Abstract
BackgroundNatural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.MethodsWe used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model.ResultsThe conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km2 (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value.ConclusionsThis study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.
Highlights
Forests have pivotal ecological functions and provide diverse ecosystem services, such as global climate regulation, habitat provision, water and soil conservation, biodiversity preservation, and carbon (2021) 8:73 altered by natural environmental changes and disturbances, such as wildfires, insect infestations, flooding, and drought, over many decades (FAO and UNEP 2020).Most forest disturbances occur at small spatial scales, and regional patterns evolve over long periods (Kim et al 2014)
A comprehensive understanding of long-term forest dynamics over vast geographical areas is crucial for sustainable forest management, biodiversity conservation and achieving carbon neutrality goals
We accurately mapped the fine-resolution spatiotemporal patterns and causes of forest disturbances over the Hengduan Mountains Region (HDMR)’s forested ecosystems from 1990 to 2020 by combining the LandTrendr algorithm with the random forest (RF) model, with forest disturbance mapping accuracies of 92.3% and 97.70% ± 0.06% and a disturbance attribution mapping accuracy of 85.80%
Summary
Forests have pivotal ecological functions and provide diverse ecosystem services, such as global climate regulation, habitat provision, water and soil conservation, biodiversity preservation, and carbon (2021) 8:73 altered by natural environmental changes and disturbances, such as wildfires, insect infestations, flooding, and drought, over many decades (FAO and UNEP 2020).Most forest disturbances occur at small spatial scales, and regional patterns evolve over long periods (Kim et al 2014). A wealth of forest cover change products from local to global scales have been generated from a medium- or high-frequency Landsat time series using these algorithms (Cohen et al 2016; Czerwinski et al 2014; Hansen et al 2016; Margono et al 2012; Masek et al 2008; Potapov et al 2012; Schroeder et al 2007, 2011; White et al 2017). Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation
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