项目名称: 基于多模态感知数据耦合的森林碳汇计量模型研究
项目编号: No.31300539
项目类型: 青年科学基金项目
立项/批准年度: 2014
项目学科: 农业科学
项目作者: 胡军国
作者单位: 浙江农林大学
项目金额: 25万元
中文摘要: 碳交易是应对气候变化、缓解CO2排放的有效途径,而碳汇计量是碳交易的技术基础。但是不同研究者使用不同模型和方法得到的碳汇数据存在巨大的差异,表明目前技术对于碳汇的测算存在很大的不确定性,因此迫切需要有更新的方法准确计量碳汇数据。本项目针对天目山森林生态系统,拟利用无线传感器网络采集的温度、湿度、风速、风向、光强等9个不同感知源、不同时空、不同量纲的多模态数据进行碳汇计量,开展如下工作:(1)研究基于传感器网络联合校正的涡度相关技术,(2)研究基于改进APAR测算和融合CO2因子的光能利用率技术,(3)研究基于人工神经网络耦合的碳汇优化模型。本项目的研究目的是利用改进的涡度相关技术和光能利用率技术计算出碳汇数据,并进一步利用人工神经网络模型耦合优化碳汇值,以提高森林碳汇计量的准确性,并为其它测量方法提供校验标准,具有重要的理论意义和应用价值。
中文关键词: 森林碳汇;无线传感网;神经网络算法;生态模型;通量网
英文摘要: Carbon trading is an effective way to tackle climate change and relieve CO2 emissions.The basis of carbon trading is how to measure forest carbon sinks.But different researchers obtain different carbon sinks data by using different models and methods,so it indicates that there are a great deal of uncertainty of measuring carbon sinks.Therefore We need study innovative methods to measure carbon sinks data.Aimed at the TianMuShan forest ecological system,the project plans to use the the wireless sensor networks to collect nine multi-modal data including temperature, humidity, wind speed, wind direction and so on.We solve three problems as following:(1)Study of eddy covariance technique based on the joint correction of sensor networks.(2)Study of light use efficiency based on improved calculation of APAR and fusion of CO2 factor.(3)Study of carbon sinks optimization model based on artificial neural network.The objective of this project is using the improved eddy coariance technique and the light use efficiency technology to calculate the carbon data, and further using artificial neural network model to couple and optimize carbon sinks value.From this studying,we can improve the accuracy of forest carbon sinks and provide criterions for other methods.In a word, this project has great significance in theory and appli
英文关键词: forest carbon sink;wireless sensor network;neural network algorithm;ecological model;flux net