Quantifying the Intra-Habitat Variation of Seagrass Beds with Unoccupied Aerial Vehicles (UAVs)
Quantifying the Intra-Habitat Variation of Seagrass Beds with Unoccupied Aerial Vehicles (UAVs)
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DOI：
10.3390/rs14030480
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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：
Price, David M.;Felgate, Stacey L.;Evans, Claire
鉴于海草栖息地的生态和经济意义，准确了解海草栖息地的空间范围对于监测和管理目的至关重要。范围数据通常以二进制（存在/不存在）或任意、半定量密度带的形式呈现，这些密度带源自低分辨率卫星图像，无法解析精细尺度特征和栖息地内的变异性。消费级无人飞行器 (UAV) 的最新进展提高了我们以更高分辨率和更低的成本勘测大面积区域的能力。这提高了发展中国家沿海国家测绘技术的可及性，世界上大部分海草栖息地都分布在这些国家。在这里，我们介绍了无人机采集图像的应用来确定海草栖息地范围和树冠覆盖百分比。在伯利兹的特内夫环礁海洋保护区对四个对比地点进行了调查，并从现场样方对海草冠层覆盖进行了地面实况调查。根据无人机收集的图像为每个站点创建正射马赛克图像。测试了三种建模技术，将样方的结果推断为空间信息，生成二元（随机森林）和冠层覆盖（随机森林回归和 beta 回归）栖息地地图。最稳健的模型（随机森林回归）的平均绝对误差为 6.8-11.9%（SE 为 8.2-14），该模型建立在之前通过卫星图像绘制海草密度的尝试之上，该模型的误差约为 15-20%。由此产生的地图显示出巨大的栖息地内异质性和不同程度的斑块性，这归因于地点能量学以及可能的物种组成。冠层覆盖图中的额外信息为关键管理决策提供了更多细节和信息，并为未来的空间研究和监测计划提供了基础。
Accurate knowledge of the spatial extent of seagrass habitats is essential for monitoring and management purposes given their ecological and economic significance. Extent data are typically presented in binary (presence/absence) or arbitrary, semi-quantitative density bands derived from low-resolution satellite imagery, which cannot resolve fine-scale features and intra-habitat variability. Recent advances in consumer-grade unoccupied aerial vehicles (UAVs) have advanced our ability to survey large areas at higher resolution and at lower cost. This has improved the accessibility of mapping technologies to developing coastal nations, where a large proportion of the world's seagrass habitats are found. Here, we present the application of UAV-gathered imagery to determine seagrass habitat extent and percent of canopy cover. Four contrasting sites were surveyed in the Turneffe Atoll Marine Reserve, Belize, and seagrass canopy cover was ground truthed from in situ quadrats. Orthomosaic images were created for each site from the UAV-gathered imagery. Three modelling techniques were tested to extrapolate the findings from quadrats to spatial information, producing binary (random forest) and canopy cover (random forest regression and beta regression) habitat maps. The most robust model (random forest regression) had an average absolute error of 6.8-11.9% (SE of 8.2-14), building upon previous attempts at mapping seagrass density from satellite imagery, which achieved errors between 15-20% approximately. The resulting maps exhibited great intra-habitat heterogeneity and different levels of patchiness, which were attributed to site energetics and, possibly, species composition. The extra information in the canopy cover maps provides greater detail and information for key management decisions and provides the basis for future spatial studies and monitoring programmes.