Accurate bathymetric maps are essential to understand marine and coastal ecosystems. With the development of satellite and sensor technology, satellite-derived bathymetry (SDB) has been widely used to measure the depth of nearshore waters. Employment of physics-based methods requires a series of optical parameters of the water column and seafloor, which limits the application of these methods to shallow-water bathymetry. Due to convenience, low costs, and high efficiency, empirical methods based on in situ measurements and satellite imagery are increasingly used for nearshore bathymetry. These measurements are required to calibrate empirical models, so that reasonable accuracy can be achieved. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the advanced topographic laser altimeter system, provides a novel opportunity for nearshore bathymetry. Using the new measurement strategy of photon counting, ICESat-2 can provide accurate bathymetric points from spaceborne observations, which can be used in place of in situ water depth data. In this study, ICESat-2 bathymetric points and multispectral images were used to train four typical models and produce bathymetric maps for Shanhu Island, Ganquan Island, and Lingyang Reef in the Xisha Islands of China. We evaluated the bathymetric results by comparing them with reference depth data from airborne light detection and ranging. All models had a satisfactory accuracy, as well as multimodel and multisource image consistency. With the ICESat-2 bathymetric points, SDB is no longer limited by in situ measurements. Hence, this approach could be extended to a larger scale to obtain nearshore bathymetric maps of coastal areas, surrounding islands, and reefs using free and open-access satellite data.

NoSQL database can provide massive, high concurrency, and scalable services for storing different types of data. HBase, a type of NoSQL database, in which columns are grouped into column families, is very suitable for storing semi-structured or unstructured spatial vector data. However, since there are few rules and constraints to be followed for NoSQL database, design of storage schema for spatial data based on NoSQL is difficult. In this paper, based on our early work, an improved Z curve storage schema is proposed for spatial vector data. According to our new schema, row key of a geometric object is the Z curve code of the spatial grids intersected with the geometric object. Moreover, geometric objects with the same row key are stored in a column family. Our proposed method has two features. First, geometric objects adjacent in location are adjacent in physical storage. Second, redundant exists in storage for improving query accuracy. In our experiments, we compare the improved Z curve storage schema with a Quadtree storage schema, a R-tree storage schema and the previous Z curve storage schema. Query response time, memory usage, and query accuracy of spatial query on point and range are used to verify the validity of our proposed method. Experimental results show that the two storage schemas based on Z curve achieves higher query efficiency than the two storage schemas based on tree - the Quadtree storage schema and the R-tree storage schema. More importantly, the query results of the improved Z curve schema are completely correct, while the query results of the previous Z curve schema are not.

Solving ordinary differential equations (ODEs) is vital in diverse fields. However, it is difficult to obtain the exact analytical solutions of ODEs due to their changeable mathematical forms. Traditional numerical methods can find approximate solutions for specific ODEs. Unfortunately, they often suffer from ODEs’ forms and characteristics. To approximate different types of ODEs, this paper proposes a generic method based on adaptive differential evolution. Besides, in order to further reduce the error of the obtained approximate solutions, an improved Fourier periodic expansion function is developed, which is then combined with the least square weight method to formulate the ODEs as an optimization problem. Since the proposed method is not limited to ODEs’ forms and constraint conditions, it can be used to approximate any ODEs, including linear ODEs and nonlinear ODEs. The proposed method is evaluated on twenty popular test cases. The results indicate that the proposed method is able to accurately approximate different ODEs with better performance compared with other methods.

Photon-counting LiDAR systems are an efficient method for nearshore bathymetry and topographic mapping. Current single-photon LiDAR systems predominantly utilize lasers with wavelengths of 1550 or 532 nm and typically have a repetition rate of less than 100 kHz, which fails to meet the integrated marine and terrestrial detection needs of remote sensing. Addressing these limitations, this study has developed a novel dual-frequency photon-counting LiDAR system that incorporates a miniaturized high-repetition-rate blue-green laser, a wide dynamic range highly sensitive photon-resolvable detector, and an embedded real-time photon data high-speed sampling and storage mechanism. This system achieves high-repetition-rate laser pulses of 1000 kHz and energy detection ranging from pico-watts to micro-watts. The bathymetric capabilities are improved to 2.5 times the Secchi disk depth, with bathymetric accuracy better than 0.2 m, ranging accuracy better than 0.1 m, and horizontal positioning accuracy better than 0.5 m. Moreover, weighing less than 8 kg, it is compatible with mainstream airborne platforms, significantly enhancing the integrated marine and terrestrial detection capabilities of remote sensing.