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Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna

机译:巴西大草原的Landsat物候指标及其与地上碳的关系

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摘要

BackgroundThe quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs). Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon.
机译:背景技术对陆地生态系统中的碳储量进行量化和空间明晰的绘制对于更好地理解全球碳循环以及监测和报告变化过程非常重要,尤其是在诸如REDD +等国际政策机制或实施国家自主贡献(NDC)的情况下以及联合国可持续发展目标(SDG)。尤其是在稀树草原等异类生态系统中,仍然缺乏准确的碳定量,因为那里的植被密度变化很大,而且季节性强,阻碍了数据的连续采集。为了解决这些挑战,我们分析了从填空的8天Landsat时间序列进行碳测绘得出的地表物候指标的潜力。我们选择了位于巴西中部地区不同分区域的三个区域,这是萨凡纳(Savanna)拥有大量碳储量的典型例子,该土地正经历着广泛的土地覆盖转换。在这里,使用Random Forest回归模型将2014/2015年季节的物候指标与塞拉多森严密植被的地上碳田样本相结合,以绘制区域碳分布图,并分析物候指标与地上碳之间的关系。

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