The Ice, Cloud, and Elevation Satellite-2 (ICESat-2) has been witnessed to improve the performance of ground information retrieval. Yet, extracting high-precision ground photons over expansive areas remains a significant challenge, particularly in forested regions with steep slopes. Ground photons can not be extracted correctly in a large slope area because of the unclear spatial distribution difference between ground and canopy photons. To address the abovementioned issue, we propose a novel slope correction method to extract ground photons for sub-canopy terrain retrieval accurately. Our approach begins with a way to find the terrain trend of the photon cloud. Turning points are then identified from the generated terrain trend to segment the signal photon cloud into distinct terrain regions. We detrend the photon cloud in each segment by a horizontal rotation, making the spatial distribution of ground and canopy photons clear. Finally, an iterative method based on the percentile range of photons’ elevation to extract fine ground photons. We validated the proposed method using nine datasets from three rugged forest areas. The filtering process effectively denoised photon cloud data, while turning point detection achieved high accuracy, with F-scores ranging from 0.89 to 0.98. Terrain retrieval results demonstrated remarkable accuracy, yielding RMSE values of 3.82 m, 3.61 m, and 2.45 m across the study areas. Ablation experiments underscored the effectiveness of the slope correction and ground photon extraction techniques, showing RMSE reductions of 10.6%, 6.5%, and 3.6% after slope correction and a decrease in unusable terrain ratios (Inv+Res>3) by 8.1%, 3.6%, and 20%. Compared to conventional methods, our approach reduced RMSE values (from 0.55 m to 2.61 m) and Inv+Res>3 ratios (from 0.04 to 0.37). Furthermore, the method outperformed ATL08 terrain estimates, providing superior accuracy in sub-canopy terrain reconstruction.

The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) plays an important role in the scientific task of global terrain mapping. ICESat-2 records high-precision object information in the form of photon clouds. Ground photon extraction is a key step in retrieving high-precision terrain. However, it is a difficult task to extract ground photons in mountainous forest areas due to the influence of terrain slopes. We proposed a slope correction method to mitigate the effect of terrain slope on ground photon extraction. First, the background noise photon is removed by a photon cloud filtering method. Second, we used the Douglas–Peucker algorithm to find the special terrain points (STPs), the photon cloud then was divided into a series of segments by STPs. We calculated the slope in each segment to form a rotation matrix, and corrected the slope of the photon cloud in each segment. The results show that the obtained STPs have high accuracy, and these STPs divided the photon cloud into a series of segments accurately. Compared with the reference slopes in each segment, the obtained slopes have a small RMSE of 2.3° and a high R2of 0.96. Additionally, the results indicate that the discrimination between ground photons and canopy photons in mountainous forest areas is significantly obvious after a slope correction.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been widely used to obtain high-precision sub-canopy topography. However, due to the vegetation cover over densely forested areas, the ground photons are sparse, which makes it difficult to accurately estimate the sub-canopy topography over densely forested areas. In this paper, we proposed a novel method for retrieving sub-canopy topography over densely forested areas from ICESat-2/ATL03 data. First, the proposed method used an improved elevation frequency histogram statistics (imEFHS) method to obtain candidate ground seed photons (GSPs). In densely forested areas, the obtained candidate GSPs are easily misclassified as canopy photons. Therefore, we performed a gradient-constrained morphological operation to identify erroneous GSPs. Finally, an erroneous GSPs refinement approach was derived to correct erroneous GSPs over densely forested areas. In addition, the sub-canopy topography can be presented by the refined GSPs with cubic spline interpolation. ICESat-2/ATL03 data acquired over densely forested areas were selected for testing the proposed method. The results in the given test sites show that the proposed method can extract sub-canopy topography accurately, with a root-mean-square error (RMSE) of 1.71 m over densely forested areas. We also compared the retrieved sub-canopy topography results with NASA ATL08 terrain samples. We found that the ratio of useful sub-canopy topography results (Residual2) between the retrieved sub-canopy topography results and the reference high-precision DTMs reached 0.93, which is much higher than that of the ATL08 terrain samples (R2= 0.63).

Photon cloud data filtering is crucial when obtaining forest vertical structure parameters from photon-counting LiDAR data. The proposed method, for the first time, takes into account the influence of the density difference between canopy photons and ground photons. A moving overlapping window approach is introduced to reduce the impact of an uneven background noise environment first. In each window, a modified elevation histogram statistics vector in the elevation direction is proposed to increase the density difference between signal and noise photons while also reducing the density difference between canopy and ground photons. The filtering results show that the average overall accuracy (OA) and standard deviation of the proposed method reach almost 0.99 and 0.01, respectively, which are much better results than those of the other existing filtering methods. Specifically, with the increase in the ratio of canopy photons to ground photons, the F-measure value of the proposed method reaches almost 0.99, and is also stable, which demonstrates that the proposed approach can almost completely eliminate the influence of the density difference between canopy photons and ground photons on the filtering results. In addition, the forest canopy heights obtained based on the proposed filtering method achieve the lowest root-mean-square error (RMSE) value of 3.18 m, compared to the other filtering methods. In summary, the proposed photon cloud data filtering method can retrieve reliable forest canopy height information from photon cloud data, and outperforms the other compared filtering methods in the given test site.

Lots of noise photons limit the application of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) in forest canopy height retrieval. Abundant noise photons lead to unstable filtering results, thus affecting the canopy top photons classification. Therefore, this study proposes a method that takes account of the background noise level in the photon cloud. First, we propose an improved Differential, Regressive, and Gaussian Adaptive Nearest Neighbor (DRAGANN) filtering approach based on the DRAGANN filtering method. To obtain a more stable filtering result, large-scale and small-scale search radiuses are combined to improve the DRAGANN filtering performance. Second, we retrieve sub-canopy terrain topography by the multi-scale window detection method from the filtered photon cloud. Finally, a robust photon acquisition criterion based on the elevation difference of the filtered photons and the uneven density of signal photons is proposed to extract the canopy-top surface, which aims to mitigate the influence of inconsistencies of residual noise photons along the track. In addition, considering the fluctuation of the canopy surface, we use a non-spline interpolation method to obtain a continuous canopy surface, which avoids the Runge phenomenon caused by the spline interpolation method. The ICESat-2 data acquired in the Harvard Forest Region (HARV) is selected to assess the proposed method. The filtering performance of the improved DRAGANN approach shows stable than that of the DRAGANN approach. The proposed method’s root means square error (RMSE) and coefficient of determination (R2) reach 3.85 m and 0.55, respectively. The results indicate that the proposed method can retrieve reliable forest canopy height from the ICESat-2 data and performs significantly better than the ATL08 canopy height product in the test site.

Vegetation height estimation of desert ecosystems is important for understanding the groundwater cycle. ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) provides an opportunity to measure vegetation heights on a global scale. This letter proposed a method for retrieving low and sparse vegetation heights in desert ecosystems. Considering the significant difference in density between the vegetation photons and the ground photons, the ground photons were removed based on the terrain-adaptive method first. The localized density parameter was then introduced to distinguish the vegetation photons and the noise photons. Finally, the vegetation heights were obtained by the elevation percentile approach. The proposed method was tested using the ICESat-2 data acquired over a desert located in Arizona. The vegetation height results derived by the proposed method have an RMSE of 0.78 m which is significantly less than that of ATL08 with an RMSE of 4.26 m, which demonstrates it is feasible to extract low and sparse vegetation height in desert areas. The results showed that ICESat-2 photon cloud lidar data are suitable for low and sparse vegetation height investigations in desert ecosystems.

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), launched in May 2019, increased the availability of different types of spaceborne laser altimetry data. But the obtained photon point cloud, especially those for the forest area with steep terrains, contains a lot of background noise that may greatly decrease the accuracy of the extracted digital elevation model (DEM) and forest height. Therefore, removing the background noise photons mixed up with the signal photons is necessary. We proposed a method for photon point cloud filtering using the backward elliptical distance (BED). First, we used the BED to express the spatial distance of the photon point cloud. On this basis, the backward local density was derived to identify signal photons and noise photons. Then we divided the data into several segments and set a local threshold for each segment to identify signal photons and noise photons. We validated the proposed method in the forested area with steep terrains in Washington State and Spain, and compared the results with that of other filtering methods. The comparison shows that the proposed method separates signal photons and noise photons better than other methods. The comprehensive evaluation indexes$F$in Spain and that of the left, center, and right channels in Washington reach 0.9892, 0.9899, 0.9905, and 0.9915, respectively. In addition, compared with the global threshold selection, the local threshold selection is more stable.

The existing local distance statistics-based filtering method for photon point cloud data is greatly affected by the input parameter (number of photon neighbors) and has a poor ability to remove noise photons that are adjacent to signal photons. In this letter, the relative neighboring relationship (RNR) is proposed to describe the relative density distribution of the neighboring photon points around two photon points. The mean local weighted distance is then defined, which is used to enhance the discrimination between the noise photons adjacent to the signal photons and the signal photons. Finally, according to the statistical characteristics of the mean local weighted distance, two strategies for threshold selection are used to separate signal photons from noise photons. ICESat-2 data acquired over tropical forest were used to verify the performance of the proposed method, and the results showed that: 1) the proposed method has a better ability to remove the noise photons adjacent to signal photons and 2) its performance is not greatly dependent on the input parameter.