Yanqiu Xing

Boreal forests play an important role in the global terrestrial carbon cycle, yet the effects of climate-driven permafrost degradation on ecosystem carbon dynamics remain poorly represented in current models. Earth system models often simplify subsurface thermal and hydrological processes, limiting their ability to capture ecosystem responses in permafrost-affected regions. Here, we improve the soil module of the LPJ-GUESS model by implementing a deep soil profile (3 m) with refined vertical discretization (30 layers) and explicit ice-impedance effects. The improved LPJ-GUESS configuration is then applied to simulate carbon and hydrothermal dynamics in the climate-sensitive boreal forests of Northeast China. Model evaluation indicates that the improved configuration generally enhances the simulation of soil hydrothermal dynamics relative to the default LPJ-GUESS configuration. The correlation coefficient for deep soil temperature (1.0 m) increases from 0.818 to 0.894, while the systematic overestimation of soil water content is substantially reduced, with the bias at 1.0 m reduced from 0.321 to −0.006. These improvements are associated with better agreement between simulated and observed vegetation composition. The simulated dominance of needleleaf forests decreases from near-complete dominance to 24.1 %, resulting in vegetation patterns that are more consistent with regional observations. Consequently, biases in carbon estimates are reduced, with simulated net primary productivity decreasing from 583.5 to 560.1 g C m ⁻² yr ⁻¹ and aboveground biomass from 112.9 to 99.0 t ha ⁻¹ , resulting in better agreement with estimates from satellite-based observations. Model sensitivity analysis indicates that vegetation composition is highly sensitive to changes in soil hydrothermal conditions compared with the control simulation. Pronounced shifts in vegetation composition occur when soil temperature increases exceed 0.6 °C and soil water deficits exceed 0.6 m ³ m ⁻³ compared to the default model configuration. Overall, this study highlights the importance of improving soil hydrothermal representation for simulating vegetation dynamics and carbon cycling in permafrost-affected boreal ecosystems.

Boreal forests constitute a major component of the global terrestrial carbon sink, yet how climate-driven permafrost degradation alters their ecosystem carbon dynamics remains poorly constrained. Despite their importance, the persistence of this carbon sink remains highly uncertain, partly due to limitations in Earth system model projections of future carbon dynamics in permafrost-affected regions. Here, we improve the soil module of the LPJ-GUESS model by implementing a deep soil profile (3 m) with refined vertical discretization (30 layers) and explicit ice-impedance effects. The enhanced model (LPJ-GUESS-Cryo) is then used to simulate carbon and hydrothermal dynamics in the climate-sensitive boreal forests of Northeast China. Model evaluation shows that LPJ-GUESS-Cryo substantially improves the simulation of soil hydrothermal dynamics relative to the default configuration. The correlation coefficient for deep soil temperature (1 m) increases from 0.818 to 0.894, while the systematic overestimation of soil water content is effectively corrected, with the bias at 1 m reduced from 0.321 to -0.006. These process-level improvements lead to a more realistic simulation of vegetation composition. The dominance of needleleaf forests is corrected from near-complete saturation to 24.1 %, consistent with observed vegetation patterns. Consequently, biases in carbon estimates are reduced, with simulated net primary productivity decreasing from 583.5 to 560.1 g C m^-2 yr^-1 and aboveground biomass from 112.9 to 99.0 t ha^-1, resulting in better agreement with estimates from satellite-based observations. Model sensitivity analysis indicates that vegetation composition is highly sensitive to changes in soil hydrothermal conditions relative to the control simulation. Pronounced shifts in vegetation composition occur when soil temperature increases exceed 0.6 °C and soil water deficits exceed 0.6 m³ m^-3 compared to the default model configuration. Overall, this study highlights that accurately representing permafrost processes in dynamic global vegetation models is critical for reducing uncertainties in high-latitude climate-carbon feedbacks.

Boreal forests constitute a major component of the global terrestrial carbon sink, yet how climate-driven permafrost degradation alters their ecosystem carbon dynamics remains poorly constrained. Despite their importance, the persistence of this carbon sink remains highly uncertain, partly due to limitations in Earth system model projections of future carbon dynamics in permafrost-affected regions. Here, we improve the soil module of the LPJ-GUESS model by implementing a deep soil profile (3 m) with refined vertical discretization (30 layers) and explicit ice-impedance effects. The enhanced model (LPJ-GUESS-Cryo) is then used to simulate carbon and hydrothermal dynamics in the climate-sensitive boreal forests of Northeast China. Model evaluation shows that LPJ-GUESS-Cryo substantially improves the simulation of soil hydrothermal dynamics relative to the default configuration. The correlation coefficient for deep soil temperature (1 m) increases from 0.818 to 0.894, while the systematic overestimation of soil water content is effectively corrected, with the bias at 1 m reduced from 0.321 to -0.006. These process-level improvements lead to a more realistic simulation of vegetation composition. The dominance of needleleaf forests is corrected from near-complete saturation to 24.1 %, consistent with observed vegetation patterns. Consequently, biases in carbon estimates are reduced, with simulated net primary productivity decreasing from 583.5 to 560.1 g C m^-2 yr^-1 and aboveground biomass from 112.9 to 99.0 t ha^-1, resulting in better agreement with estimates from satellite-based observations. Model sensitivity analysis indicates that vegetation composition is highly sensitive to changes in soil hydrothermal conditions relative to the control simulation. Pronounced shifts in vegetation composition occur when soil temperature increases exceed 0.6 °C and soil water deficits exceed 0.6 m³ m^-3 compared to the default model configuration. Overall, this study highlights that accurately representing permafrost processes in dynamic global vegetation models is critical for reducing uncertainties in high-latitude climate-carbon feedbacks.

The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) provides highly dense full waveform LiDAR data for global carbon monitoring. However, in forest environments, the complex canopy structure and terrain introduce significant background noise, making effective signal extraction challenging. To address this issue, an adaptive waveform denoising framework is proposed in this study. Bayesian optimization (BO) is first employed to adaptively determine the effective signal threshold for each waveform by minimizing structural volatility within background noise segments, thereby achieving reliable separation between effective signals and background noise. Subsequently, a structure-aware adaptive Gaussian filtering (SAAF) method is developed. Residual noise in the effective signal is suppressed by dynamically adjusting the Gaussian kernel width based on structural factors, including notch density, flat peak characteristics, peak skewness, and multipeak structures. The results indicate that, compared to the official TECIS parameter extraction (TPE) method, the BO method removes an additional 5.14% of redundant background noise energy, yielding a cleaner, more physically realistic effective signal. Furthermore, relative to manually annotated effective signal start and end points, the localization accuracy of the extracted start and end points is improved by 77.5% and 78.3%, respectively. For effective signal smoothing, SAAF maintains high precision across varying slopes and forest types, with peak signal-to-noise ratio (PSNR) consistently exceeding 44 dB and the correlation coefficient (CC) remaining above 0.96. This framework provides reliable, structurally complete waveform data for subsequent inversion of forest structure parameters.

Boreal forests constitute a major component of the global terrestrial carbon sink, yet how climate-driven permafrost degradation alters their ecosystem carbon dynamics remains poorly constrained. Despite their importance, the persistence of this carbon sink remains highly uncertain, partly due to limitations in Earth system model projections of future carbon dynamics in permafrost-affected regions. Here, we improve the soil module of the LPJ-GUESS model by implementing a deep soil profile (3 m) with refined vertical discretization (30 layers) and explicit ice-impedance effects. The enhanced model (LPJ-GUESS-Cryo) is then used to simulate carbon and hydrothermal dynamics in the climate-sensitive boreal forests of Northeast China. Model evaluation shows that LPJ-GUESS-Cryo substantially improves the simulation of soil hydrothermal dynamics relative to the default configuration. The correlation coefficient for deep soil temperature (1 m) increases from 0.818 to 0.894, while the systematic overestimation of soil water content is effectively corrected, with the bias at 1 m reduced from 0.321 to -0.006. These process-level improvements lead to a more realistic simulation of vegetation composition. The dominance of needleleaf forests is corrected from near-complete saturation to 24.1 %, consistent with observed vegetation patterns. Consequently, biases in carbon estimates are reduced, with simulated net primary productivity decreasing from 583.5 to 560.1 g C m-2 yr-1 and aboveground biomass from 112.9 to 99.0 t ha-1, resulting in better agreement with estimates from satellite-based observations. Model sensitivity analysis indicates that vegetation composition is highly sensitive to changes in soil hydrothermal conditions relative to the control simulation. Pronounced shifts in vegetation composition occur when soil temperature increases exceed 0.6 °C and soil water deficits exceed 0.6 m3 m-3 compared to the default model configuration. Overall, this study highlights that accurately representing permafrost processes in dynamic global vegetation models is critical for reducing uncertainties in high-latitude climate-carbon feedbacks.

Forest fires endanger ecosystems and human life, making early detection crucial for effective prevention. Traditional detection methods are often inadequate due to large coverage areas and inherent limitations. However, drone technology combined with deep learning holds promise. This study investigates using small drones equipped with lightweight deep learning models to detect forest fires early. A high-quality dataset constructed through aerial image analysis supports robust model training. The proposed YOLO-UFS network, based on YOLOv5s, integrates enhancements such as the C3-MNV4 module, BiFPN, AF-IoU loss function, and NAM attention mechanism. These modifications achieve a 91.3% mAP on the self-built early forest fire dataset. Compared to the original model, YOLO-UFS improves accuracy by 3.8%, recall by 4.1%, and average accuracy by 3.2%, while reducing computational parameters by 74.7% and 78.3%. It outperforms other mainstream YOLO algorithms on drone platforms, balancing accuracy and real-time performance. In generalization experiments using public datasets, the model’s mAP0.5 increased from 85.2% to 86.3%, and mAP0.5:0.95 from 56.7% to 57.9%, with an overall mAP gain of 3.3%. The optimized model runs efficiently on the Jetson Nano platform with 258 GB of RAM, 7.4 MB of storage memory, and an average frame rate of 30 FPS. In this study, airborne visible light images are used to provide a low-cost and high-precision solution for the early detection of forest fires, so that low-computing UAVs can achieve the requirements of early detection, early mobilization, and early extinguishment. Future work will focus on multi-sensor data fusion and human–robot collaboration to further improve the accuracy and reliability of detection.

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features.

Accurately localizing individual trees and identifying species distribution are critical tasks in forestry remote sensing. Visual Simultaneous Localization and Mapping (visual SLAM) algorithms serve as important tools for outdoor spatial positioning and mapping, mitigating signal loss caused by tree canopy obstructions. To address these challenges, a semantic SLAM algorithm called LPD-SLAM (Line-Point-Distance Semantic SLAM) is proposed, which integrates stereo cameras with an inertial measurement unit (IMU), with contributions including dynamic feature removal, an individual tree data structure, and semantic point distance constraints. LPD-SLAM is capable of performing individual tree localization and tree species discrimination tasks in forest environments. In mapping, LPD-SLAM reduces false species detection and filters dynamic objects by leveraging a deep learning model and a novel individual tree data structure. In optimization, LPD-SLAM incorporates point and line feature reprojection error constraints along with semantic point distance constraints, which improve robustness and accuracy by introducing additional geometric constraints. Due to the lack of publicly available forest datasets, we choose to validate the proposed algorithm on eight experimental plots, which are selected to cover different seasons, various tree species, and different data collection paths, ensuring the dataset’s diversity and representativeness. The experimental results indicate that the average root mean square error (RMSE) of the trajectories of LPD-SLAM is reduced by up to 81.2% compared with leading algorithms. Meanwhile, the mean absolute error (MAE) of LPD-SLAM in tree localization is 0.24 m, which verifies its excellent performance in forest environments.

Forest disturbance is one of the main drivers of forest carbon flux change. How to accurately estimate the carbon flux caused by forest disturbance is an important research problem. In a previous study, the authors proposed a Grid-based Carbon Accounting (GCA) model that used remote sensing data to estimate forest carbon fluxes in North Carolina from 1986 to 2010. However, the original model was unable to track legacy emissions from previously harvested wood products and was unable to consider forest growth conditions before and after forest disturbance. This paper made some improvements to the original GCA model to enable it to track fluxes between all major aboveground live carbon pools, including pre-disturbance growth and growth of undisturbed forests, which were not included in the initial model. Based on existing timber product output (TPO) survey data and annual TPO records inversed from remote sensing data, we also worked to clarify the distribution ratios of removed C between slash and different wood product pools. Specifically, the average slash ratio for North Carolina was calculated from the difference between the C removed and the C flowing into the wood product as calculated from TPO survey data. County- and year-specific ratios were then calculated using the annual TPO records obtained from remote sensing and TPO survey data, dividing the removed remaining C into pools P1, P10, and P100, which were then applied to each 30 m pixel based on the county and year to which the pixel belonged. After compensating for these missing legacy emissions and adjusting forest growth rates from Forest Inventory and Analysis (FIA) data, we estimated a net carbon sink of 218.1 Tg of the flux associated with live aboveground biomass and harvested wood products from North Carolina woodlands over the 25-year study period (1986–2010). This estimate is close to the greenhouse gas emission and sink data provided by the U.S. Department of Agriculture for North Carolina and is comparable to estimates reported by several other studies.