Forests are facing several challenges related to forest deforestation mostly due to the actions of man. The study used a CA-Markov model to examine land use/land cover dynamics from 1986 to 2022, as well as estimate f...Forests are facing several challenges related to forest deforestation mostly due to the actions of man. The study used a CA-Markov model to examine land use/land cover dynamics from 1986 to 2022, as well as estimate future changes from 2022 to 2052 in the Mount Nlonako forest and peripheries. Three types of Landsat images (Landsat 4 - 5 Thematic Mapper (TM) images of 1986 and 2004, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022) were used for diachronic analysis. The results revealed six major land use/land cover classes namely: Dense forest, Clear forest, Farmland, Savannah, Built-up Area and Bare floor. Accuracy rates for land use/land cover classification ranged from 89.85% to 93.11%. The prediction model was accepted with an overall satisfaction rate of 84.08%. The Dense Forest class has been steadily decreasing from 138320.94 ha (75.42%) in 1986 to 84161.34 ha (45.89%) in 2022, corresponding to a total loss of 54159.6 ha (29.53%) over the 36-year period and is projected to reach 39028.34 ha (21.28%) in 2052 corresponding to a future loss of 45133 ha (24.61%) over a period of 30 years. Anthropogenic factors (mainly agriculture and industrial logging) and natural factors (excess rainfall) were responsible for the degradation of the area. Regardless of the limitations of the CA-Markov model due to the non integration of socio-economic factors, this study is a crucial alert to decison and policy makers to undergo protection procedures for this area to be protected, thereby involving the local communities in the management and restoration of the area through participatory management.展开更多
Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations...Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.展开更多
The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns ...The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns indicate that deforestation primarily occurs at lower elevations and near the boundaries of forests and settlements,often associated with shifting cultivation by local farmers.This study adopts a process-based modelling approach,specifically the agent-based model,to simulate land changes,particularly farmers'expansion of agricultural land around the Mutis mountain forest.The underlying concept of this agent-based approach is the interaction between the human and environmental systems.Farmers,representing the human system,interact with the land,which represents the environmental system,through land use decision-making mechanisms.The research was conducted in the Community Forest of the Timor Tengah Utara District,one of the sites within the Mutis-Timau Forest Complex with the highest deforestation rate.Land use change simulations were performed using agent-based modelling from 1999 to 2030,considering the socio-economic conditions of farmers,spatial preferences,land use decisions,and natural transitions.The results revealed that the agricultural area increased by 14%under the Business as Usual scenario and 5%under the Reducing Emission from Deforestation and Forest Degradation scenario,compared to the initial agricultural area of 245 hectares.The probability of farmers deciding to extend agricultural activities was positively associated with the number of livestock maintained by farmers and the size of the village area.Conversely,the likelihood of farmers opting for agricultural extensification decreased with an increase in the area of private land and the farmer's age.These findings are crucial for the managers of the Mutis-Timau Forest Complex and other relevant stakeholders,as they aid in arranging actions to combat deforestation,designing proper forest-related policies,and providing support for initiatives such as reducing emissions from deforestation and forest degradation programs or further incentive schemes.展开更多
Tropical forests store more than half of the world's terrestrial carbon(C)pool and account for one-third of global net primary productivity(NPP).Many terrestrial biosphere models(TBMs)estimate increased productivi...Tropical forests store more than half of the world's terrestrial carbon(C)pool and account for one-third of global net primary productivity(NPP).Many terrestrial biosphere models(TBMs)estimate increased productivity in tropical forests throughout the 21st century due to CO_(2)fertilization.However,phosphorus(P)liaitations on vegetation photosynthesis and productivity could significantly reduce the CO_(2)fertilization effect.Here,we used a carbon-nitrogen-phosphorus coupled model(Dynamic Land Ecosystem Model;DLEM-CNP)with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during1860-2018.Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity.We further compared the simulations from DLEM-CNP,DLEM-CN,and DLEMC and the results showed that the inclusion of P processes reduced the CO_(2)fertilization effect on gross primary production(GPP)by 25%and 45%,and net ecosystem production(NEP)by 28%and 41%,respectively,relative to CN-only and C-on ly models.From the 1860s to the 2010s,the DLEM-CNP estimated that in tropical forests GPP increased by 17%,plant respiration(Ra)increased by 18%,ecosystem respiration(Rh)increased by 13%,NEP increased by 121%per unit area,respectively.Additionally,factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO_(2) fertilization effect had been offset by 135%due to deforestation from the 1860s to the 2010s.Our study highlights the importance of P limitation on the C cycle and the weakened CO_(2)fertilization effect resulting from P limitation in tropical forests.展开更多
Climate change and forest management are recognized as pivotal factors influencing forest ecosystem services and thus multifunctionality.However,the magnitude and the relative importance of climate change and forest m...Climate change and forest management are recognized as pivotal factors influencing forest ecosystem services and thus multifunctionality.However,the magnitude and the relative importance of climate change and forest management effects on the multifunctionality remain unclear,especially for natural mixed forests.In this study,our objective is to address this gap by utilizing simulations of climate-sensitive transition matrix growth models based on national forest inventory plot data.We evaluated the effects of seven management scenarios(combinations of various cutting methods and intensities)on the future provision of ecosystem services and multifunctionality in mixed conifer-broad-leaved forests in northeastern China,under four climate scenarios(SSP1-2.6,SSP2-4.5,SSP5-8.5,and constant climate).Provisioning,regulating,cultural,and supporting services were described by timber production,carbon storage,carbon sequestration,tree species diversity,deadwood volume,and the number of large living trees.Our findings indicated that timber production was significantly influenced by management scenarios,while tree species diversity,deadwood volume,and large living trees were impacted by both climate and management separately.Carbon storage and sequestration were notably influenced by both management and the interaction of climate and management.These findings emphasized the profound impact of forest management on ecosystem services,outweighing that of climate scenarios alone.We found no single management scenario maximized all six ecosystem service indicators.The upper story thinning by 5%intensity with 5-year interval(UST5)management strategy emerged with the highest multifunctionality,surpassing the lowest values by more than 20%across all climate scenarios.In conclusion,our results underlined the potential of climate-sensitive transition matrix growth models as a decision support tool and provided recommendations for long-term strategies for multifunctional forest management under future climate change context.Ecosystem services and multifunctionality of forests could be enhanced by implementing appropriate management measures amidst a changing climate.展开更多
Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully...Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.展开更多
To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section,...To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.展开更多
Forest hydrology,the study of water dynamics within forested catchments,is crucial for understanding the intricate relationship between forest cover and water balances across different scales,from ecosystems to landsc...Forest hydrology,the study of water dynamics within forested catchments,is crucial for understanding the intricate relationship between forest cover and water balances across different scales,from ecosystems to landscapes,or from catchment watersheds.The intensified global changes in climate,land use and cover,and pollution that occurred over the past century have brought about adverse impacts on forests and their services in water regulation,signifying the importance of forest hydrological research as a re-emerging topic of scientific interest.This article reviews the literature on recent advances in forest hydrological research,intending to identify leading countries,institutions,and researchers actively engaged in this field,as well as highlighting research hotspots for future exploration.Through a systematic analysis using VOSviewer,drawing from 17,006 articles retrieved from the Web of Science Core Collection spanning 2000–2022,we employed scientometric methods to assess research productivity,identify emerging topics,and analyze academic development.The findings reveal a consistent growth in forest hydrological research over the past two decades,with the United States,Charles T.Driscoll,and the Chinese Academy of Sciences emerging as the most productive country,author,and institution,respectively.The Journal of Hydrology emerges as the most co-cited journal.Analysis of keyword co-occurrence and co-cited references highlights key research areas,including climate change,management strategies,runoff-erosion dynamics,vegetation cover changes,paired catchment experiments,water quality,aquatic biodiversity,forest fire dynamics and hydrological modeling.Based on these findings,our study advocates for an integrated approach to future research,emphasizing the collection of data from diverse sources,utilization of varied methodologies,and collaboration across disciplines and institutions.This holistic strategy is essential for developing sustainable approaches to forested watershed planning and management.Ultimately,our study provides valuable insights for researchers,practitioners,and policymakers,guiding future research directions towards forest hydrological research and applications.展开更多
BACKGROUND Patients with chronic obstructive pulmonary disease(COPD)frequently experience exacerbations requiring multiple hospitalizations over prolonged disease courses,which predispose them to generalized anxiety d...BACKGROUND Patients with chronic obstructive pulmonary disease(COPD)frequently experience exacerbations requiring multiple hospitalizations over prolonged disease courses,which predispose them to generalized anxiety disorder(GAD).This comorbidity exacerbates breathing difficulties,activity limitations,and social isolation.While previous studies predominantly employed the GAD 7-item scale for screening,this approach is somewhat subjective.The current literature on predictive models for GAD risk in patients with COPD is limited.AIM To construct and validate a GAD risk prediction model to aid healthcare professionals in preventing the onset of GAD.METHODS This retrospective analysis encompassed patients with COPD treated at our institution from July 2021 to February 2024.The patients were categorized into a modeling(MO)group and a validation(VA)group in a 7:3 ratio on the basis of the occurrence of GAD.Univariate and multivariate logistic regression analyses were utilized to construct the risk prediction model,which was visualized using forest plots.The model’s performance was evaluated using Hosmer-Lemeshow(H-L)goodness-of-fit test and receiver operating characteristic(ROC)curve analysis.RESULTS A total of 271 subjects were included,with 190 in the MO group and 81 in the VA group.GAD was identified in 67 patients with COPD,resulting in a prevalence rate of 24.72%(67/271),with 49 cases(18.08%)in the MO group and 18 cases(22.22%)in the VA group.Significant differences were observed between patients with and without GAD in terms of educational level,average household income,smoking history,smoking index,number of exacerbations in the past year,cardiovascular comorbidities,disease knowledge,and personality traits(P<0.05).Multivariate logistic regression analysis revealed that lower education levels,household income<3000 China yuan,smoking history,smoking index≥400 cigarettes/year,≥two exacerbations in the past year,cardiovascular comorbidities,complete lack of disease information,and introverted personality were significant risk factors for GAD in the MO group(P<0.05).ROC analysis indicated that the area under the curve for predicting GAD in the MO and VA groups was 0.978 and 0.960.The H-L test yieldedχ^(2) values of 6.511 and 5.179,with P=0.275 and 0.274.Calibration curves demonstrated good agreement between predicted and actual GAD occurrence risks.CONCLUSION The developed predictive model includes eight independent risk factors:Educational level,household income,smoking history,smoking index,number of exacerbations in the past year,presence of cardiovascular comorbidities,level of disease knowledge,and personality traits.This model effectively predicts the onset of GAD in patients with COPD,enabling early identification of high-risk individuals and providing a basis for early preventive interventions by nursing staff.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms ...BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms for predicting the risk of inhospital mortality in children with dengue shock syndrome(DSS).AIM To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.METHODS This single-center retrospective study was conducted at tertiary Children’s Hospital No.2 in Viet Nam,between 2013 and 2022.The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit(PICU).Nine significant features were predetermined for further analysis using machine learning models.An oversampling method was used to enhance the model performance.Supervised models,including logistic regression,Naïve Bayes,Random Forest(RF),K-nearest neighbors,Decision Tree and Extreme Gradient Boosting(XGBoost),were employed to develop predictive models.The Shapley Additive Explanation was used to determine the degree of contribution of the features.RESULTS In total,1278 PICU-admitted children with complete data were included in the analysis.The median patient age was 8.1 years(interquartile range:5.4-10.7).Thirty-nine patients(3%)died.The RF and XGboost models demonstrated the highest performance.The Shapley Addictive Explanations model revealed that the most important predictive features included younger age,female patients,presence of underlying diseases,severe transaminitis,severe bleeding,low platelet counts requiring platelet transfusion,elevated levels of international normalized ratio,blood lactate and serum creatinine,large volume of resuscitation fluid and a high vasoactive inotropic score(>30).CONCLUSION We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS.The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.展开更多
The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the d...The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the data from Hongshi Forestry Bureau, in Changbai Mountain region, Jilin Province, China. The data were measured in 232 permanent sample plots. With the data of permanent sample plots, the parameters of transition probability and ingrowth models were estimated, and some models were compared and partly modified. During the simulation of stand structure, four factors such as largest diameter residual tree （LDT）, the ratio of the number of trees in a given diameter class to those in the next larger diameter class （q）, residual basal area （RBA） and selective cutting cycle （C） were considered. The simulation results showed that the optimum stand structure parameters for large diameter trees are as follows： q is 1.2, LDT is 46cm, RBA is larger than 26 m^2 and selective cutting cycle time （C） is between 10 and 20 years.展开更多
This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting de...This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.展开更多
Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Orient...Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.展开更多
Background:We used mixed models with random components to develop height-diameter(h-d) functions for mixed,uneven-aged stands in northwestern Durango(Mexico),considering the breast height diameter(d) and stand variabl...Background:We used mixed models with random components to develop height-diameter(h-d) functions for mixed,uneven-aged stands in northwestern Durango(Mexico),considering the breast height diameter(d) and stand variables as predictors.Methods:The data were obtained from 44 permanent plots used to monitor stand growth under forest management in the study area.Results:The generalized Bertalanffy-Richards model performed better than the other generalized models in predicting the total height of the species under study.For the genera Pinus and Quercus,the models were successfully calibrated by measuring the height of a subsample of three randomly selected trees close to the mean d,whereas for species of the genera Cupressus,Arbutus and Alnus,three trees were also selected,but they are specifically the maximum,minimum and mean d trees.Conclusions:The presented equations represent a new tool for the evaluation and management of natural forest in the region.展开更多
Permanent plots in the montane tropical rain forests in Xishuangbanna, southwest China, were established, and different empirical models, based on observation data of these plots in 1992, were built to model diameter ...Permanent plots in the montane tropical rain forests in Xishuangbanna, southwest China, were established, and different empirical models, based on observation data of these plots in 1992, were built to model diameter frequency distributions. The focus of this study is on predicting accuracy of stem number in the larger diameter classes, which is much more important than that of the smaller trees, from the view of forest management, and must be adequately considered in the modelling and estimate. There exist 3 traditional ways of modelling the diameter frequency distribution: the negative exponential function model, limiting line function model, and Weibull distribution model. In this study, a new model, named as the logarithmic J-shape function, together with the others, was experimented and was found as a more suitable model for modelling works in the tropical forests.展开更多
Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale....Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.展开更多
Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a bo...Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.展开更多
The nutrient cycling model NuCM is one of the most detailed models for simulating processes that influence nutrient cycling in forest ecosystems. A field study was conducted at Tieshanping, a Masson pine (Pinus masson...The nutrient cycling model NuCM is one of the most detailed models for simulating processes that influence nutrient cycling in forest ecosystems. A field study was conducted at Tieshanping, a Masson pine (Pinus massoniana Lamb.) forest site, in Chongqing, China, to monitor the impacts of acidic precipitation on nutrient cycling. NuCM simulations were compared with observed data from the study site. The model produced an approximate fit with the observed data. It simulated the mean annual soil solution concentrations in the two simulation years, whereas it sometimes failed to reproduce seasonal variation. Even though some of the parameters required by model running were measured in the field, some others were still highly uncertain and the uncertainties were analyzed. Some of the uncertain parameters necessary for model running should be measured and calibrated to produce a better fit between modeled results and field data.展开更多
文摘Forests are facing several challenges related to forest deforestation mostly due to the actions of man. The study used a CA-Markov model to examine land use/land cover dynamics from 1986 to 2022, as well as estimate future changes from 2022 to 2052 in the Mount Nlonako forest and peripheries. Three types of Landsat images (Landsat 4 - 5 Thematic Mapper (TM) images of 1986 and 2004, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022) were used for diachronic analysis. The results revealed six major land use/land cover classes namely: Dense forest, Clear forest, Farmland, Savannah, Built-up Area and Bare floor. Accuracy rates for land use/land cover classification ranged from 89.85% to 93.11%. The prediction model was accepted with an overall satisfaction rate of 84.08%. The Dense Forest class has been steadily decreasing from 138320.94 ha (75.42%) in 1986 to 84161.34 ha (45.89%) in 2022, corresponding to a total loss of 54159.6 ha (29.53%) over the 36-year period and is projected to reach 39028.34 ha (21.28%) in 2052 corresponding to a future loss of 45133 ha (24.61%) over a period of 30 years. Anthropogenic factors (mainly agriculture and industrial logging) and natural factors (excess rainfall) were responsible for the degradation of the area. Regardless of the limitations of the CA-Markov model due to the non integration of socio-economic factors, this study is a crucial alert to decison and policy makers to undergo protection procedures for this area to be protected, thereby involving the local communities in the management and restoration of the area through participatory management.
基金This research was funded by the National Natural Science Foundation of China(Grant Nos.31870426).
文摘Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.
基金funded by the Ministry of Environment and Forestry of the Republic of Indonesia through the research funding assistance program。
文摘The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns indicate that deforestation primarily occurs at lower elevations and near the boundaries of forests and settlements,often associated with shifting cultivation by local farmers.This study adopts a process-based modelling approach,specifically the agent-based model,to simulate land changes,particularly farmers'expansion of agricultural land around the Mutis mountain forest.The underlying concept of this agent-based approach is the interaction between the human and environmental systems.Farmers,representing the human system,interact with the land,which represents the environmental system,through land use decision-making mechanisms.The research was conducted in the Community Forest of the Timor Tengah Utara District,one of the sites within the Mutis-Timau Forest Complex with the highest deforestation rate.Land use change simulations were performed using agent-based modelling from 1999 to 2030,considering the socio-economic conditions of farmers,spatial preferences,land use decisions,and natural transitions.The results revealed that the agricultural area increased by 14%under the Business as Usual scenario and 5%under the Reducing Emission from Deforestation and Forest Degradation scenario,compared to the initial agricultural area of 245 hectares.The probability of farmers deciding to extend agricultural activities was positively associated with the number of livestock maintained by farmers and the size of the village area.Conversely,the likelihood of farmers opting for agricultural extensification decreased with an increase in the area of private land and the farmer's age.These findings are crucial for the managers of the Mutis-Timau Forest Complex and other relevant stakeholders,as they aid in arranging actions to combat deforestation,designing proper forest-related policies,and providing support for initiatives such as reducing emissions from deforestation and forest degradation programs or further incentive schemes.
基金partially supported by the US National Science Foundation(1903722,1243232)。
文摘Tropical forests store more than half of the world's terrestrial carbon(C)pool and account for one-third of global net primary productivity(NPP).Many terrestrial biosphere models(TBMs)estimate increased productivity in tropical forests throughout the 21st century due to CO_(2)fertilization.However,phosphorus(P)liaitations on vegetation photosynthesis and productivity could significantly reduce the CO_(2)fertilization effect.Here,we used a carbon-nitrogen-phosphorus coupled model(Dynamic Land Ecosystem Model;DLEM-CNP)with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during1860-2018.Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity.We further compared the simulations from DLEM-CNP,DLEM-CN,and DLEMC and the results showed that the inclusion of P processes reduced the CO_(2)fertilization effect on gross primary production(GPP)by 25%and 45%,and net ecosystem production(NEP)by 28%and 41%,respectively,relative to CN-only and C-on ly models.From the 1860s to the 2010s,the DLEM-CNP estimated that in tropical forests GPP increased by 17%,plant respiration(Ra)increased by 18%,ecosystem respiration(Rh)increased by 13%,NEP increased by 121%per unit area,respectively.Additionally,factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO_(2) fertilization effect had been offset by 135%due to deforestation from the 1860s to the 2010s.Our study highlights the importance of P limitation on the C cycle and the weakened CO_(2)fertilization effect resulting from P limitation in tropical forests.
基金funded by the National Key R&D Program of China(Grant No.2022YFD2200500)the Forestry Public Welfare Scientific Research Project(Grant No.201504303)。
文摘Climate change and forest management are recognized as pivotal factors influencing forest ecosystem services and thus multifunctionality.However,the magnitude and the relative importance of climate change and forest management effects on the multifunctionality remain unclear,especially for natural mixed forests.In this study,our objective is to address this gap by utilizing simulations of climate-sensitive transition matrix growth models based on national forest inventory plot data.We evaluated the effects of seven management scenarios(combinations of various cutting methods and intensities)on the future provision of ecosystem services and multifunctionality in mixed conifer-broad-leaved forests in northeastern China,under four climate scenarios(SSP1-2.6,SSP2-4.5,SSP5-8.5,and constant climate).Provisioning,regulating,cultural,and supporting services were described by timber production,carbon storage,carbon sequestration,tree species diversity,deadwood volume,and the number of large living trees.Our findings indicated that timber production was significantly influenced by management scenarios,while tree species diversity,deadwood volume,and large living trees were impacted by both climate and management separately.Carbon storage and sequestration were notably influenced by both management and the interaction of climate and management.These findings emphasized the profound impact of forest management on ecosystem services,outweighing that of climate scenarios alone.We found no single management scenario maximized all six ecosystem service indicators.The upper story thinning by 5%intensity with 5-year interval(UST5)management strategy emerged with the highest multifunctionality,surpassing the lowest values by more than 20%across all climate scenarios.In conclusion,our results underlined the potential of climate-sensitive transition matrix growth models as a decision support tool and provided recommendations for long-term strategies for multifunctional forest management under future climate change context.Ecosystem services and multifunctionality of forests could be enhanced by implementing appropriate management measures amidst a changing climate.
基金supported by the National Technology Extension Fund of Forestry,Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm ([2019]06)Fundamental Research Funds for the Central Universities (No.PTYX202107).
文摘Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.
文摘To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.
基金supported by Yibin University,Sichuan,China and Hebei University,Baoding,China(Grant No.521100221033).
文摘Forest hydrology,the study of water dynamics within forested catchments,is crucial for understanding the intricate relationship between forest cover and water balances across different scales,from ecosystems to landscapes,or from catchment watersheds.The intensified global changes in climate,land use and cover,and pollution that occurred over the past century have brought about adverse impacts on forests and their services in water regulation,signifying the importance of forest hydrological research as a re-emerging topic of scientific interest.This article reviews the literature on recent advances in forest hydrological research,intending to identify leading countries,institutions,and researchers actively engaged in this field,as well as highlighting research hotspots for future exploration.Through a systematic analysis using VOSviewer,drawing from 17,006 articles retrieved from the Web of Science Core Collection spanning 2000–2022,we employed scientometric methods to assess research productivity,identify emerging topics,and analyze academic development.The findings reveal a consistent growth in forest hydrological research over the past two decades,with the United States,Charles T.Driscoll,and the Chinese Academy of Sciences emerging as the most productive country,author,and institution,respectively.The Journal of Hydrology emerges as the most co-cited journal.Analysis of keyword co-occurrence and co-cited references highlights key research areas,including climate change,management strategies,runoff-erosion dynamics,vegetation cover changes,paired catchment experiments,water quality,aquatic biodiversity,forest fire dynamics and hydrological modeling.Based on these findings,our study advocates for an integrated approach to future research,emphasizing the collection of data from diverse sources,utilization of varied methodologies,and collaboration across disciplines and institutions.This holistic strategy is essential for developing sustainable approaches to forested watershed planning and management.Ultimately,our study provides valuable insights for researchers,practitioners,and policymakers,guiding future research directions towards forest hydrological research and applications.
基金Supported by the Henan Provincial Health Commission,No.232102310145.
文摘BACKGROUND Patients with chronic obstructive pulmonary disease(COPD)frequently experience exacerbations requiring multiple hospitalizations over prolonged disease courses,which predispose them to generalized anxiety disorder(GAD).This comorbidity exacerbates breathing difficulties,activity limitations,and social isolation.While previous studies predominantly employed the GAD 7-item scale for screening,this approach is somewhat subjective.The current literature on predictive models for GAD risk in patients with COPD is limited.AIM To construct and validate a GAD risk prediction model to aid healthcare professionals in preventing the onset of GAD.METHODS This retrospective analysis encompassed patients with COPD treated at our institution from July 2021 to February 2024.The patients were categorized into a modeling(MO)group and a validation(VA)group in a 7:3 ratio on the basis of the occurrence of GAD.Univariate and multivariate logistic regression analyses were utilized to construct the risk prediction model,which was visualized using forest plots.The model’s performance was evaluated using Hosmer-Lemeshow(H-L)goodness-of-fit test and receiver operating characteristic(ROC)curve analysis.RESULTS A total of 271 subjects were included,with 190 in the MO group and 81 in the VA group.GAD was identified in 67 patients with COPD,resulting in a prevalence rate of 24.72%(67/271),with 49 cases(18.08%)in the MO group and 18 cases(22.22%)in the VA group.Significant differences were observed between patients with and without GAD in terms of educational level,average household income,smoking history,smoking index,number of exacerbations in the past year,cardiovascular comorbidities,disease knowledge,and personality traits(P<0.05).Multivariate logistic regression analysis revealed that lower education levels,household income<3000 China yuan,smoking history,smoking index≥400 cigarettes/year,≥two exacerbations in the past year,cardiovascular comorbidities,complete lack of disease information,and introverted personality were significant risk factors for GAD in the MO group(P<0.05).ROC analysis indicated that the area under the curve for predicting GAD in the MO and VA groups was 0.978 and 0.960.The H-L test yieldedχ^(2) values of 6.511 and 5.179,with P=0.275 and 0.274.Calibration curves demonstrated good agreement between predicted and actual GAD occurrence risks.CONCLUSION The developed predictive model includes eight independent risk factors:Educational level,household income,smoking history,smoking index,number of exacerbations in the past year,presence of cardiovascular comorbidities,level of disease knowledge,and personality traits.This model effectively predicts the onset of GAD in patients with COPD,enabling early identification of high-risk individuals and providing a basis for early preventive interventions by nursing staff.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
文摘BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms for predicting the risk of inhospital mortality in children with dengue shock syndrome(DSS).AIM To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.METHODS This single-center retrospective study was conducted at tertiary Children’s Hospital No.2 in Viet Nam,between 2013 and 2022.The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit(PICU).Nine significant features were predetermined for further analysis using machine learning models.An oversampling method was used to enhance the model performance.Supervised models,including logistic regression,Naïve Bayes,Random Forest(RF),K-nearest neighbors,Decision Tree and Extreme Gradient Boosting(XGBoost),were employed to develop predictive models.The Shapley Additive Explanation was used to determine the degree of contribution of the features.RESULTS In total,1278 PICU-admitted children with complete data were included in the analysis.The median patient age was 8.1 years(interquartile range:5.4-10.7).Thirty-nine patients(3%)died.The RF and XGboost models demonstrated the highest performance.The Shapley Addictive Explanations model revealed that the most important predictive features included younger age,female patients,presence of underlying diseases,severe transaminitis,severe bleeding,low platelet counts requiring platelet transfusion,elevated levels of international normalized ratio,blood lactate and serum creatinine,large volume of resuscitation fluid and a high vasoactive inotropic score(>30).CONCLUSION We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS.The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.
基金This paper was supported by National Strategy Key Project, Research and Paradigm on Ecological Harvesting and Regeneration Tech-nique for Northeast Natural Forest (2001BA510B07-02)
文摘The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the data from Hongshi Forestry Bureau, in Changbai Mountain region, Jilin Province, China. The data were measured in 232 permanent sample plots. With the data of permanent sample plots, the parameters of transition probability and ingrowth models were estimated, and some models were compared and partly modified. During the simulation of stand structure, four factors such as largest diameter residual tree （LDT）, the ratio of the number of trees in a given diameter class to those in the next larger diameter class （q）, residual basal area （RBA） and selective cutting cycle （C） were considered. The simulation results showed that the optimum stand structure parameters for large diameter trees are as follows： q is 1.2, LDT is 46cm, RBA is larger than 26 m^2 and selective cutting cycle time （C） is between 10 and 20 years.
基金This work was supported in part by the National Natural Science Foundation of China(61601418,41602362,61871259)in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-5)+1 种基金in part by the Qilian Mountain National Park Research Center(Qinghai)(grant number:GKQ2019-01)in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province,Grant No.QHDX-2019-01.
文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
基金This research received no specific grant from any funding agency in the public,commercial,or not-for-profit sectors
文摘Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.
基金financially supported by the"Programa de Mejoramiento del Profesorado"(project:Seguimiento y Evaluacion de Sitios Permanentes de Investigación Forestal y el Impacto Socioeconómico delManejo Forestal en Norte de México)supported by"Programa Banco Santander-USC"(becas para estancias predoctorales destinadas a docentes e investigadores de America Latina)
文摘Background:We used mixed models with random components to develop height-diameter(h-d) functions for mixed,uneven-aged stands in northwestern Durango(Mexico),considering the breast height diameter(d) and stand variables as predictors.Methods:The data were obtained from 44 permanent plots used to monitor stand growth under forest management in the study area.Results:The generalized Bertalanffy-Richards model performed better than the other generalized models in predicting the total height of the species under study.For the genera Pinus and Quercus,the models were successfully calibrated by measuring the height of a subsample of three randomly selected trees close to the mean d,whereas for species of the genera Cupressus,Arbutus and Alnus,three trees were also selected,but they are specifically the maximum,minimum and mean d trees.Conclusions:The presented equations represent a new tool for the evaluation and management of natural forest in the region.
文摘Permanent plots in the montane tropical rain forests in Xishuangbanna, southwest China, were established, and different empirical models, based on observation data of these plots in 1992, were built to model diameter frequency distributions. The focus of this study is on predicting accuracy of stem number in the larger diameter classes, which is much more important than that of the smaller trees, from the view of forest management, and must be adequately considered in the modelling and estimate. There exist 3 traditional ways of modelling the diameter frequency distribution: the negative exponential function model, limiting line function model, and Weibull distribution model. In this study, a new model, named as the logarithmic J-shape function, together with the others, was experimented and was found as a more suitable model for modelling works in the tropical forests.
文摘Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.
基金financially supported by the Special Fund for Forest Scientific Research in the Public Welfare(No.201404402)Fundamental Research Funds for the Central Universities(Nos.C2572014BA23 and 2572019BA03)。
文摘Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.
基金the Chinese-Norwegian Cooperation Project Integrated Monitoring Program on Acidification of Chinese Terrestrial Systems (IMPACTS)the Chinese Academy of Forestry (No.CAFYBB200700X)
文摘The nutrient cycling model NuCM is one of the most detailed models for simulating processes that influence nutrient cycling in forest ecosystems. A field study was conducted at Tieshanping, a Masson pine (Pinus massoniana Lamb.) forest site, in Chongqing, China, to monitor the impacts of acidic precipitation on nutrient cycling. NuCM simulations were compared with observed data from the study site. The model produced an approximate fit with the observed data. It simulated the mean annual soil solution concentrations in the two simulation years, whereas it sometimes failed to reproduce seasonal variation. Even though some of the parameters required by model running were measured in the field, some others were still highly uncertain and the uncertainties were analyzed. Some of the uncertain parameters necessary for model running should be measured and calibrated to produce a better fit between modeled results and field data.
