Please enter verification code
Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise
American Journal of Remote Sensing
Volume 9, Issue 1, June 2021, Pages: 1-15
Received: Dec. 14, 2020; Accepted: Dec. 25, 2020; Published: Jan. 4, 2021
Views 17      Downloads 19
Alfred Homere Ngandam Mfondoum, StatsN'Maps, Private Consulting Firm, Dallas, USA; Laboratory of Natural Resources Management, Department of Geography, University of Yaounde I, Yaounde, Cameroon
Paul Gerard Gbetkom, Department of Geography, University of Aix-Marseille, Marseille, France
Sofia Hakdaoui, Earth Observation Department, Geo-Biodiversity and Natural Patrimony Laboratory, Geophysics, Natural Patrimony and Green Chemistry Research Centre, Scientific Institute, Mohamed V University, Rabat, Morocco
Ryan Cooper, Erik Jonsson School of Engineering and Computer Science, University of Texas in Dallas, Richardson, Texas, USA
Armel Fabrice Mvogo Moto, Laboratory of Natural Resources Management, Department of Geography, University of Yaounde I, Yaounde, Cameroon; I Love Geomatics Association, Yaoundé, Cameroon
Brian Njumeneh, I Love Geomatics Association, Yaoundé, Cameroon
Article Tools
Follow on us
Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.
Brightness-shadowing, Built-up, Multi-Layers Perceptron, Linear Regression, Noise, Unplanned Cities
To cite this article
Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, Brian Njumeneh, Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise, American Journal of Remote Sensing. Vol. 9, No. 1, 2021, pp. 1-15. doi: 10.11648/j.ajrs.20210901.11
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Esch, T., Heldens, W., Hirner, H., Keil, M., Marconcini, M., Roth, A., Zeidler, J., Dech, S., and Strano, E., Breaking new ground in mapping human settlements from space - The Global Urban Footprint. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 134, 30-42.
Karlsson, A. Classification of high-resolution satellite images. 2003.
Lu, D. et Weng, Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 2007, 28, 823-870.
Masek, J. G., Lindsay, F. E. and Goward, S. N., Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observation. International Journal of Remote Sensing, 2000, 21, 3473-3486.
Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P. and Zhang, S., Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors, 2018, 18, 1-21.
Kawamura, M., Jayamanna, S. and Tsujiko, Y., Relation between social and environmental conditions in Colombo Sri Lanka and urban index estimated by satellite remote sensing data. The International Archives of Photogrammetry and Remote Sensing, 1996, 31, 321-326.
Zha, Y., Gao, J. and Ni, S., Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583-594 (2003).
Jieli, C., Manchun, L., Liu, Y., Chenglei, S. and Wei, H., Extract residential areas automatically by New Built-up Index. IEEE, 2010.
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, 2012, 4, 2957-2970.
Hanqiu, X., Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI). Photogrammetry. Engineering and Remote Sensing, 2010, 76, 557-565.
Sun, Z., Wang, C., Guo, W and Shang, R. A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery. Remote Sensing, 2017, 9, (942).
Chen, J., Yang, K., Chen, S., Yang, C., Zhang, S. and He, L., Enhanced normalized difference index for impervious surface area estimation at the plateau basin scale. Journal of Applied Remote Sensing, 2019, 13.
Xu, H. A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 2008, 29, 4269-4276.
Sinha, P., Verma, N. K. and Ayele, E., Urban built-up area extraction and change detection of Adama municipal area using time-series Landsat images. International Journal of Advanced Remote Sensing and GIS, 2016, 5, 1886-1895.
Ukhnaa, M., Huo, X. and Gaudel, G., Modification of urban built-u area extraction method based on thematic index-derived bands. Earth and Environmental Science, 2019, 227.
Kaimaris, D. and Patias, P. Identification and area measurement of the built-up area with the Built-up Index (BUI). International Journal of Advanced Remote Sensing and GIS, 2016, 6, 1844-1858.
Razul, A., Baltzter, H., Ibrahim, G. R. F., Hameed, H. M., Wheeler, J., Adamu, B., Ibrahim, S. and Najmaddin, P. M. Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land, 2018, 7.
Nichol, J. E., High-resolution surface temperature patterns related to urban morphology in a tropical city: a satellite-based study. Journal of Applied Meteorology, 1996, 35, 135-146.
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, 2010, 1, 213-221.
Streutker, D. R. A remote sensing study of the urban heat island of Houston, Texas. International Journal Remote Sensing, 2002, 23.
Mohamed, A. A., Odindi, J. and Mutanga, O., Land surface temperature and emissivity estimation for Urban Heat Island assessment using medium- and low-resolution space-borne sensors: A review. Geocarto International, 2017, 32, 455-470.
Li, E., Du, P., Samat, A., Xia, J. and Che, M., An automatic approach for urban land-cover classification from Landsat-8 OLI data. International Journal Remote Sensing, 2015, 36, 5983-6007.
Slonecker, E. T., Jennings, D. B. and Garofalo, D., Remote sensing of impervious surfaces: a review. Remote Sensing Reviews, 2001, 20, 227-255.
Gbetkom, P. G., Gadal, S., El Aboudi, A., Ngandam Mfondoum, A. H., et al. Mapping of built-up areas in Cameroonians shores of lake Chad and its hinterland through based object classification of Sentinel 2 images. Proceedings of GEOBIA conference, ″From pixels to ecosystems and global sustainability″, Montpellier, France, 18-22 June, 2018.
Shukla, J. and Mintz, Y. Influence of land-surface evapotranspiration on the earth’s climate. Science, 1981, 215, 1498–150.
Brubaker, K. L. and Entekhabi, D. Analysis of feedback mechanisms in land-atmosphere interaction. Water Resources Research, 1996, 32, 1343–1357.
Mustafa, E. K., Liu, G., Abd El-Hamid, H. and Kaloop, M. R., Simulation of land use dynamics and impact on land surface temperature using satellite data. Geojournal, 2019.
Ahmed, B., Kamruzzaman, Md., Zhu, X., Rahman, S. Md and Choi, K., Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sensing, 2016, 5, 5969–5998.
Bernales, A. M. J., Antolihao, J. A., Samonte, C., Campomanes, F. P., Rojas, R. J., Dela Serna, A. M. and Silapan, J., Modelling the relationship between land surface temperature and landscape patterns of land use land cover classification using multi linear regression models. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B8.
Rikimaru, P. S. Roy and S. Miyatake., Tropical forest cover density mapping. Tropical Ecology, 2002, 43, 39–47.
Deng, Y., Wu, C., Li, M. and Chen, R., RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation, 2015, 39, 40-48.
Ngandam Mfondoum, A. H., Gbetkom, P. G., Cooper, R., Hakdaoui, S. and Wokwenmendam Nguet, P., Assessing the spatial metabolism of a mid-urban/mid-rural city: the relationship built-up, vegetation, bare soil and urban heat in Foumban (west-Cameroon, Central Africa) – periods 1987-2003-2019. Proceedings of the 3rd International Land Use Symposium, ILUS, ″Land use changes: trends and projections″, Paris, France, 4-6 December, 10.13140/RG.2.2.13421.33766, 2019.
Ngandam Mfondoum, A. H., Gbetkom, P. G., Cooper, R., Hakdaoui, S. and Mansour Badamassi, M. B., Extraction of Built-up features in complex biophysical environments by using a Landsat bands ratio. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, XLIV-M-2-2020, 79-85.
Congalton, R. G. and Green, K. Assessing the Accuracy of Remotely Sensed Data-Principles and Practices. 2nd ed. CRC Press: Boca Raton, FL, USA, p. 183, 2009.
Schowengerdt, R. A. Remote Sensing, Models, and Methods for Image Processing. Academic Press, p. 522, 1997.
Zhao, H., Chen, X. and Tian, L., A new method to retrieve topographic shadow based on multi-spectral operation. IEEE, 2006, 2750-2753.
Chavez, P. S. Image-based atmospheric corrections–revisited and improved. Photogrammetry Engineering and Remote Sensing. 1996, 9, 1025–1036.
Barsi, J. A., Schott, J. R., Hook, S. J. Raqueno, N. G., Markham, B. L. and Radocinski, R. G., Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 2014, 6, 11607–11626.
United States Geological Survey. Landsat 8 (L8) data users handbook version 5.0. P. 106, 2019.
Barsi, J. A., Lee, K., Kvaran, G., Markham, B. L. and Pedelty, J. A., The Spectral Response of the Landsat-8 Operational Land Imager. Remote Sensing, 2014, 6, 10232-10251.
Lu, D. and Weng, Q. Use of impervious surface in urban land-use classification. Remote Sensing of the Environment, 2006, 102, 146-160.
Wu, C. and Murray, T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of the Environment, 2003, 84, 493-505.
Lu, D., Moran, E. and Hetrick, S. Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 298-306.
Moayedi, H.; Rezaei, A. An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Computing and Applications, 2019, 31, 327–336.
Moayedi, H.; Hayati, S. Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile. International Journal Geomechanics, 2018, 18.
Rosenblatt, F., Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington, DC, 1961.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186