Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data
Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data
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DOI：
10.3390/drones3030060
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发表时间：
2019-07
期刊：
影响因子：
4.8
通讯作者：
Bo Yang;Timothy L. Hawthorne;Hannah R. Torres;M. Feinman
中科院分区：
文献类型：
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作者：
Bo Yang;Timothy L. Hawthorne;Hannah R. Torres;M. Feinman
沿海生境的高分辨率制图对于资源清查、变化检测和水产养殖应用的清查是非常宝贵的。然而，沿海地区，特别是红树林内部，往往难以进入。配备多光谱传感器的无人机（UAV）提供了一个机会，以改善卫星图像的海岸管理，因为非常高的空间分辨率，多光谱能力，并有机会收集实时观测。尽管无人机测绘应用最近发展迅速，但很少有文章定量比较无人机多光谱测绘方法与更传统的遥感数据（如卫星图像）相比有多大的改进。本文的目的是定量地证明改进的多光谱无人机测绘技术，用于先进的测绘和评估沿海土地覆盖的高分辨率图像。我们在佛罗里达大西洋中部海岸的印第安河泻湖沿着进行了多光谱无人机测绘野外工作试验。收集地面控制点（GCPs）以生成严格的无人机图像地理参考数据集，并支持与地理参考卫星和航空图像的比较。还获得了多光谱卫星图像（哨兵-2），以绘制同一地区的土地覆盖图。利用归一化差异植被指数和面向对象的分类方法对无人驾驶飞机和卫星制图能力进行了比较。与佛罗里达环境保护部的航空影像相比，本研究中使用的无人机多光谱制图方法提供了研究区域的物理条件的先进信息，改进了土地特征的划定，并且制图产品明显优于分辨率较低的卫星影像。该研究展示了一种可复制的无人机多光谱测绘方法，可用于缺乏高质量数据的研究地点。
High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.