**5.3 Polar mapping**

In recent years, polar ice sheet mass balance studies have revealed that the Antarctic and Greenland ice sheets as a whole are in a state of accelerated melting, which has important implications for both global sea level rise and climate change [85, 86]. Satellite laser altimetry, represented by ICESat-2, is an important tool for mapping changes in polar ice sheet elevation and thus analyzing the ice sheet material balance [87–90].

Mapping polar ice cap elevation changes is one of the main scientific objectives of ICESat-2. At present, the main methods for mapping the elevation change of the polar ice cap surface using ICESat-2 include the intersection point method and the repeated trajectory method. The former uses multiple functions to fit the satellite lift orbits and calculate the ice cap elevation change at the orbit intersection points and their locations. The latter method divides the satellite orbit into kilometer-scale segments and uses least squares to fit the segment function model to calculate the ice cap elevation change. Compared with the traditional single-beam measurement mode used by ICESat, the multi-beam measurement of ICESat-2 greatly increases the number of intersections and effectively improves the ability to obtain details of the ice cap surface elevation change. At the same time, ICESat-2's paired laser beams can independently determine the local surface slope, making it possible to determine surface elevation changes using a single repeated reference track.

ICESat-2 features multiple beams, high repetition frequency, and micro-pulses, which greatly enhance the accuracy and reliability of polar observation results. On the spatial scale of 100 km<sup>2</sup> , ICESat-2 has an accuracy of better than 0.25 m/year for ice sheet elevation change measurements and can provide high-precision observations of better than 0.4 cm/year over the entire Antarctic or Greenland ice sheets [53]. **Figure 15** shows the Greenland ice sheet elevation change from 2003 to 2019 based on the intersection of ICESat and ICESat-2. The high-resolution and high-precision measurements provided by ICESat-2 will greatly improve the mapping accuracy in the ice sheet edge areas with large topographic relief and provide more reliable data support for the analysis of the polar ice sheet material balance.

### **5.4 Three-dimensional topographic mapping**

The satellite-based laser survey can quickly and accurately acquire threedimensional information on the earth's surface, providing a new means for terrestrial elevation three-dimensional mapping. Due to the high altitude of the satellite platform, unlike the airborne platform scanning measurement mode, the satellite-based LiDAR measurement adopts a multi-beam push measurement mode similar to the optical remote sensing satellite line array push imaging. Although the photon counting point cloud data in the flight direction forms a continuous surface elevation profile

**Figure 15.**

*Changes in the elevation of the Greenland ice sheet from 2003 to 2019 based on the intersection of ICESat and ICESat-2.*

due to high sampling frequency, and the ground sampling distance can reach submeter level, the sampling interval in the vertical orbit direction may reach hundreds to thousands of meters in magnitude due to the limitation of the number of beams, and the sampling spacing between the two directions is large, making it difficult to effectively measure the object shape. Icesat-2, for example, has a sampling interval of 0.7 meters in the along-track direction and more than 3.3 kilometers in the vertical-track direction, so it is difficult to meet the requirements of three-dimensional terrain mapping with a single pass, which limits the scope of application.

However, as the satellite cycle operation can realize the same area repeatedly observed several times, the sampling distance between sparse beams is gradually reduced, and the point cloud data interval in the vertical track direction can be effectively reduced by using joint processing of multi-track data, **Figure 16** shows the results of multi-track photon counting point cloud data processing using airborne 51 beam LiDAR. The sampling interval between the two directions gradually converges to the same, and finally realizes 3D mapping of the surface object, which can provide a better data source for 3D model reconstruction.

The simulation of satellite operation by airborne experiment shows that although the laser points are widely spaced in the vertical orbit direction, the dense point cloud with more uniform distribution can be obtained by multi-track coverage, which can better reflect the three-dimensional shape of the ground surface. Further, the laser point cloud can be filtered and classified, and a typical algorithm such as the adaptive progressive triangular network filtering method can classify the point cloud into two categories: ground points and non-ground points. Then, according to the spatial distribution of nonground points, they are further divided into buildings, vegetation and others. In the case of building point clouds, RANSAC plane detection and clustering as well as key point/ corner point and boundary extraction are used to achieve automatic reconstruction of

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

**Figure 16.** *Airborne multi-orbit photon counting point cloud data and processing results.*

**Figure 17.** *Automatic building extraction from 5m laser point cloud data and LOD1 level 3D reconstruction.*

LOD1 level building 3D models, as shown in **Figure 17**, and higher detail levels require further integration of multi-source data such as high-resolution multi-view images, spectral information, geographic information and even mobile phone videos.

At the same time, in order to further improve the efficiency and fineness of the target 3D measurement, the combination of LiDAR measurement and oblique photogrammetry to achieve rapid 3D mapping of the target is an effective technical approach. At present, a payload device Leica CityMapper, which integrates two detection instruments, has emerged in the aerial photogrammetry field, collecting 3D city oblique photography images and laser point cloud data simultaneously, which can effectively improve the productivity and data quality of creating digital city 3D models, realizing efficient and low-cost acquisition of highly detailed and accurate 3D data, and making the widespread use of 3D models a reality. This provides a good reference for the design of a satellite-based hybrid 3D mapping system.
