Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites
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DOI：
10.1088/1748-9326/abbf7d
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发表时间：
2020-12-01
影响因子：
6.7
通讯作者：
Daskalova, Gergana N.
中科院分区：
文献类型：
--
作者：
Assmann, Jakob J.;Myers-Smith, Isla H.;Daskalova, Gergana N.
需要跨尺度的数据来监测生态系统对北极快速变暖的反应，并解释冻原绿化趋势。在这里，我们测试了卫星和无人机派生的冻土带绿度的季节性变化之间的对应关系，以确定最佳的空间尺度在加拿大育空地区的Qikiqtaruk-Herschel岛的植被监测。我们将多光谱无人机图像和卫星数据（Sentinel-2，Landsat 8和MODIS）的归一化差异植被指数（NDVI）的时间序列与两个生长季节（2016年和2017年）的地面观测相结合。我们发现，在8个一公顷的地块上，地块平均绿度（无人机-卫星斯皮尔曼的rho 0.67-0.87）和逐像素绿度（无人机-卫星R-2 0.58-0.69）具有较高的跨季节对应性，无人机捕获的NDVI值相对于卫星较低。我们确定了高原苔原绿度的空间变化在距离约半米的地块，这表明这些粒度是最佳的监测这种变化在岛上的两个最常见的植被类型。我们进一步观察到，当从超细颗粒雄蜂像素（约1000个像素）聚集时，景观绿色度的空间异质性（46.2%-63.9%）的季节变化明显减少。0.05 m）到中等粒度卫星像素的大小（10-30 m）。最后，季节性变化的无人机衍生的绿色高度相关的测量叶片生长的地面验证图（平均斯皮尔曼的ρ 0.70）。这些研究结果表明，多光谱无人机测量可以捕获苔原景观中的植物生长动态。总的来说，我们的研究结果表明，无人机平台和紧凑型多光谱传感器等新技术使我们能够在以前无法达到的尺度上研究生态系统，并填补了我们对苔原生态系统过程理解的空白。捕捉苔原景观的精细尺度变化将改善对北极环境变化的生态影响和气候反馈的预测。
Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk-Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman's rho 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R-2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2%-63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10-30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman's rho 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.