Search Results (39)

Excessive tailwind, threatening the safety of aircraft takeoff and landing, is one of the prominent research topics in the field of aviation meteorology. This paper analyzes the causes of tailwinds at Beijing Daxing International Airport (BDIA), based on coherent Doppler wind lidar (CDWL) and ERA5 reanalysis data. CDWL with high spatiotemporal resolution is utilized to detect variations in the low-level wind field in the vicinity of airport areas. ERA5 reanalysis data are employed to investigate the distribution characteristics of meteorological elements such as wind fields, pressure, and temperature in the Beijing surrounding regions. The study of two typical tailwind events reveals that frontal activity, through the combined effects of pressure gradient adjustment and topographic constraints from the Taihang Mountains, drives the development of low-level southerly jets. It serves as the key mechanism triggering excessive tailwind. By integrating CDWL and ERA5 data for local and regional analysis, this study contributes to enhancing understanding of tailwind causal mechanisms and provides critical support for aviation meteorological disaster early warning. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

To address the critical challenges of wind erosion mitigation and sustainable soil management in the fragile agroecosystem of the black soil region in the foothills of the Daxing’anling Mountains, this study evaluated five tillage practices—conventional ridge tillage (CP), no tillage with straw removal (NT), no tillage with straw mulching (R+NT), autumn strip tillage with straw mulching (R+ST_A), and spring strip tillage with straw mulching (R+ST_S)—across two landforms: gently sloped uplands and flat depressions. The results demonstrated that R+ST_S achieved superior performance across both landscapes, exhibiting a 42.99% reduction in the wind erosion rate, a 48.88% decrease in soil sediment discharge, and a 52.26% reduction in the soil creep amount compared to CP. These improvements were mechanistically linked to the enhanced surface microtopography (aerodynamic roughness increased by 1.8–2.3 fold) and optimized straw coverage (68–72%). R+ST_S also enhanced the topsoil fertility, increasing the total nitrogen (TN), soil organic carbon (SOC), alkaline nitrogen (AN), available phosphorus (AP), and rapidly available potassium (AK) by 22.07%, 12.94%, 14.92%, 32.94%, and 9.52%, respectively. Furthermore, it improved maize emergence and its yield by 10.04% and 9.99% compared to R+NT. Mantel tests and SEM revealed strong negative correlations between erosion and nutrients, identifying nitrogen availability as the key yield driver. R+STS offers a sustainable strategy for erosion control and productivity improvement in the black soil region. Full article

Soil microorganisms are crucial in global biogeochemical cycles, impacting ecosystems’ energy flows and material cycling. This study, via high-throughput sequencing in four forests—the original Larix gmelinii (Rupr.) Kuzen. forest (LG), the conifer–broad-leaved mixed Pinus sylvestris var. mongolica Litv. forest (PS), the original pure Betula platyphylla Sukaczev forest (BP), and the original pure Populus L. forest (PL) in Shuanghe National Nature Reserve, Daxing’anling mountains—explored soil microbial community structures and diversities. The results indicated that the BP and PL forests had the lowest soil bacterial ACE and Chao1 indices, and the original pure birch forest’s Shannon index was higher than that of the poplar forest. The soil’s fungal Chao1 index of the birch forest was higher than that of the larch forests. Bradyrhizobium and Roseiarcus were the dominant soil bacterial genera; the dominant soil fungal genera were Podila, Russula, and Sebacina. RDA and mantel analyses indicated that soil microbial community structures varied across forest types mainly because of the effective phosphorous and pH levels, soil’s total nitrogen level, and available phosphorus level. This study offers a scientific foundation for cold-temperate-forest ecosystem management regarding soil microbial diversity and community structural changes in different forest types. Full article

Fire is important in boreal forest ecosystems, but comprehensive recovery analysis is lacking for soil nutrients and plant traits in China boreal forests, where the strict “extinguish at sight” fire prevention policy has been implemented. Based on over 50 years of forest fire recordings in the Daxing’anling Mts, 48 pairs of burnt and unburnt controls (1066 plots) were selected for 0–20 cm soil sampling and plant surveys. We recorded 18 plant parameters of the abundance of each tree, shrub, grass, and plant size (height, diameter, and coverage), 7 geo-topographic data parameters, and 2 fire traits (recovery year and burnt area). We measured eight soil properties (soil organic carbon, SOC; total nitrogen, TN; total phosphorus, TP; alkali-hydrolyzed P, AP; organic P, Po; inorganic P, Pi; total glomalin-related soil protein, T-GRSP; easily-extracted GRSP, EE-GRSP). Paired T-tests revealed that the most significant impact of the fire was a 25%–48% reduction in tree sizes, followed by decline in the plant diversity of arbors and shrubs but increasing plant diversity in herbs. GRSP showed an >18% increase and P_o decreased by 17% (p < 0.05). Redundancy ordination showed that the post-fire recovery years and burnt area were the most potent explainer for the variations (p < 0.05), strongly interacting with latitudes and longitudes. Plant richness and tree size were directly affected by fire traits, while the burnt area and recovery times indirectly increased the GRSP via plant richness. A fire/control ratio chronosequence found that forest community traits (tree size and diversity) and soil nutrients could be recovered to the control level after ca. 30 years. This was relatively shorter than in reports on other boreal forests. The possible reasons are the low forest quality from overharvesting in history and the low fire severity from China’s fire prevention policy. This policy reduced the human mistake-related fire incidence to <10% in the 2010s in the studied region. Chinese forest fire incidences were 3% that of the USA. The burnt area/fire averaged 5 hm² (while the USA averaged 46 hm², Russia averaged 380 hm², and Canada averaged 527 hm²). Overharvesting resulted in the forest height declining at a rate of >10 cm/year. Our finding supports forest management and the evaluation of forest succession after wildfires from a holistic view of plant–soil interactions. Full article

Carbon (C), nitrogen (N), and phosphorus (P) are vital nutrients in the soil, exerting a profound influence on the primary productivity of ecosystems. However, our understanding of how the understory influences soil nutrients and their stoichiometry remains limited, especially in cold-temperate forests where the understory plays a crucial role in mediating soil nutrient cycling. To elucidate the effect of understory vegetation on soil nutrients, three typical larch forests, namely Sphagnum–Bryum–Rhododendron tomentosum–Larix gmelinii forest (SLL), Rhododendron dauricum–Larix gmelinii forest (RL), and Rhododendron tomentosum–Larix gmelinii forest (LL), were selected in the typical cold-temperate region of northeast China to determine the soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP) contents, and their stoichiometric characteristics in 0–100 cm soil depth. The results revealed the following: (1) Significant differences in soil nutrient and its stoichiometry existed among the three different forest types (p < 0.001), with the SLL displaying the highest mean SOC, TN, and TP contents, as well as soil C:N, C:P, and N:P ratios, whereas the RL exhibited the lowest values (p < 0.05). (2) Across the 0–100 cm soil profile, the soil nutrient content and stoichiometry showed decreasing trends with soil depth, with significant differences among the soil layers. (3) Variations in soil stoichiometry were significantly correlated with soil bulk density, pH, soil temperature, soil water content, total porosity, and capillary porosity (p < 0.05). This study underscores the necessity of further consideration of the impact of understory vegetation in future research on soil stoichiometry in forest ecosystems. Full article

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones. Full article

Supercooled water in mixed-phase clouds plays a significant role in precipitation formation, atmospheric radiation, weather modification, and aircraft flight safety. Identifying supercooled water in mixed-phase clouds is a crucial-frontier scientific issue in atmospheric detection research. In this study, we propose a new algorithm for identifying supercooled water based on the multi-spectral peak characteristics in cloud radar power spectra, combined with radar reflectivity factor and mean Doppler velocity. Using microwave radiometer data, we conducted retrieval analyses on two stratocumulus cases in the spring over the northeastern Daxing’anling region, China. The retrieval results show that the supercooled water in the spring stratocumulus clouds over the region is widespread, with liquid water content (LWC) ranging around 0.1 ± 0.05 g/m³, and particle sizes not exceeding 10 μm. The influence of updrafts on supercooled water is evident, with both showing good consistency in spatiotemporal variation trends. Comparing the liquid water path (LWP) variations retrieved from cloud radar and microwave radiometer, both showed good consistency in variation trends and high LWC areas, indicating the reliability of the identification algorithm developed in this study. Full article

The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm for AGB downscaling that integrated a ML for AGB mapping with another for residual mapping. The accuracies of six bias-corrected ensemble ML algorithms were evaluated at resolutions of 0.05°, 0.025°, and 0.01°. Moreover, a step-by-step downscaling (SBSD) method was introduced, utilizing bias-corrected ensemble ML algorithms to downscale AGB from 0.1° to 0.05°, 0.025°, and 0.01° resolutions and was compared with the direct downscaling (DD) at three scales. A comparative analysis was conducted in the Daxing’anling Mountains and Xiaoxing’anling Mountains. AGB and corresponding uncertainty maps at three scales were generated using SBSD. The results showed that the efficacy of the XGBoost-based AGB model combined with the random forest-based residual correction model was superior. Spatial patterns in AGB maps generated by SBSD and DD were found to be similar. Notably, SBSD yielded enhanced accuracy in the Daxing’anling Mountains with complex topography, while both performed comparably in the Xiaoxing’anling Mountains with milder topography, highlighting SBSD’s advantages in high heterogeneity areas. Full article

With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm². Meanwhile, the real fire area in FIRER is 5800.9 hm²; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires. Full article

In recent years, the influence of extreme weather patterns has led to an alarming increase in the frequency and severity of sub-surface forest fires in boreal forests. The Ledum palustre-Larix gmelinii forests of the Daxing’an Mountains of China have emerged as a hotspot for sub-surface fires, and terrain slope has been recognized as a pivotal factor shaping forest fire behavior. The present study was conducted to (1) study the effect of terrain slope on the smoldering temperature and spread rate using simulated smoldering experiments and (2) establish occurrence probability prediction model of the sub-surface fires’ smoldering with different slopes based on the random forest model. The results showed that all the temperatures with different slopes were high, and the highest temperature was 947.91 °C. The spread rates in the horizontal direction were higher than those in the vertical direction, and the difference increased as the slope increased. The influence of slope on the peak temperature was greater than that of spread rate. The peak temperature was extremely positively correlated with the slope, horizontal distance and vertical depth. The spread rate was extremely positively correlated with the slope. The spread rate in the vertical direction was strongly positively correlated with the depth, but was strongly negatively correlated with the horizontal distance; the horizontal spread rate was opposite. The prediction equations for smoldering peak temperature and spread rate were established based on slope, horizontal distance, and vertical depth, and the model had a good fit (p < 0.01). Using random forest model, we established the occurrence prediction models for different slopes based on horizontal distance, vertical depth, and combustion time. The models had a good fit (AUC > 0.9) and high prediction accuracy (accuracy > 80%). The study proved the effect of slope on the characteristics of sub-surface fire smoldering, explained the variation in peak temperature and spread rate between different slopes, and established the occurrence prediction model based on the random forest model. The selected models had a good fit, and prediction accuracy met the requirement of the sub-surface fire prediction. Full article

We used tree-ring width data of Larix gmelinii and Pinus sylvestris var. mongolica from the northern region of the Daxing’an Mountains, China; normalized difference vegetation index (NDVI) data; and microtopographic information (elevation, slope direction, slope gradient, and topographic location index) to assess spatiotemporal dynamics in the growth of the boreal forest and topographic patterns of forest decline under the background of climate warming. Forest growth trends were determined based on tree growth decline indicators and NDVI time series trends, and topographic patterns of forest decline were analyzed using the C5.0 decision tree model. More climatic information was present in the radial growth of the trees at higher elevations, and P. sylvestris var. mongolica was influenced strongly by climatic factors of the previous year. Since 1759, tree radial growth trends in the study area have experienced two recessions during 1878–1893 and 1935–1943, which were characterized by persistent narrow whorls of tree rings of below-average growth. Changes in NDVI and tree-ring information were similar, and they together indicate a high risk of declining forest growth in the northern Daxing’an Mountains after 2010, especially at higher elevations. The NDVI time series showed that the high temperatures in 2003 negatively affected forest growth in the study area, which was confirmed by the tree-ring data. The decision tree terrain model results had an accuracy of 0.861, and elevation was the most important terrain factor affecting forest decline. The relative importance of elevation, topographic position index, aspect, and slope was 58.41%, 17.70%, 16.81%, and 7.08%, respectively. Classification rule-based decision tree models can be used to quantify the effects of terrain factors on tree growth. This research methodology can aid the management of regional forestry resources and the conservation of forest resources under the background of climate change, which increases the risk of forest decline. Full article

(1) Background: Korean pine (Pinus koraiensis) and Manchurian walnut (Juglans mandshurica) are the main tree species for plantation regeneration in Northeast China, and the mixed plantation of them is one of the typical measures adopted to address the decline in stand productivity in long-term monocultures. However, little is known about the effects of Korean pine and Manchurian walnut monocultures and mixed plantations on soil microbial diversity, composition, and functional groups. (2) Methods: We used ITS and 16S rRNA gene sequencing to detect fungal and bacterial communities and used the FUNGuild, FAPROTAX, and Bugbase databases to predict their functional groups. (3) Results: Fungal and bacterial alpha diversity were always higher in Manchurian walnut monocultures than in Korean pine monocultures. The plantation type had a greater impact on the fungal composition than the bacterial composition. The fungal functional groups were significantly affected by the plantation type (p < 0.05), while the bacterial functional groups were barely changed among all plantation types. The soil available nutrient content was the most important soil factor in shaping the microbial community structures and functional groups. (4) Conclusions: Shifts in fungal community compositions and functional groups might play a dominant role in soil nutrient cycling across the different plantation types in Northeast China. Full article

Using a static chamber-gas chromatography method, we investigate the characteristics of soil CO₂, CH₄, and N₂O fluxes and their relationships with environmental factors during the growing season in four typical Larix gmelinii forests (moss–Larix gmelinii forest, Ledum palustre–Larix gmelinii forest, herbage–Larix gmelinii forest, and Rhododendron dauricum–Larix gmelinii forest) in the Greater Khingan Mountains. Our results show that all four forest types are sources of CO₂ emissions, with similar average emission fluxes (146.71 mg·m⁻² h⁻¹–211.81 mg·m⁻² h⁻¹) and no significant differences. The soil in the moss–Larix gmelinii forest emitted CH₄ (43.78 μg·m⁻² h⁻¹), while all other forest types acted as CH₄ sinks (−56.02 μg·m⁻² h⁻¹–−28.07 μg·m⁻² h⁻¹). Although all forest types showed N₂O uptake at the beginning of the growing season, the N₂O fluxes (4.03 μg·m⁻² h⁻¹–5.74 μg·m⁻² h⁻¹) did not differ significantly among the four forest types for the entire growing season, and all acted as sources of N₂O emissions. The fluxes of CO₂, CH₄, and N₂O were significantly correlated with soil temperature and soil pH for all four forest types. Multiple regression analysis shows that considering the interactive effects of soil temperature and moisture could better explain the changes in greenhouse gas emissions among different forest types. The average Q₁₀ value (8.81) of the moss–Larix gmelinii forest is significantly higher than that of the other three forest types (3.16–3.54) (p < 0.05), indicating that the soil respiration in this forest type is more sensitive to temperature changes. Full article

This study investigates the evolution of soil nitrogen (N) contents and forms along a 17-year wildfire chronosequence in the Daxing’an Mountains. Surface soil and subsoil samples were collected during different recovery periods after wildfires. Then, the mineral N (i.e., NH₄⁺-N and NO₃⁻-N) and amino acid-N (AAN) contents in the soil extracts were measured and used to calculate the different ratios as indicators of the N forms. The results showed that the NH₄⁺-N, NO₃⁻-N, and AAN contents increased immediately after the wildfire. With vegetation restoration, the NH₄⁺-N and NO₃⁻-N contents became similar to those of unburned forests nine years and two months after the wildfire, respectively. The AAN content was mostly recovered one year post-fire. The wildfire did not lead to substantial changes in the mineral N form, but the ratio significantly increased and recovered after nine years. The soil available N form was altered by wildfires. After the wildfire, the dominant available N form changed from equivalent AAN and mineral N to a predominance of AAN in the growing season, and the predominance of AAN decreased to varying degrees in the non-growing season. With the recovery of the white birch and Dahurian larch, AAN again became the dominant N form, but the predominance of AAN was low before the freeze-up. Our study demonstrates that wildfires directly affect the soil N contents and forms, and such effects could be diminished by the restoration of the soil environment and vegetation over time. Full article