2026-07-02T11:02:36Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/118812026-06-30T00:05:36Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378

Diez Pastor, José Francisco González Moya, Francisco Javier Latorre Carmona, Pedro Pérez-Barbería, Francisco Javier Kuncheva, Ludmila I. . Canepa Oneto, Antonio Jesús Arnaiz González, Álvar García Osorio, César 2026-06-29T16:17:10Z 2026-06-29T16:17:10Z 2026-02 2027-02-05 0143-1161 https://hdl.handle.net/10259/11881 10.1080/01431161.2026.2618658 1366-5901 Detection of spatial areas where biodiversity is at risk is of paramount importance for the conservation and monitoring of ecosystems. Large terrestrial mammalian herbivores are keystone species as their activity not only has deep effects on soils, plants, and animals but also shapes landscapes, as large herbivores act as allogenic ecosystem engineers. One key landscape feature that indicates intense herbivore activity and potentially impacts biodiversity is the formation of grazing trails. Grazing trails are formed by the continuous trampling activity of large herbivores that can produce complex networks of tracks of bare soil. Here, we evaluated different algorithms based on machine learning techniques to identify grazing trails. Our goal is to automatically detect potential areas with intense herbivory activity, which might be beneficial for conservation and management plans. We have applied five semantic segmentation methods combined with fourteen encoders aimed at mapping grazing trails on aerial images. Our results indicate that in most cases the chosen methodology successfully mapped the trails, although there were a few instances where the actual trail structure was underestimated. The UNet architecture with the MambaOut encoder was the best architecture for mapping trails. The proposed approach could be applied to develop tools for mapping and monitoring temporal changes in these landscape structures to support habitat conservation and land management programmes. This is the first time, to the best of our knowledge, that competitive image segmentation results are obtained for the detection and delineation of trails of large herbivorous mammals.Footnote This work was supported by the Asturias Biodiversity Complementary Program BIO06 (Next Generation EU/PRTR), Strategic Projects Oriented Towards Ecological and Digital Transition (TED2021-131388B-100), Spanish Knowledge Generation Projects (PID2023-146074OB-I00) funded by EU Next Generation and Spanish Research Agency, Spanish Research Council Tenured Scientist Incorporation Grants 2022 (202230I041) application/pdf eng Taylor and Francis International Journal of Remote Sensing. 2026, V. 47, n. 6, p. 2581-2604 https://doi.org/10.1080/01431161.2026.2618658 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/embargoedAccess Semantic segmentation Deep learning Grazing trails Herbivory Biodiversity Monitoring Pastoreo Grazing Biodiversidad-Conservación Biodiversity conservation Teledetección Remote sensing Remote sensing colour image semantic segmentation of large herbivorous mammal trails info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion International Journal of Remote Sensing 47 6 2581 2604