Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning

• Main Authors: Khaldi, Rohaifa, Tabik, Siham, Puertas-Ruiz, Sergio, de Giles, Julio Peñas, Correa, José Antonio Hódar, Zamora, Regino, Segura, Domingo Alcaraz
• Format: Journal Article
• Language: English
• Published: 31-01-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2401.17985

Monitoring the distribution and size of long-living large shrubs, such as
junipers, is crucial for assessing the long-term impacts of global change on
high-mountain ecosystems. While deep learning models have shown remarkable
success in object segmentation, adapting these models to detect shrub species
with polymorphic nature remains challenging. In this research, we release a
large dataset of individual shrub delineations on freely available satellite
imagery and use an instance segmentation model to map all junipers over the
treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize
performance, we introduced a novel dual data construction approach: using
photo-interpreted (PI) data for model development and fieldwork (FW) data for
validation. To account for the polymorphic nature of junipers during model
evaluation, we developed a soft version of the Intersection over Union metric.
Finally, we assessed the uncertainty of the resulting map in terms of canopy
cover and density of shrubs per size class. Our model achieved an F1-score in
shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2
and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for
canopy cover, and 0.90 and 20.62 for shrub density. The greater density of
larger shrubs in lower altitudes and smaller shrubs in higher altitudes
observed in the model outputs was also present in the PI and FW data,
suggesting an altitudinal uplift in the optimal performance of the species.
This study demonstrates that deep learning applied on freely available
high-resolution satellite imagery is useful to detect medium to large shrubs of
high ecological value at the regional scale, which could be expanded to other
high-mountains worldwide and to historical and forthcoming imagery.