应用遥感、人工神经网络叶面积模型和模拟过程模型估计湿地松人工林碳储量

全球气候变化引起人们对森林碳固定作用的关注。碳存储速率依赖于生态系统流通量(光合作用和生态系统呼吸),量化为净生态系统二氧化碳交换。在没有密集采样点的情况下,我们需要采用估测森林净生态系统交换的方法准确地估计林分水平和更大尺度的碳固定量。本文通过祸合遥感估算的叶面积指数和生长过程拟合模型,估计了佛罗里达州内9 770公顷湿地松人工林一年里净生态系统交换总量。地面图神经网络模型和陆地卫星数据估计的森林叶面积指数平均值是1.06(数值范围0-3.93,包括森林边界)。输入神经网络叶面积指数值,湿地松拟合模型(SPM2)估计的森林净生态交换值在-5.52 Mg·hm-2·a-1到11.06Mg·hm-2·a-1之间,平均值是3.47 Mg·hm-2·a-1。年总的碳储量是33920t,约合3.5 t/hm2。估计的叶面积指数和森林净生态交换均对对施肥高度敏感。图3表1参30。

Application of remote sensing,an artificial neural network leaf area model,and a process-based simulation model to estimate carbon storage in Florida slash pine plantations

Carbon sequestration in forests is of great interest due to concerns about global climate change.Carbon storage rates depend on ecosystem fluxes(photosynthesis and ecosystem respiration),typically quantified as net ecosystem exchange(NEE).Methods to estimate forest NEE without intensive site sampling are needed to accurately assess rates of carbon sequestration at stand-level and larger scales.We produced spatially-explicit estimates of NEE for 9 770 ha of slash pine(Pinus elliottii) plantations in North-Ce...