Using Unoccupied Aerial Vehicles (UAVs) to Map Seagrass Cover from Sentinel-2 Imagery
Using Unoccupied Aerial Vehicles (UAVs) to Map Seagrass Cover from Sentinel-2 Imagery
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DOI：
10.3390/rs14030477
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发表时间：
2022-02-01
期刊：
影响因子：
5
通讯作者：
Evans, Claire
中科院分区：
文献类型：
--
作者：
Carpenter, Stephen;Byfield, Val;Evans, Claire
海草栖息地具有生态价值，在固碳和储存碳方面发挥着重要作用。因此，需要估计不同环境中的海草覆盖率，以支持减缓气候变化、海洋空间规划和沿海地区管理。现场方法虽然准确，但耗时、昂贵，并且可能无法代表卫星成像收集的更大空间单位。因此，需要一种一致的方法，使用准确的基于点的实地调查来提供大空间尺度海草覆盖百分比的高质量绘图。在这里，我们开发了一种三步法，结合现场（象方）、航空（无人飞行器-无人机）和卫星数据来绘制北半球最大环礁伯利兹图尔内夫环礁的海草覆盖百分比。首先，利用四张无人机图像的光学波段结合现场数据来计算海草覆盖度。然后，使用无人机计算出的海草覆盖率来开发训练和验证数据集，以估计 Sentinel-2 像素中的海草覆盖率。接下来，在 Sentinel-2 数据中识别出非海草区域，并通过基于对象的分类将其删除，然后进行基于像素的回归来计算海草覆盖百分比。使用这种方法，使用无人机（观察到的分布和绘制的分布之间的 R-2 = 0.91）和 Sentinel-2 数据（R-2 = 0.73）绘制了海草覆盖百分比。这项工作提供了第一个公开且可探索的特内夫环礁海草覆盖率地图，我们估计那里大约有 242 公里(2) 的海草覆盖率高于 10%。我们估计，这种方法为训练卫星数据提供的数据比传统方法多 30 倍，因此每点数据的成本大幅降低。此外，数据的增加有助于提供高质量的海草覆盖图，适合以 10 m(2) 分辨率解析海草环境恶化、稳定或恢复的趋势，以支持海草的循证管理和保护。
Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle-UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R-2 = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R-2 = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km(2) of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m(2) resolution to underpin evidence-based management and conservation of seagrass.