Abstract
Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlap** tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-017-0683-9/MediaObjects/12524_2017_683_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-017-0683-9/MediaObjects/12524_2017_683_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-017-0683-9/MediaObjects/12524_2017_683_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-017-0683-9/MediaObjects/12524_2017_683_Fig4_HTML.gif)
Similar content being viewed by others
References
Alig, R. J., & Healy, R. G. (1987). Urban and built-up land area changes of determinants. Land Economics, 63(3), 215–226.
Briggs, D. J., Gulliver, J., Fecht, D., & Vienneau, D. M. (2007). Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sensing of Environment, 108(4), 451–466.
Caflisch, R. E. (1998). Monte Carlo and quasi-Monte Carlo methods. Acta Numerica, 7, 1–49.
Cramer, J. S. (2003). The origins and development of the logit model. Logit Models from Economics and Other Fields, 2003, 1–19.
Data School. (2014). Simple guide to confusion matrix terminology. http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/. 12 April 2017.
Dobson, J. E., et al. (2016). LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 66(7), 849–857. http://cat.inist.fr/?aModele=afficheN&cpsidt=1420537. 30 Nov 2016.
Fisher, P. F., & Langford, M. (1996). Modeling sensitivity to accuracy in classified imagery: A study of areal interpolation by dasymetric map**. Professional Geographer, 48(3), 299–309. http://hdl.handle.net/2381/19665.
Flowerdew, R., & Green, M. (1989). Statistical methods for inference between incompatible zonal systems. London: Taylor & Francis.
Holt, J. B., Lo, C. P., & Hodler, T. W. (2004). Dasymetric estimation of population density and areal interpolation of census data. Cartography and Geographic Information Science, 31(2), 103–121.
Jia, P., Qiu, Y., & Gaughan, A. E. (2014). A fine-scale spatial population distribution on the high-resolution gridded population surface and application in Alachua County, Florida. Applied Geography, 50, 99–107. doi:10.1016/j.apgeog.2014.02.009.
Kim, H., & Choi, J. (2011). A hybrid dasymetric map** for population density surface using remote sensing data. Journal of the Korean Geographical Society, 46(1), 67–80. http://www.kgeography.or.kr/homepage/kgeography/www/old/publishing/journal/46/01/07.PDF.
Langford, M. (2003). Refining methods for dasymetric map** using satellite remote sensing. In V. Mesev (Ed.), Remotely sensed cities. London: Taylor & Francis.
Langford, M., & Harvey, J. T. (2001). The use of remotely sensed data for spatial disaggregation of published census population counts. In IEEE/ISPRS joint workshop on remote sensing and data fusion over urban areas (cat. no. 01EX482) (Vol. 61, pp. 260–264).
Langford, M., Higgs, G., Radcliffe, J., & White, S. (2008). Urban population distribution models and service accessibility estimation. Computers, Environment and Urban Systems, 32, 66–80.
Langford, M., Maguire, D. J., & Unwin, D. J. (1991). The areal interpolation problem: Estimating population using remote sensing in a GIS framework. In I. Masser & M. Blakemore (Eds.), Handling geographical information. New York: Longman Scientific & Technical Location.
Langford, M., & Unwin, D. J. (1994). Generating and map** population density surfaces within a geographical information system. The Cartographic Journal, 31, 21–26.
Latimer, A. M., Shanshan, W., Gelfand, A. E., & Silander, J. A. (2006). Building statistical models to analyze species distributions. Ecological Applications, 16(1), 33–50.
Lung, T., Lübker, T., Ngochoch, J. K., & Schaab, G. (2013). Human population distribution modelling at regional level using very high resolution satellite imagery. Applied Geography, 41, 36–45. doi:10.1016/j.apgeog.2013.03.002.
Mennis, J. (2003). Generating surface models of population using dasymetric map**. The Professional Geographer, 55(August 2002), 31–42.
Miller, T. W. (2013). Modeling techniques in predictive analytics: Business problems and solutions with R. 1st ed. New Jersey: Pearson Education, Inc.
Nagle, N. N., Buttenfield, B. P., Leyk, S., & Speilman, S. (2014). Dasymetric modeling and uncertainty. Annals of the Association of American Geographers, 104(1), 80–95.
Petrov, A. (2012). One hundred years of dasymetric map**: Back to the origin. The Cartographic Journal, 49(3), 256–264.
Powers, D. M. W. (2007). Evaluation: From precision, recall and F-factor to ROC, informedness, markedness and correlation. Adelaide, South Australia: School of Informatics and Engineering, Flinders University of South Australia.
Reibel, M., & Agrawal, A. (2007). Areal interpolation of population counts using pre-classified land cover data. Population Research and Policy Review, 26(5–6), 619–633.
Reibel, M., & Bufalino, M. E. (2005). Street-weighted interpolation techniques for demographic count estimation in incompatible zone systems. Environment and Planning A, 37(1), 127–139.
Salim, A. M., Nadarajan, K. D., & Mutunayagam, N. B. (1967). Towards a settlement structure for Kerala, part II. http://digitalcommons.unl.edu/arch_crp_facultyschol/12/?utm_source=digitalcommons.unl.edu%2Farch_crp_facultyschol%2F12&utm_medium=PDF&utm_campaign=PDFCoverPages.
Tian, Y., Yue, T., Zhu, L., & Clinton, N. (2005). Modeling population density using land cover data. Ecological Modelling, 189(1–2), 72–88.
Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural–urban land conversions. International Journal of Geographical Information Science, 16(8), 795–818.
Wu, S.-S., Qiu, X., & Wang, L. (2005). Population estimation methods in GIS and remote sensing: A review. GIScience and Remote Sensing, 42(1), 80–96. http://bellwether.metapress.com/openurl.asp?genre=article&id=doi:10.2747/1548-1603.42.1.80%5Cnhttp://www.researchgate.net/profile/Shuo_Sheng_Wu/publication/245440370_Population_Estimation_Methods_in_GIS_and_Remote_Sensing_A_Review/links/54aeaf540cf29661a3.
Yasodharan, E. P. (2007). State of environment report: Kerala 2007 volume 1. Land environment, wetlands of Kerala and environmental health. In E. P. Yasodharan, K. Kokkal, & P. Harinarayanan (Eds.), (1st ed.). Thiruvananthapuram: KSCSTE, Government of Kerala.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Puthuvayi, B., Anilkumar, P.P. A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover. J Indian Soc Remote Sens 46, 145–155 (2018). https://doi.org/10.1007/s12524-017-0683-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-017-0683-9