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

Román, Alejandro; Navarro, Gabriel; Caballero, Isabel; Tovar-Sánchez, Antonio

doi:10.1080/15481603.2022.2101702

Cited by 10 publications

(5 citation statements)

References 72 publications

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“…The results obtained were compared pixel-by-pixel and band-by-band with the reflectance data obtained using a photogrammetric software. It was considered the most reliable data in the absence of in situ data, and previous research validated the accuracy of photogrammetry-derived products [5,29,56,57]. In particular, this study was limited by the distance from the coastline, since photogrammetric software works well when finding common points near coastal regions.…”

Section: Performance Of the Reflectance-retrieval Methods From Indivi...mentioning

confidence: 91%

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

et al. 2022

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Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, since the sea surface is constantly changing, commonly used photogrammetric methods fail when applied to UAV images captured over water areas. In this work, we evaluate the applicability of a five-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters such as chlorophyll-a (Chl-a) concentration and total suspended solids (TSS), including a new Python workflow for the manual generation of an orthomosaic in aquatic areas exclusively based on the sensor’s metadata. We show water-quality details in two different sites along the Maltese coastline on the centimetre-scale, improving the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m3 and 10 to 20 mg/m3, respectively. Spectral comparisons were also carried out along with some statistics calculations such as RMSE, MAE, or bias in order to validate the obtained results.

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Section: Performance Of the Reflectance-retrieval Methods From Indivi...mentioning

confidence: 91%

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

et al. 2022

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“…The spatial-temporal monitoring of the Antarctic ecosystem has seen a shift towards remote sensing as an alternative to conventional methods, particularly with the widespread availability of high-resolution satellite imagery [ 10 , 27 , 28 , 29 , 30 ]. In particular, the last decade has witnessed an increase in UAV use in Antarctic research, offering a unique perspective on monitoring and mapping of vegetation [ 29 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…This variability includes sparse vegetation, isolated individuals scattered amidst soil and rocks, small communities forming biocrusts on soil or rock, and more extensive vegetation mats in larger communities [ 10 ]. 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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“…Remote sensing data have been applied to agriculture [11][12][13], forestry [14,15], meteorology [16,17], and other fields successfully, including images from satellites [18,19] and unmanned aerial vehicles (UAVs) [20,21]. Remote sensing has the advantages of wide observation range, fast speed, and short cycle of obtaining data with high spatial resolution, so detecting landslides with remote sensing technology has become a trend [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal and Multi-View Strategic Hybrid Deep Learning for Old Landslide Detection in the Loess Plateau, Northwest China

Gao,

Xi,

et al. 2024

Remote Sensing

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Old landslides in the Loess Plateau, Northwest China usually occurred over a relatively long period, and their sizes are usually smaller compared to old landslides in the alpine valley areas of Sichuan, Yunnan, and Southeast Tibet. These landslide areas may have been changed either partially or greatly, and they are usually covered with vegetation and similar to their surrounding environment. Therefore, it is a great challenge to detect them using high-resolution remote sensing images with only orthophoto view. This paper proposes the optimal-view and multi-view strategic hybrid deep learning (OMV-HDL) method for old loess landslide detection. First, the optimal-view dataset in the Yan’an area (YA-OP) was established to solve the problem of insufficient optical features in orthophoto images. Second, in order to make the process of interpretation more labor-saving, the optimal-view and multi-view (OMV) strategy was proposed. Third, hybrid deep learning with weighted boxes fusion (HDL-WBF) was proposed to detect old loess landslides effectively. The experimental results with the constructed optimal-view dataset and multi-view data show that the proposed method has excellent performance among the compared methods—the F1 score and AP (mean) of the proposed method were improved by about 30% compared with the single detection model using traditional orthophoto-view data—and that it has good detection performance on multi-view data with the recall of 81.4%.

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

Abstract: Sánchez (2022) High-spatial resolution UAV multispectral data complementing satellite imagery to characterize a chinstrap penguin colony ecosystem on deception island (Antarctica),

Cited by 10 publications

References 72 publications

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

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

Optimal and Multi-View Strategic Hybrid Deep Learning for Old Landslide Detection in the Loess Plateau, Northwest China

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