1. Congalton, R.G., “A review of assessing the accuracy of classifications of remotely sensed data.” Remote Sensing of Environment, Vol. 37, No. 1, (1991), 35–46.
2.Weng, Q., “Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic 652 M. Ichsan Ali et al. / IJE TRANSACTIONS B: Applications Vol. 32, No. 5, (May 2019) 647-653modelling.” Journal of Environmental Management, Vol. 64, No. 3, (2002), 273–284.
3.Millette, T.L., Tuladhar, A.R., Kasperson, R.E., and Turner II, B.L., “The use and limits of remote sensing for analyzing environmental and social change in the Himalayan Middle Mountains of Nepal.” Global Environmental Change, Vol. 5, No. 4, (1995), 367–380.
4.Masek, J.G., Lindsay, F.E., and Goward, S.N., “Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations.” International Journal of Remote Sensing, Vol. 21, No. 18, (2000), 3473–3486
5.Maru, R., Baharuddin, I.I., Zhiddiq, S., Arfan, A., and Bayudin, B., “Trend Analysis of Urban Heat Island Phenomenon in the City of Makassar, South Sulawesi, Indonesia using Landsat.” Asian Journal of Applied Sciences, Vol. 3, No. 5, (2015), 477–484.
6.Barnes, K.B., Morgan III, J.M., Roberge, M.C., and Lowe, S., “Sprawl development: its patterns, consequences, and measurement.” Towson University, Towson, (2001), 1–24.
7.Weng, Q., “Remote sensing of impervious surfaces.” Boca Raton, Florida, USA: CRC Press, Taylor & Francis Group, 2007.
8.Xu, H., “A new index for delineating builtāup land features in satellite imagery.” International Journal of Remote Sensing, Vol. 29, No. 14, (2008), 4269–4276.
9.Chen, X.L., Zhao, H.M., Li, P.X., and Yin, Z.Y., “Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes.” Remote Sensing of Environment, Vol. 104, No. 2, (2006), 133–146.
10.Zha, Y., Gao, J., and Ni, S., “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery.” International Journal of Remote Sensing, Vol. 24, No. 3, (2003), 583–594.
11.Zhao, H., and Chen, X., “Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+.” In Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE International IEEE, 2005, 1666–1668
12.Rikimaru, A., and Miyatake, S., “Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow.” In Presented paper for the 18th ACRS Kuala Lumpur, Malaysia, 1997.
13.As-syakur, A.R., Adnyana, I.W.S., Arthana, I.W., and Nuarsa, I.W., “Enhanced built-UP and bareness index (EBBI) for mapping built-up and bare land in an urban area.” Remote Sensing, Vol. 4, No. 10, (2012), 2957–2970.
14.He, C., Shi, P., Xie, D., and Zhao, Y., “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach.” Remote Sensing Letters, Vol. 1, No. 4, (2010), 213–221.
15.Wicaksono, P., Danoedoro, P., Hartonom., and Nehren, U., “Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing.” International Journal of Remote Sensing, Vol. 37, No. 1, (2016), 26–52.
16.Irons, J.R.; Dwyer, J.L.; and Barsi, J.A., “The next Landsat satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment, (2012), 1–11.
17.Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., and Zhang, X., “Impacts of urban biophysical composition on land surface temperature in urban heat island clusters.” Landscape and Urban Planning, Vol. 135, (2015), 1–10.
18.Kawamura, M., Jayamana, S., and Tsujiko, Y., “Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data.” International Archives of the Photogrammetry, Remote Sensing, Vol. 31, (1996), 321–326.
19.U.S. Geological Survey, “Product Guide: Landsat Surface Reflectance-Derived Spectral Indices.” Reston, Virginia: Department of the Interior, 2017.
20.Lillesand, T.M., Kiefer, R.W., and Chipman, J.W., “Remote sensing and image interpretation.” John Wiley & Sons Inc.: New York, (2004).
21.Yüksel, A.; Akay, A.E.; and Gundogan, R., “Using ASTER imagery in land use/cover classification of eastern Mediterranean landscapes according to Corine land cover project.”Sensors, Vol. 8, No. 2, (2008), 1237–1251.
22.Tran, T.D.-B., Puissant, A.; Badariotti, D., and Weber, C., “Optimizing spatial resolution of imagery for urban form detection: the cases of France and Vietnam.” Remote Sensing, Vol. 3, No. 10, (2011), 2128–2147.
23.Soegaard, H., and Møller-Jensen, L., “Towards a spatial CO2 budget of a metropolitan region based on textural image classification and flux measurements.” Remote Sensing of Environment, Vol. 87, No. 2–3, (2003), 283–294.
24.Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., and Ferreira, L.G., “Overview of the radiometric and biophysical performance of the MODIS vegetation indices.” Remote Sensing of Environment, Vol. 83, No. 1–2, (2002), 195–213
25.Weng, Q., Lu, D., and Schubring, J., “Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies.” Remote sensing of Environment, Vol. 89, No. 4, (2004), 467–483.