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

Yang Chen,Dongsheng Yu,Kaiyue Feng,Xin Wang,Jie Song,Lixia Ma

doi:10.3390/rs14010148

Abstract

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

Highlights

Subtropical forests cover approximately 26% of China’s land area and play an important role in preserving plant and animal biodiversity, regulating terrestrial carbon cycles, preventing land degradation, and fostering regional and global ecological balance [1,2,3].From the 1950s to the 1990s, soil erosion, wildfires, deforestation, and over-cultivation led to severe forest degradation in southern China [4]
Among the vegetation spectral indices, NDVI, GNDVI, and ARVI had the highest correlation with the leaf area index (LAI) in Xingguo during winter, while ARVI, IRECI, IPVI, and NDVI showed the strongest correlation with the LAI in Gandong during the summer
This work investigated the potential of integrating multi-sensor imagery from the Sentinel-1, Sentinel-2, and ALOS-1 digital elevation model (DEM) using the stacking learning (SL) machine learning algorithm, for retrieving the forest LAI in the subtropical forests of southern China

Summary

Introduction

From the 1950s to the 1990s, soil erosion, wildfires, deforestation, and over-cultivation led to severe forest degradation in southern China [4]. To reverse this trend, China initiated a Grain-for-Green Project in the late 20th century [5]. Forests in this region have been restored and vegetation cover and productivity have gradually increased [6]. The leaf area index (LAI), defined as one-half of the total leaf area per unit ground area, is a vital physiophysical parameter for characterizing forest structure because it is used to estimate forest biomass, health, longevity, and productivity [8]. The LAI is a key input for the ecosystem-level

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