Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
研究了Fenton法氧化降解MC-LR的影响因素,包括反应时间、pH、H2O2浓度、Fe2+/H2O2、Fe3+等。正交试验表明,各个因素的影响作用为H2O2浓度>Fe2+浓度>反应时间>pH。最佳试验条件为:H2O2浓度1.8 mmol/L、Fe2+/H2O2=1∶18、初始pH=...研究了Fenton法氧化降解MC-LR的影响因素,包括反应时间、pH、H2O2浓度、Fe2+/H2O2、Fe3+等。正交试验表明,各个因素的影响作用为H2O2浓度>Fe2+浓度>反应时间>pH。最佳试验条件为:H2O2浓度1.8 mmol/L、Fe2+/H2O2=1∶18、初始pH=3、反应时间30 m in、反应温度(25±1)℃,在该条件下,MC-LR初始质量浓度为2.0μg/L时,去除率可达94.4%。展开更多
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
文摘研究了Fenton法氧化降解MC-LR的影响因素,包括反应时间、pH、H2O2浓度、Fe2+/H2O2、Fe3+等。正交试验表明,各个因素的影响作用为H2O2浓度>Fe2+浓度>反应时间>pH。最佳试验条件为:H2O2浓度1.8 mmol/L、Fe2+/H2O2=1∶18、初始pH=3、反应时间30 m in、反应温度(25±1)℃,在该条件下,MC-LR初始质量浓度为2.0μg/L时,去除率可达94.4%。
