Tall forests exhibit heightened recovery following severe drought: a remote sensing perspective

Abstract Context Climate change is increasing the frequency and severity of droughts worldwide, with profound consequences for forest ecosystems. Although previous studies suggest that taller forests are more vulnerable during drought, their capacity to recover following drought remains poorly understood, creating substantial uncertainty for forest management. Objectives This study aimed to quantify how canopy height influences post-drought forest recovery at a regional scale. Methods We investigated evergreen broad-leaved forests in southwest China following a severe drought in 2010. We used the MODIS Enhanced Vegetation Index (EVI) to derive forest recovery metrics, the Standardized Precipitation Evapotranspiration Index (SPEI) to classify drought severity, and a LiDAR-derived canopy height map. We then used linear regression and ANOVA to evaluate relationships between forest recovery and canopy height. Results Short-term (1 year) recovery increased with the intensity of the preceding drought. Under severe and extreme drought conditions, recovery increased significantly with canopy height ( p < 0.001). In contrast, long-term (3 years) recovery did not differ significantly among canopy height classes. Conclusions Our results indicated that although tall forests may experience greater drought impacts, they exhibit stronger short-term recovery, resulting in comparable long-term resilience across canopy-height classes. This size-dependent recovery dynamic is a previously underappreciated mechanism that should be incorporated into forest dynamics models and climate-adaptive management strategies.

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Publication Details

Journal
Landscape Ecology
Published
2026-03-21
DOI
https://doi.org/10.1007/s10980-026-02313-2
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
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article

Tall forests exhibit heightened recovery following severe drought: a remote sensing perspective

Yuan Zhang, Ying Yang, Ting Dong, Mark Shelan et al.
Landscape Ecology
Remote Sensing and LiDAR Applications
article

Tall forests exhibit heightened recovery following severe drought: a remote sensing perspective

Yuan Zhang, Ying Yang, Ting Dong, Mark Shelan, Peipei Xu, Wei Fang, Shuai Liu, Hua Yang, Yun Zhang
article en

Abstract

Abstract Context Climate change is increasing the frequency and severity of droughts worldwide, with profound consequences for forest ecosystems. Although previous studies suggest that taller forests are more vulnerable during drought, their capacity to recover following drought remains poorly understood, creating substantial uncertainty for forest management. Objectives This study aimed to quantify how canopy height influences post-drought forest recovery at a regional scale. Methods We investigated evergreen broad-leaved forests in southwest China following a severe drought in 2010. We used the MODIS Enhanced Vegetation Index (EVI) to derive forest recovery metrics, the Standardized Precipitation Evapotranspiration Index (SPEI) to classify drought severity, and a LiDAR-derived canopy height map. We then used linear regression and ANOVA to evaluate relationships between forest recovery and canopy height. Results Short-term (1 year) recovery increased with the intensity of the preceding drought. Under severe and extreme drought conditions, recovery increased significantly with canopy height ( p < 0.001). In contrast, long-term (3 years) recovery did not differ significantly among canopy height classes. Conclusions Our results indicated that although tall forests may experience greater drought impacts, they exhibit stronger short-term recovery, resulting in comparable long-term resilience across canopy-height classes. This size-dependent recovery dynamic is a previously underappreciated mechanism that should be incorporated into forest dynamics models and climate-adaptive management strategies.

Landscape Ecology
Pace University (US), Beijing Normal University (CN), Culture Resource (EG), Wuhu Institute of Technology (CN), State Key Laboratory of Remote Sensing Science (CN), Anhui Normal University (CN)
Climate action
Openalex Percentile: Top 13%
Remote Sensing and LiDAR Applications
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