IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p748-d480155.html
   My bibliography  Save this article

Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?

Author

Listed:
  • Iana Rufino

    (PPGECA, Center for Natural Resources and Technology, Federal University of Campina Grande, Campina Grande, PB 58428-830, Brazil)

  • Slobodan Djordjević

    (Centre for Water Systems, University of Exeter, Exeter EX4 4QF, UK)

  • Higor Costa de Brito

    (PPGECA, Center for Natural Resources and Technology, Federal University of Campina Grande, Campina Grande, PB 58428-830, Brazil)

  • Priscila Barros Ramalho Alves

    (Centre for Water Systems, University of Exeter, Exeter EX4 4QF, UK)

Abstract

The northeastern Brazilian region has been vulnerable to hydrometeorological extremes, especially droughts, for centuries. A combination of natural climate variability (most of the area is semi-arid) and water governance problems increases extreme events’ impacts, especially in urban areas. Spatial analysis and visualisation of possible land-use change (LUC) zones and trends (urban growth vectors) can be useful for planning actions or decision-making policies for sustainable development. The Global Human Settlement Layer (GHSL) produces global spatial information, evidence-based analytics, and knowledge describing Earth’s human presence. In this work, the GHSL built-up grids for selected Brazilian cities were used to generate urban models using GIS (geographic information system) technologies and cellular automata for spatial pattern simulations of urban growth. In this work, six Brazilian cities were selected to generate urban models using GIS technologies and cellular automata for spatial pattern simulations of urban sprawl. The main goal was to provide predictive scenarios for water management (including simulations) and urban planning in a region highly susceptible to extreme hazards, such as floods and droughts. The northeastern Brazilian cities’ analysis raises more significant challenges because of the lack of land-use change field data. Findings and conclusions show the potential of dynamic modelling to predict scenarios and support water sensitive urban planning, increasing cities’ coping capacity for extreme hazards.

Suggested Citation

  • Iana Rufino & Slobodan Djordjević & Higor Costa de Brito & Priscila Barros Ramalho Alves, 2021. "Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?," Sustainability, MDPI, vol. 13(2), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:748-:d:480155
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/748/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/2/748/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nyamekye, Clement & Kwofie, Samuel & Ghansah, Benjamin & Agyapong, Emmanuel & Boamah, Linda Appiah, 2020. "Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality," Land Use Policy, Elsevier, vol. 99(C).
    2. P. A. C. Libanio, 2018. "Two decades of Brazil’s participatory model for water resources management: from enthusiasm to frustration," Water International, Taylor & Francis Journals, vol. 43(4), pages 494-511, May.
    3. Carlos Enrique Tupiño Salinas & Vládia Pinto Vidal de Oliveira & Liana Brito & André V. Ferreira & José Carlos de Araújo, 2019. "Social impacts of a large-dam construction: the case of Castanhão, Brazil," Water International, Taylor & Francis Journals, vol. 44(8), pages 871-885, November.
    4. Britaldo Silveira Soares-Filho & Daniel Curtis Nepstad & Lisa M. Curran & Gustavo Coutinho Cerqueira & Ricardo Alexandrino Garcia & Claudia Azevedo Ramos & Eliane Voll & Alice McDonald & Paul Lefebvre, 2006. "Modelling conservation in the Amazon basin," Nature, Nature, vol. 440(7083), pages 520-523, March.
    5. Hermanns, Till & Helming, Katharina & König, Hannes J. & Schmidt, Katharina & Li, Qirui & Faust, Heiko, 2017. "Sustainability impact assessment of peatland-use scenarios: Confronting land use supply with demand," Ecosystem Services, Elsevier, vol. 26(PB), pages 365-376.
    Full references (including those not matched with items on IDEAS)

    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. Sébastien Marchand, 2011. "Technical Efficiency, Farm Size and Tropical Deforestation in the Brazilian Amazonian Forest," CERDI Working papers halshs-00552981, HAL.
    2. Changchun Feng & Hao Zhang & Liang Xiao & Yongpei Guo, 2022. "Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective," Land, MDPI, vol. 11(2), pages 1-18, February.
    3. Claudia Suzanne Marie Nathalie Vitel & Gabriel Cardoso Carrero & Mariano Colini Cenamo & Maya Leroy & Paulo Mauricio Lima A. Graça & Philip Martin Fearnside, 2013. "Land-use Change Modeling in a Brazilian Indigenous Reserve: Construction of a Reference Scenario for the Suruí REDD Project," Post-Print hal-01466513, HAL.
    4. Jose Manuel Ochoa-Quintero & Charlotte H. Chang & Toby A. Gardner & Mariluce Rezende Messias & William J. Sutherland & Fernanda A. C. Delben, 2017. "Habitat Loss on Rondon’s Marmoset Potential Distribution," Land, MDPI, vol. 6(1), pages 1-15, January.
    5. Gabriela Simonet & Julie Subervie & Driss Ezzine-De-Blas & Marina Cromberg & Amy Duchelle, 2015. "Paying smallholders not to cut down the amazon forest: impact evaluation of a REDD+ pilot project," Working Papers 1514, Chaire Economie du climat.
    6. José Gomis-Cebolla & Juan C. Jiménez-Muñoz & José A. Sobrino, 2016. "MODIS-Based Monthly LST Products over Amazonia under Different Cloud Mask Schemes," Data, MDPI, vol. 1(2), pages 1-10, July.
    7. Zhiwei Deng & Bin Quan, 2022. "Intensity Characteristics and Multi-Scenario Projection of Land Use and Land Cover Change in Hengyang, China," IJERPH, MDPI, vol. 19(14), pages 1-18, July.
    8. Santos, Mário & Bastos, Rita & Cabral, João Alexandre, 2013. "Converting conventional ecological datasets in dynamic and dynamic spatially explicit simulations: Current advances and future applications of the Stochastic Dynamic Methodology (StDM)," Ecological Modelling, Elsevier, vol. 258(C), pages 91-100.
    9. Yanli Gao & Hongbo Li & Yan Song, 2021. "Interaction Relationship between Urbanization and Land Use Multifunctionality: Evidence from Han River Basin, China," Land, MDPI, vol. 10(9), pages 1-21, September.
    10. Franklin, Sergio L. & Pindyck, Robert S., 2018. "Tropical Forests, Tipping Points, and the Social Cost of Deforestation," Ecological Economics, Elsevier, vol. 153(C), pages 161-171.
    11. Bernardo F. T. Rudorff & Marcos Adami & Joel Risso & Daniel Alves De Aguiar & Bernardo Pires & Daniel Amaral & Leandro Fabiani & Izabel Cecarelli, 2012. "Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative," Sustainability, MDPI, vol. 4(5), pages 1-15, May.
    12. Jules Degila & Ida Sèmévo Tognisse & Anne-Carole Honfoga & Sèton Calmette Ariane Houetohossou & Fréjus Ariel Kpedetin Sodedji & Hospice Gérard Gracias Avakoudjo & Souand Peace Gloria Tahi & Achille Ep, 2023. "A Survey on Digital Agriculture in Five West African Countries," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    13. Kaushal, Kevin R. & Navrud, Ståle, 2018. "Global Biodiversity Costs of Climate Change. Improving the damage assessment of species loss in Integrated Assessment Models," Working Paper Series 4-2018, Norwegian University of Life Sciences, School of Economics and Business.
    14. Changjun Gu & Pei Zhao & Qiong Chen & Shicheng Li & Lanhui Li & Linshan Liu & Yili Zhang, 2020. "Forest Cover Change and the Effectiveness of Protected Areas in the Himalaya since 1998," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    15. Carrero, Gabriel Cardoso & Walker, Robert Tovey & Simmons, Cynthia Suzanne & Fearnside, Philip Martin, 2022. "Land grabbing in the Brazilian Amazon: Stealing public land with government approval," Land Use Policy, Elsevier, vol. 120(C).
    16. Courage Kamusoko & Yukio Wada & Toru Furuya & Shunsuke Tomimura & Mitsuru Nasu & Khamma Homsysavath, 2013. "Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management," Land, MDPI, vol. 2(1), pages 1-19, January.
    17. Ze Zhou & Bin Quan & Zhiwei Deng, 2023. "Effects of Land Use Changes on Ecosystem Service Value in Xiangjiang River Basin, China," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    18. Sun, Jianing & Zhou, Tao & Wang, Di, 2022. "Relationships between urban form and air quality: A reconsideration based on evidence from China’s five urban agglomerations during the COVID-19 pandemic," Land Use Policy, Elsevier, vol. 118(C).
    19. A.S. Duden & P.A. Verweij & A.P.C. Faaij & D. Baisero & C. Rondinini & F. van der Hilst, 2020. "Biodiversity Impacts of Increased Ethanol Production in Brazil," Land, MDPI, vol. 9(1), pages 1-17, January.
    20. de Oliveira Barros, Erison Rosa & Oliveira de Andrade, Maurício & de Souza Júnior, Fernando Lourenço, 2022. "Time-space modeling of irregular occupations around Brazilian highways, based on static grids: Case study of BR-408," Land Use Policy, Elsevier, vol. 114(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:jsusta:v:13:y:2021:i:2:p:748-:d:480155. 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.