A Deep Learning-Based Approach for Mapping Shrubs in Arctic Tundra from Very high-Resolution Imagery
Document Type
Preprint
Publication Date
9-23-2026
Journal / Book Title
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Abstract
Arctic shrub expansion is accelerating due to climate warming, but accurately mapping it at fine spatial scales remains challenging due to spectral overlap between shrubs and surrounding vegetation, heterogeneous tundra landscapes, and limited availability of very high-resolution imagery. This study presents a novel mapping and validation approach that integrates very high-resolution (< 1 m) QuickBird (QB) and WorldView (WV) satellite imagery across 12 North Slope sites, using deep learning architectures to map changes in shrub cover from 2002 to 2022. Deep learning models, including Convolutional Neural Network (CNN), ResNet, VGG, U-Net, and Vision Transformer, were trained on 4,100 3-channel pansharpened multispectral images (200×200 m). Two traditional machine learning models (Random Forest and Support Vector Machine) served as a control group. Leave-one-site-out cross-validation showed that all models performed well (AUC-ROC of 0.78–0.86, F1 at validation of 0.33-0.46) and had a moderate to strong agreement with field reference data (R2 = 0.76–0.87). The pretrained VGG and ResNet models achieved the strongest field-reference alignments for shrub cover prediction, while the difference with traditional models was small. Temporal trend analyses for July and August demonstrate a cover change of 0.06–0.45% yr-1 with unweighted mean area slopes and 0.01–0.92% yr-1 inverse-variance weighting. Growth rates were found to depend on the analytical design. Furthermore, the limiting factor in mapping shrub cover was not model complexity but the quality of the training data labels (annotation method explained ∼26% of the error variance). Per-scene cover error was seen across architectures (ρ = 0.67). Comparison with NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT)-derived Canopy Height Models revealed a weak-to-moderate relationship (R2 = 0.04–0.77), suggesting horizontal cover and vertical canopy structure capture complementary aspects of shrub abundance. These results demonstrate the potential of deep learning to map decadal shrub expansion at sub-meter resolution.
DOI
10.1109/JSTARS.2026.3737076
MSU Digital Commons Citation
Radakovic, Darko; Chopping, Mark; Cook, Bruce; Varde, Aparna S.; and Robila, Stefan A., "A Deep Learning-Based Approach for Mapping Shrubs in Arctic Tundra from Very high-Resolution Imagery" (2026). Department of Earth and Environmental Studies Faculty Scholarship and Creative Works. 836.
https://digitalcommons.montclair.edu/earth-environ-studies-facpubs/836
Rights
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Published Citation
D. Radakovic, M. Chopping, B. Cook, A. S. Varde and S. Robila, "A Deep Learning-Based Approach for Mapping Shrubs in Arctic Tundra from Very high-Resolution Imagery," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2026.3737076.