Estimating food resource availability in arid environments with Sentinel 2 satellite imagery
Estimating food resource availability in arid environments with Sentinel 2 satellite imagery
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DOI：
10.7717/peerj.9209
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发表时间：
2020-05
期刊：
影响因子：
2.7
通讯作者：
C. Funghi;René Hans-Jürgen Heim;W. Schuett;S. Griffith;J. Oldeland
中科院分区：
文献类型：
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作者：
C. Funghi;René Hans-Jürgen Heim;W. Schuett;S. Griffith;J. Oldeland
在干旱环境中，植物初级生产力通常较低，且在空间和时间上变化很大。资源在空间和时间上分布不均匀（例如，土壤养分、水），并取决于全球（厄尔尼诺/南方涛动）和当地气候参数。作为欧洲哥白尼方案一部分的哨兵2号卫星的发射，导致提供了免费提供的高空间分辨率（每像素10米）数据。在这里，我们的目的是测试是否哨兵2-图像可以用来量化的空间变异的一个小草丛草（九毛蒿属）。在澳大利亚干旱地区，我们是否可以识别不同的植被覆盖（例如，草从灌木）沿着不同的时间情景。虽然持续时间很短，但九芒草草原已被确定为干旱环境中动物的主要食物来源。如果我们能够远程识别和监测这一物种的生产力，它将提供一个重要的新工具，研究食物资源动态和随后的动物对它们在干旱栖息地的反应。方法结合野外植被调查和Sentinel 2卫星影像，通过GLST-R检验卫星光谱数据是否能预测九芒草随时间的空间变异。此外，基于2016年10月的九芒草种子生产力和总植被覆盖率的聚类分析（“高尔”距离，“完整”方法）确定了三个聚类：裸地，草为主和灌木为主。我们比较了2016年10月至2017年1月这些不同聚类之间的植被指数。结果MSAVI 2和NDVI与九芒草带种比例相关，且这种关系随时间而变化。两个植被指数（MSAVI 2和NDVI）较高的补丁，种子生产力高的九芒草比裸露的土壤，但只有在10月，气候有利的时期，在此期间，这种优势草达到高峰种子生产力。讨论MSAVI 2和NDVI提供了可靠的估计植被类型的异质性，只有在南方的春天测量的景观。这意味着草覆盖与种子生产力有关，并且有可能远程和可靠地预测干旱生境中的食物资源供应，但只有在某些条件下。在夏季集群之间缺乏显着差异，可能是由于研究中的植被的短暂性和稀疏的草为主的植被，而灌木植被集群，特别是测量的归一化植被指数。结论总的来说，我们的研究突出了哨兵2图像的潜力，估计和监测在干旱环境中远程草种可用性的变化。然而，草原覆盖的异质性并不像其他类型的植被那样可靠，只有在生产力高峰期才能很好地检测到（例如，2016年10月）。
Background In arid environments, plant primary productivity is generally low and highly variable both spatially and temporally. Resources are not evenly distributed in space and time (e.g., soil nutrients, water), and depend on global (El Niño/ Southern Oscillation) and local climate parameters. The launch of the Sentinel2-satellite, part of the European Copernicus program, has led to the provision of freely available data with a high spatial resolution (10 m per pixel). Here, we aimed to test whether Sentinel2-imagery can be used to quantify the spatial variability of a minor tussock grass (Enneapogon spp.) in an Australian arid area and whether we can identify different vegetation cover (e.g., grass from shrubs) along different temporal scenarios. Although short-lasting, the Enneapogon grassland has been identified as a key primary food source to animals in the arid environment. If we are able to identify and monitor the productivity of this species remotely, it will provide an important new tool for examining food resource dynamics and subsequent animal responses to them in arid habitat. Methods We combined field vegetation surveys and Sentinel2-imagery to test if satellite spectral data can predict the spatial variability of Enneapogon over time, through GLMMs. Additionally, a cluster analysis (‘gower’ distance, ‘complete’ method), based on Enneapogon seed-productivity, and total vegetation cover in October 2016, identified three clusters: bare ground, grass dominated and shrub dominated. We compared the vegetation indices between these different clusters from October 2016 to January 2017. Results We found that MSAVI2 and NDVI correlated with the proportion of Enneapogon with seeds across the landscape and this relationship changed over time. Both vegetation indices (MSAVI2 and NDVI) were higher in patches with high seed-productivity of Enneapogon than in bare soil, but only in October, a climatically-favorable period during which this dominant grass reached peak seed-productivity. Discussion MSAVI2 and NDVI provided reliable estimates of the heterogeneity of vegetation type across the landscape only when measured in the Austral spring. This means that grass cover is related to seed-productivity and it is possible to remotely and reliably predict food resource availability in arid habitat, but only in certain conditions. The lack of significant differences between clusters in the summer was likely driven by the short-lasting nature of the vegetation in the study and the sparseness of the grass-dominated vegetation, in contrast to the shrub vegetation cluster that was particularly well measured by the NDVI. Conclusions Overall, our study highlights the potential for Sentinel2-imagery to estimate and monitor the change in grass seed availability remotely in arid environments. However, heterogeneity in grassland cover is not as reliably measured as other types of vegetation and may only be well detected during periods of peak productivity (e.g., October 2016).