Abstract
Optical remote sensing technology has been widely used in forest resources inventory. Due to the influence of satellite orbits, sensor parameters, sensor errors, and atmospheric effects, there are great differences in vegetation spectral information captured by different satellite sensor images. Spectral fusion technology can couple the advantages of different multispectral sensor images to produce new multispectral data with high spatial and spectral resolution, it has great potential for improving the spectral sensitivity of forest vegetation and alleviating the spectral saturation. However, how to quickly and effectively select the multi-spectral fusion data suitable for forest above-ground biomass (AGB) estimation is a very critical issue. This study proposes a scheme (RF-S) to comprehensively evaluate multispectral fused images and develop the appropriate model for forest AGB estimation, on the basis of random forest (RF) and the stacking ensemble algorithm. First, four classic fusion methods are used to fuse the preprocessed GaoFen-2 (GF-2) multispectral image with Sentinel-2 image to generate 12 fused Sentinel-like images. Secondly, we apply a comprehensive evaluation method to quickly select the optimal fused image for the follow-up research. Subsequently, two feature combination optimization methods are used to select feature variables from the three feature sets. Finally, the stacking ensemble algorithm based on model dynamic integration and hyperparameter automatic optimization, as well as some classic machine learners, are used to construct the forest AGB estimation model. The results show that the fused image NND_B3 (based on nearest neighbor diffusion pan sharpening method and Band3_Red) selected by the evaluation method proposed in this study has the best performance in AGB estimation. Using the stacking ensemble method and NND_B3 image, we get the highest estimation accuracy, with the adjusted R2 and relative root mean square error (RMSEr) of 0.6306 and 15.53%, respectively. The AGB estimation RMSEr of NND_B3 is 19.95% and 24.90% lower than those of GF-2 and Sentinel-2, respectively. We also found that the multi-window texture factor has better performance in the area with low AGB, and it can suppress the overestimation significantly. The AGB spatial distribution estimated using the NND_B3 image matches the field observations well, indicating that the multispectral fusion image combined with the Stacking algorithm can increase the accuracy and saturation of the AGB estimates.
Highlights
The forest ecosystem is very important to the Earth’s ecosystem
This study has proved the superiority of multi-spectral fusion image combined with stacking integrated modeling method in estimating the aboveground biomass (AGB) of Chinese fir plantation, but there are some limitations in the application
The random forest (RF)-S method was proposed for estimating the AGB of Chinese fir plantations in south China
Summary
The forest ecosystem is very important to the Earth’s ecosystem. Its biomass and carbon storage play a very important role in global climate change and material circulation, Remote Sens. 2021, 13, 3910 and it can directly or indirectly regulate and buffer the global climate change [1,2]. 70% to 90% of the total forest biomass, is one of the significant carbon pools in forest ecosystems [4,5]. As a basic quantitative characteristic of forest ecosystems, AGB can be used to assess the growth and health of forests. Fast and accurate acquisition of AGB information is extremely important for forest management and understanding of ecosystems, carbon cycles and carbon dynamics [6,7]
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