摘要
利用北京市延庆县不同密度抚育后林分、林缘和农田在2010年4月、7月及10月的季节性小气候监测数据,构建了林缘—农田和林内—农田的立体水热空间的BP定量预测模型和MLR模型,拟达到定量评价林业生态工程生态效益、预测农田小气候进而服务林业生产的目的。结果表明:(1)对于集合小气候环境梯度CMG,林缘—农田的BP模型预测精度整体高于林内—农田的BP模型预测精度;(2)林缘—农田BP模型在整个生长季预测相关性均高于林内—农田的BP模型;林缘—农田的MLR模型仅10月较林内—农田的MLR模型预测有紧密的相关性,而4月和7月却相反。(3)林缘—农田的两种模型的季节预测精度均为7月>10月>4月;林内—农田的BP模型在生长初期中高密度林分Ⅱ的预测精度最高,在生长季中后期高密度林分Ⅰ的预测精度最高;而林内—农田的MLR模型在整个生长季均为中高密度林分Ⅱ的预测精度最高。(4)构建BP模型所需参数少,预测精度高,在样本数据量足够的情况下,有一定的外推能力。
If is an important aspect to evaluate the ecological benefits of forestry projects. Using observed seasonal meteorological data of April, July and October （2010） in different density of Pinus tabulaeformis plantations in Yan- qing county, Beijing, we established the BP neural network model and MLR model for stereoscopic hydrothermal space of the forest edge-farmland and forest-farmland. The possibility of quantitatively evaluating and forecasting the farmland microclimate by these models were studied. The results showed： （1） The precision of forest edge-farmland BP neural network model was higher than that of forest-farmland BP neural network model in congregate microclimate- gradient （CMG） ;（2） The observed-simulated correlation （OSC） of forest edge-farmland BP model was higher than that of forest-farmland BP model during the whole growing season; while the higher OSC of forest edge-farmland MLR model only occurred in October, but forest-farmland MLR model had the higher OSC in April and July. （3） The pre- cision order of these two types of forest edge-farmland model followed as July 〉 October 〉 April; The forest-farm-land BP model of density II had the highest precision in April, but forest-farmland BP model of density I was the highest in July and October. The precision of forest-farmland MLR model of density II remained highest during the whole growing season. （4） The BP neural network model had high precision with few parameters, and was able to extrapolate when enough observed data were provided.
出处
《浙江农业学报》
CSCD
北大核心
2013年第2期359-364,共6页
Acta Agriculturae Zhejiangensis
基金
高等学校博士学科点专项科研基金资助项目(20090014110011)
国家自然科学基金资助项目(30972353)
北京市教育委员会学科建设与研究生教育建设项目"北京市生态公益林质量升级关键技术研究"产学研联合培养研究生基地资助项目(CXYBL2008-2010)
关键词
BP人工神经网络
森林生态效益
立体水热空间
小气候
BP artificial neural network
forest ecological benefits
stereoscopic hydrothermal space
microclimate
