High-spatial resolution UAV multispectral data complementing satellite imagery to characterize a chinstrap penguin colony ecosystem on deception island (Antarctica)

• Published in: GIScience & Remote Sensing
• Main Authors: Román, Alejandro, Navarro, Gabriel, Caballero, Isabel, Tovar-Sánchez, Antonio
• Other Authors: Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), European Commission, Ministerio de Ciencia, Innovación y Universidades (España)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis 2022
• Subjects: Micasense; UAV; Sentinel-2; Landsat 8; Penguin colony; Remote sensing; Antarctic; Deception Island; Guano; The Antarctic; Antarc*; Antarctica; antarcticus; Chinstrap penguin
• Online Access: http://hdl.handle.net/10261/285342; https://doi.org/10.1080/15481603.2022.2101702; https://doi.org/10.13039/501100011033; https://doi.org/10.13039/501100000780; https://doi.org/10.13039/501100004837

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. This research was funded by grants/projects RTI2018-098048-B-100 (PiMetAn), EQC2018-004275-P, EQC2019-005721, RTI2018-098784-J-I00 and IJC2019-039382-I funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way ...