Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island, China

Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers,... This study aims to examine vegetation cover dynamics through quantification of its greenness index, magnitude of change, nature of transition, and fragmentation levels using Landsat images from four periods, 1984, 1996, 2007 and 2017, together with physical and socioeconomic drivers. The results show high spatiotemporal variations of vegetation cover that generally increased from plain to mountain and towards the recent decade (2007–2017). While low vegetation cover (fractional vegetation cover < 50% and NDVI < 0.3) remained to be the dominant cover type in Pingtan, spatiotemporal variations were observed during different time periods. Overall, forestland has increased by 11.43% (3682 ha), and shrub land has expanded by 0.28% (91 ha). In contrast, grassland has reduced by 5.41% (1743 ha). Considering all vegetation types, the expanded area dominated the vegetation cover change resulting in a net gain of 6.3% (2030 ha). The increase in vegetation cover was contributed mainly by farmland. The landscape pattern of each vegetation type shows variation across periods and some metrics have inconsistent trend with change in class size. Generally, patches’ shape complexity, size variability and dominance reduced, while fragmentation level increased during the study period. Slope, elevation, strong wind and policy affected vegetation cover positively. Most socio economic variables showed significant influence (variable importance in the projection > 1) on both woodland (+) and grassland (−) in first latent factor that consists the highest proportion variance in the models. Having quantitative results of vegetation change using several metrics and its change maps would provide valuable information for comprehensive understanding of vegetation cover change and valuable inputs for environmental management planners. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Modeling Earth Systems and Environment Springer Journals

Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island, China

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Publisher
Springer International Publishing
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Earth Sciences; Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Applications in the Physical Sciences; Ecosystems; Environment, general
ISSN
2363-6203
eISSN
2363-6211
D.O.I.
10.1007/s40808-018-0473-6
Publisher site
See Article on Publisher Site

Abstract

This study aims to examine vegetation cover dynamics through quantification of its greenness index, magnitude of change, nature of transition, and fragmentation levels using Landsat images from four periods, 1984, 1996, 2007 and 2017, together with physical and socioeconomic drivers. The results show high spatiotemporal variations of vegetation cover that generally increased from plain to mountain and towards the recent decade (2007–2017). While low vegetation cover (fractional vegetation cover < 50% and NDVI < 0.3) remained to be the dominant cover type in Pingtan, spatiotemporal variations were observed during different time periods. Overall, forestland has increased by 11.43% (3682 ha), and shrub land has expanded by 0.28% (91 ha). In contrast, grassland has reduced by 5.41% (1743 ha). Considering all vegetation types, the expanded area dominated the vegetation cover change resulting in a net gain of 6.3% (2030 ha). The increase in vegetation cover was contributed mainly by farmland. The landscape pattern of each vegetation type shows variation across periods and some metrics have inconsistent trend with change in class size. Generally, patches’ shape complexity, size variability and dominance reduced, while fragmentation level increased during the study period. Slope, elevation, strong wind and policy affected vegetation cover positively. Most socio economic variables showed significant influence (variable importance in the projection > 1) on both woodland (+) and grassland (−) in first latent factor that consists the highest proportion variance in the models. Having quantitative results of vegetation change using several metrics and its change maps would provide valuable information for comprehensive understanding of vegetation cover change and valuable inputs for environmental management planners.

Journal

Modeling Earth Systems and EnvironmentSpringer Journals

Published: Jun 6, 2018

References

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