{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:40:44Z","timestamp":1781282444713,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T00:00:00Z","timestamp":1645920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Land"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/land11030351","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:47:17Z","timestamp":1645994837000},"page":"351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2127","authenticated-orcid":false,"given":"Jagannath","family":"Aryal","sequence":"first","affiliation":[{"name":"Faculty of Engineering and IT, The University of Melbourne, Parkville, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4564-2985","authenticated-orcid":false,"given":"Chiranjibi","family":"Sitaula","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC 3800, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6639-6824","authenticated-orcid":false,"given":"Sunil","family":"Aryal","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Waurn Ponds, VIC 3216, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,27]]},"reference":[{"key":"ref_1","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land Cover Classification Using Landsat 8 Time Series, European Commission Joint Research Center."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_3","unstructured":"(2020, September 10). Copernicus Open Access Portal. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_4","unstructured":"(2020, November 23). EO Browser. Available online: https:\/\/www.sentinel-hub.com\/explore\/eobrowser\/."},{"key":"ref_5","unstructured":"(2020, November 23). Earth Explorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_6","first-page":"78242A","article-title":"A high-resolution index for vegetation extraction in IKONOS images","volume":"Volume 7824","author":"Taleb","year":"2010","journal-title":"Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, Toulouse, France, 20\u201322 September 2010"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"867","DOI":"10.5194\/isprs-archives-XLI-B8-867-2016","article-title":"Urban Vegetation mapping based on the hj-a ndvi reconstruction","volume":"41","author":"Li","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"461","DOI":"10.14358\/PERS.70.4.461","article-title":"Satellite observations of the seasonal vegetation growth in central asia: 1982\u20131990","volume":"70","author":"Yu","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2374","DOI":"10.1080\/01431161.2019.1688419","article-title":"Non-stationary and unequally spaced NDVI time series analyses by the LSWAVE software","volume":"41","author":"Ghaderpour","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M., Al, A., Hassan, Q.K., and Dewan, A. (2019). Spatio-temporal patterns of land use\/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens., 11.","DOI":"10.3390\/rs11070790"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kwan, C., Gribben, D., Ayhan, B., Li, J., Bernabe, S., and Plaza, A. (2020). An accurate vegetation and non-vegetation differentiation approach based on land cover classification. Remote Sens., 12.","DOI":"10.3390\/rs12233880"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1016\/j.rse.2007.09.007","article-title":"The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI","volume":"112","author":"Montandon","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4614","DOI":"10.5897\/AJAR11.1825","article-title":"Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods","volume":"8","author":"Sahebjalal","year":"2013","journal-title":"Afr. J. Agric. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ufug.2016.07.001","article-title":"Normalized difference vegetation index (NDVI) as a marker of surrounding greenness in epidemiological studies: The case of Barcelona city","volume":"19","author":"Gascon","year":"2016","journal-title":"Urban For. Urban Green."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/24749508.2019.1608409","article-title":"Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification","volume":"4","author":"Salami","year":"2020","journal-title":"Geol. Ecol. Landscapes"},{"key":"ref_16","first-page":"145","article-title":"Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana","volume":"22","author":"Mensah","year":"2019","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105686","DOI":"10.1016\/j.compag.2020.105686","article-title":"Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data","volume":"177","author":"Daryaei","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","first-page":"343","article-title":"Estimating urban greenness index using remote sensing data: A case study of an affluent vs poor suburbs in the city of Johannesburg","volume":"24","author":"Abutaleb","year":"2020","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/JSTARS.2019.2962550","article-title":"Estimating the urban fractional vegetation cover using an object-based mixture analysis method and Sentinel-2 MSI imagery","volume":"13","author":"Cai","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, T., Su, J., Liu, C., Chen, W.H., Liu, H., and Liu, G. (2017, January 7\u20138). Band selection in Sentinel-2 satellite for agriculture applications. Proceedings of the 23rd International Conference on Automation and Computing (ICAC), Huddersfield, UK.","DOI":"10.23919\/IConAC.2017.8081990"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gong, W., Hu, X., and Gong, J. (2018). Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data. Remote Sens., 10.","DOI":"10.3390\/rs10060946"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vasilakos, C., Kavroudakis, D., and Georganta, A. (2020). Machine learning classification ensemble of multi-temporal sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sens., 12.","DOI":"10.3390\/rs12122005"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/17445760.2019.1597084","article-title":"The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images","volume":"35","author":"Wei","year":"2020","journal-title":"Int. J. Parallel Emergent Distrib. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., and Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Timilsina, S., Aryal, J., and Kirkpatrick, J. (2020). Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN). Remote Sens., 12.","DOI":"10.3390\/rs12183017"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5632","DOI":"10.1080\/01431161.2016.1246775","article-title":"Stacked Autoencoder-based deep learning for remote-sensing image classification: A case study of African land-cover mapping","volume":"37","author":"Li","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liang, P., Shi, W., and Zhang, X. (2018). Remote sensing image classification based on stacked denoising autoencoder. Remote Sens., 10.","DOI":"10.3390\/rs10010016"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/isprs-archives-XLII-3-79-2018","article-title":"Extraction of built-up areas using convolutional neural networks and transfer learning from sentinel-2 satellite images","volume":"42","author":"Bramhe","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, X., Tong, X., Hu, Z., and Wu, G. (2020). Improving urban land cover\/use mapping by integrating a hybrid convolutional neural network and an automatic training sample expanding strategy. Remote Sens., 12.","DOI":"10.3390\/rs12142292"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.ecolmodel.2009.01.037","article-title":"Using single-and multi-target regression trees and ensembles to model a compound index of vegetation condition","volume":"220","author":"Kocev","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2015.69","article-title":"Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery","volume":"2","author":"Sheffield","year":"2015","journal-title":"Sci. Data"},{"key":"ref_33","unstructured":"QGIS Development Team (2009). QGIS Geographic Information System, Open Source Geospatial Foundation."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. (Methodol.)"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data","volume":"57","author":"Abdi","year":"2020","journal-title":"Giscience Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference Computer vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_42","unstructured":"(2020, September 10). Victorian LGA, Available online: https:\/\/discover.data.vic.gov.au\/dataset\/lga\/."},{"key":"ref_43","unstructured":"(2020, November 23). Victorian Vegetation. Available online: http:\/\/www.vicveg.net.au\/vvPlantNote2.aspx\/."},{"key":"ref_44","unstructured":"(2020, November 23). Victorian Vegetation Communities, Available online: https:\/\/www.necma.vic.gov.au\/Solutions\/Plants-Animals\/Native-Plants-Animals\/Vegetation-communities-revegetation\/."},{"key":"ref_45","unstructured":"(2020, November 23). Victorian Climate Temperature, Available online: http:\/\/vro.agriculture.vic.gov.au\/dpi\/vro\/vrosite.nsf\/pages\/climate-temperature\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/22797254.2019.1582840","article-title":"Copernicus Sentinel-2 Calibration and Validation","volume":"52","author":"Szantoi","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_47","unstructured":"(2020, November 22). Sentinel-2A Products. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-2-msi\/product-types\/."},{"key":"ref_48","unstructured":"(2020, November 22). Sentinel-2A Guidelines. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/technical-guides\/sentinel-2-msi\/level-2a\/algorithm\/."},{"key":"ref_49","unstructured":"(2020, July 11). Sentinel-2A Processing Levels. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-2-msi\/processing-levels\/level-2\/."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"126450","DOI":"10.1016\/j.ufug.2019.126450","article-title":"Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning","volume":"46","author":"Shekhar","year":"2019","journal-title":"Urban For. Urban Green."},{"key":"ref_52","unstructured":"(2020, December 03). Australian Bureau of Statistics, Available online: https:\/\/itt.abs.gov.au\/itt\/r.jsp?databyregion\/."},{"key":"ref_53","unstructured":"R Core Team (2013). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_54","unstructured":"Ranghetti, L., and Busetto, L. (2020, December 03). sen2r: Find, Download and Process Sentinel-2 Data. Available online: https:\/\/sen2r.ranghetti.info\/."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"237","DOI":"10.5194\/isprs-archives-XLII-4-W16-237-2019","article-title":"Urban vegetation classification with NDVI thresold value method with very high resolution (VHR) PLEIADES Imagery","volume":"42","author":"Hashim","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.proenv.2015.10.043","article-title":"Measuring land cover change in Seremban, Malaysia using NDVI index","volume":"30","author":"Aburas","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zaitunah, A., Samsuri, A., and Safitri, R. (2018, January 26\u201327). Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia. Proceedings of the IOP Conference Series: Earth and Environmental Science, Banda Aceh, Indonesia.","DOI":"10.1088\/1755-1315\/126\/1\/012112"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gessesse, A.A., and Melesse, A.M. (2019). Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia. Extreme Hydrology and Climate Variability, Elsevier.","DOI":"10.1016\/B978-0-12-815998-9.00008-7"},{"key":"ref_59","unstructured":"W\u00fcstemann, H., and Kalisch, D. (2016). Towards a National Indicator for Urban Green Space Provision and Environmental Inequalities in Germany: Method and Findings, Technische Universit\u00e4t Berlin. Technical Report, SFB 649 Discussion Paper."},{"key":"ref_60","first-page":"321","article-title":"Evaluation of changes in per capita green space through remote sensing data","volume":"1","author":"Beiranvand","year":"2013","journal-title":"Int. J. Adv. Biol. Biomed. Res."},{"key":"ref_61","unstructured":"Franco Gantiva, J.A., P\u00e1ez, D., and Rajabifard, A. (2018). Methodological Proposal for Measuring and Predicting Urban Green Space Per Capita in a Land-Use Cover Change Model: Case Study in Bogot\u00e1, Colombia. [Master\u2019s Thesis, Uniandes]."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"064072","DOI":"10.1088\/1748-9326\/ac03dc","article-title":"Mapping the maximum extents of urban green spaces in 1039 cities using dense satellite images","volume":"16","author":"Huang","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kuras, A., Brell, M., Rizzi, J., and Burud, I. (2021). Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13173393"}],"container-title":["Land"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-445X\/11\/3\/351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:28:40Z","timestamp":1760135320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-445X\/11\/3\/351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,27]]},"references-count":63,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["land11030351"],"URL":"https:\/\/doi.org\/10.3390\/land11030351","relation":{},"ISSN":["2073-445X"],"issn-type":[{"value":"2073-445X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,27]]}}}