorological elements into a same value range; S2, taking the meteorological elements as nodes in the graph, and describing relationships among the nodes by an adjacency matrix of graph; then learning node information by a stepwise learning strategy and continuously updating a state of the adjacency matrix; S3, training the directed graph neural network model after determining a loss function, obtaining a model satisfying requirements by adjusting a learning rate, an optimizer and regularization parameters as a forecast model, and saving the forecast model; and S4, inputting historical multivariable time series into the forecast model, changing an output stride according to demands, and thereby obtaining high-temperature disaster forecast for a future period of time.

Abstract: The underwater acoustic test system comprises an underwater acoustic transmitting unit, an underwater acoustic parabolic reflector, an underwater acoustic receiving unit, an orientation control system, and a computer measurement and control system. The underwater acoustic transmitting unit comprises an underwater acoustic signal generator and a transmitting transducer. The underwater acoustic parabolic reflector comprises a central main reflecting area and an edge diffraction processing area, wherein the central main reflecting area is configured for reflecting acoustic wave signals, and the edge diffraction processing area is configured for reducing the influence of the underwater acoustic parabolic reflector on a test area. The underwater acoustic receiving unit comprises a receiving transducer and an underwater acoustic signal receiver. The orientation control system comprises a traveling crane and a test turntable.

Abstract: The present application provides a method and system for identifying extreme climate events. The method acquires climate index (CI) grid data of a to-be-identified region within an extreme climate time period, and gradually expands each of event centers in the to-be-identified region, until CI values of all grids adjacent to the event center are not greater than a CI threshold. The method can obtain extreme climate impacted areas of extreme climate events in the to-be-identified region, and can further obtain CI intensities of the extreme climate events by average calculation. The method can obtain three pieces of dimension information on each of the extreme climate events in the to-be-identified region, including an extreme climate impacted area, a CI intensity and a duration. Therefore, the method can identify the extreme climate events more comprehensively.