Abiotic disturbances affect forest short-term vegetation cover and phenology in Southwest China. (May 2021)

• Record Type: Journal Article
• Authors: Deng, Ying; Wang, Ming; Yousefpour, Rasoul; Hanewinkel, Marc
• Is Part Of: Ecological indicators. Volume 124(2021)
• Publication Date: 2021-05
• Subjects: NDVI normalized difference vegetation index -- EVI enhanced vegetation index -- TM and ETM+ thematic mapper and enhanced thematic mapper -- MODIS moderate resolution imaging spectroradiometer -- Landtrendr landsat-based detection of trends in disturbance and recovery -- BFAST breaks for additive seasonal and trend -- DBEST detecting breakpoints and estimating segments in trend -- IPCC United Nations intergovernmental panel on climate change -- MVC maximum value composite -- SG Savitzky Golay -- ECE extreme cold event -- SPEI standardized precipitation evapotranspiration index -- SGS start of the growing season -- LGS length of the growing season -- SGS end of the growing season; Forest disturbances -- Change detection -- Breaks for additive seasonal and trend (BFAST) -- Post-disturbance effects; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.107393
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: BFAST was developed to reconstruct historical forest disturbance events. Fires, extreme cold events, and droughts were identified. Fires led to largest damages and altered regular phenology. Legacy effects existed 1–2 years after cold and drought events. Ambient climate contributed to modulating post-disturbance effects. Abstract: Forest disturbances significantly affect the global carbon cycle by, for example, vegetation loss or changing forest phenology. However, the lack of historical disturbance events constitutes a challenge for in-depth temporal and spatial analysis. Available remote sensing time series and combined climate data may have great potential to quickly and consistently detect and identify forest disturbances events. We employed time-series data (2001–2014) of a vegetation index (normalized difference vegetation index, NDVI) and a change detection algorithm (the breaks for additive seasonal and trend, BFAST) to detect forest disturbances in a sub-tropical area located in Southwest China. Remote sensing and meteorological data were combined to distinguish among the typical forest disturbances: fires, extreme cold events in winter (ECE), and droughts. With the reconstructed historical disturbance events, post-disturbance vegetation loss, short-term vegetation cover, and phenology changes were analyzed. Our results show that fires and droughts caused severe damage to forest cover (NDVI anomalies can reach up to −1.84 and −1.11, respectively). Fire changed the regular phenological periods which last 3–4 years, and it also took 1–2 years for vegetation greenness to recover after ECE and droughts, which triggered carbon emissions and reduced forest stocks. Warmer areas were vulnerable to ECE effects as well and should be paid more attention. Post-disturbance effects show complex patterns: characteristics of disturbances, climatic conditions, and multiple events overlaying contribute to modifying forest vegetation. Hence, forest disturbances cannot be neglected but should be emphasized in future forest ecosystem modeling or analyzing. The approach used in the study can be a crucial step in detecting and assessing the effects of various disturbances on forest vegetation and phenology and, thereby, contributes to improved risk analysis and management in forestry.