Document Type
Article
Publication Date
11-22-2025
Abstract
Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 encoder backbones, resulting in four model configurations for comparative evaluation. Among the tested models, U-Net with a VGG19 backbone achieved the highest performance, attaining an F1 score of 0.90, demonstrating superior capability for habitat mapping in arid environments. The resulting map segmented the dendritic wadi network, Rawdat depressions, and sand-plateau contacts with sharp boundaries. Aligned with the IUCN habitat scheme, Rawdat, seasonal vegetation-bearing desert carbonate sinkholes, are introduced as a new IUCN habitat class. The map produces verifiable indicators relevant to Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), supporting protection, restoration targeting, and monitoring. Therefore, the proposed deep learning hyperspectral framework can be applied to other arid and semi-arid regions worldwide to upgrade terrestrial ecosystem mapping and conservation planning.
Recommended Citation
Elgendy, A., Morgan, H., Tran, B., Thomas, R., Ismail, T., Shehadeh, Y.K., ElGharib, A., Al-Dughairi, A.A., El Mubarak, A., Harhash, K.A., El-Askary, H., 2025. Harnessing hyperspectral imaging and deep learning for terrestrial habitat mapping in arid landscapes: A case study in Saudi Arabia. Ecol. Inform. 92, 103534. https://doi.org/10.1016/j.ecoinf.2025.103534
Appendix A. Supplementary data
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Environmental Health and Protection Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Other Ecology and Evolutionary Biology Commons, Remote Sensing Commons
Comments
This article was originally published in Ecological Informatics, volume 92, in 2025. https://doi.org/10.1016/j.ecoinf.2025.103534