The number of bamboo stem at different ages and the mean diameter at breast height(DBH)which are the important target in evaluating productivity of bamboo stand were investigated in 50 plots established in Jianou city...The number of bamboo stem at different ages and the mean diameter at breast height(DBH)which are the important target in evaluating productivity of bamboo stand were investigated in 50 plots established in Jianou city, Fujian Province in this paper, and the authors selected the method of artificial neural network to biuld the simulative and predictive model of mean DBH for bamboo stands. Artificial neural network is a good method in handling the overall nonlinear mapping problems between input variables and output ones, which has a wide application in many research fields, such as system simulating, automation controlling, paralleled data processing and so on. In this paper, the input variables were the number of different age and the total number of stand, the output variable was mean DBH for bamboo stands, the number of neurons of hide level( M ) was M=2L+1=3 according to the last document ( L is the number of factors of input level), and the network activity function is Sigmiod function as follows: F(x)=1/(1+e -x ). Using the built BP network, the samples were trained until E j(W 1 lm ,W 2 mn )=Nn=1(O nj -Y nj ) 2 =min, where O nj and Y nj are output values of network and really values of DBH for bamboo stands respectively, N is the number of trained samples, and E j is sum of square deviation of BP network. If E j didn’t converge, the weights and thresholds of BP network were adjusted as follow: ΔW ij (n+1)=βλ jX i+αΔW ij (n) and Δη j(n+1)=-βλ j+αΔη j(n) .. The results showed that the mean simulative accuracy and the mean predictive accuracy of mean D.B.H BP model for bamboo stands were all satisfactory, which were 89 95% and 89 26% respectively. Therefore, it provided a scientific basis for evaluating the productivity and realizing high yield for bamboo stands.展开更多
入侵植物通常由于具有较强的适应性而能够快速繁殖扩散,影响本土物种的生长繁殖,进而威胁到当地生态安全、景观格局和农业生产等。西藏生态环境非常脆弱,一旦发生恶性物种大面积入侵,生态后果不堪设想。为了探究入侵植物印加孔雀草(Tage...入侵植物通常由于具有较强的适应性而能够快速繁殖扩散,影响本土物种的生长繁殖,进而威胁到当地生态安全、景观格局和农业生产等。西藏生态环境非常脆弱,一旦发生恶性物种大面积入侵,生态后果不堪设想。为了探究入侵植物印加孔雀草(Tagetes minuta L.)对西藏东南生态安全的影响趋势,基于野外实地调查数据,采用最大熵(MaxEnt)模型,应用R语言平台对模型和数据进行优化筛选,探讨影响其地理分布的主要环境因子,并模拟预测了当代及2种气候变化情景(RCP 4.5、RCP 8.5)下,其在西藏的潜在适生区分布情况。结果表明:(1)训练数据集和测试数据集的受试者工作特征曲线下的面积(AUC)均为0.997,模拟效果较好;底层土壤酸碱度、最暖季降水量、土壤有效含水量、最暖月最高温度为影响印加孔雀草分布的主导环境因子,贡献率总和超过90%。(2)加查县、朗县是印加孔雀草分布密集区域,米林县、林芝市区、察隅县、墨脱县等地为入侵高风险地区。(3)中短期(2050年)内印加孔雀草适生面积增加明显,2070年时面积则会减少;印加孔雀草适生区在藏东南地区进一步向东北区域扩张,分布质心由当前的墨脱县域向波密县域转移。总体而言,印加孔雀草分布受土壤环境、温度和降水影响较大,气候变化将使其向西藏东部、南部扩张。研究结果对于西藏自治区制定植物入侵防控管理办法具有重要参考价值。展开更多
文摘The number of bamboo stem at different ages and the mean diameter at breast height(DBH)which are the important target in evaluating productivity of bamboo stand were investigated in 50 plots established in Jianou city, Fujian Province in this paper, and the authors selected the method of artificial neural network to biuld the simulative and predictive model of mean DBH for bamboo stands. Artificial neural network is a good method in handling the overall nonlinear mapping problems between input variables and output ones, which has a wide application in many research fields, such as system simulating, automation controlling, paralleled data processing and so on. In this paper, the input variables were the number of different age and the total number of stand, the output variable was mean DBH for bamboo stands, the number of neurons of hide level( M ) was M=2L+1=3 according to the last document ( L is the number of factors of input level), and the network activity function is Sigmiod function as follows: F(x)=1/(1+e -x ). Using the built BP network, the samples were trained until E j(W 1 lm ,W 2 mn )=Nn=1(O nj -Y nj ) 2 =min, where O nj and Y nj are output values of network and really values of DBH for bamboo stands respectively, N is the number of trained samples, and E j is sum of square deviation of BP network. If E j didn’t converge, the weights and thresholds of BP network were adjusted as follow: ΔW ij (n+1)=βλ jX i+αΔW ij (n) and Δη j(n+1)=-βλ j+αΔη j(n) .. The results showed that the mean simulative accuracy and the mean predictive accuracy of mean D.B.H BP model for bamboo stands were all satisfactory, which were 89 95% and 89 26% respectively. Therefore, it provided a scientific basis for evaluating the productivity and realizing high yield for bamboo stands.
文摘入侵植物通常由于具有较强的适应性而能够快速繁殖扩散,影响本土物种的生长繁殖,进而威胁到当地生态安全、景观格局和农业生产等。西藏生态环境非常脆弱,一旦发生恶性物种大面积入侵,生态后果不堪设想。为了探究入侵植物印加孔雀草(Tagetes minuta L.)对西藏东南生态安全的影响趋势,基于野外实地调查数据,采用最大熵(MaxEnt)模型,应用R语言平台对模型和数据进行优化筛选,探讨影响其地理分布的主要环境因子,并模拟预测了当代及2种气候变化情景(RCP 4.5、RCP 8.5)下,其在西藏的潜在适生区分布情况。结果表明:(1)训练数据集和测试数据集的受试者工作特征曲线下的面积(AUC)均为0.997,模拟效果较好;底层土壤酸碱度、最暖季降水量、土壤有效含水量、最暖月最高温度为影响印加孔雀草分布的主导环境因子,贡献率总和超过90%。(2)加查县、朗县是印加孔雀草分布密集区域,米林县、林芝市区、察隅县、墨脱县等地为入侵高风险地区。(3)中短期(2050年)内印加孔雀草适生面积增加明显,2070年时面积则会减少;印加孔雀草适生区在藏东南地区进一步向东北区域扩张,分布质心由当前的墨脱县域向波密县域转移。总体而言,印加孔雀草分布受土壤环境、温度和降水影响较大,气候变化将使其向西藏东部、南部扩张。研究结果对于西藏自治区制定植物入侵防控管理办法具有重要参考价值。
