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Vegetation Resilience Dataset for Urban Green Spaces in Shanghai (2001-2022, V1.0)


SUN Daoqian1SUN Weiran1CHENG Xiaoyan2WANG Jie*2CHENG Fangyan3
1 National Forestry and Grassland Administration,Beijing 100010,China2 Shanghai Gardening-Landscaping Construction Co.,Ltd.,Shanghai 200335,China3 Zhejiang A&F University,Hangzhou 311300,China

DOI:10.3974/geodb.2024.12.04.V1

Published:Dec. 2024

Visitors:919       Data Files Downloaded:20      
Data Downloaded:14.18 MB      Citations:

Key Words:

Critical Slowing Down theory,green space,resilience

Abstract:

Based on MODIS NDVI data (MOD13Q1 V6, 250m resolution) from 2001 to 2022, combined with Dynamic World land use data and Amap administrative boundary data, the authors constructed a vegetation resilience dataset for urban green spaces in Shanghai using the theory of critical slowing down, through simplified STL method and moving average combined with harmonic analysis. The dataset includes the following data in Shanghai: (1) boundary of study area; (2) spatial distribution of green space coverage; (3) vegetation resilience based on AR(1) index using simplified STL method (STL_AR(1)); (4) vegetation resilience based on AR(1) index using moving average and harmonic analysis (V2_AR(1)); (5) vegetation resilience based on variance index using moving average and harmonic analysis (V2_VAR). The dataset is archived in .shp and .tif data formats, the raster data is in 250 m resolution. The dataset consists of 28 data files with data size of 812 KB (Compressed into one file with 726 KB).

Foundation Item:

Science and Technology Commission of Shanghai Municipality (22dz1209403); Shanghai Construction Group Co., Ltd. (24JCSF-24); Zhejiang A&F University (2024LFR069)

Data Citation:

SUN Daoqian, SUN Weiran, CHENG Xiaoyan, WANG Jie*, CHENG Fangyan. Vegetation Resilience Dataset for Urban Green Spaces in Shanghai (2001-2022, V1.0)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.12.04.V1.

References:


     [1] Overpeck, J. T., Breshears, D. D. The growing challenge of vegetation change [J]. Science, 2021, 372(6544): 786-787.
     [2] Vieira, J., Matos, P., Mexia, T., et al. Green spaces are not all the same for the provision of air purification and climate regulation services: The case of urban parks [J]. Environmental Research, 2018, 160: 306-313.
     [3] Dakos, V., van Nes, E. H., D'Odorico, P., et al. Robustness of variance and autocorrelation as indicators of critical slowing down [J]. Ecology, 2012, 93(2): 264-271.
     [4] Majumder, S., Tamma, K., Ramaswamy, S., et al. Inferring critical thresholds of ecosystem transitions from spatial data [J]. Ecology, 2019, 100(7): e02722.
     [5] Smith, T., N. Boers. Reliability of vegetation resilience estimates depends on biomass density [J]. Nature Ecology & Evolution, 2023, 7(11): 1799-1808.
     [6] Lenton, T. M., Buxton, J. E., Armstrong McKay, D. I., et al. A resilience sensing system for the bio-sphere [J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2022, 377(1857): 20210383.
     [7] O'Leary, J. K., Micheli, F., Airoldi, L., et al. The Resilience of Marine Ecosystems to Climatic Disturbances [J]. BioScience, 2017, 67(3): 208-220.
     

Data Product:

ID Data Name Data Size Operation
1 SH_Green_Resilience_1.0.rar 726.13KB
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