Decreasing the forest ecosystem leaf-area index error(LAIe)helps accurately estimate the growth and light energy utilization of aboveground foliage.Analyzing light transmission in forest ecosystems can effectively det...Decreasing the forest ecosystem leaf-area index error(LAIe)helps accurately estimate the growth and light energy utilization of aboveground foliage.Analyzing light transmission in forest ecosystems can effectively determine LAIe.The LAI-2200 plant canopy analyzer(PCA)is used extensively for rapid field-effective LAI(LAIe)measurements and primarily to measure forest canopy LAIe values.However,sometimes this parameter must also be measured in forests with small clearings.In this study,we used the LAI-2200 PCA to obtain one A-value and four B-values each for the canopy,herbaceous layer,and forest ecosystem LAIe.Field measurements showed that the three LAIe types were obviously different.In certain quadrats,the average herbaceous layer(Dicranopteris dichotoma Bernh.)LAIe apparently exceeded that of the Pinus massoniana forest ecosystem.The sources of this error were measuring and recording A-value readings for small canopies and underestimating the ecosystem LAIe.We obtained similar coefficients of determination for both the pre-recomputation and post-recomputation of the canopy and forest ecosystem LAIe(R^2C 0.96 and R^2C 0.99,respectively);thus,the error was decreased.Measuring field LAIe with the LAI-2200 PCA and recomputation should compensate for LAIe underestimation in complex forest ecosystems.展开更多
We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised clas...We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41401385 and 31770760)the Foundation of College of Forestry,Fujian Agricultural and Forest University(Grant No.61201400833)
文摘Decreasing the forest ecosystem leaf-area index error(LAIe)helps accurately estimate the growth and light energy utilization of aboveground foliage.Analyzing light transmission in forest ecosystems can effectively determine LAIe.The LAI-2200 plant canopy analyzer(PCA)is used extensively for rapid field-effective LAI(LAIe)measurements and primarily to measure forest canopy LAIe values.However,sometimes this parameter must also be measured in forests with small clearings.In this study,we used the LAI-2200 PCA to obtain one A-value and four B-values each for the canopy,herbaceous layer,and forest ecosystem LAIe.Field measurements showed that the three LAIe types were obviously different.In certain quadrats,the average herbaceous layer(Dicranopteris dichotoma Bernh.)LAIe apparently exceeded that of the Pinus massoniana forest ecosystem.The sources of this error were measuring and recording A-value readings for small canopies and underestimating the ecosystem LAIe.We obtained similar coefficients of determination for both the pre-recomputation and post-recomputation of the canopy and forest ecosystem LAIe(R^2C 0.96 and R^2C 0.99,respectively);thus,the error was decreased.Measuring field LAIe with the LAI-2200 PCA and recomputation should compensate for LAIe underestimation in complex forest ecosystems.
文摘We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.
文摘偏航角零点漂移严重影响风电机组性能,将之消除的前提是对其进行可靠且快速的检测。基于风能捕获机理,该文提出一种运用机器学习算法的偏航角零点漂移诊断方法。首先,采用孤立森林(isolated forest,IF)异常值检测算法对数据进行预处理;其次,建立非参数模型稀疏高斯过程回归(sparse Gaussian process regression,SGPR)估计偏航角零点漂移;最后,利用多个风电场的风电机组实际运行数据对所提方法进行验证,并分析不同诊断模型对数据量的依赖性。结果表明:IF+SGPR方法准确性高,所需数据量少,能够快速诊断偏航角零点漂移;该诊断方法能够应用于各种电场不同型号的风电机组,普适性较高。
文摘以动态数据驱动技术为基础,通过对林火蔓延模拟精度验证方法和模拟误差的分析,确定模拟误差修正参数及其计算方法,并通过神经网络技术自动生成模拟误差修正参数,从而实现林火蔓延模型模拟误差的在线自适应修正。以王正非林火蔓延模型为例,采用历史记录火场数据对模拟误差的自适应修正过程进行验证试验,结果表明,在预测的16条记录中,有14条与计算结果误差小于预定的0.20 m.min-1,2条误差超过0.20 m.
