Burned vegetation recovery trajectory and its driving factors using satellite remote-sensing datasets in the Great Xing’An forest region of Inner Mongolia.

Zhang, Qiyue; Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356; Zhao, Pengwu et Zhou, Mei (2023). Burned vegetation recovery trajectory and its driving factors using satellite remote-sensing datasets in the Great Xing’An forest region of Inner Mongolia. International Journal of Wildland Fire , vol. 32 , nº 2. pp. 244-261. DOI: 10.1071/WF21167.

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Résumé

Forest fire is one of the most important factors that alter a forest ecosystem’s biogeochemical cycle. Large-scale distributed burned areas lose their original vegetation structure and are more impacted by climate change in the vegetation recovery process, thus making it harder to restore their original vegetation structure. In this study, we used historical Landsat imagery and the LandTrendr algorithm in the Google Earth Engine platform to study and identify post-fire stages in the Great Xing’An Range of Inner Mongolia. Moreover, we categorized different post-fire vegetation recovery trajectories. The usefulness of spectral indices was also evaluated in the study region. We applied the Geodetector model to analyze the driving factors of the burned area vegetation regeneration process. The results show that burn severity and earth–atmosphere hydrological cycle are two main impacting factors in the short term after the fire (e.g. 5–6 years). Other climatical conditions affect vegetation recovery, including prolonged vegetation recovery process, hydrothermal circulation process and topographical conditions, seasonally frozen soil, freeze–thaw processes, and climate events. This study improves understanding of the dynamic successional processes in the burned area and the driving factors. Also, the outcomes can facilitate and support sustainable forest management of the Great Xing’An Range.

Type de document:	Article
Mots-clés libres:	burned area; driving factors; geodector; Great Xing’An Range; Inner Mongolia; LandTrendr; remote sensing data; vegetation recovery trajectory
Centre:	Centre Eau Terre Environnement
Date de dépôt:	17 mai 2023 15:14
Dernière modification:	17 mai 2023 15:14
URI:	https://espace.inrs.ca/id/eprint/13313

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