Journal Article

Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China

Zhang, Shaoyu, Luo, Yichen, Hu, Bisong, Qi, Shuhua, Tian, Ye, Liao, Kaitao

2023

Overview

Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R2 = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution.

• Publisher: MDPI AG
• Subject: aboveground biomass / Accuracy / Age / Algorithms / altimeters / Canopies / canopy height / carbon sinks / Case studies / China / Climate change / Contours (Cartography) / decision support systems / Decision trees / Deforestation / Dependent variables / Elevation / Environmental aspects / Extrapolation / forest canopy / Forest degradation / forests / Forests and forestry / High resolution / ice / Imagery / Laser altimeters / Lasers / Leaf area / Leaf area index / Lidar / Machine learning / Mapping / Performance prediction / radar / Remote sensing / Root-mean-square errors / Satellite imaging / Spatial distribution / Support vector machines / texture / Topographic maps / Topography / trees / Vegetation