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

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.

Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests

For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25,000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (≤20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0 to 7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps.

Abiotic Drivers of Seedling Bank Diversity in Subtropical Forests of Southern China

Abiotic factors are important to shape plant community composition and diversity through processes described as environmental filtering. Most studies on plant diversity in forests focus on adult trees, while the abiotic drivers of forest seedling community characteristics are less understood. Here, we studied seedling banks’ composition, richness, diversity, and abundance, and investigated their relationships with microsite abiotic conditions along a wide elevational gradient. We sampled seedling communities in 312 1-m2 quadrats, distributed in 13 one-ha plots in four subtropical forests in south China, covering an elevation gradient of 1500 m, for 2 years. We measured light availability, slope, and 11 soil nutrients for each seedling quadrat. We used analysis of similarities and multivariate analysis of variance to compare the composition and abiotic drivers of the four forests’ seedling communities. We then used mixed models and structural equation modeling to test the direct and indirect effects of abiotic factors on seedling species richness, diversity, and abundance. The differences in seedling community composition among these forests were mostly explained by differences in elevations and soil nutrients. Seedling diversity as Shannon and Simpson diversity index decreased with increasing elevation and increased with increasing slope, but seedling abundance and species richness did not. Elevation had an indirect effect on Simpson’s diversity index through modulating the direct effects of soil properties. Our findings show that soil properties play a prominent role in favoring differentiation in species composition among the four forests we studied and provide additional evidence to decreasing species diversity with elevation. However, this was reflected in decreasing Shannon and Simpson indices rather than species richness, which is more commonly studied. Whether and to what extent future environmental changes in climate and soil acidification will alter future forest composition and diversity needs to be investigated.

Soil Nitrogen and Sulfur Leaching in a Subtropical Forest at a Transition State under Decreasing Atmospheric Deposition

Anthropogenic emissions of nitrogen- (N) and sulfur (S)-containing pollutants have declined across China in recent years. However, the responses of N and S depositions and dynamics in soil remain unclear in subtropical forests. In this study, the wet and throughfall depositions of dissolved inorganic N (DIN) and SO42− were continuously monitored in a mildly polluted subtropical forest in Southeast China in 2017 and 2018. Moreover, these solutes in soil water along the soil profile were monitored in 2018. Throughfall deposition of DIN and S decreased by 59% and 53% in recent 3 years, respectively, which can be majorly attributed to the decreases in wet depositions of NO3− and SO42−. Meanwhile, NH4+ deposition remained relatively stable at this site. Even though N deposition in 2018 was below the N saturation threshold for subtropical forests, significant N leaching still occurred. Excess export of N occurred in the upper soil layer (0–15 cm), reaching 6.86 ± 1.54 kg N/ha/yr, while the deeper soil (15–30 cm) was net sink of N as 8.29 ± 1.71 kg N/ha/yr. Similarly, S was excessively exported from the upper soil with net flux of 14.7 ± 3.15 kg S/ha/yr, while up to 6.37 ± 3.18 kg S/ha/yr of S was retained in the deeper soil. The significant N and S leaching under declined depositions suggested that this site possibly underwent a transition state, recovering from historically high acid deposition. Furthermore, the rainfall intensity remarkably regulated leaching and retention of SO42− and DIN at this site. The impacts of climate changes on N and S dynamics require further long-term monitoring in subtropical forests.