Scale-dependency of Arctic ecosystem properties revealed by UAV
Scale-dependency of Arctic ecosystem properties revealed by UAV
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无人机揭示的北极生态系统特性的尺度依赖性
DOI：
10.1088/1748-9326/aba20b
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发表时间：
2020
影响因子：
6.7
通讯作者：
J. Olofsson
中科院分区：
文献类型：
--
作者：
M. Siewert;J. Olofsson
面对气候变化，估计植物生物量和总初级生产力（GPP）等关键生态系统特性的变化非常重要。地面实况估计，特别是实验是在小空间尺度（0.01-1 m2）下进行的，并使用粗尺度卫星遥感产品进行放大。当关系不是在与遥感产品相同的空间尺度上发展时，这将导致异构环境中非线性相关属性的缩放偏差。我们证明，即使在高度异质的北极苔原地形中，无人机（UAV）也可以以厘米分辨率可靠地测量归一化植被指数（NDVI）。这表明，这种比例偏差在非常精细的分辨率下增加最多，但无人机可以通过在发生生态变化时生成相同比例的遥感产品来克服这一问题。使用 Landsat 30 m 规模卫星图像在 0.0625 m2 和 1 m2 处生成的地面真实数据，得出的低估值很大（生物量为 8.9%–17.0%，GPP600 为 5.0%–9.7%），其程度与数十年气候变化的预期影响相当。纠正这种放大偏差的方法是存在的，但依赖于子像素信息。我们的数据表明，这种规模依赖性在不同地区和不同季节之间会有很大差异，因此很难得出补偿它的广义函数。这与北极绿化尤其相关，北极绿化主要是异质土地覆盖、强烈的季节性和亚米级尺度的大量实验研究，但也适用于其他异质景观。这些结果证明了无人机对于卫星验证的价值。无人机可以在生态实地调查中使用的地块尺度和与地球系统模型相关的卫星监测中的粗尺度之间建立桥梁。由于未来的气候变化预计会改变景观异质性，因此季节性更新的无人机图像将成为正确预测生态系统特性景观规模变化的重要工具。
In the face of climate change, it is important to estimate changes in key ecosystem properties such as plant biomass and gross primary productivity (GPP). Ground truth estimates and especially experiments are performed at small spatial scales (0.01–1 m2) and scaled up using coarse scale satellite remote sensing products. This will lead to a scaling bias for non-linearly related properties in heterogeneous environments when the relationships are not developed at the same spatial scale as the remote sensing products. We show that unmanned aerial vehicles (UAVs) can reliably measure normalized difference vegetation index (NDVI) at centimeter resolution even in highly heterogeneous Arctic tundra terrain. This reveals that this scaling bias increases most at very fine resolution, but UAVs can overcome this by generating remote sensing products at the same scales as ecological changes occur. Using ground truth data generated at 0.0625 m2 and 1 m2 with Landsat 30 m scale satellite imagery the resulting underestimation is large (8.9%–17.0% for biomass and 5.0%–9.7% for GPP600) and of a magnitude comparable to the expected effects of decades of climate change. Methods to correct this upscaling bias exist but rely on sub-pixel information. Our data shows that this scale-dependency will vary strongly between areas and across seasons, making it hard to derive generalized functions compensating for it. This is particularly relevant to Arctic greening with a predominantly heterogeneous land cover, strong seasonality and much experimental research at sub-meter scale, but also applies to other heterogeneous landscapes. These results demonstrate the value of UAVs for satellite validation. UAVs can bridge between plot scale used in ecological field investigations and coarse scale in satellite monitoring relevant for Earth System Models. Since future climate changes are expected to alter landscape heterogeneity, seasonally updated UAV imagery will be an essential tool to correctly predict landscape-scale changes in ecosystem properties.