IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i3p351-d759983.html
   My bibliography  Save this article

NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Author

Listed:
  • Jagannath Aryal

    (Faculty of Engineering and IT, The University of Melbourne, Parkville, VIC 3010, Australia)

  • Chiranjibi Sitaula

    (Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC 3800, Australia)

  • Sunil Aryal

    (School of Information Technology, Deakin University, Waurn Ponds, VIC 3216, Australia)

Abstract

Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index ( UGSI ) and Per Capita Green Space ( PCGS ) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.

Suggested Citation

  • Jagannath Aryal & Chiranjibi Sitaula & Sunil Aryal, 2022. "NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia," Land, MDPI, vol. 11(3), pages 1-21, February.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:3:p:351-:d:759983
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/3/351/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/3/351/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kocev, Dragi & Džeroski, Sašo & White, Matt D. & Newell, Graeme R. & Griffioen, Peter, 2009. "Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition," Ecological Modelling, Elsevier, vol. 220(8), pages 1159-1168.
    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. Mara Ottoboni & Salvatore Eugenio Pappalardo & Massimo De Marchi & Fabrizio Ungaro, 2023. "Characterization and Mapping of Public and Private Green Areas in the Municipality of Forlì (NE Italy) Using High-Resolution Images," Land, MDPI, vol. 12(3), pages 1-18, March.

    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. Jeongsub Choi & Mengmeng Zhu & Jihoon Kang & Myong K. Jeong, 2024. "Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing," Annals of Operations Research, Springer, vol. 339(1), pages 185-201, August.
    2. Moung-Jin Lee & Wonkyong Song & Saro Lee, 2015. "Habitat Mapping of the Leopard Cat ( Prionailurus bengalensis ) in South Korea Using GIS," Sustainability, MDPI, vol. 7(4), pages 1-21, April.
    3. Holguin-Gonzalez, Javier E. & Boets, Pieter & Alvarado, Andres & Cisneros, Felipe & Carrasco, María C. & Wyseure, Guido & Nopens, Ingmar & Goethals, Peter L.M., 2013. "Integrating hydraulic, physicochemical and ecological models to assess the effectiveness of water quality management strategies for the River Cuenca in Ecuador," Ecological Modelling, Elsevier, vol. 254(C), pages 1-14.
    4. Choi, Jong-Kuk & Oh, Hyun-Joo & Koo, Bon Joo & Ryu, Joo-Hyung & Lee, Saro, 2011. "Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS," Ecological Modelling, Elsevier, vol. 222(8), pages 1522-1533.
    5. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    6. Wen Song & Wei Song & Haihong Gu & Fuping Li, 2020. "Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas," IJERPH, MDPI, vol. 17(6), pages 1-17, March.
    7. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
    8. Meenakshi Sharma & Prashant Kaushik & Aakash Chawade, 2021. "Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research," Sustainability, MDPI, vol. 13(15), pages 1-14, August.
    9. Sinclair, Steve J. & Avirmed, Otgonsuren & White, Matthew D. & Batpurev, Khorloo & Griffioen, Peter A. & Liu, Canran & Jambal, Sergelenkhuu & Sime, Hayley & Olson, Kirk A., 2021. "Rangeland condition assessment in the Gobi Desert: A quantitative approach that places stakeholder evaluations front and Centre," Ecological Economics, Elsevier, vol. 181(C).
    10. Yujing Zhou & Dubo He, 2024. "Multi-Target Feature Selection with Adaptive Graph Learning and Target Correlations," Mathematics, MDPI, vol. 12(3), pages 1-24, January.
    11. Shijie Li & Zuoqin Qian & Ji Liu, 2024. "Multi-Output Regression Algorithm-Based Non-Dominated Sorting Genetic Algorithm II Optimization for L-Shaped Twisted Tape Insertions in Circular Heat Exchange Tubes," Energies, MDPI, vol. 17(4), pages 1-22, February.
    12. Kocev, Dragi & Naumoski, Andreja & Mitreski, Kosta & Krstić, Svetislav & Džeroski, Sašo, 2010. "Learning habitat models for the diatom community in Lake Prespa," Ecological Modelling, Elsevier, vol. 221(2), pages 330-337.
    13. Mannan Karim & Jiqiu Deng & Muhammad Ayoub & Wuzhou Dong & Baoyi Zhang & Muhammad Shahzad Yousaf & Yasir Ali Bhutto & Muhammad Ishfaque, 2023. "Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices," Land, MDPI, vol. 12(10), pages 1-24, October.
    14. Everaert, Gert & Boets, Pieter & Lock, Koen & Džeroski, Sašo & Goethals, Peter L.M., 2011. "Using classification trees to analyze the impact of exotic species on the ecological assessment of polder lakes in Flanders, Belgium," Ecological Modelling, Elsevier, vol. 222(14), pages 2202-2212.
    15. Schmid, Lena & Gerharz, Alexander & Groll, Andreas & Pauly, Markus, 2023. "Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones," Computational Statistics & Data Analysis, Elsevier, vol. 179(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:11:y:2022:i:3:p:351-:d:759983. 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.