Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future.

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R² and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km³/year and −1.29 km³/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R² and RMSE were 1.00 and 0.36 km³, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km³/year and −0.58 km³/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) can measure the elevations of the Earth’s surface using a sampling strategy with unprecedented spatial detail. In the daytime of mountainous areas where the signal–noise ratio (SNR) of weak beam data is very low, current algorithms do not always perform well on extracting signal photons from weak beam data (i.e., many signal photons were missed). This paper proposes an effective algorithm to extract signal photons from the weak beam data of ICESat-2 in mountainous areas. First, a theoretical equation of SNR for ICESat-2 measured photons in mountainous areas was derived to prove that the available information provided by strong beam data can be used to assist the signal extraction of weak beam data (that may have very low SNR in mountainous areas). Then, the relationship between the along-track slope and the noise level was used as the bridge to connect the strong and weak beam data. To be specific, the along-track slope of the weak beam was inversed by the slope–noise relationship obtained from strong beam data, and then was used to rotate the direction of the searching neighborhood in the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. With the help of this process, the number of signal photons included in the searching neighborhood will significantly increase in mountainous areas and will be easily detected from the measured noisy photons. The proposed algorithm was tested in the Tibetan Plateau, the Altun Mountains, and the Tianshan Mountains in different seasons, and the extraction results were compared with the results from the ATL03 datasets, the ATL08 datasets, and the classical DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the parameters of the classification precision, recall, and F-score of our algorithm and three other algorithms were calculated. The modified DBSCAN could achieve a good balance between the classification precision (93.49% averaged) and recall (89.34% averaged), and its F-score (more than 0.91) was higher than that of the other three methods, which successfully obtained a continuous surface profile from weak beam data with very low SNRs. In the future, the detected signal photons from weak beam data are promising to assess the elevation accuracy achieved by ICESat-2, estimate the along-track and cross-track slope, and further obtain the ground control points (GCPs) for stereo-mapping satellites in mountainous areas.

Slope and roughness are basic geophysical properties of terrain surface, and also sources of error in satellite laser altimetry systems. The full-waveform satellite laser altimeter records the complete echo waveform backscattered from the target surface worldwide, so it may be used for both range measurement and inversion analysis of geometric parameters of the target surface. This paper proposes a new method for inversion of slope and roughness of the bare or near-bare terrain within laser footprint using full-waveform satellite laser altimeter data, Shuttle Radar Topographic Mission (SRTM) and topographic prior knowledge. To solve the non-uniqueness of the solution to the inversion problem, this paper used the SRTM and airborne Light Detection and Ranging (LiDAR) data in North Rhine-Westphalia, Germany, to establish a priori hypothesis about real information of topographic parameters. Then, under the constraints of prior hypothesis, the theoretical formulas and rules for slope and roughness inversion using the pulse-width broadening knowledge of satellite laser altimeter echo full-waveform were developed. Finally, based on the full-waveform data from the Geoscience Laser Altimeter System (GLAS) that was borne on ICE, Cloud, and Land Elevation Satellite (ICESat) and SRTM in the West Valley City, Utah and Jackson City, Wyoming, United States of America, the inversion was carried out. The experiment compares the results of proposed method with those of existing ones and evaluates the inversion results using high precision terrain slope and roughness information, which indicates that our proposed method is superior to the state-of-the-art methods, and the inversion accuracy for slope is 0.667$°$ (Mean Absolute Error, MAE) and 1.054$°$ (Root Mean Square Error, RMSE), the inversion accuracy for roughness is 0.171 m (MAE) and 0.250 m (RMSE).