Identifying phase-specific environmental drivers of drought-induced vegetation dynamics via spectral Proxies

Drought-induced canopy browning and recovery dynamics threaten ecosystem stability worldwide, with Australia serving as a representative case. This study examined the 2019–2020 drought and subsequent recovery in Eucalyptus forests across two bioregions of the Australian state of New South Wales (NSW): the North Coast and South Eastern Highlands. Canopy browning and recovery were quantified using a 2010–2022 Sentinel-2 time series of Normalized Burn Ratio (NBR), which has been previously identified as the most effective spectral index for detecting drought-related declines in canopy greenness, and were validated with field-measured canopy health. Artificial neural networks were used to link NBR z-scores with climatic (precipitation, temperature, potential evapotranspiration), topographic (Topographic Wetness Index, aspect, slope), soil, and vegetation variables. Lagged and cumulative precipitation and temperature emerged as the dominant drivers of canopy browning, while recovery was influenced by potential evapotranspiration and temperature. Regional contrasts underscored the role of local climate, topography, and vegetation composition in shaping drought impacts and post-drought recovery trajectories. • Drought-induced canopy browning (decline in spectral greenness) and subsequent recovery were driven by distinct environmental factors. • Extreme heat and rapid soil moisture loss caused immediate canopy browning. • Recovery required post-drought rain, moderate heat, and favorable topography. • Poorly recovered forests were highly sensitive to past climate and site factors.

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Publication Details

Journal
Environmental Modelling & Software
Published
2026-02-25
DOI
https://doi.org/10.1016/j.envsoft.2026.106928
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Identifying phase-specific environmental drivers of drought-induced vegetation dynamics via spectral Proxies

Belinda E. Medlyn, Rachid Oucheikh, Samuel Hislop, Brendan Choat et al.
Environmental Modelling & Software
Remote Sensing in Agriculture
article

Identifying phase-specific environmental drivers of drought-induced vegetation dynamics via spectral Proxies

Belinda E. Medlyn, Rachid Oucheikh, Samuel Hislop, Brendan Choat, Nuwanthi Arampola, Ali Mansourian, Benjamin Smith, Pengxiang Zhao
article en

Abstract

Drought-induced canopy browning and recovery dynamics threaten ecosystem stability worldwide, with Australia serving as a representative case. This study examined the 2019–2020 drought and subsequent recovery in Eucalyptus forests across two bioregions of the Australian state of New South Wales (NSW): the North Coast and South Eastern Highlands. Canopy browning and recovery were quantified using a 2010–2022 Sentinel-2 time series of Normalized Burn Ratio (NBR), which has been previously identified as the most effective spectral index for detecting drought-related declines in canopy greenness, and were validated with field-measured canopy health. Artificial neural networks were used to link NBR z-scores with climatic (precipitation, temperature, potential evapotranspiration), topographic (Topographic Wetness Index, aspect, slope), soil, and vegetation variables. Lagged and cumulative precipitation and temperature emerged as the dominant drivers of canopy browning, while recovery was influenced by potential evapotranspiration and temperature. Regional contrasts underscored the role of local climate, topography, and vegetation composition in shaping drought impacts and post-drought recovery trajectories. • Drought-induced canopy browning (decline in spectral greenness) and subsequent recovery were driven by distinct environmental factors. • Extreme heat and rapid soil moisture loss caused immediate canopy browning. • Recovery required post-drought rain, moderate heat, and favorable topography. • Poorly recovered forests were highly sensitive to past climate and site factors.

Environmental Modelling & SoftwareVol. 199
Lund University (SE), MIT University (MK), Geospatial Research (United Kingdom) (GB), Western Sydney University (AU), RMIT University (AU)
Climate action
Openalex Percentile: Top 52%
Remote Sensing in Agriculture
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