DOI: 10.6084/m9.figshare.30978076.v2
title: The code of the paper named Structural-Functional Decoupling in Post-Disturbance Forest Recovery: Canopy Cover Lags Hydrological Function Across Chinese Forests .

description: This study investigated the asynchronous changes between canopy cover and evapotranspiration before and after forest disturbance across different vegetation zones, and analyzed the effects of habitat conditions and site-specific factors on evapotranspiration recovery. During the analysis, a series of vegetation greenness indices, forest functional parameters, and climatic and environmental variables were used. All of these variables are publicly available and can be accessed through open platforms, including:<br>1.This study utilized a series of vegetation greenness indices and forest functional parameters derived from MODIS and Landsat satellite data on the GEE platform. The ET data were obtained from the "MODIS/061/MOD16A2GF" dataset (Running, Mu, Zhao, & Moreno, 2019); EVI and NDVI data were obtained from the "MODIS/061/MOD13Q1" dataset (Didan, 2021); LAI and FPAR data were obtained from the "MODIS/061/MOD15A2H" dataset (Myneni, Knyazikhin, & Park, 2015); NBR data were obtained from the public dataset "LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NBR"; GEDI data were obtained from the public dataset "LARSE/GEDI/GEDI02_A_002_MONTHLY".2.Environmental and climatic data for the forest plots were obtained from the ERA5 dataset developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Store, 2017). Topographic data were obtained from the global 30 m × 30 m resolution digital elevation model (DEM) dataset released by the European Space Agency (ESA) (Riegler, Hennig, & Weber, 2015). Canopy cover data were obtained from the 1985–2023 time series of tree cover in China developed by Cai et al. (2023).3.This study referenced the forest disturbance distribution maps of China (1986–2020) produced by Liu et al. (2023), as well as the 30-meter resolution land use change maps of China developed by Yang et al. (2021), to identify forest areas with potential for recovery following disturbance events in 2005 and 2006.<br>References<br>Cai, Y., Xu, X., Zhu, P., Nie, S., Wang, C., Xiong, Y., & Liu, X. (2024). Unveiling spatiotemporal tree cover patterns in China: The first 30 m annual tree cover mapping from 1985 to 2023. ISPRS Journal of Photogrammetry and Remote Sensing, 216, 240-258. Didan, K. (2021). MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V061. (No Title). Liu, Z., Wang, W. J., Ballantyne, A., He, H. S., Wang, X., Liu, S., . . . Yu, K. (2023). Forest disturbance decreased in China from 1986 to 2020 despite regional variations. Communications Earth & Environment, 4(1), 15. Myneni, R., Knyazikhin, Y., & Park, T. (2015). MOD15A2H MODIS/Terra leaf area Index/FPAR 8-Day L4 global 500m SIN grid V006. (No Title). Riegler, G., Hennig, S., & Weber, M. (2015). WorldDEM–A novel global foundation layer. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 183-187. Running, S., Mu, Q., Zhao, M., & Moreno, A. (2019). MOD16A2GF MODIS/Terra net evapotranspiration gap-filled 8-day L4 global 500 m SIN grid V006. (No Title). Store, C. C. C. S. C. D. (2017). Copernicus Climate Change Service (C3 S)(2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Yang, J., & Huang, X. (2021). 30 m annual land cover and its dynamics in China from 1990 to 2019. Earth System Science Data Discussions, 2021, 1-29.<br>