A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019

Abstract

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas.

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

“…In recent years, ICESat-2/ATLAS, a satellite-borne lidar system of the latest iteration, has played an increasingly important role in a number of disciplines, including measurements of ice-cover thickness and surface elevation [26,27], the dynamic monitoring of lake water levels [28], and the imaging of forest physical parameters. In the estimation and mapping of forest physical parameters, ICESat-2 data are frequently used in synergy with satellite optical imagery for forest aboveground biomass inversion [29][30][31] and tree height mapping at the regional scale [32][33][34], and have become the crucial data source for large-scale forest aboveground biomass and tree height mapping. However, no studies on forest biodiversity monitoring using the novel satellite-based LIDAR ICESat-2/ATLAS in conjunction with satellite optical remote sensing have been reported to date, and there has been little exploration of the potential of combining horizontal spectral variation and vertical structural variation features of satellite remote sensing data for application in forest tree species diversity modeling [4].…”

A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data

“…Spaceborne LiDAR has been widely used in global forest canopy height estimation [ 1 , 2 , 3 ] due to its rapid and cost-effective detection of surface typography and vertical forest structure [ 2 , 4 , 5 , 6 ]. The accuracy of global forest height data evaluated by the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat) has been proven over the past few decades [ 7 ].…”