The forest recovery path after drought dependence on forest type and stock volume

• Published in: Environmental research letters Vol. 17; no. 5; pp. 55006 - 55019
• Main Authors: Luo, Hui, Zhou, Tao, Yu, Peixin, Yi, Chuixiang, Liu, Xia, Zhang, Yajie, Zhou, Peifang, Zhang, Jingzhou, Xu, Yixin
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2022
• Subjects: Coniferous forests; Density; Drought; drought legacy effects; Evapotranspiration; Forest ecosystems; Forest management; forest recovery; Forests; Information management; NDVI; Normalized difference vegetative index; planted and natural forests; Recovery; SPEI; stock volume; Terrestrial ecosystems
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/ac57e5

Abstract Drought legacy effects of forest ecosystems have been widely observed. However, the influence of forest type and stock volume on its recovery path is poorly understood. In this research, we first used the Standardized Precipitation Evapotranspiration Index to identify a drought event. Then, we applied the normalized difference vegetation index deficit and forest property maps derived from forest inventories to investigate the potential impacts of forest properties on forest recovery paths. The results showed that the legacy effects 1–3 years after a drought event were pervasive, but the forest recovery path was highly dependent on the forest type and forest stock volume. The recovery of forests with low stock volume densities (<60 m 3 ha −1 ) was mostly stronger than that of forests with high stock volume densities (≥60 m 3 ha −1 ) by the second year. Although all forests with different stock volume densities approximately returned to a normal status by the third year, they followed various paths to recovery. Natural coniferous forests in China that have a similar stock volume density (<60 m 3 ha −1 ) took longer to recover than planted coniferous forests and exhibited a lower magnitude of recovery. These findings highlight that drought legacy effects are greater for natural coniferous forests with high stock volume densities, which provides insightful forest management information on how to speed up forest recovery with forest density control and type control.