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
|