Biodiversity Assessment in Drylands Using Augmented Satellite Imagery Through Deep Learning Models

As global biodiversity faces increasing threats from climate change, habitat loss, and human activities, effective methods for assessing and monitoring biodiversity are crucial. Drylands are particularly vulnerable to the impacts of climate change, and integrating remote sensing technology with ecological research can help protect these environments. Identification of individual trees and shrubs is fundamental to assess biodiversity and can also improve carbon stocks estimates. However, identifying individual trees using medium resolution satellite images is often not feasible. The use of advanced technologies, such as machine learning and satellite imagery in environmental management plays a key role in biodiversity conservation and can potentially fill this gap. The use of readily available Maxar satellite imagery makes conservation approaches accessible and cost-effective, which is crucial for widespread adoption, especially in resource-limited settings or for large-scale studies. This study aims to improve the identification of vegetation in dryland ecosystems by integrating deep learning methods to remote sensing. The primary objective was to distinguish vegetation from rocks or shadows in these areas, which is often challenging due to the dark appearance of vegetation and the similar visual features of the landscape, such as landforms textures, water bodies or man-made objects.. To address this challenge, a Vision Transformer (ViT) deep learning model was developed to estimate near infrared (NIR) spectral bands from high resolution Maxar Satellite images. By enhancing the spectral richness, the model aids in the differentiation of vegetation. Maxar satellite imagery was primarily used because of its accessibility through Google services, making it ideal for planning initial surveys to identify areas of interest for more detailed study. The accuracy of the model was validated against high-resolution SkySat NIR imagery, and achieved an R2 of 0.92. The obtained NIR band helped to clearly distinguish vegetation from non-vegetative surfaces such as soil, rocks, and water, which were not as discernible in RGB imagery alone. The use of a deep learning method to estimate a synthetic NIR band proved to be cost-effective and efficient for large-scale identification of individual trees and shrubs in drylands, overcoming the limitations of medium-resolution satellite imagery. The findings of the study are crucial to the conservation of biodiversity and offer a practical approach for environmentalists and researchers. Future work includes expanding the dataset to include various dryland environments, and integrating additional data sources such as soil data and topographic features for a more comprehensive analysis.