Sains Malaysiana 45(7)(2016): 1025–1034

 

The Use of WorldView-2 Satellite Data in Urban Tree Species Mapping by Object-Based Image Analysis Technique

(Penggunaan Data Satelit World View-2 bagi Pemetaan Spesies Pokok Bandar menggunakan Teknik Analisis Imej berasaskan Objek)

 

 

RAZIEH SHOJANOORI1, HELMI Z.M. SHAFRI1*, SHATTRI MANSOR1 & MOHD HASMADI ISMAIL2

 

1Department of Civil Engineering and, Geospatial Information Science Research Centre (GISRC)

Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan

Malaysia

 

2Forest Survey and Engineering Laboratory, Faculty of Forestry, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia

 

Received: 25 March 2015/Accepted: 3 December 2015

 

ABSTRACT

The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling their growth and decline. This study focused on the detection of urban tree species by considering three types of tree species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of maximum likelihood classification and support vector machines. These methods were then compared with object-based image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy.

 

Keywords: Object-based classification; pixel-based classification; urban tree species; WorldView-2

 

ABSTRAK

Pembangunan kawasan penempatan dan komersial mengancam tumbuhan dan ekosistem. Maka isu pengurusan bandar termasuk mengenal pasti keadaan dan kuantiti spesies pokok bandar untuk melindungi alam sekitar dan juga mengawal pertumbuhan serta kemerosotan mereka perlu dijalankan dengan segera. Kajian ini memfokuskan kepada pengesanan spesies pokok bandar dengan mengambil kira tiga spesies yang dikenali sebagai Mesua ferrea L., Samanea saman dan Casuarina sumatrana. Set peraturan baharu dibangunkan untuk mengesan tiga spesies ini. Dengan ini, dua teknik pengelasan berasaskan piksel diaplikasi dan dibandingkan menggunakan teknik kebolehjadian maksimum dan mesin penyokong vektor. Teknik ini kemudian dibandingkan dengan pengelasan analisis imej berasakan objek (OBIA). Teknik OBIA kemudian digunakan untuk membangunkan set peraturan dengan mengekstrak ciri reruang, spektrum, tekstur dan warna serta lain-lain yang berkaitan. Akhirnya set peraturan baharu diguna pakai kepada imej WorldView-2. Hasilnya menunjukkan teknik OBIA berasaskan set peraturan yang baharu tersebut menunjukkan potensi yang signifikan untuk mengesan spesies pokok dengan ketepatan yang tinggi.

 

Kata kunci: Pengelasan berasaskan objek; pengelasan berasaskan piksel; spesies pokok bandar; WorldView-2

 

REFERENCES

 

Adeline, K.R.M., Briottet, X., Paparoditis, N. & Gastellu- Etchegorry, J.P. 2013. Material reflectance retrieval in urban tree shadows with physics-based empirical atmospheric correction. IEEE Urban Remote Sensing Event (JURSE), São Paulo, Brazil, April 21-23.

Akamphon, S. & Akamphon, K. 2014. Cost and benefit tradeoffs in using a shade tree for residential building energy saving. Thai Society of Higher Education Institutes on the Environment (TSHE) 7: 19-24.

Ardila, J., Bijker, W., Tolpekin, V. & Stein, A., 2012. Gaussian localized active contours for multitemporal analysis of urban tree crowns. IEEE International Geoscience and Remote Sensing Symposium. pp. 6971-6974.

Cho, M.A., Mathieu, R., Asner, G.P., Naidoo, L., Aardt, J.V., Ramoelo, A., Debba, P., Wessels, K., Main, R., Smit, I.P.J. & Erasmus, B. 2012. Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125: 214-226.

Chonglu, Z., Yong, Z., Yu, C., Zhen, C., Qingbin, J., Pinyopusarerk, K. & Franche, C. 2010. Potential Casuarina species and suitable techniques for the GGW. In Le projetmajeurafricain de la Grande MurailleVerte: Concepts etmiseenoeuvre. IRD Éditions, edited by Dia, A. & Duponnois, R. http://books. openedition.org/irdeditions/2123.

Conine, A., Xiang, W.N., Young, J. & Whitley, D. 2004. Planning for multi-purpose greenways in Concord, North Carolina. Landscape Urban Plan. 68: 271-287.

Flanders, D., Hall-Beyer, M. & Perverzoff, J. 2003. Preliminary evaluation of eCognition object based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing 29(4): 441-452.

Forest Research Institute Malaysia. 2014. http://www.frim.gov. my/attractions/colours-of-frim/.

Forzieri, G., Tanteri, L., Moser, G. & Catani, F. 2013. Mapping natural and urban environments using airborne multi-sensor ADS40–MIVIS–LiDAR synergies. Int. J. Appl. Earth ObsGeoinf. 23: 313-323.

Gong, C., Yu, S., Joesting, H. & Chen, J., 2013. Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images. Landscape and Urban Planning 117: 57-65.

Gobster, P.H. & Westphal, L.M. 2004. The human dimensions of urban greenways: planning for recreation and related experiences. Landscape Urban Plan. 68: 147-165.

Hájek, F. 2006. Object-oriented classification of Ikonos satellite data for the identification of tree species composition. Journal of Forest Science 52(4): 181-187.

Hao, Z., Heng-Jia, S. & Bo-Chun, Y. 2011. Application of hyper spectral remote sensing for urban forestry monitoring in natural disaster zones. IEEE International Conference on Computer and Management (CAMAN). pp. 1-4.

Huang, C., Shao, Y., Chen, J., Liu, J., Chen, J. & Li, J. 2007. A strategy for analyzing urban forest using landsat ETM+ Imagery. IEEE International Geoscience and Remote Sensing Symposium. pp. 1990-1993.

Immitzer, M., Atzberger, C. & Koukal, T. 2012. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens. 4: 2661-2693.

Iovan, C., Cournede, P.H., Guyard, T., Bayol, B., Boldo, D. & Cord, M. 2014. Model-based analysis–synthesis for realistic tree reconstruction and growth simulation. IEEE Trans Geosci. Remote Sens. 52: 1438-1450.

Iovan, C., Boldo, D. & Cord, M. 2008. Detection, characterization, and modeling vegetation in urban areas from high-resolution aerial imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1(3): 206-213.

Johnson, B. & Xie, Z. 2013. Classifying a high resolution image of an urban area using super-object information. ISPRS Journal of Photogrammetry and Remote Sensing 83: 40-49.

Ke, Y. & Quackenbush, L.J. 2007. Forest species classification and tree crown delineation using QuickBird imagery. In Proceedings of the AS- PRS Annual Conference, May 7-11; Tampa (FL). American Society for Photogrammetry and Remote Sensing, edited by Bethesda, M.

Kong, F., Yin, H. & Nakagoshi, N. 2007. Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China. Landscape and Urban Planning 79: 240-252.

Kong, F. & Nakagoshi, N. 2005. Changes of urban green spaces and their driving forces: a case study of Jinan City, China. Journal of International Development and Cooperation 11(2): 97-109.

Latif, Z.A., Zamri, I. & Omar, H. 2012. Determination of trees species using WorldView-2 data. IEEE 8th Int. Conf. on Signal Process Appl. and Technol. (ICSPAT). pp. 383-387.

Li, C., Yin, J. & Zhao, J. 2010. Extraction of urban vegetation from high resolution remote sensing image. International Conference on Computer Design and Applications (ICCDA) 4: 403-406.

Lobo, A. 1997. Image segmentation and discriminant analysis for the identification of land cover units in ecology. IEEE Trans Geosci. Remote Sens. 35: 1136-1145.

Ma, J., Ju, W., 2011. Mapping Leaf Area Index for the Urban Area of Nanjing City, China Using IKONOS Remote Sensing Data. IEEE, 978-1-61284-848-8/11/$26.00.

Mora, B., Wulder, M.A. & White, J.C. 2010. Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment. Remote Sensing of Environment 114(11): 2474-2484.

Marshall, V., Lewis, M. & Ostendorf, B. 2012. Do additional bands (coastal, NIR-2, red-edge and yellow) in WorldView-2 multispectral imagery improve discrimination of an Invasive Tussock, Buffel Grass (Cenchrus Ciliaris). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol XXXIX-B8.

Nouri, H., Beecham, S., Anderson, S. & Nagler, P. 2014. High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors. Journal of Remote Sensing 6: 580-602.

Nowak, D.J. & Dwyer, J.F. 2007. Understanding the benefits and costs of urban forest ecosystems. In Urban and Community Forestry in the Northeast. 2nd ed, edited by Kuser, J.E. Netherland: Springer. pp. 25-46.

Pu, R. & Landry, S. 2012. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment 124: 516-533.

Puissant, A., Rougier, S. & Stumpf, A. 2014. Object-oriented mapping of urban trees using Random Forest classifiers. International Journal of Applied Earth Observation and Geoinformation 26: 235-245.

Rapinal, S., Clement, B., Magnanon, S., Sellin, V. & Hubert-Moy, L. 2014. Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image. Journal of Environmental Management 144: 236-246.

Shafri, H.Z.M., Taherzadeh, E., Mansor, S. & Ashurov, R. 2012. Hyperspectral remote sensing of urban areas: an overview of techniques and applications. Research Journal of Applied Sciences, Engineering and Technology 4: 1557-1565.

Shahidan, M.F., Shariff, M.K.M., Jones, P., Salleh, E. & Abdullah, A.M. 2010. A comparison of Mesua ferrea L. and Hura crepitans L. for shade creation and radiation modification in improving thermal comfort. Landscape and Urban Planning 97: 168-181.

Shouse, M., Liang, L. & Fei, S. 2013. Identification of understory invasive exotic plants with remote sensing in urban forests. International Journal of Applied Earth Observation and Geoinformation 21: 525-534.

Sugumaran, R., Pavuluri, M.K. & Zerr, D. 2003. The use of high resolution imagery for identification of urban climax forest species using traditional and rule-based classification approach. IEEE Transactions on Geoscience and Remote Sensing 41(9): 1933-1939.

Tigges, J., Lakes, T. & Hostert, P. 2013. Urban vegetation classification: benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment 136: 66-75.

Voss, M. & Sugumaran, R. 2008. Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors 8: 3020-3036.

Wania, A. & Weber, C. 2007. Hyperspectral imagery and urban green observation. Urban Remote Sens Event (JURSE), Paris. pp. 1-8.

Youjing, Z. & Hengtong, R. 2007. Identification scales for urban vegetation classification using high spatial resolution satellite data. In IEEE International Geoscience and Remote Sensing Symposium, (IGARSS), Barcelona, Spain. pp. 1472-1475.

Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M. & Schirokauer, D. 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing 72: 799-811.

Yuan, F. & Bauer, M.E. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment 106: 375-386.

Zhang, C. & Qiu, F. 2012. Mapping individual tree species in an urban forest using airborne LiDAR data and hyperspectral imagery. Photogramm Eng Remote Sens. 78: 1079-1087.

Zhou, W. 2013. An object-based approach for urban land cover classification: Integrating LiDAR height and intensity data. IEEE Geoscience and Remote Sensing Letters 10(4): 928-931.

 

 

*Corresponding author; email: hzms04@gmail.com

 

 

 

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