A green fingerprint of Antarctica: drones, hyperspectral imaging, and machine learning for moss and lichen classification
- Author:
- Sandino J., Bollard B., Doshi A., Randall K., Barthelemy J., Robinson S.A. & Gonzalez F.
- Year:
- 2023
- Journal:
- Remote Sensing
- Pages:
- 15(24): 5658 [26 p.]
- Url:
- https://doi.org/10.3390/rs15245658
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.
Keywords: Antarctic Specially Protected Area (ASPA); data fusion; environmental monitoring;
hyperspectral imaging (HSI); unmanned aerial system (UAS); unmanned aerial vehicle (UAV).
- Id:
- 35978
- Submitter:
- zpalice
- Post_time:
- Thursday, 07 December 2023 19:24