A Green Fingerprint of Antarctica: Drones, Hyperspectral Imaging, and Machine Learning for Moss and Lichen Classification

被引:5
|
作者
Sandino, Juan [1 ,2 ]
Bollard, Barbara [3 ,4 ]
Doshi, Ashray [3 ]
Randall, Krystal [3 ,4 ]
Barthelemy, Johan [3 ,5 ]
Robinson, Sharon A. [3 ,4 ]
Gonzalez, Felipe [1 ,2 ]
机构
[1] Queensland Univ Technol, Securing Antarct Environm Future, 2 George St, Brisbane, Qld 4000, Australia
[2] Queensland Univ Technol, QUT Ctr Robot, 2 George St, Brisbane, Qld 4000, Australia
[3] Univ Wollongong, Securing Antarcticas Environm Future, Northfields Ave, Wollongong, NSW 2522, Australia
[4] Univ Wollongong, Sch Earth Atmospher & Life Sci, Northfields Ave, Wollongong, NSW 2522, Australia
[5] NVIDIA, Santa Clara, CA 95051 USA
基金
澳大利亚研究理事会;
关键词
Antarctic Specially Protected Area (ASPA); data fusion; environmental monitoring; hyperspectral imaging (HSI); unmanned aerial system (UAS); unmanned aerial vehicle (UAV); CHLOROPHYLL CONCENTRATION; TERRESTRIAL VEGETATION; REFLECTANCE INDEX; UAV; ECOSYSTEMS; HEALTH; COLOR; LEAF; SOIL;
D O I
10.3390/rs15245658
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that enables precise mapping and monitoring of vegetation in ASPAs. The processing pipeline of this workflow integrates small unmanned aerial vehicles (UAVs)-or drones-to collect hyperspectral and multispectral imagery (HSI and MSI), global navigation satellite system (GNSS) enhanced with real-time kinematics (RTK) to collect ground control points (GCPs), and supervised machine learning classifiers. This workflow was validated in the field by acquiring ground and aerial data at ASPA 135, Windmill Islands, East Antarctica. The data preparation phase involves a data fusion technique to integrate HSI and MSI data, achieving the collection of georeferenced HSI scans with a resolution of up to 0.3 cm/pixel. From these high-resolution HSI scans, a series of novel spectral indices were proposed to enhance the classification accuracy of the model. Model training was achieved using extreme gradient boosting (XGBoost), with four different combinations tested to identify the best fit for the data. The research results indicate the successful detection and mapping of moss and lichens, with an average accuracy of 95%. Optimised XGBoost models, particularly Model 3 and Model 4, demonstrate the applicability of the custom spectral indices to achieve high accuracy with reduced computing power requirements. The integration of these technologies results in significantly more accurate mapping compared to conventional methods. This workflow serves as a foundational step towards more extensive remote sensing applications in Antarctic and ASPA vegetation mapping, as well as in monitoring the impact of climate change on the Antarctic ecosystem.
引用
收藏
页数:26
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