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Urban Biophysical Quality Modelling Based On Remote Sensing Data In Semarang, Indonesia

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Cities are centres of economic growth with fascinating dynamics, including persistent urbanisation that encroaches adjacent arable lands to build urban physical features and sustain services offered by urban ecosystems. Even though industrial revolution, economic dynamics, and environmental changes affect spatial feasibility for housing, complex urban growth is always followed by the development of environmentally friendly cities. However, with such quality having multiple facets, it is necessary to assess and map liveable areas from a more comprehensive and objective perspective. This study aimed to assess, map and identify the biophysical quality of an urban environment using a straightforward technique that allows rapid assessment for early detection of changes in the quality. It proposed a multi-index approach termed the urban biophysical environmental quality (UBEQ) based on spectral characteristic of remote sensing data for residential areas calculated using various data derived from remote sensing. Statistical analyses were performed to test data reliability and normality. Further, many indices were analysed, then employed as indicators in UBEQ modelling and tested with sensitivity and factor analysis to obtain the best remote sensing index in the study area. Based on PCA Results, it was found that the built-up land index and vegetation index mainly contributed to the UBEQ index. The generated model had 86.5% accuracy. Also, the study area, Semarang City, had varying UBEQ index values, from high to low levels.

About the Authors

Iswari Nur Hidayati
Universitas Gadjah Mada
Russian Federation

Department of Geographic Information Science, Faculty of Geography

Sekip Utara, Bulaksumur, Yogyakarta, Indonesia, 55281

Karunia Pasya Kusumawardani
Universitas Gadjah Mada
Russian Federation

Department of Geographic Information Science, Faculty of Geography

Sekip Utara, Bulaksumur, Yogyakarta, Indonesia, 55281

A. G. Ayudyanti
Universitas Gadjah Mada
Russian Federation

Department of Geographic Information Science, Faculty of Geography

Sekip Utara, Bulaksumur, Yogyakarta, Indonesia, 55281

R. R. Prabaswara
Universitas Gadjah Mada
Russian Federation

Department of Geographic Information Science, Faculty of Geography

Sekip Utara, Bulaksumur, Yogyakarta, Indonesia, 55281


1. Adiana R.S. & Pigawati B. (2015). Kajian Perkembangan Kecamatan Mijen Sebagai Dampak Pembangunan Bukit Semarang Baru (Bsb City). Teknik PWK (Perencanaan Wilayah Kota), 4(1), 66-77.

2. Aditya R.B., Ulul M., Ningam L., Program R.P. & Mada U.G. (2021). Assessing City Greenness using Tree Canopy Cover: The Case of Yogyakarta, Indonesia. Geography, Environment, Sustainability, 14(1), 71-80.

3. Campbell J. & Wynne, R. (2011). Introduction to Remote Sensing (5th Ed) (5th ed.). New York: The Guilford Press.

4. Chander G., Markham B.L. & Helder D.L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893-903, DOI: 10.1016/j.rse.2009.01.007.

5. Charreire H., Weber C., Chaix B., Salze P.,Casey R., Banos A. ... Oppert J.M. (2012). Identifying built environmental patterns using cluster analysis and GIS: Relationships with walking, cycling and body mass index in French adults. International Journal of Behavioral Nutrition and Physical Activity, 9, 1-11, DOI: 10.1186/1479-5868-9-59.

6. Cuchiara G.C., RappenglQck B., Rubio M.A., Lissi E., Gramsch E. & Garreaud R.D. (2017). Modeling study of biomass burning plumes and their impact on urban air quality; a case study of Santiago de Chile. Atmospheric Environment, 166, 79-91, DOI: 10.1016/j.atmosenv.2017.07.002.

7. Danoedoro P. (2012). Pengantar Pengolahan Citra Digital. Yogyakarta: Andi Offset.

8. Fu B., Yu D. & Zhang Y. (2019). The livable urban landscape: GIS and remote sensing extracted land use assessment for urban livability in Changchun Proper, China. Land Use Policy, 87(February), DOI: 10.1016/j.landusepol.2019.104048.

9. Gultom L.H. & Sunarti (2017). Pengaruh Penataan Permukiman Kumuh Untuk Mencapai Livable Settlement Di Kelurahan Tambakrejo Kota Semarang. Jurnal Pengembangan Kota, 5(2), 140-148, DOI: 10.14710/jpk.5.2.140-148.

10. Guo G., Wu Z., Xiao R., Chen Y., Liu X. & Zhang X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135, 1-10, DOI: 10.1016/j.landurbplan.2014.11.007.

11. Haidir H. & Rudiarto I. (2019). Lahan Potensial Permukiman Di Kota Semarang. Tataloka, 21(4), 575, DOI: 10.14710/tataloka.21.4.575-588.

12. Hidayati I.N. (2019). Development of urban biophysical environmental quality yogyakarta urban area based on image spectral characteristic and multiresolution data. Dissertation. Gadjah Mada University.

13. Hidayati I.N., Suharyadi & Danoedoro P. (2019a). A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI. Forum Geografi Indonesian Journal of Spatial and Regional Analysis, 33(2).

14. Hidayati I.N., Suharyadi R. & Danoedoro P. (2019b). Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery. KnE Social Sciences, 2019, 363-379, DOI: 10.18502/kss.v3i21.4981.

15. Huete A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(March 2014), 295-309, DOI: 10.1016/0034-4257(88)90106-X.

16. Li D., Zhao X. & Li X. (2016). Remote sensing of human beings - a perspective from nighttime light. Geo-Spatial Information Science, 19(1), 69-79, DOI: 10.1080/10095020.2016.1159389.

17. Liang B. & Weng Q. (2011). Assessing Urban Environmental Quality Change of Indianapolis, United States, by the Remote Sensing and GIS Integration. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 43-55, DOI: 10.1109/JSTARS.2010.2060316.

18. Mao Q., Yuan Y. & Shuai Y. (2014). Effects of atmospheric aerosol on the direct normal irradiance on the earth's surface. International Journal of Hydrogen Energy, 39(12), 6364-6370, DOI: 10.1016/j.ijhydene.2014.02.053.

19. Morisson. (2012). Metode Penelitian Survey. Jakarta: Kencana Prenada Media Group.

20. Muladica N., Murtini T. W., & Suprapti A. (2018). Transformation of Settlement caused by Housing Development in Suburbs of Semarang. Jurnal Teknik Sipil Dan Perencanaan, 20(2), 71-80, DOI: 10.15294/jtsp.v20i2.15171.

21. Pan Z., Wang G., Hu Y & Cao B. (2019). Characterizing urban redevelopment process by quantifying thermal dynamic and landscape analysis. Habitat International, 86(483), 61-70, DOI: 10.1016/j.habitatint.2019.03.004.

22. Psaltoglou A. & Calle E. (2018). Enhanced connectivity index - A new measure for identifying critical points in urban public transportation networks. International Journal of Critical Infrastructure Protection, DOI: 10.1016/j.ijcip.2018.02.003.

23. Rezvani M.R., Mansourian H. & Sattari M.H. (2013). Evaluating Quality of Life in Urban Areas (Case Study: Noorabad City, Iran). Social Indicators Research, 112(1), 203-220, DOI: 10.1007/s11205-012-0048-2.

24. Silva L.T. & Mendes J.F.G. (2012). City Noise-Air: An environmental quality index for cities. Sustainable Cities and Society, 4, 1-11, DOI: 10.1016/j.scs.2012.03.001.

25. Soemarwoto O. (1983). Ekologi Lingkungan dan Pembangunan. Jakarta: Djambatan Publisher.

26. Stossel Z., Kissinger M. & Meir A. (2017). Modeling the Contribution of Existing and Potential Measures to Urban Sustainability Using the Urban Biophysical Sustainability Index (UBSI). Ecological Economics, 139, 1-8, DOI: 10.1016/j.ecolecon.2017.03.039.

27. Weber N., Haase D. & Franck U. (2014). Science of the Total Environment Zooming into temperature conditions in the city of Leipzig : How do urban built and green structures in fl uence earth surface temperatures in the city ? Science of the Total Environment, The, 496, 289298, DOI: 10.1016/j.scitotenv.2014.06.144.

28. Xiao X.D., Dong L., Yan H., Yang N. & Xiong Y. (2018). The influence of the spatial characteristics of urban green space on the urban heat island effect in Suzhou Industrial Park. Sustainable Cities and Society, 40(April 2017), 428-439, DOI: 10.1016/j.scs.2018.04.002

29. Xu H., Wang X. & Xiao G. (2000). A remote sensing and gis integrated study on urbanization with its impact on arable lands : fuqing city, fujian province , CHINA, 314, 301-314.

30. 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(3), 375-386, DOI: 10.1016/j.rse.2006.09.003.

31. Zha Y, Gao J. & Ni S. (2003). Use of normalized di ff erence built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594.


For citations:

Hidayati I., Kusumawardani K., Ayudyanti A.G., Prabaswara R.R. Urban Biophysical Quality Modelling Based On Remote Sensing Data In Semarang, Indonesia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2021;14(3):14-23.

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ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)