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通过综合多源地面和遥感数据评估森林净初级生产力。

Assessment of forest net primary production through the elaboration of multisource ground and remote sensing data.

作者信息

Maselli Fabio, Chiesi Marta, Barbati Anna, Corona Piermaria

机构信息

IBIMET-CNR, Via Madonna del Piano 10, 50019 Sesto Fiorentino, FI, Italy.

出版信息

J Environ Monit. 2010 May;12(5):1082-91. doi: 10.1039/b924629k.

Abstract

This paper builds on previous work by our research group which demonstrated the applicability of a parametric model, Modified C-Fix, for the monitoring of Mediterranean forests. Specifically, the model is capable of combining ground and remote sensing data to estimate forest gross primary production (GPP) on various spatial and temporal scales. Modified C-Fix is currently applied to all Italian forest areas using a previously produced data set of meteorological data and NDVI imagery descriptive of a ten-year period (1999-2008). The obtained GPP estimates are further elaborated to derive forest net primary production (NPP) averages for 20 Italian Regions. Such estimates, converted into current annual increment of standing volume (CAI) through the use of specific coefficients, are compared to the data of a recent national forest inventory (INFC). The results obtained indicate that the modelling approach tends to overestimate the ground CAI values for all forest types. The correction of a drawback in the current model implementation leads to reduce this overestimation to about 9% of the INFC increments. The possible origins of this overestimation are investigated by examining the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis.

摘要

本文基于我们研究小组之前的工作,该工作证明了参数模型Modified C-Fix在地中海森林监测中的适用性。具体而言,该模型能够结合地面和遥感数据,在各种空间和时间尺度上估算森林总初级生产力(GPP)。目前,Modified C-Fix正在使用先前生成的描述十年期(1999 - 2008年)的气象数据和归一化植被指数(NDVI)图像数据集应用于意大利所有森林地区。所获得的GPP估算值经过进一步细化,以得出意大利20个地区的森林净初级生产力(NPP)平均值。通过使用特定系数将这些估算值转换为立木材积的年生长量(CAI),并与最近一次国家森林资源清查(INFC)的数据进行比较。所得结果表明,该建模方法往往会高估所有森林类型的地面CAI值。对当前模型实施中的一个缺陷进行修正后,可将这种高估降低至INFC增量的约9%。通过研究先前研究和早期森林资源清查的结果,对这种高估的可能来源进行了调查。最后,鉴于在国家层面评估森林碳预算,讨论了使用不同NPP估算方法的影响。

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