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首页> 外文期刊>Forest Ecology and Management >Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity.
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Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity.

机译:使用基于生理的模型评估澳大利亚和新西兰的森林生产力,该模型由平均每月天气数据和卫星得出的冠层光合能力估算值驱动。

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摘要

In order to evaluate the effects of spatial variation in climate and soils on forest productivity across broad regions, an approach is required that can be widely applied and tested. Detailed physiological and micro-meteorological studies have recently led to new insights that greatly simplify the prediction of above-ground net primary production (NPP), a variable closely related to conventional measures of forest growth, such as mean annual increment (MAI) of stemwood. These simplifications were applied in a monthly time-step model - a version of the simple physiological processes predicting growth (3-PG) model, developed by Landsberg & Waring in 1997 (Forest Ecology and Management 95 (3) 209-228) - modified to allow remotely sensed observationsto be input to it. The modified model is called 3-PGS, and is driven by estimates of the fraction of light intercepted by green canopies, derived from near-infrared and red reflectances monitored from National Oceanographic and Atmospheric Administration(NOAA) weather satellites, and from equations utilizing local temperature and rainfall records. Absorbed photosynthetically active radiation (APAR) was estimated from global solar radiation, derived from an established empirical relationship based on average maximum and minimum temperatures, and from a linear relation with the satellite-derived normalized difference vegetation index (NDVI) which represents the photosynthetic capacity of all vegetation within a cell for a given month and is often correlated with the fraction of PAR absorbed (fPAR). Monthly values of environmental constraints on productivity were expressed by modifiers calculated from the vapour pressure deficit (VPD) of the atmosphere, soil water deficit, or frost. This procedure leadsto estimates of utilizable radiation (APARu). Gross primary production (GPP) was calculated by multiplying APARu by a constant canopy quantum efficiency (1.8 g C MJ-1) and total NPP has been shown, in a number of studies, to approximate 0.45 ?.05 of GPP. The model partitions NPP into root and above-ground foliage and stem mass. The fraction of total NPP allocated to root growth increases from 0.2 to 0.6 as the ratio APARu:APAR decreases from 1.0 to 0.2. Above-ground NPP (NPPA) predicted by the model was compared with estimated above-ground NPP derived at 8 contrasting forested sites in Australia (dominated by Callitris glaucophylla, various Eucalyptus spp. or Pinus radiata) and New Zealand (P. radiata). There was a linear relation between predicted NPPA and measured wood production (r2 0.82). The analysis also provided an assessment of the relative importance of various climatic variables upon production, which varied extensively from site to site.
机译:为了评估气候和土壤空间变化对整个区域森林生产力的影响,需要一种可以广泛应用和测试的方法。最近详细的生理学和微气象学研究带来了新的见解,这些见解极大地简化了地上净初级生产力(NPP)的预测,该变量与森林常规生长测度密切相关的变量,例如枯木的年均增幅(MAI) 。这些简化方法应用于每月的时间步长模型-一种简单的生理过程预测生长(3-PG)模型,由Landsberg&Waring在1997年开发(森林生态与管理95(3)209-228)-进行了修改以便将遥感观测输入到其中。修改后的模型称为3-PGS,由对绿色冠层截获的光的比例进行估算,该估算是从国家海洋和大气管理局(NOAA)气象卫星监测到的近红外和红色反射率得出的,以及从利用本地温度和降雨记录。吸收的光合作用活性辐射(APAR)是根据全球太阳辐射估算得出的,该辐射是基于平均最高和最低温度建立的经验关系以及与代表光合作用能力的卫星衍生归一化差异植被指数(NDVI)的线性关系得出的给定月份中一个单元中所有植被的数量,通常与吸收的PAR的比例(fPAR)相关。生产力对环境的限制的月度值由修正值表示,这些修正值是根据大气的蒸气压不足(VPD),土壤水分不足或霜冻计算得出的。该过程导致可利用辐射(APARu)的估计。通过将APARu乘以恒定的冠层量子效率(1.8 g C MJ-1)可以计算出总初级生产(GPP),在许多研究中,总NPP已显示约为GPP的0.45〜.05。该模型将NPP分为根部和地上的树叶和茎质量。随着APARu:APAR的比例从1.0降低到0.2,分配给根生长的总NPP比例从0.2增加到0.6。将模型预测的地上NPP(NPPA)与来自澳大利亚的8个对比林地(以Callitris glaucophylla,各种桉树或Pinus radiata为主)和新西兰(P. radiata)得出的估计地上NPP进行了比较。预测的NPPA与测得的木材产量之间存在线性关系(r2 0.82)。该分析还评估了各种气候变量在生产时的相对重要性,这些变量在不同地点之间差异很大。

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