Multispectral aerial monitoring of a patchy vegetation oasis composed of different vegetation classes. UAV-based study exploiting spectral reflectance indices

Váczi, Peter; Barták, Miloš

doi:10.5817/cpr2022-1-10

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…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. These works emphasise the need for novel spectral indices and robust validation methods, which remain unstandardised.…”

Section: Discussionmentioning

confidence: 89%

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

Sandino,

Bollard,

Doshi

et al. 2023

Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 89%

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

Sandino,

Bollard,

Doshi

et al. 2023

Remote Sensing

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“…Such variability makes drones an attractive option due to the high resolution of imagery compared to satellites, but also means fewer studies have integrated UAV and AI for monitoring and mapping vegetation in the Antarctic environment [ 27 , 28 , 39 ]. In addition, only a limited number of studies have incorporated multispectral sensors to enhance the precision and accuracy of vegetation mapping in various environments [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

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

Raniga,

Amarasingam,

Sandino

et al. 2024

Sensors

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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.

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“…Spectral reflectance signatures of different components of the Antarctic terrestrial vegetation measured in situ by using a high-resolution multispectral image is important for adjustment and interpretation of remote sensing data. Therefore, as demonstrated by Váczi and Barták (2022) field data, obtained typically by UAV or contact measurement, are of great importance. Therefore, spectral pattern of several species of mosses, lichens, and of alga from hyperspectral data obtained recently in situ by da Rosa et al (2022) in Harmony Point, maritime Antarctica are of a great value.…”

Section: Introductionmentioning

confidence: 99%

Spectral characteristics of bryophyte carpet and mat subformation showing a vitality-dependent color pattern: Comparison for two distant regions of maritime Antarctica

Puhovkin,

Smykla,

Váczi

et al. 2023

Czech Polar Rep.

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Spectral characteristics of the bryophyte carpet and mat subformation on Nelson Island (South Shetlands Islands) and Galindez Island (Argentine Islands, Graham Coast) were analyzed using spectral reflectance characteristics. A set of 9 specific reflectance indices were calculated and compared between two locations for the same type of moss vegetation formed by Sanionia georgicouncinata and Warnstorfia spp. The Normalized Difference Vegetation Index (NDVI) is efficient in discriminating between the two contrasting color/ecological moss community classes, i.e. such as less vigorous or dead and vigorous. However, NDVI is not sufficiently sensitive to discriminate intermediate vitality states. Presented data also demonstrates that complementary application of two indices, NDVI and Photochemical Reflectance Index (PRI), can be promising for follow-up studies focused on the determination of the color differences attributed to ecolophysiological state of a moss community. With the same values of NDVI, bryophyte carpet and mat subformation on Galindez Island are characterized by higher values of the OSAVI, which can be used as an indicator for further monitoring.

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Multispectral aerial monitoring of a patchy vegetation oasis composed of different vegetation classes. UAV-based study exploiting spectral reflectance indices

Cited by 4 publications

References 40 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

Spectral characteristics of bryophyte carpet and mat subformation showing a vitality-dependent color pattern: Comparison for two distant regions of maritime Antarctica

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