UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Sotille, Maria Eliza; Bremer, Ulisses Franz; Vieira, Gonçalo; Velho, Luiz Felipe Machado; Petsch, Carina; Auger, Jeffrey D.; Simões, Jefferson Cárdia

doi:10.1016/j.ecoinf.2022.101768

Cited by 14 publications

(2 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advancements and challenges associated with Antarctic vegetation mapping are reflected in recent research, which highlights the innovative use of UAVs and ML techniques to differentiate and assess vegetation health. This work contributes to the growing body of knowledge that supports the use of UAVs, MSI and HSI data, and ML in ecological monitoring and extends the application of these techniques to the unique and challenging environment of the Antarctic [19,68]. However, these studies, including those by Turner et al [24,27,28], Bollard-Breen et al [23], Váczi and Barták [69], and King et al [17], tend to focus on the technology's potential rather than its systematic application.…”

Section: Discussionmentioning

confidence: 86%

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

Sandino,

Bollard,

Doshi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 86%

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

Sandino,

Bollard,

Doshi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Eischeid et al had an F1-score of 85% for the Random Forest (RF) algorithms used for disturbance mapping on tundra vegetation in the Artic [ 64 ]. Likewise, Sotille et al used an RF classifier on Antarctic vegetation and got an accuracy of 96.6% [ 65 ]. This supports the findings of Sotille et al and Turner et al as more ground truth data would have allowed the RF model to be more transferable to new data [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Raniga,

Amarasingam,

Sandino

et al. 2024

Sensors

View full text Add to dashboard Cite

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.

show abstract

Predictive mapping of glacial sediment properties (Bellingshausen Dome, King George Island, Antarctica)

Suleymanov,

Nizamutdinov,

Mavlyudov

et al. 2024

Environ Earth Sci

View full text Add to dashboard Cite

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Cited by 14 publications

References 80 publications

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

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

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Predictive mapping of glacial sediment properties (Bellingshausen Dome, King George Island, Antarctica)

Contact Info

Product

Resources

About