Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

Abstract

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

“…In accordance with previous studies, the MSI was more useful than the SAR and DEM for the LAI estimation, providing a wider range of the predicted LAI values. This is likely because the MSI data consist of optical sensor readings; spectral indices from the narrow-band, red-edge, and SWIR spectral regions are sensitive to green vegetation and are useful for retrieving forest phenology, physiology, and structure [13,23,28]. However, the MSI suffers from data saturation due to the forest canopy's shading of solar radiation in thick forest zones, which causes the MSI to a record horizontal vegetation structure more effectively than vertical biophysical characteristics [49].…”

“…We also found that the NBL significantly affected the SL model performance. However, many previous prediction studies [25,[27][28][29][30] did not attempt to optimize the NBL when generating SL models. In this work, the best NBL for estimating the LAI was determined through a comparative analysis of SL models (Figure 4), which revealed that the use of too many or too few base learners reduced the accuracy of SL models.…”

“…Additionally, the LAI is a key input for the ecosystem-level correlation [26]. A group of SL models has been developed and successfully used to monitor static and dynamic forest traits including forest change [27], forest canopy height [28], vegetation cover [29], growing stem volume [30], and aboveground biomass [25]. The use of SL was shown to increase the accuracy when compared to the use of individual ML methods because it uses both simple numerical information from instruments and the outputs of low-level models as input data [26,31].…”

Assessing GEDI-NASA system for forest fuels classification using machine learning techniques

“…For instance, the NASA's Geoscience Laser Altimeter System (GLAS) was used to estimate fire fuel models (e.g., Ashworth et al, 2010), canopy fuel properties for crown fire behavior (e.g., García et al, 2012), and canopy structure and fuel data (e.g., Peterson et al, 2013). The NASA's Advanced Topographic Laser Altimeter System (ATLAS) instrument, launched in 2018, can also be used for vegetation characterization (Narine et al, 2020), although few studies have been conducted to date (e.g., Jiang et al, 2021;Lin et al, 2020;Narine et al, 2019) and none of them on the estimation of forest fuels. However, it should be noted that both GLAS and ATLAS systems were not initially optimized for vegetation and forest structure characterization (Leite et al, 2022, Potapov et al, 2021.…”

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

“…The airborne laser radar can obtain very high inversion accuracy, but only on a small scale. Spaceborne lidar can cover the entire world, but can only provide sampling spot data [14]. At present, ICESat-1 (ice, cloud, and land Elevation Satellite 1), ICESat-2 (ice, cloud, and land Elevation Satellite 2) and GEDI (Global Ecosystem Dynamics Investigation) are the main spaceborne lidars for forest parameter structure measurement.…”