A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

Abstract

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.

Assessing the Capability and Potential of LiDAR for Weed Detection

“…The logistic regression results showed a lower detection rate of smaller rods at greater LiDAR scanning distances. The small RMSE between the actual and the LiDAR estimated rods heights confirmed the LiDAR as a capable device for target height estimation [44]. Furthermore, the estimated height of the targets increased above the actual height as both LiDAR horizontal scanning angle (from straight ahead) and distance of the target increased, which was also demonstrated by Alwan, Wagner [43].…”

Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

“…In our experiment, there are still some limitations: (1) Although there was a random deviation of several meters between the simulation footprints and the actual elevation, the real photon-counting lidar measurement results also contain a large amount of background noise, and noise filtering is still a big challenge [43][44][45]. 2In addition, in our simulation, the change in target reflectivity within the footprint is not considered.…”

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

“…The main purpose of the preprocessing is to decrease the number of background photons and to reduce the amount of calculation needed in the subsequent steps. Image processing [25,28,29], envelope curves [26], and histogram statistics [26,[30][31][32][33] are the commonly used methods in the preprocessing step. 1) Image processing: The first preprocessing method involves rasterizing the profile point cloud along the orbit into a 2D image according to the density of the photons.…”

“…The cut-off threshold was set as 3 times sigma. In 2018, Nie et al [32] and Zhu et al [30] used Moussavi's method to extract possible surface photons. Popescu et al [26] also proposed a preprocessing method based on histogram statistics.…”

“…3) Feature calculation: The third surface detection method is a key step in surface detection. There are four main features that have been calculated in the existing studies, namely, the data distribution feature [28,39], the photon density [25,[30][31][32][40][41][42][43][44][45][46][47][48][49], the k-nearest neighbor distance [24,50], and the local outlier factor [51,52]. Among the different features, the photon density is the most commonly adopted feature.…”