Jinning Shi

Timely and reliable estimation of forest stock volume is essential for sustainable forest management and conservation. Light detection and ranging (LiDAR) data can provide an effective depiction of the three-dimensional structure information of forests, but its large-scale application is hampered by spatial continuity. This study aims to construct a LiDAR sampling framework, combined with multi-sensor imagery, to estimate the regional forest stock volume of natural secondary forests in Northeast China. Two sampling approaches were compared, including systematic sampling and classification-based sampling. First, the forest stock volume was mapped using a combination of field measurement data and full-coverage LiDAR data. Then, the forest stock volume obtained in the first step of estimation was used as a reference value, and optical images and topographic features were combined for secondary modeling to compare the effectiveness and accuracy of different sampling methods, including 12 systematic sampling and classification-based sampling methods. Our results show that the root mean square error (RMSE) of the 12 systematic sampling approaches ranged from 55.81 to 57.42 m³/ha, and the BIAS ranged from 21.55 to 24.89 m³/ha. The classification-based LiDAR sampling approach outperformed systematic sampling, with an RMSE of 55.56 (3/ha) and a BIAS of 20.68 (3/ha). This study compares different LiDAR sampling approaches and explores an effective LiDAR sample collection scheme for estimating forest stock, while balancing cost and accuracy. The classification-based LiDAR sampling approach described in this study is easy to apply and portable and can provide a reference for future LiDAR sample collection.

The CO₂ fertilization effect (CFE) plays a crucial role in the amelioration of climate change. Many physiological and environmental factors, such as stand age, stand type, and site conditions, may affect the extent of the CFE. However, the relationship between the CFE and these factors remains elusive. In this study, we used the emerging gross primary production (GPP) remote sensing products, with GPP predicted using eddy covariance–light use efficiency models (EC-LUE GPP) based on satellite near-infrared reflectance of vegetation (NIRv GPP) and assessed with a random forest model to explore the CFE trends with stand age in a coniferous forest and a broad-leaved forest in Heilongjiang Province, China. We additionally compared the differences among the CFEs under different site conditions. The CFEs in coniferous forests and broad-leaved forests both showed a rapid increase in stands of 10 to 20 years of age, followed by a decline after reaching a maximum, with the rate of decline reducing with age. Eventually, CFE remained stable in stands near 100 years of age. However, the CFE in coniferous forests exhibited more extended periods of rapid increase and a higher maximum than in broad-leaved forests. Moreover, in this study, we used the site class index (SCI) to grade site conditions. The results demonstrate that the CFE differed significantly under different levels of site conditions, and these differences gradually decreased with age. The site with the highest SCI had fewer environmental restrictions on the CFE, and consequently, the CFE rate of decline was faster. Our results are of significance in understanding the CFE and adapting to future changes in atmospheric CO₂ concentration.