A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images

Lovitt, Julie; Richardson, Galen; Rajaratnam, Krishan; Leblanc, Sylvain G.; He, Li; Nielsen, Scott E.; Hillman, Ashley; Schmelzer, Isabelle; Arsenault, André

doi:10.1080/07038992.2022.2144179

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…These technological tools, often accompanied by classical machine learning (ML) and deep learning (DL) methodologies, enhance the accuracy and efficiency of different vegetation mapping [ 18 , 19 , 20 , 21 , 22 , 23 ]. Studies have demonstrated the effectiveness of employing DL for accurately monitoring and classifying these delicate species like moss and lichen in diverse environmental settings [ 24 , 25 , 26 ].…”

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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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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“…Another type of model that has been used for lichen mapping is Convolutional Neural Networks (CNNs). Unlike single-pixel classifiers, CNNs can create inferences based on image texture, intensity, and other spatial patterns, and use these along with spectral information to classify pixels in an image [22,23]. CNNs have many different remote sensing applications, such as detecting trees and buildings [24,25], segmenting ecotypes [22], and detecting landcover change [26].…”

Section: Introductionmentioning

confidence: 99%

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada

Richardson,

Knudby,

Chen

et al. 2023

PLoS ONE

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Lichen mapping is vital for caribou management plans and sustainable land conservation. Previous studies have used random forest, dense neural network, and convolutional neural network models for mapping lichen coverage. However, to date, it is not clear how these models rank in this task. In this study, these machine learning models were evaluated on their ability to predict lichen percent coverage in Sentinel-2 imagery in Québec and Labrador, Canada. The models were trained on 10-m resolution lichen coverage (%) maps created from 20 drone surveys collected in July 2019 and 2022. The dense neural network achieved a higher accuracy than the other two, with a reported mean absolute error of 5.2% and an R2 of 0.76. By comparison, the random forest model returned a mean absolute error of 5.5% (R2: 0.74) and the convolutional neural network had a mean absolute error of 5.3% (R2: 0.74). A regional lichen map was created using the trained dense neural network and a Sentinel-2 imagery mosaic. There was greater uncertainty on land covers that the model was not exposed to in training, such as mines and deep lakes. While the dense neural network requires more computational effort to train than a random forest model, the 5.9% performance gain in the test pixel comparison renders it the most suitable for lichen mapping. This study represents progress toward determining the appropriate methodology for generating accurate lichen maps from satellite imagery for caribou conservation and sustainable land management.

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A novel deep learning change detection approach for estimating spatiotemporal crop field variations from Sentinel-2 imagery

Dahiya,

Singh,

Gupta

et al. 2024

Remote Sensing Applications: Society and Environment

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A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images

Cited by 5 publications

References 37 publications

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

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

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada

A novel deep learning change detection approach for estimating spatiotemporal crop field variations from Sentinel-2 imagery

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