VEGETATION EXTRACTION WITH PIXEL BASED CLASSIFICATION APPROACH IN URBAN PARK AREA
DOI:
https://doi.org/10.21837/pm.v19i16.956Keywords:
Remote Sensing, Urban Vegetation, High Resolution, Pixel-Based Classification and Support Vector Machine.Abstract
Information on urban vegetation and land use is critical for sustainable environmental management in cities. In general, urban vegetation is important for urban planning because it helps to maintain a balance between the natural environment and the built-up region. The assessment of the composition and configuration of the vegetation is important to highlight the urban ecosystem. Thus, obtaining information about urban vegetation is critical for developing a sustainable urban development strategy. Remote sensing is increasingly being used to generate such data for mapping and monitoring changes in urban vegetation. The aim of this study is to identify and classify vegetation using the high-resolution Pleiades satellite image in urban park areas using pixel-based image analysis. Pixel based method was applied and support vector machine algorithm was used for classification of urban vegetation. Comparison of accuracy was made from the error matrices, overall accuracy and kappa coefficient for vegetation and non-vegetation classes. The overall accuracy for the classification approach was 98.98% and a kappa value of 0.97. The result demonstrates the ability of high-resolution imagery to accurately extract urban vegetation despite the complex surface of the urban area. The findings can be used to support other research and applications related to urban green space monitoring, conservation, and future urban vegetation planning.Downloads
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Alonzo, M., McFadden, J.P., Nowak, D.J., & Roberts, D.A. (2016). Mapping urban forest structure and function using hyperspectral imagery and LiDAR data. Urban Forestry & Urban Greening, 17, 135-147. https://dx.doi.org/10.1016/j.ufug.2016.04.003
Belgiu, M., & Dr?gu?, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114(Supplement C), 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Caccetta, P., Collings, S., Hingee, K., McFarlane, D., & Wu, X., (2011). Fine-scale monitoring of complex environments using remotely sensed aerial, satellite and other spatial data.
International Symposium on Image and data fusion (ISIDF 2011). http://hdl.handle.net/102.100.100/103346?index=1
Deilmai, B.R., Ahmad, B., & Zabihi, H. (2014). Comparison of two classification methods (MLC and SVM) to extract land use land cover in Johor, Malaysia. IOP Conference Series: Earth and Environmental Science 20. https://doi.org/10.1088/1755-1315/20/1/012052
Erasu, D. (2017). Remote Sensing-based urban land use/land cover change detection and monitoring. Journal of Remote Sensing & GIS, 6(2). https://doi.org/10.4172/2469-4134.1000196
Gasparovic, M., & Dobrinic, D. (2020). Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery. Remote Sensing, 12(12), 1952. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs12121952
Gao, Z., & Liu, X. (2014). Support vector machine and object-oriented classification for urban impervious surface extraction from satellite imagery. Paper presented at the 2014 The Third International Conference on Agro-Geoinformatics. https://doi.org/10.1109/Agro-Geoinformatics.2014.6910661
Hashim, H., Abd Latif, Z., and Adnan, N.A (2019). Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery, Int. Arch. Photogram. Remote Sens. Spatial Inf Sci. XLII-4/W16, 237-240. https://doi.org/10.5194/isprs-archives-XLII-4-W16-237-2019.
Hamzah, H., Othman, N., Mohd Hussain, N. H. (2020). Setting the criteria for urban tree vandalism assessment. Planning Malaysia Journal of the Malaysian Institute of Planners, 18(4), 12-32. http://dx.doi.org/10.21837/pm.v18i14.815
Hamzah, H., Othman, N., Mohd Hussain, N. H., & Simis, M. (2018). The criteria of urban trees regarding the issues of tree vandalism. IOP Conference Series: Earth and Environmental Science, 203, 1–6. https://doi.org/10.1088/1755-1315/203/1/012023
Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band World View-2 satellite data. Remote Sensing, 4, 2661-2693. https://doi.org/10.3390/rs4092661
Ibrahim, N., Ujang, U., Desa, G., & Ariffin, A. (2015). Analysing the sustainability of urban development: A review on the potential use of volunteered geographic information. ISPRS Annal of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 169-174. https://doi.org/10.5194/isprsannals-II-2-W2-169-2015
JPBD Semenanjung Malaysia. (2011). PPA 06 – Panduan Perlaksanaan Akta 172: Perintah Pemeliharaan Pokok [Guidelines of Act 172: Tree Preservation Order]. 1-24.
Kamagata, N., Hara, K., Mori, M., Akamatsu, Y., Li, Y. & Hoshino, Y. (2006). A new method of vegetation mapping by object-based classification using high resolution satellite data. Journal of the Japan Society of Photogrammetry and Remote Sensing, 45(1), 43-49. https://doi.org/10.4287/jsprs.45.43
Latif, Z.A., Zamri, I. & Omar, H. (2012). Determination of trees species using WorldView-2 data. IEEE 8th Int. Conf. on Signal Process Application. and Technology (ICSPAT), 383–387. https://doi.org/10.1109/CSPA.2012.6194754
Law of Malaysia (2006). Town and Country Planning Act 1976 (Act 172). The Commissioner of Law Revision Malaysia. Malaysia: Percetakan Nasional Malaysia Bhd. 1-132. Retrieved from https://www.planmalaysia.gov.my/index.php/en/lihat-akta-172
Lehman, I., Mathey, J. Rößler, S., Bräuer, A., Goldberg, V. (2014). Urban vegetation structure types as a methodological approach for identifying ecosystem services – Application to the analysis of micro-climatic effects. Ecological Indicators, 42, 58 – 72. https://doi.org/10.1016/j.ecolind.2014.02.036
Lillesand, T.M., Kiefer, R.W. & Chipman, J.W. (2008). Remote Sensing and image interpretation 6th edition (New York: John Wiley & Sons Ltd).
Maktav, D., Erbek, F.S., & Jurgen, C. (2005). Remote Sensing of urban areas. International Journal of Remote Sensing, 26, 655-659. https://doi.org/10.1080/01431160512331316469
Nichol, J., & Lee, C.M. (2005). Urban vegetation monitoring in Hong Kong sings high resolution multispectral images. International Journal of Remote Sensing, 26, 903. https://doi.org/10.1080/01431160412331291198
Niemela, J. (2014). Ecology of urban green spaces: The way forward in answering major research questions. Landscape and Urban Planning, 125, 298–303. https://doi.org/10.1016/j.landurbplan.2013.07.014
Nurul Iman Saiful, B., Asmala, A., & Burhanuddin Mohd, A. (2014). Application of support vector machine for classification of multispectral data. IOP Conference Series: Earth and Environmental Science, 20(1), 012038. https://doi.org/10.1088/1755-1315/20/1/012038
Nurul Shakila, K. Nurul Faizah, B. & Hazlina, H. (2018). Improving quality of life through recreational behaviour in urban park. Asian Journal of Quality of Life, 3(13), 80-88. https://doi.org/10.17576/geo-2021-1701-08
Pu, R.L., & Landry, S. (2012). A comparative analysis of high resolution IKONOS and World View-2 imagery for mapping urban tree species. Remote Sensing of Environment, 124, 516-533. https://doi.org/10.1016/j.rse.2012.06.011
Pu, R., Gong, P., Michishita, R., & Sasagawa, T. (2006). Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sensing of Environment, 104, 211–225. https://doi.org/10.1016/j.rse.2005.09.022
Pu, R. (2009). Broadleaf species recognition within situ hyperspectral data. International Journal of Remote Sensing, 30(11), 2759–2779. https://doi.org/10.1080/01431160802555820
Razak, M.A.W.A., Othman, N., & Nazir, N.N.M. (2016). Connecting people with nature: Urban park and human well-being. Procedia-Social and Behavioural Sciences, 222, 476-484. https://doi.org/10.1016/j.sbspro.2016.05.138
Rosli, S., Leh, O.L.H., Mohd Adzmi, N.A., & Marzukhi, M.A. (2020). Relationship between quality of urban parks and physical activity: A case study in Changkat public park, Batu Gajah, Perak. Planning Malaysia Journal of the Malaysian Institute of Planners, 18(4), 158-172. http://dx.doi.org/10.21837/pm.v18i14.824
Rougier, S., & Puissant, A. (2014). Improvements of urban vegetation segmentation and classification using multi-temporal Pleiades images. South Eastern European Journal of Earth Observation and Geomatics, 3(2). 409-414.
Roy, P., & Giriraj, A. (2008). Land use land cover analysis in Indian context. Journal of Applied Sciences, 8, 1346-1353. https://doi.org/10.3923/jas.2008.1346.1353
Simarmata, M. (2012). Kajian Ketercukupan Ruang Terbuka Hijau Berdasarkan Jumlah Penduduk Kota Pematangsiantar. Jurnal Akar, 1, 91-103
Szuster, B.W., Chen, Q., & Borger, M. (2012). A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Journal of Applied Geography 31, 525-532. https://doi.org/10.1016/j.apgeog.2010.11.007
Trisakti, B. (2017). Vegetation type classification and vegetation cover percentage estimation in urban green zone using Pleiades imagery. IOP Conference Series: Earth and Environmental Science 54(2017) 012003. Doi: 10.1088/1755-1315/54/1/012003. https://doi.org/10.1088/1755-1315/54/1/012003
Tigges, J., Lakes, T., & Hostert, P. (2013). Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment, 136, 66-75. https://doi.org/10.1016/j.rse.2013.05.001
Tooke, R.T., Coops, Goodwin, N.R., Voog, J.A. (2009). Extracting urban vegetation characteristics using spectral mixture analysis and tree classification. Remote Sensing of Environment, 113, 398-407.
Ouerghemmi, W., Gadal, S., Mozgeris, G., & Jonikavicius, D. (2018). Urban vegetation mapping by airborne hyperspectral imagery; feasibility and limitations. 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). https://doi.org/10.1109/WHISPERS.2018.8747112
Weber, C. & Hirsch, J. (1992). Some urban measurement from SPOT data: Urban life quality indices. International Journal of Remote Sensing, 13, 3251-3261. https://doi.org/10.1080/01431169208904116
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Wolch, J. R., Byrne, J., Newell, J. P. (2014). Urban green space, public health and environmental justice: The challenge of making cities just green enough. Landscape and Urban Planning, 125, 234–244. https://doi.org/10.1016/j.landurbplan.2014.01.017
Wong, C.H., Mew, A.A., & Perumal, B. (2017). Urban Community Forest, Bukit Persekutuan. Malaysian Nature Society. Kuala Lumpur, Malaysia. Retrieved from https://ucfnetworkbukitpersekutuan.files.wordpress.com/2017/11/ucf-booklet.pdf
Yu, L., Porwal, A., Holden, E.J., & Denith, M.C. (2012). Towards automatic lithological classification from remote sensing data using support vector machines. Journal of Computer & Geosciences, 45, 229-239. https://doi.org/10.1016/j.cageo.2011.11.019
Zylshal, Sulma, S., Yulianto, F., Nugroho, J. T., & Sofan, P. (2016). A Support Vector Machine object-based image analysis approach on urban green space extraction using Pleiades-1A imagery. Modelling Earth Systems Environment 2(2). https://doi.org/10.1007/s40808-016-0108-8
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