ABSTRACT

Remote sensing has evolved as an alternative to traditional techniques in the spatio-temporal monitoring of the Antarctic ecosystem, especially with the rapid expansion of the use of Unmanned Aerial Vehicles (UAVs), providing a centimeter-scale spatial resolution. In this study, the potential of a high-spatial resolution multispectral sensor embedded in a UAV is compared to medium resolution satellite remote sensing (Sentinel-2 and Landsat 8) to monitor the characteristics of the Vapor Col Chinstrap penguin (Pygoscelis antarcticus) colony ecosystem (Deception Island, South Shetlands Islands, Antarctica). Our main objective is to generate precise thematic maps of the typical ecosystem of penguin colonies derived from the supervised analysis of the spectral information obtained with these remote sensors. For this, two parametric classification algorithms (Maximum Likelihood, MLC, and Spectral Angle, SAC) and two non-parametric machine learning classifiers (Support Vector Machine, SVM, and Random Forest, RFC) are tested with UAV imagery, obtaining the best results with the SVM classifier (93.19% OA). Our study shows that the use of UAV outperforms satellite imagery (87.26% OA with Sentinel-2 Level 2 (S2L2) and 70.77% OA with Landsat 8 Level 2 (L8L2) in SVM classification) in the characterization of the substrate due to a higher spatial resolution, although differences between UAV and S2L2 are minimal. Thus, both sensors used in tandem could provide a broader and more precise view of how the area covered by the different elements of these ecosystems can change over time in a global climate change scenario. In addition, this study represents a precise UAV monitoring that takes place in this Chinstrap penguin colony, estimating a total coverage of approximately 20,000 m² of guano areas in the study period.

摘要

遥感已经发展成为南极生态系统时空监测中传统技术的替代方法，特别是随着无人机（UAV）使用的迅速扩大，提供了厘米级的空间分辨率。在这项研究中，将嵌入在无人机中的高空间分辨率多光谱传感器的潜力与中分辨率卫星遥感（Sentinel-2 和 Landsat 8）进行了比较，以监测 Vapor Col 帽带企鹅 ( Pygoscelis antarcticus) 殖民地生态系统（欺骗岛、南设得兰群岛、南极洲）。我们的主要目标是通过对这些遥感器获得的光谱信息进行监督分析，生成企鹅群落典型生态系统的精确专题图。为此，使用无人机图像测试了两种参数分类算法（最大似然，MLC 和光谱角，SAC）和两种非参数机器学习分类器（支持向量机，SVM 和随机森林，RFC），获得了最佳结果使用 SVM 分类器 (93.19% OA)。我们的研究表明，在 SVM 分类中，无人机的使用优于卫星图像（87.26% OA 与 Sentinel-2 Level 2 (S2L2) 和 70.77% OA 与 Landsat 8 Level 2 (L8L2) 在 SVM 分类中）由于更高空间分辨率，尽管 UAV 和 S2L2 之间的差异很小。因此，串联使用的两个传感器可以提供更广泛和更精确的视图，了解这些生态系统的不同元素所覆盖的区域在全球气候变化情景中如何随时间变化。此外，这项研究代表了在这个 Chinstrap 企鹅群落中进行的精确无人机监测，估计总覆盖范围约为 20,000 m^{研究期间的2}个鸟粪区。