Search Results (8)

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework innovatively combines DEM data with Faster Regions with Convolutional Neural Networks (Faster R-CNN) and CNN-based methods, breaking through the limitations of traditional approaches that rely on manual feature extraction. It is capable of automatically identifying critical terrain features, such as mountain peaks and water systems, with higher accuracy and efficiency. DEMs provide high-resolution topographical information, which deep learning models leverage to accurately identify and delineate key geographical features. Our results show that the integration of DEMs and deep learning significantly improves the accuracy of fire risk assessment by offering detailed and precise terrain analysis, thereby providing more reliable inputs for fire behavior prediction. The extracted mountain peaks and water systems, as fundamental inputs for fire behavior prediction, enable more accurate predictions of fire spread and potential impact areas. This study not only highlights the great potential of combining geospatial data with advanced machine learning techniques but also offers a scalable and efficient solution for forest fire risk management in mountainous regions. Future work will focus on expanding the dataset to include more environmental variables and validating the model in different geographical areas to further enhance its robustness and applicability. Full article

This research investigated the phytoplankton ecological stoichiometry characteristics and the balance of the relationship between elements in a mountain river in a cold region. The samples of phytoplankton of four seasons were collected in May 2020, August 2020, November 2020, and February 2021 from the Taizicheng River in Chongli, Zhangjiakou City, China. We determined the contents of carbon (C), nitrogen (N), phosphorus (P), sulfur (S), hydrogen (H), and iron (Fe), and analyzed their ecological stoichiometric characteristics and correlation. Our results showed that the contents of C, N, P, S, H, and Fe in phytoplankton were 82.14 ± 32.12 g/kg, 9.22 ± 3.5 g/kg, 1.46 ± 0.55 g/kg, 1.96 ± 0.86 g/kg, 2.36 ± 1.36 g/kg, and 12.64 ± 10.57 g/kg, respectively. Generally, the contents of C, N, and P were relatively stable, while the contents of S, H, and Fe fluctuated greatly, and the coefficient of variation of Fe content was as high as 83.62%. The elemental molar composition of phytoplankton in the Taizicheng River is C_156.00N_15.41S_1.54H_51.17Fe_5.10P, which showed a significant difference compared with the classical Redfield ratio C₁₀₆N₁₆P. The high proportion of element C indicated that phytoplankton in the Taizicheng River have a high demand for C and a strong ability to consolidate C. The ratio of N:P was consistent with previous research results. The N:P ratio of phytoplankton in the Taizicheng River was 15.41, suggesting that the growth of phytoplankton in the Taizicheng River was restricted by both N and P. The contents of C, N, and P were positively correlated, while there was no significant correlation among S, H, and Fe. C:P was significantly positively correlated with C:N and N:P, while there were no strong correlations between C:N and C:P, as well as H:S, Fe:S, and H:Fe, indicating that the coupling correlation between phytoplankton elements was different and C, N, and P were highly correlated as important phytoplankton nutrient elements. This study contributes to our understanding of the phytoplankton ecological stoichiometry characteristics and the limiting factors of nutrients in a mountain river and provides a scientific basis for further ecological conservation and management efforts. Full article

Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R² = 0.811, broadleaf forest R² = 0.356, coniferous forest R² = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires. Full article

The new particle formation (NPF) process is a significant source of atmospheric secondary particles, which has remarkable impacts on the regional air quality and global radiative forcing. Most NPF studies conduct their measurements at a single site, which can hardly provide information about the regionality of NPF events at large scales (>100 km). During the 2022 Winter Olympic and Paralympic Games, simultaneous measurements of particle number size distributions and NPF-associated precursors were conducted at a mountainous site close to the Winter Olympic Village in Chongli (CL), Zhangjiakou, and an urban site in Beijing (BJ) located 150 km southeast of the CL site. High NPF frequencies were observed at the CL (50%) and BJ (52%) sites; however, the fraction of concurrent NPF events was smaller than the results in other regions. In addition, the wind distributions exhibited distinct air mass origins at the two sites during the concurrent NPF events. Compared with the BJ site, the NPF growth rates were higher at the CL site due to higher levels of volatile organic compounds (VOCs) and radiation. Surprisingly, the formation rates at the CL site were lower than at the BJ site, even with a higher sulfuric acid concentration and lower CS, which may be attributed to lower dimethylamine concentrations in the mountainous area. This study reveals that, although NPF events are commonly thought to occur on regional scales, their intensity and mechanisms may have significant spatial inhomogeneity. Further studies are required to reduce the uncertainty when expanding the mechanisms based on the urban conditions to regional or global scales in the models. Full article

Modeling and prediction of forest fire occurrence play a key role in guiding forest fire prevention. From the perspective of the whole world, forest fires are a natural disaster with a great degree of hazard, and many countries have taken mountain fire prediction as an important measure for fire prevention and control, and have conducted corresponding research. In this study, a forest fire prediction model based on LSTNet is proposed to improve the accuracy of forest fire forecasts. The factors that influence forest fires are obtained through remote sensing satellites and GIS, and their correlation is estimated using Pearson correlation analysis and testing for multicollinearity. To account for the spatial aggregation of forest fires, the data set was constructed using oversampling methods and proportional stratified sampling, and the LSTNet forest fire prediction model was established based on eight influential factors. Finally, the predicted data were incorporated into the model and the predicted risk map of forest fires in Chongli, China was drawn. This paper uses metrics such as RMSE to compare with traditional machine learning methods, and the results show that the LSTNet model proposed in this paper has high accuracy (ACC 0.941). This study illustrates that the model can effectively use spatial background information and the periodicity of forest fire factors, and is a novel method for spatial prediction of forest fire susceptibility. Full article

Spurred by the degraded forest in the 2022 Chongli Winter Olympic Games area, the Chinese government initiated a national program for mountain forest rehabilitation. We developed a method to assess the quality of mountain forests using an index system composed of stand structure, site conditions, and landscape aesthetics at three criteria levels. The method involves index weights determined by the analytical hierarchy process (AHP) and entropy method. The results show that landscape aesthetics was the most important measure for the criterion layer. Slope aspect and naturalness were the most and second-most important indices, respectively, for the alternative layer. The quality of the mountain forest in the Chongli area was divided into four grades. The area had 7.8% with high quality, 46.7% with medium quality, 36.6% with low quality, and 8.9% with inferior quality. In total 76.6% of the damaged forest were distributed on sloping and steep sloping ground at 1700 to 2050 m altitude, and Betula platyphylla Sukaczev and Larix gmelinii var. principis-rupprechtii (Mayr) Pilg. were the predominating trees. The damaged forest was divided into over-dense, over-sparse, degraded, inappropriate tree species, and inferior landscape forest. For different types of damaged forest, corresponding modification measures were proposed. The methods developed in this study can be used for rehabilitation projects to improve the quality of degraded forests in mountainous temperate areas. Full article

Understanding the response of plant species richness to environmental filters is critical for conservation management as there is an increasing emphasis on plant restoration in urban/rural planning. However, empirical studies on the effects that the regional species pool has on plant species richness often overlook small spatial scales, therefore requiring more comprehensive approaches. As mountains can act as barriers to plant dispersal, the impact on the species pool, particularly, should be a priority. This study aimed to investigate how the regional species pool affects the local plant species richness in a multivariate context. We sampled vascular plant communities along three transects located in three valleys across the Chongli District, China, where four common habitat types were selected for sampling: grassland, shrubbery, pure forest, and mixed forest. We compared the differences in the multi-scale species richness and species composition between habitats and regions and used piecewise structural equation modeling to analyze the relative importance of the regional species pool, habitat species pool, soil resource availability, and exposure for local plant richness. The β-diversity had the highest contribution to the total species richness between valleys and habitats. The species composition between regions and habitats showed a significant difference and the local species richness was most strongly affected by the soil characteristics, but effects from the regional species pool still played an important role. Conservation efforts and urban/rural planning should use a multi-level and multi-scale approach based on a detailed structural investigation. Full article

To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region. Full article