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Article

Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery

1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Nanping 354300, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(22), 5426; https://doi.org/10.3390/rs15225426
Submission received: 1 September 2023 / Revised: 16 November 2023 / Accepted: 16 November 2023 / Published: 20 November 2023
(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)

Abstract

Understanding accurate and continuous forest dynamics is of key importance for forest protection and management in the Greater Khingan Mountains (GKM). There has been a lack of finely captured and long-term information on forest disturbance and recovery since the mega-fire of 1987 which may limit the scientific assessment of the GKM’s vegetation conditions. Therefore, we proposed a rapid and robust approach to track the dynamics of forest disturbance and recovery from 1987 to 2021 using Landsat time series, LandTrendr, and random forests (RF) models. Furthermore, we qualified the spatial characteristics of forest changes in terms of burn severity, topography, and distances from roads and settlements. Our results revealed that the integrated method of LandTrendr and RF is well adapted to track forest dynamics in the GKM, with an overall accuracy of 0.86. From 1987 to 2021, forests in the GKM showed a recovery trend with a net increase of more than 4.72 × 104 ha. Over 90% of disturbances occurred between 1987 and 2010 and over 75% of recovery occurred between 1987 and 1988. Mildly burned areas accounted for 51% of forest disturbance and severely burned areas contributed to 45% of forest recovery. Forest changes tended to occur in zones with elevations of 400–650 m, slopes of less than 9°, and within 6 km of roads and 24 km of settlements. Temporal trends of forest disturbance and recovery were mainly explained by the implementation timelines of major forestry policies. Our results provide high-resolution and time-series information on forest disturbance and recovery in the GKM which could support scientific decisions on forest management and sustainable utilization.
Keywords: forest disturbance; forest recovery; LandTrendr; random forests (RF); TimeSync-Plus; Greater Khingan Mountains (GKM) forest disturbance; forest recovery; LandTrendr; random forests (RF); TimeSync-Plus; Greater Khingan Mountains (GKM)

Share and Cite

MDPI and ACS Style

Ren, H.; Ren, C.; Wang, Z.; Jia, M.; Yu, W.; Liu, P.; Xia, C. Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery. Remote Sens. 2023, 15, 5426. https://doi.org/10.3390/rs15225426

AMA Style

Ren H, Ren C, Wang Z, Jia M, Yu W, Liu P, Xia C. Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery. Remote Sensing. 2023; 15(22):5426. https://doi.org/10.3390/rs15225426

Chicago/Turabian Style

Ren, Huixin, Chunying Ren, Zongming Wang, Mingming Jia, Wensen Yu, Pan Liu, and Chenzhen Xia. 2023. "Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery" Remote Sensing 15, no. 22: 5426. https://doi.org/10.3390/rs15225426

APA Style

Ren, H., Ren, C., Wang, Z., Jia, M., Yu, W., Liu, P., & Xia, C. (2023). Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery. Remote Sensing, 15(22), 5426. https://doi.org/10.3390/rs15225426

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