Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery

• Main Authors: Wenjuan Yu, Weiqi Zhou, Zhaxi Dawa, Jia Wang, Yuguo Qian, Weimin Wang
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Remote Sensing
• Subjects: urban landscape dynamics; change process; temporally variation; Continuous Change Detection and Classification; vegetation greening
• Online Access: https://www.mdpi.com/2072-4292/13/16/3217

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000–2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management.