针对SCOPE(soil canopy observation of photosynthesis and energy fluxes)模型模拟冠层净辐射(0.4~2.5μm短波净辐射+2.5~50μm长波净辐射)时假设叶片空间随机分布的问题,开发考虑叶片空间聚集的冠层净辐射模拟新模型。将SCOPE模型的...针对SCOPE(soil canopy observation of photosynthesis and energy fluxes)模型模拟冠层净辐射(0.4~2.5μm短波净辐射+2.5~50μm长波净辐射)时假设叶片空间随机分布的问题,开发考虑叶片空间聚集的冠层净辐射模拟新模型。将SCOPE模型的短波净辐射模块和长波净辐射模块分别用考虑叶片空间聚集的GOST2模型和UFR97模型替换,形成新的冠层净辐射模拟模型NRC(modeling canopy net radiation considering spatial clumping index of leaves);通过浙江省安吉县1个毛竹(Phyllostachys edulis)林样地(1~4年生异龄林,4500株/hm^(2))2023年整年的观测数据验证,对比SCOPE模型和NRC模型对冠层净辐射的模拟结果。SCOPE模型和NRC模型对冠层净辐射的模拟结果都与观测值有强相关性,决定系数(R^(2))分别为0.97和0.99,均方根误差(RMSE)分别为47.24和13.31 W/m^(2)。SCOPE模型模拟得到的短波净辐射(R_(notot))存在低估(R^(2)=0.96,平均偏差MBE=-14.17 W/m^(2)),长波净辐射(R nttot)存在高估(R^(2)=0.46;MBE=50.27 W/m^(2)),而NRC模型分别成功模拟了R_(notot)(R^(2)=0.99,MBE=1.44 W/m^(2))和R nttot(R^(2)=0.71;MBE=1.34 W/m^(2))。NRC模型具备模拟叶片空间聚集条件下冠层净辐射的潜力。展开更多
We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In...We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.展开更多
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
文摘针对SCOPE(soil canopy observation of photosynthesis and energy fluxes)模型模拟冠层净辐射(0.4~2.5μm短波净辐射+2.5~50μm长波净辐射)时假设叶片空间随机分布的问题,开发考虑叶片空间聚集的冠层净辐射模拟新模型。将SCOPE模型的短波净辐射模块和长波净辐射模块分别用考虑叶片空间聚集的GOST2模型和UFR97模型替换,形成新的冠层净辐射模拟模型NRC(modeling canopy net radiation considering spatial clumping index of leaves);通过浙江省安吉县1个毛竹(Phyllostachys edulis)林样地(1~4年生异龄林,4500株/hm^(2))2023年整年的观测数据验证,对比SCOPE模型和NRC模型对冠层净辐射的模拟结果。SCOPE模型和NRC模型对冠层净辐射的模拟结果都与观测值有强相关性,决定系数(R^(2))分别为0.97和0.99,均方根误差(RMSE)分别为47.24和13.31 W/m^(2)。SCOPE模型模拟得到的短波净辐射(R_(notot))存在低估(R^(2)=0.96,平均偏差MBE=-14.17 W/m^(2)),长波净辐射(R nttot)存在高估(R^(2)=0.46;MBE=50.27 W/m^(2)),而NRC模型分别成功模拟了R_(notot)(R^(2)=0.99,MBE=1.44 W/m^(2))和R nttot(R^(2)=0.71;MBE=1.34 W/m^(2))。NRC模型具备模拟叶片空间聚集条件下冠层净辐射的潜力。
文摘We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.
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.
