Abstract
Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.
Highlights
As essential terrestrial ecosystems, forests form an important part of the earth’s carbon cycle [1,2]
A new canopy height estimation method was developed by combining the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) data with ZY-3 stereo images
This study proposes a new means with which to estimate forest canopy height, which used a combination of ICESat-2 ATLAS data and ZY-3 stereo images to extract a discontinuous canopy height model (CHM) dataset as training samples and extrapolated the backpropagation artificial neural network (BP-ANN) model to the whole study area with ten vegetation index bands from Landsat 8 images
Summary
Forests form an important part of the earth’s carbon cycle [1,2]. The accurate estimation of terrestrial forest biomass is conducive to better understanding the crucial role of forests in the cycle of global carbon. The accurate estimation of AGB can reduce the uncertainty of terrestrial carbon quantification [10,11]. For many current estimates of global carbon flux and biomass distribution in forests, there is still too much uncertainty due to the coarse estimation of vegetation structures for many scientific research and policy applications [10,12,13]. In regional or global mapping, the estimation of forest canopy height and AGB is directly related to the allometric growth equation. Improving the accuracy of forest canopy height estimation and enriching data sources are conducive to improving the accuracy of AGB estimation, which is an important research focus [14]
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