Author
Listed:
- Pengyun Ma
(College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Key Laboratory of Submarine Geosciences and Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China)
- Yilin Liu
(College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China)
- Xibin Han
(College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Key Laboratory of Submarine Geosciences and Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China)
- Xiangfeng Geng
(Shandong Provincial Geo-Mineral Engineering Exploration Institute, Jinan 250014, China)
- Xiaodong Cui
(College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)
- Lihong Zhao
(College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)
- Yun Liu
(College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Key Laboratory of Submarine Geosciences and Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China)
- Rui Han
(College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Key Laboratory of Submarine Geosciences and Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China)
Abstract
Impervious surfaces serve as critical indicators for monitoring urbanization processes and assessing urban ecological conditions. The precise extraction and analysis of the spatiotemporal variations in impervious surfaces are essential for informing urban planning strategies. The unique location advantage of Jiaozhou Bay makes it an important urban gathering area. Based on Landsat remote sensing image data, the extraction effect and accuracy of urban built-up area index, biophysical index, and random forest classification were compared and analyzed. Then, the optimal random forest method was used to extract impervious water information from 8 Landsat satellite images of the coastal area of Jiaozhou Bay from 1986 to 2022. Over the past four decades, the impervious surface area in the Jiaozhou Bay coastal region has expanded dramatically from 71.53 km 2 in 1986 to 1049.16 km 2 in 2022, with the most significant increase, nearly doubling, occurring between 2011 and 2017. Spatially, the distribution of impervious surfaces has expanded progressively from coastal to inland areas and from central to peripheral zones, particularly toward the southwest in Huangdao District and Jiaozhou City. The distribution of impervious surfaces in the Jiaozhou Bay coastal area is primarily confined to flat and gently sloping nearshore regions due to natural constraints like terrain slope. Concurrently, policy initiatives, along with population and economic growth, have catalyzed the rapid expansion of these surfaces. These insights are invaluable for comprehending the urban spatiotemporal dynamics and patterns along the Jiaozhou Bay coast and offer fresh perspectives for research into urban transformations and the sustainable development of ecological environments in other coastal regions.
Suggested Citation
Pengyun Ma & Yilin Liu & Xibin Han & Xiangfeng Geng & Xiaodong Cui & Lihong Zhao & Yun Liu & Rui Han, 2024.
"Analysis of the Spatiotemporal Evolution and Driving Mechanisms of Impervious Surfaces along the Jiaozhou Bay (China) Coast over the Past Four Decades,"
Sustainability, MDPI, vol. 16(13), pages 1-20, July.
Handle:
RePEc:gam:jsusta:v:16:y:2024:i:13:p:5659-:d:1427638
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5659-:d:1427638. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.