使用广泛的北方森林清单数据库评估森林辐射传输模型

IF 5.7 Q1 ENVIRONMENTAL SCIENCES
Ranjith Gopalakrishnan , Lauri Korhonen , Matti Mõttus , Miina Rautiainen , Aarne Hovi , Lauri Mehtätalo , Matti Maltamo , Heli Peltola , Petteri Packalen
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引用次数: 0

摘要

森林反射率和透射率模型(FRT)适用于大片北方森林景观,主要是因为其林分特定输入可以从标准森林库存变量中生成。我们首次在大范围内量化了该模型的准确性。这是通过对芬兰南部进行的森林管理操作清单中的大量(12369)地理参考林地进行模拟研究来实现的。我们将FRT模拟的双向反射因子(BRF)与陆地卫星8号卫星操作陆地成像仪(OLI)测量的双向反射系数进行了比较。我们还使用线性混合效应建模框架量化了影响测量和模拟BRF之间差异大小的几个解释因素的相对重要性。FRT高估BRF的普遍趋势出现在所有被检查的树种和光谱带中:红色波段高达~0.05,近红外波段高达~0.10。与高估相关的重要解释因素包括优势树种、林地的林下类型、木材量(作为林分成熟度的指标)、植被异质性和一年中的时间。我们的分析表明,大约20%的误差是由冠层树叶和下层林的非代表性光谱引起的。我们的研究结果证明了从不同的林分和整个季节收集代表性光谱的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database

The forest reflectance and transmittance model (FRT) is applicable over a wide swath of boreal forest landscapes mainly because its stand-specific inputs can be generated from standard forest inventory variables. We quantified the accuracy of this model over an extensive region for the first time. This was done by carrying out a simulation study over a large number (12,369) of georeferenced forest plots from operational forest management inventories conducted in Southern Finland. We compared the FRT simulated bidirectional reflectance factors (BRF) with those measured by Landsat 8 satellite Operational Land Imager (OLI). We also quantified the relative importance of several explanatory factors that affected the magnitude of the discrepancy between the measured and simulated BRFs using a linear mixed effects modelling framework. A general trend of FRT overestimating BRFs is seen across all tree species and spectral bands examined: up to ∼0.05 for the red band, and ∼0.10 for the near infrared band. The important explanatory factors associated with the overestimations included the dominant tree species, understory type of the forest plot, timber volume (acts as a proxy for stand maturity), vegetation heterogeneity and time of the year. Our analysis suggests that approximately 20% of the error is caused by the non-representative spectra of canopy foliage and understory. Our results demonstrate the importance of collecting representative spectra from a diverse set of forest stands, and over the full range of seasons.

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