IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v34y2007i3p556-569.html
   My bibliography  Save this article

Detecting Spatiotemporal Dynamic Landscape Patterns Using Remote Sensing and the Lacunarity Index: A Case Study of Haikou City, China

Author

Listed:
  • Guangjin Tian
  • Zhifeng Yang
  • Yichun Xie

    (Department of Geography and Geology, Institute for Geo-spatial Research and Education, Eastern Michigan University, Ypsilanti, MI 48197, USA)

Abstract

Quantifying the landscape pattern and its dynamics is essential for the monitoring and assessment of the ecological consequences of urbanization. As one of the Special Economic Zones, Haikou is one of the fastest growing regions among all Chinese cities, owing to rapid real estate development. Using a GIS-based land-use dataset from 1986, 1996, and 2000, in combination with a lacunarity index, we attempt to quantify the spatial pattern in the Haikou metropolitan area. After the landscape structure changes over the periods 1986–96 and 1996–2000 are analyzed, a Markov conversion matrix is applied in order to study the sources and destinations of landscape dynamic changes. The lacunarity index is calculated in order to measure the landscape dynamics, with respect to several major land-use types, at a range of spatial scales. The findings indicate that the leapfrog development of real estate and the rapid economic growth of Haikou City have had a great impact on the dynamic landscape patterns. From 1986 to 1996 urban land expanded dramatically and clustered, while cropland was encroached upon and fragmented. From 1996 to 2000, after the government had implemented the strict cropland protection measures, urban expansion and cropland misuse were controlled to a large degree, and a lot of cropland was reclaimed in certain areas. We investigate the dynamic landscape pattern and process, and their implications in policy and economic development.

Suggested Citation

  • Guangjin Tian & Zhifeng Yang & Yichun Xie, 2007. "Detecting Spatiotemporal Dynamic Landscape Patterns Using Remote Sensing and the Lacunarity Index: A Case Study of Haikou City, China," Environment and Planning B, , vol. 34(3), pages 556-569, June.
    • Handle: RePEc:sae:envirb:v:34:y:2007:i:3:p:556-569
    DOI: 10.1068/b3155
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b3155
    Download Restriction: no
    File URL: https://libkey.io/10.1068/b3155?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Karen C. Seto & Robert K. Kaufmann, 2003. "Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data," Land Economics, University of Wisconsin Press, vol. 79(1), pages 106-121.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tian, Guangjin & Ouyang, Yun & Quan, Quan & Wu, Jianguo, 2011. "Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA," Ecological Modelling, Elsevier, vol. 222(5), pages 1129-1138.
    2. Tian, Guangjin & Jiang, Jing & Yang, Zhifeng & Zhang, Yaoqi, 2011. "The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China," Ecological Modelling, Elsevier, vol. 222(3), pages 865-878.
    3. Tian, Guangjin & Qiao, Zhi & Zhang, Yaoqi, 2012. "The investigation of relationship between rural settlement density, size, spatial distribution and its geophysical parameters of China using Landsat TM images," Ecological Modelling, Elsevier, vol. 231(C), pages 25-36.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jianglong Chen & Jinlong Gao & Feng Yuan, 2016. "Growth Type and Functional Trajectories: An Empirical Study of Urban Expansion in Nanjing, China," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    2. Weiguo Liu & Karen C Seto, 2008. "Using the ART-MMAP Neural Network to Model and Predict Urban Growth: A Spatiotemporal Data Mining Approach," Environment and Planning B, , vol. 35(2), pages 296-317, April.
    3. Michail Fragkias & Karen C Seto, 2007. "Modeling Urban Growth in Data-Sparse Environments: A New Approach," Environment and Planning B, , vol. 34(5), pages 858-883, October.
    4. Shu, Cheng & Xie, Hualin & Jiang, Jinfa & Chen, Qianru, 2018. "Is Urban Land Development Driven by Economic Development or Fiscal Revenue Stimuli in China?," Land Use Policy, Elsevier, vol. 77(C), pages 107-115.
    5. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    6. Prashanti Sharma & Rajesh Bahadur Thapa & Mir Abdul Matin, 2020. "Examining forest cover change and deforestation drivers in Taunggyi District, Shan State, Myanmar," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5521-5538, August.
    7. Deininger, Klaus & Jin, Songqing, 2009. "Securing property rights in transition: Lessons from implementation of China's rural land contracting law," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 22-38, May.
    8. Tao Yu & Anming Bao & Wenqiang Xu & Hao Guo & Liangliang Jiang & Guoxiong Zheng & Ye Yuan & Vincent NZABARINDA, 2019. "Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta," IJERPH, MDPI, vol. 17(1), pages 1-21, December.
    9. He, Qingsong & Xu, Min & Xu, Zike & Ye, Yanmei & Shu, Xianfan & Xie, Peng & Wu, Jiayu, 2019. "Promotion incentives, infrastructure construction, and industrial landscapes in China," Land Use Policy, Elsevier, vol. 87(C).
    10. Xu HENGZHOU & Yin HONGCHUN & Guo YUYAN, 2013. "Fiscal decentralization, local government competition and farmland conversion in China: the co-integration analysis and the GMM estimation based on the inter-provincial panel data," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(9), pages 420-429.
    11. Ran Guo & Hong Leng & Qing Yuan & Shiyi Song, 2022. "Impact of Urban Form on CO 2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China," Land, MDPI, vol. 11(5), pages 1-20, May.
    12. Jinlong Gao & Zhixuan Wu & Jianglong Chen & Wen Chen, 2020. "Beyond the bid‐rent: Two tales of land use transition in contemporary China," Growth and Change, Wiley Blackwell, vol. 51(3), pages 1336-1356, September.
    13. Wang Man & Qin Nie & Lizhong Hua & Xuewen Wu & Hui Li, 2019. "Spatio–Temporal Variations in Impervious Surface Patterns during Urban Expansion in a Coastal City: Xiamen, China," Sustainability, MDPI, vol. 11(8), pages 1-12, April.
    14. Yongwei Liu & Xiaoshu Cao & Tao Li, 2020. "Identifying Driving Forces of Built-Up Land Expansion Based on the Geographical Detector: A Case Study of Pearl River Delta Urban Agglomeration," IJERPH, MDPI, vol. 17(5), pages 1-17, March.
    15. Lijing Tang & Dongyan Wang, 2018. "Optimization of County-Level Land Resource Allocation through the Improvement of Allocation Efficiency from the Perspective of Sustainable Development," IJERPH, MDPI, vol. 15(12), pages 1-19, November.
    16. Burak Güneralp & Michael K Reilly & Karen C Seto, 2012. "Capturing Multiscalar Feedbacks in Urban Land Change: A Coupled System Dynamics Spatial Logistic Approach," Environment and Planning B, , vol. 39(5), pages 858-879, October.
    17. Yuhao Lu & Nicholas C Coops, 2018. "Bright lights, big city: Causal effects of population and GDP on urban brightness," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    18. Chau, Nancy H. & Qin, Yu & Zhang, Weiwen, 2016. "Leader Networks and Transaction Costs: A Chinese Experiment in Interjurisdictional Contracting," IZA Discussion Papers 9641, Institute of Labor Economics (IZA).
    19. Xiuyan Zhao & Changhong Miao, 2022. "Spatial-Temporal Changes and Simulation of Land Use in Metropolitan Areas: A Case of the Zhengzhou Metropolitan Area, China," IJERPH, MDPI, vol. 19(21), pages 1-27, October.
    20. Xiaoqing Song & Kang-tsung Chang & Liang Yang & Jürgen Scheffran, 2016. "Change in Environmental Benefits of Urban Land Use and Its Drivers in Chinese Cities, 2000–2010," IJERPH, MDPI, vol. 13(6), pages 1-21, May.

    More about this item

    Statistics

    Access and download statistics

    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:sae:envirb:v:34:y:2007:i:3:p:556-569. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.