ABSTRACT

Small unmanned aerial systems, or sUAS, remote sensing has much potential for monitoring vegetation cover and other phenomena due to its innate ability to capture very high spatial resolution imagery at low altitudes, for small areas, and with rapid planning. Remote sensing methodologies used in monitoring change from orbital and manned aerial remote sensing have been extensively researched and implemented. However, these same historic change detection methodologies have not been thoroughly tested with the new, very high spatial resolution imagery captured by sUAS, particularly the modest sUAS proliferating in the resource management agencies around the world. This study seeks to examine and understand the variability involved in using sUAS for change detection of vegetation cover by designing a confidence model, calibrating the model, and demonstrating the use of the model. Our research design involves a novel approach using multiple collections in a 1.6-hour period over a controlled environment where the land cover did not change. A confidence model was developed and calibrated in the controlled environment. A demonstration of the developed confidence model from the control environment was used for a hurricane impacted area. Coastal dune vegetation cover, essential for dune strength and growth, was monitored before and after Hurricane Irma impacted Harbor Island in coastal South Carolina. The results indicate that even though no actual change occurred during the controlled experiment, an average of 5.6% of the pixels indicated a false change of land cover. These false land cover discrepancies are caused from slight shadow movements, georegistration accuracies, multiple look angles and variable spectral response from a mosaic (i.e. 120 images) of imagery. It was also determined that much change in vegetation cover occurred as a result of inundation from Hurricane Irma, and confidences were high in assessing the change.

摘要

小型无人航空系统或sUAS遥感具有监测植被覆盖和其他现象的潜力，这是由于其固有的能力，即在低海拔，小范围内并通过快速规划，可以捕获非常高的空间分辨率图像。用于监测轨道和载人航空遥感变化的遥感方法已得到广泛研究和实施。但是，这些相同的历史变化检测方法尚未用sUAS捕获的新的非常高分辨率的新图像进行彻底测试，特别是在全球资源管理机构中激增的适度sUAS。本研究旨在通过设计置信度模型，校准模型，并演示该模型的使用。我们的研究设计涉及一种新颖的方法，即在土地覆盖率不变的受控环境下，在1.6小时内使用多个集合。建立了置信度模型并在受控环境中进行了校准。来自控制环境的已开发置信度模型的演示用于飓风影响区域。在沙特飓风艾尔玛袭击南卡罗来纳州沿海的海港岛之前和之后，对沙丘强度和生长必不可少的沿海沙丘植被覆盖度进行了监测。结果表明，即使在对照实验中未发生实际变化，平均5.6％的像素也表明土地覆被发生了错误的变化。这些错误的土地覆盖差异是由于轻微的阴影移动，地理配准精度，影像的马赛克（即120张图像）的多个视角和可变的光谱响应。还确定，由于飓风“艾尔玛”（Irma）淹没，植被的覆盖率发生了很大变化，因此评估这种变化的可信度很高。