Remote Sensing-Based Monitoring of Postfire Recovery of Persistent Shrubs: The Case of Juniperus communis in Sierra Nevada (Spain)

Javier Blanco Sacristan, Emilio Guirado, José Luis Molina-Pardo, Javier Cabello, Esther Giménez-Luque, Domingo Alcaraz-Segura

Research output: Contribution to journalArticlepeer-review


Wildfires affect the structure, functioning, and composition of ecosystems. Long-term monitoring of the occurrence, abundance, and growth of plant species is key to assessing the responses of the dynamics of plant populations with regard to environmental disturbances, such as wildfires. In this work, we evaluated the changes in the number of individuals and the canopy cover extent of a population of Juniperus communis L. during a four-decade period following a wildfire in a Mediterranean high-mountain ecosystem (Sierra Nevada, Spain). To do this, we used object-based image analysis (OBIA) applied to very high-resolution aerial images. Our study also provides a new approach to optimize the shrub identification process and to semi-automatically evaluate the accuracy of the number of shrubs and their canopy cover. From the 752 individuals present in 1977, only 433 remained immediately after a fire (1984), a few more disappeared one decade later (420 shrubs in 1997), while by 2008, the population had partially recovered to 578 shrubs. The wildfire decreased juniper canopy cover from 55,000 m2 to 40,000 m2, but two decades later it had already recovered to 57,000 m2. The largest shrubs were more resistant to fire than the smallest ones and recovered in a shorter time period. The protection measures introduced with the park declaration seemed to have contributed to the post-fire recovery. The potential of this methodology in the management and conservation of biodiversity in the future is also discussed.
Original languageEnglish (US)
Pages (from-to)4
Issue number1
StatePublished - Dec 22 2022

Bibliographical note

KAUST Repository Item: Exported on 2023-02-16
Acknowledgements: This work has received funding from the European Research Council (ERC grant agreement 647038 [BIODESERT]), the ADAPTAMED LIFE14 CCA/ES/000612 project, the RH2O-ARID project (P18-RT-5130), and the RESISTE project (P18-RT-1927), funded by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía; and by the A-TIC-458-UGR18 and DETECTOR (A-RNM-256-UGR18) projects, with the contribution of the European Funds for Regional Development. E.G. was supported by the Generalitat Valenciana and the European Social Fund (APOSTD/2021/188). This work is part of the “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01) project, which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01_WP-8, LifeWatch-2019-10-UGR-01_WP-7, and LifeWatch-2019-10-UGR-01_WP-4.


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