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Influence of landscape heterogeneity on spatial patterns of wood productivity, wood specific density and above ground biomass in Amazonia

机译:景观异质性对亚马逊地区木材生产力,木材比重和地上生物量空间格局的影响

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摘要

Long-term studies using the RAINFOR network of forest plots have generated significant insights into the spatial and temporal dynamics of forest carbon cycling in Amazonia. In this work, we map and explore the landscape context of several major RAINFOR plot clusters using Landsat ETM+ satellite data. In particular, we explore how representative the plots are of their landscape context, and test whether bias in plot location within landscapes may be influencing the regional mean values obtained for important forest biophysical parameters. Specifically, we evaluate whether the regional variations in wood productivity, wood specific density and above ground biomass derived from the RAINFOR network could be driven by systematic and unintentional biases in plot location. Remote sensing data covering 45 field plots were aggregated to generate landscape maps to identify the specific physiognomy of the plots. In the Landsat ETM+ data, it was possible to spectrally differentiate three types of terra firme forest, three types of forests over Paleovarzea geomorphologycal formation, two types of bamboo-dominated forest, palm forest, Heliconia monodominant vegetation, swamp forest, disturbed forests and land use areas. Overall, the plots were generally representative of the forest physiognomies in the landscape in which they are located. Furthermore, the analysis supports the observed regional trends in those important forest parameters. This study demonstrates the utility of landscape scale analysis of forest physiognomies for validating and supporting the finds of plot based studies. Moreover, the more precise geolocation of many key RAINFOR plot clusters achieved during this research provides important contextual information for studies employing the RAINFOR database.
机译:使用RAINFOR森林地块网络进行的长期研究已经对亚马逊地区森林碳循环的时空动态产生了重要的见解。在这项工作中,我们使用Landsat ETM +卫星数据绘制并探索了几个主要RAINFOR地块的景观背景。特别是,我们探索了这些地块在其景观环境中的代表性,并测试了景观内地块位置的偏差是否会影响重要森林生物物理参数的区域平均值。具体来说,我们评估是否可以由地块位置的系统性和非故意性偏差驱动源自RAINFOR网络的木材生产率,木材比重和地上生物量的区域变化。汇总涵盖45个田地的遥感数据以生成景观图,以识别田地的特定地貌。在Landsat ETM +数据中,可以在光谱上区分三种类型的硬实森林,三种古地形地貌森林,两种以竹为主的森林,棕榈林,Heliconia单优势植被,沼泽森林,受干扰的森林和土地使用区域。总体而言,这些地块通常代表了它们所处景观中的森林地貌。此外,分析支持了在那些重要森林参数中观察到的区域趋势。这项研究证明了对森林地貌的景观尺度分析在验证和支持基于地块的研究结果中的实用性。此外,在此研究过程中获得的许多关键RAINFOR地块群集的更精确地理位置为使用RAINFOR数据库的研究提供了重要的背景信息。

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