Pemanfaatan Data Landsat Multitemporal Untuk Pemetaan Pola Ekspansi Perkotaan Secara Spasiotemporal (Studi Kasus Pada Tiga Perkotaan Metropolitan Di Pulau Jawa)

https://doi.org/10.22146/jntt.39091

Like Indrawati(1*), Ari Cahyono(2)

(1) Program Studi Diploma 3 Penginderaan Jauh dan SIG, Departemen Teknologi Kebumian, Sekolah Vokasi, UGM
(2) Departemen Sains Informasi Geografi, Fakultas Geografi, UGM
(*) Corresponding Author

Abstract


Utilization of multitemporal remote sensing data among others can be used todetermine thepattern of changes in urban expansion. One of the most important types of cities in urban systems isthe metropolitan urban area that covers several districts and cities. This is because the regiongenerally acts as the capital of the country, the provincial capital, and the center of economicactivities that are national or strategic. Understanding urban expansion at different metropolitanurban levels is important for expanding knowledge in times of urban growth and its impact on theenvironment. Aims in this study are: (1) utilization of multitemporal Landsat data for mapping urbanexpansion patterns, (2) knowing the effectiveness of object-based classification for mapping of urbansettlements and (3) spatiotemporal urban expansion pattern analysis in three metropolitan cities onJava Island.. In this study focused on three metropolitan urban in Java, namely DKI. Jakarta,Surabaya and Semarang. This study utilizing Landsat TM, ETM + and OLI image data to map urbansettlement land cover using object-based classification with Random Forest algorithm. Next,quantifying the typology of urban expansion and compare the spatiotemporal pattern of urbanexpansion during 2005-2015 on the results of land cover mapping. This research has found that (1)object-based classification with Random Forest algorithm is quite effective in terms of time of work tomap urban settlement cover on Landsat digital data having medium spatial resolution; (2) the threeurban metropolia is experiencing rapid and massive development and has a very variedspatiotemporal pattern; (3) Size of the city affect the pattern of urban expansion, followed by rapidexpansion of the region. Larger city size with relatively rapid expansion is more likely to experiencethe edge extension model, while smaller cities tend to develop with outlying models.

Keywords


Spatiotemporal pattern; urban expansion; urban expansion typhology quantification; object based image analysis

Full Text:

PDF


References

A.A. Belal,, F.S. Moghanm, (2011) Detecting urban growth using remote sensing and GIS techniquesin Al Gharbiya governorate, Egypt. The Egyptian Journal of Remote Sensing and SpaceScience. 14: 73-79

Angelica I. STAN, (2013). Morphological Patterns of Urban Sprawl Territories. Urbanism.Arhitectură. Construcţii • Vol. 4 • Nr. 4

Breiman, L., (2001). Random forest. Mach. Learning 45: 5–32

Cutler, David Richard, Thomas C. Edward, Karen, H Beard, Joshua J. Lawler, (2007). Random Forestfor Classification in Ecology, Ecology 88(11): 2783-92

Díaz-Uriarte and Sara Alvarez de Andrés (2006). Gene selection and classification of microarraydata using random forest. BMC Bioinformatics 7/3: 1-13

Duro, D.C., Franklin, S.E., Dube, M.G.,(2012). A comparison of pixel-based and object-based imageanalysis with selected machine learning algorithms for the classification of agriculturallandscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 118: 259–272.

Forman, Richard T., (1995), Land Mosaics: the ecology of landscapes and regions: Land Mosaics: theecology of landscapes and regions. Cambridge University Press, Cambridge.

Genuera, Robin, Jean-Michel Poggi, Christine Tuleau-Malotc. (2010). Variable selection usingRandom Forests. Pattern Recognition Letters 31: 142225-2236

Guo, L., Chehata, N., Mallet, C., Boukir, S., (2011). Relevance of airborne lidar and multispectralimage data for urban scene classification using Random Forests. ISPRS International Journal ofPhotogrammetry and Remote Sensing 66: 56–66

Hoffhine Wilson, E., Hurd, J.D., Civco, D.L., Prisloe, M.P., Arnold, C., 2003. Development of ageospatial model to quantify, describe and map urban growth. Remote Sens. Environ 86 (3):275–285

Huang, J., Lu, X., Sellers, J., 2007. A global comaparative analysis of urban foirm; applying spatialmetrics and remote sensing . Landscape Urban Plan 82(4) 184-197

Janthy Trilusianthy Hidajat, Santun R.P Sitorus, Ernan Rustiadi dan Machfud (2013) DinamikaPertumbuhan dan Status Keberlanjutan Kawasan Permukiman di Pinggiran Kota WilayahMetropolitan Jakarta. Globe Volume 15 No. 1Juni 2013 : 93 - 100

Karen C. Seto, Michail Fragkias, Burak Güneralp, Michael K. Reilly. (2011). A Meta-Analysis ofGlobal Urban Land Expansion. PLoS ONE, 6, e23777. [online]http://dx.doi.org/10.1371/journal.pone.0023777 (Published: August 18, 2011) (diakses : 9 Maret2017)

Lambin, E.F., 1996. Change detection at multiple scales: seasonal and annual variations in landscapevariables. Photogrammetric Engineering and Remote Sensing, 62:931-938

Lei Ma, Tengyu Fu , Thomas Blaschke , Manchun Li, Dirk Tiede , Zhenjin Zhou , Xiaoxue Ma andDeliang Chen, (2017), Evaluation of Feature Selection Methods for Object-Based Land CoverMapping of Unmanned Aerial Vehicle Imagery, ISPRS Int. J. Geo-Inf., 6: 51;doi:10.3390/ijgi6020051 www.mdpi.com/journal/ijgi

Lichtenegger, J., 1992. ERS-1: land use mapping and crop monitoring: a first close look to SAR data.Earth Observation Quarterly, (May-June):37-38.

Lo, C.P., 1981. Land use mapping of Hong Kong from landsat images: an evaluation. Int. J. Rem.Sens. 2 (3), 231–251

Luck M. and Wu J. 2002. A gradient analysis of urban landscape pattern: A case study from thePhoenix Metropolitan region of USA. Landscape Ecology 17: 327–329

Ma, L.; Cheng, L.; Li, M.; Liu, Y.; Ma, X. (2015) Training set size, scale, and features in geographicobject-based image analysis of very high resolution unmanned aerial vehicle imagery. ISPRSInternational Journal of Photogrammetry and Remote Sensing, 102: 14–27

Muchoney, D.M., Haack, B.N., 1994. Change detection for monitoring forest defoliation.Photogrammetric Engineering and Remote Sensing, 60:1243-1314

Mukherjee, S., 1987. Landuse maps for conservation of ecosystems. Geogr. Rev. Ind. 3, 23–28

Puissant, A.; Rougier, S.; Stumpf, A. (2014). Object-oriented mapping of urban trees using randomforest classifiers. Int. J. Appl. Earth Obs. Geoinf., 26: 235–245.

Quarmby, N.A., Cushine, J.L., 1989. Monitoring urban land cover changes at the urban fringe fromSPOT HRV imagery in South East England. Int. J. Rem. Sens. 10 (6), 231–251.

Sailer, C.T., Eason, E.L.E., Brickey, J.L., 1997. Operational multispectral information extraction: theDLPO image interpretation program. Photogrammetric Engineering and Remote Sensing,63:129-136

Schneider, A.; Mertes, C.M. (2014). Expansion and growth in Chinese cities, 1978–2010. Environmental Research Letters, 9: 69–75.

Sierra, P. Pons, X., Sauri., D., 2003. Post-classification change detection with data from differentsensors; some accuracy considerations. Int. J. Remote Sensing 24 (16), 3311-1140

Stumpf, A.; Kerle, N. (2011) Object-oriented mapping of landslides using random forests. RemoteSens. Environ., 115: 2564–2577

Tateishi, R., Kajiwara, K., 1991. Global Lands Cover Monitoring by NOAA NDVI Data. Proceedingof International Workshop of Environmental Monitoring from Space. Taejon, Korea, p.37-48.

Wenjia Wu, Shuqing Zhao, Chao Zhu, Jinliang Jiang. (2015). A comparative study of urban expansionin Beijing, Tianjin and Shijiazhuang over the past three decades. Landscape and UrbanPlanning 134 : 93–106

Wenjuan Yu, Weiqi Zhou, Yuguo Qian, Jingli Yan. (2016). A new approach for land coverclassification and change analysis: Integrating backdating and an object-based method. RemoteSensing of Environment 177: 37–47 [online] www.elsevier.com/locate/rse (diakses: 1 Maret2017)

Wenjuan Yu and Weiqi Zhou.(2017). The Spatiotemporal Pattern of Urban Expansion in China: AComparison Study of Three Urban Megaregions. Remote Sens. 9: 45

Wu, J.; Jenerette, G.D.; Buyantuyev, A.; Redman, C.L (2011) Quantifying spatiotemporal patterns ofurbanization: The case of the two fastest growing metropolitan regions in the United States.Ecol. Complex 8: 1–8.

Xian, G. Crane, M., 2005. Assesments of urban growth in the Tampa Bay watershed using remotesensing data. Remote Sensing Env. 97(2)203-215

Xu, C.; Liu, M.S.; Zhang, C.; An, S.Q.; Yu, W.; Chen, J.M. (2007). The spatiotemporal dynamics ofrapid urban growth in the Nanjing metropolitan region of china. Landsc. Ecol., 22: 925–937.

Van der Geer, J., Hanraads, J. A. J., & Lupton, R. A. (2010). The art of writing a scientific article.Journal of Scientific Communications, 163: 51 – 59.

BPS Kota Surabaya, Kota Surabaya Dalam Angka 2015

BPS Kota Semarang, Kota Semarang Dalam Angka 2016

BPS Statistik DKI. Jakarta. Statistik Daerah Provinsi DKI Jakarta 2015

Elena Besussi, Nancy Chin, Michael Batty, and Paul Longley (Chapter 2).T. Rashed and C. Jürgens(eds.), (2010). Remote Sensing of Urban and Suburban Areas, Remote Sensing and DigitalImage Processing, Springer Science+Business Media B.V

Forman, Richard T., (1995), Land Mosaics: the ecology of landscapes and regions: Land Mosaics: theecology of landscapes and regions. Cambridge University Press, Cambridge.

I Made Parsa dan Tatik Kartika (2015). Teknik Segmentasi dan Klasifikasi Berjenjang UntukPemetaan Lahan awah Menggunakan Citra SPOT-6 (Studi Kasus Kabupaten Maros, SulawesiSelatan). Pemmanfataan Citra Penginderaan Jauh untuk Sumberdaya Wilayah Darat. Bogor;CRESTPENT Press

Khila Dahar (2015). Urban growth forms or types. http://www.arcgis.com/home/item.html?id=325cb9c2b45a4e87b544f10ef78f51b0, diakses tanggal 13 Juli 2017

Indrawati, Like, Sudaryatno (2016). Pemetaan Berorientasi Objek Untuk Ekstraksi BangunandanVegetasi di Area Perkotaan Menggunakan Klasifikasi Random Forest Pada Citra Pleides 1-B. Prosiding Seminar Nasional Teknologi Terapan Tahun 2016. Sekolah Vokasi UGM.



DOI: https://doi.org/10.22146/jntt.39091

Article Metrics

Abstract views : 3561 | views : 4799

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Jurnal Nasional Teknologi Terapan (JNTT)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Jurnal Nasional Teknologi Terapan Indexed by:


  

Web
AnalyticsView My Stats