IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v5y2016i4p44-d84636.html
   My bibliography  Save this article

Prediction of Land Use Change in Long Island Sound Watersheds Using Nighttime Light Data

Author

Listed:
  • Ruiting Zhai

    (Department of Geography, University of Connecticut, 215 Glenbrook Rd., Storrs, CT 06269, USA)

  • Chuanrong Zhang

    (Department of Geography, University of Connecticut, 215 Glenbrook Rd., Storrs, CT 06269, USA
    Center for Environmental Sciences and Engineering, University of Connecticut, 3107 Horsebarn Hill Rd., U-4210, Storrs, CT 06269, USA)

  • Weidong Li

    (Department of Geography, University of Connecticut, 215 Glenbrook Rd., Storrs, CT 06269, USA
    Center for Environmental Sciences and Engineering, University of Connecticut, 3107 Horsebarn Hill Rd., U-4210, Storrs, CT 06269, USA)

  • Mark A. Boyer

    (Department of Geography, University of Connecticut, 215 Glenbrook Rd., Storrs, CT 06269, USA
    Center for Environmental Sciences and Engineering, University of Connecticut, 3107 Horsebarn Hill Rd., U-4210, Storrs, CT 06269, USA)

  • Dean Hanink

    (Department of Geography, University of Connecticut, 215 Glenbrook Rd., Storrs, CT 06269, USA)

Abstract

The Long Island Sound Watersheds (LISW) are experiencing significant land use/cover change (LUCC), which affects the environment and ecosystems in the watersheds through water pollution, carbon emissions, and loss of wildlife. LUCC modeling is an important approach to understanding what has happened in the landscape and what may change in the future. Moreover, prospective modeling can provide sustainable and efficient decision support for land planning and environmental management. This paper modeled the LUCCs between 1996, 2001 and 2006 in the LISW in the New England region, which experienced an increase in developed area and a decrease of forest. The low-density development pattern played an important role in the loss of forest and the expansion of urban areas. The key driving forces were distance to developed areas, distance to roads, and social-economic drivers, such as nighttime light intensity and population density. In addition, this paper compared and evaluated two integrated LUCC models—the logistic regression–Markov chain model and the multi-layer perception–Markov chain (MLP–MC) model. Both models achieved high accuracy in prediction, but the MLP–MC model performed slightly better. Finally, a land use map for 2026 was predicted by using the MLP–MC model, and it indicates the continued loss of forest and increase of developed area.

Suggested Citation

  • Ruiting Zhai & Chuanrong Zhang & Weidong Li & Mark A. Boyer & Dean Hanink, 2016. "Prediction of Land Use Change in Long Island Sound Watersheds Using Nighttime Light Data," Land, MDPI, vol. 5(4), pages 1-16, December.
  • Handle: RePEc:gam:jlands:v:5:y:2016:i:4:p:44-:d:84636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/5/4/44/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/5/4/44/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heimlich, Ralph E. & Anderson, William D., 2001. "Development At The Urban Fringe And Beyond: Impacts On Agriculture And Rural Land," Agricultural Economic Reports 33943, United States Department of Agriculture, Economic Research Service.
    2. Charlotta Mellander & José Lobo & Kevin Stolarick & Zara Matheson, 2015. "Night-Time Light Data: A Good Proxy Measure for Economic Activity?," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    3. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    4. Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
    5. Christopher D. Elvidge & Daniel Ziskin & Kimberly E. Baugh & Benjamin T. Tuttle & Tilottama Ghosh & Dee W. Pack & Edward H. Erwin & Mikhail Zhizhin, 2009. "A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data," Energies, MDPI, vol. 2(3), pages 1-28, August.
    6. Wenjie Wang & Chuanrong Zhang & Jenica M. Allen & Weidong Li & Mark A. Boyer & Kathleen Segerson & John A. Silander, 2016. "Analysis and Prediction of Land Use Changes Related to Invasive Species and Major Driving Forces in the State of Connecticut," Land, MDPI, vol. 5(3), pages 1-22, July.
    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. Ruci Wang & Hao Hou & Yuji Murayama, 2018. "Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
    2. Haghighat, Fatemeh, 2021. "Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Jian Gong & Jingye Li & Jianxin Yang & Shicheng Li & Wenwu Tang, 2017. "Land Use and Land Cover Change in the Qinghai Lake Region of the Tibetan Plateau and Its Impact on Ecosystem Services," IJERPH, MDPI, vol. 14(7), pages 1-21, July.

    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. Juan Jose Miranda & Oscar A. Ishizawa & Hongrui Zhang, 2020. "Understanding the Impact Dynamics of Windstorms on Short-Term Economic Activity from Night Lights in Central America," Economics of Disasters and Climate Change, Springer, vol. 4(3), pages 657-698, October.
    2. Boslett, Andrew & Hill, Elaine & Ma, Lala & Zhang, Lujia, 2021. "Rural light pollution from shale gas development and associated sleep and subjective well-being," Resource and Energy Economics, Elsevier, vol. 64(C).
    3. Adriana Kocornik-Mina & Thomas K. J. McDermott & Guy Michaels & Ferdinand Rauch, 2020. "Flooded Cities," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 35-66, April.
    4. Dickinson, Jeffrey, 2020. "Planes, Trains, and Automobiles: What Drives Human-Made Light?," MPRA Paper 103504, University Library of Munich, Germany.
    5. Felbermayr, Gabriel & Gröschl, Jasmin & Sanders, Mark & Schippers, Vincent & Steinwachs, Thomas, 2018. "Shedding Light on the Spatial Diffusion of Disasters," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181556, Verein für Socialpolitik / German Economic Association.
    6. John Gibson & Susan Olivia & Geua Boe‐Gibson, 2020. "Night Lights In Economics: Sources And Uses," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 955-980, December.
    7. repec:lic:licosd:41920 is not listed on IDEAS
    8. José García-Montalvo & Marta Reynal-Querol & Juan Carlos Muñoz Mora, 2021. "Measuring Inequality from Above," Working Papers 1252, Barcelona School of Economics.
    9. Jaqueson K. Galimberti, 2020. "Forecasting GDP Growth from Outer Space," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 697-722, August.
    10. Kammerlander, Andreas & Schulze, Günther G., 2023. "Local economic growth and infant mortality," Journal of Health Economics, Elsevier, vol. 87(C).
    11. Christian Otchia & Simplice Asongu, 2020. "Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(8), pages 1421-1441, December.
    12. Russ, Jason, 2020. "Water runoff and economic activity: The impact of water supply shocks on growth," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
    13. Nguyen, Cuong & Noy, Ilan, 2018. "Measuring the impact of insurance on urban recovery with light: The 2011 New Zealand earthquake," Working Paper Series 6955, Victoria University of Wellington, School of Economics and Finance.
    14. Addison,Douglas M. & Stewart,Benjamin P., 2015. "Nighttime lights revisited : the use of nighttime lights data as a proxy for economic variables," Policy Research Working Paper Series 7496, The World Bank.
    15. E. Ustaoglu & R. Bovkır & A. C. Aydınoglu, 2021. "Spatial distribution of GDP based on integrated NPS-VIIRS nighttime light and MODIS EVI data: a case study of Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10309-10343, July.
    16. Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
    17. Shapiro, Daniel & Oh, Chang Hoon & Zhang, Peng, 2023. "Nighttime lights data and their implications for IB research," Journal of International Management, Elsevier, vol. 29(5).
    18. Susanne A. Frick & Andrés Rodríguez-Pose & Michael Wong, 2018. "Towards economically dynamic Special Economic Zones in emerging countries," Papers in Evolutionary Economic Geography (PEEG) 1816, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Apr 2018.
    19. Patrick Lehnert & Michael Niederberger & Uschi Backes-Gellner & Eric Bettinger, 2020. "Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps Across Time and Space," Economics of Education Working Paper Series 0165, University of Zurich, Department of Business Administration (IBW), revised Sep 2022.
    20. Lionel Roger, 2018. "Blinded by the light? Heterogeneity in the luminosity-growth nexus and the African growth miracle," Discussion Papers 2018-04, University of Nottingham, CREDIT.
    21. Felbermayr, Gabriel & Gröschl, Jasmin & Sanders, Mark & Schippers, Vincent & Steinwachs, Thomas, 2022. "The economic impact of weather anomalies," World Development, Elsevier, vol. 151(C).

    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:jlands:v:5:y:2016:i:4:p:44-:d:84636. 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: 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.

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