**3.1 Global laser point cloud acquisition**

The satellite-based LiDAR measurement is performed by transmitting laser pulses at a certain frequency from the satellite-based laser to the ground, and the laser beam crosses the atmosphere and is scattered by the ground surface, producing a weak backscattered echo, which is received by the telescope on the satellite-based LiDAR, and the distance value between the laser and the detected ground surface is calculated through photoelectric signal conversion and time measurement, and then combined with the satellite attitude, platform position, laser pointing, and other information to finally obtain precise 3D spatial coordinates of the laser footprint point. It mainly involves the key technologies such as long-life on-board laser, multi-beam laser transmission and reception, photon level signal detection, precision orbit position determination, laser pointing accuracy determination, and joint active-passive 3D measurement. The major key technologies involved are long-life on-board laser, multi-beam laser transmission and reception, photon level signal detection, precision orbital position determination, precise determination of laser pointing, and joint active-passive 3D measurement, etc. The technology is characterized by the requirement of laser pointing accuracy to subangular second level and distance measurement accuracy to centimeter level at working altitude of hundreds of kilometers and the realization of 3D coordinate measurement accuracy of laser footprint points on the plane to meter level and elevation to decimeter level. Meanwhile, in order to improve the efficiency and accuracy of global mapping, it adopts a new system of photon counting LiDAR, which reduces the laser footprint size from tens and dozens of meters to the meter level and increases the sampling frequency from a few Hz to 10,000 Hz, improving the detection effectiveness by thousands of times compared with the traditional linear detective system, providing support for the rapid acquisition of high-density and high-quality point clouds. Unlike the traditional optical or microwave remote sensing mapping through the indirect measurement mode by the imaging to achieve three-dimensional reconstruction, the satellite-based LiDAR measurement belongs to the direct active acquisition of surface elevation information, which reduce the post-processing steps and improves the overall efficiency of surface three-dimensional information acquisition.

### **3.2 Point cloud data processing**

According to the photonic point cloud data processing process, the product definition design can be divided into five levels, mainly including original telemetry data (level 0), format decoding data (level 1), point cloud geolocation data (level 2), standard data products (level 3), and thematic data products (level 4), of which levels 0–2 are pre-processing products and levels 3–4 are professional processing products, involving the main technical processes including laser foot point 3D coordinate solution, LiDAR ranging and pointing parameters ground calibration, point cloud data pre-processing, mapping professional processing and networking services. Level 1 processing is to decode the raw package data, format conversion, and data cataloging to obtain the standard format data. The raw standard format data include photon time-of-flight data obtained after instrument delay and other corrections, laser

*Spaceborne LiDAR Surveying and Mapping DOI: http://dx.doi.org/10.5772/intechopen.108177*

emission position, and pointing data obtained using precision positional data and calibration data. Level 2 processing is to obtain the laser footprint longitude, latitude, and elevation values, and denoising and pre-classifying the point cloud [36], including (2A) un-noised point cloud and (2B) denoised point cloud. Level 3 processing is to obtain control point data, target area DSM, and polar DSM products after adjustment and gridding, including (3A) control point data, (3B) DSM, (3C) DEM, and (3D) object 3D model. Level 4 processing is for specific application needs, fusion of optical, microwave images and DSM, and other multi-source data to generate thematic products for specific applications, as well as extraction of polar ice cover, atmosphere, vegetation, lakes, and other information in point clouds, and other corresponding thematic products are obtained through specialized professional processing of mapping.

#### **3.3 Point cloud data applications**

As a direct digital representation of the global 3D physical world, laser point clouds have been applied in a wide range of directions [37–41], mainly including massive global laser point cloud 3D visualization, multiple terrain information fusion, shallow sea topography, polar elevation measurement, automatic 3D reconstruction of objects, and digital mapping based on point clouds, etc., to realize diverse spatial information product making and provide support for the construction of 3D digital earth framework. Efficient storage management retrieval and visualization of massive point cloud data are the most direct and effective applications. At present, there are successful solutions to support the storage management and visualization application of 3D point clouds with a global data volume of 100 petabytes, such as Bentley Pointools [42] and Euclideon udStream [43], whose 3D engine has the characteristics of loading unlimited spatial data in seconds to achieve rapid application. Secondly, point cloud control surveying realizes multi-source observation data fusion application. Cloud control photogrammetry has been realized in a number of system construction and engineering applications, point cloud support for multi-source terrain information, and threedimensional model fusion will be the next important research direction, to provide an effective way to quickly establish a large range of consistent accuracy, more rich information-type three-dimensional geospatial information framework. In addition, point cloud mapping to achieve automatic acquisition of 3D information from laser point clouds and transformation into geographic entity representation with structure and function has become a major application direction. Dense point cloud and fully automatic processing can be applied to urban 3D modeling, coastal zone topographic survey, polar elevation mapping, road infrastructure maintenance monitoring, and forest resources survey, etc. It generates multifaceted geospatial information products including 3D models of buildings, digital ground models and digital surface models, forestry thematic products, etc., which can provide support for global mapping database construction and thematic element information update.
