Total views : 588
A Modified and Enhanced Normalized built-up Index using Multispectral and Thermal Bands
Objective: Mapping impervious surfaces using moderate resolution satellite images is a useful technique for supporting different fields including monitoring and evaluation, planning, statistical analysis and reporting and policy development. Due to the negative impact of impervious surfaces over urban climate, the development of new techniques using spectral indices is a key parameter to extract built up areas with high accuracy. Method: In this study we propose a new concept capitalizing the existing relationships between urban heat island effects and built up areas to produce a modified index, benefiting from the high reflectivity of thermal bands, the mean-infrared and near-infrared band. In addition, spatial enhancement of multispectral data is also used to improve the accuracy of the proposed index, our approach uses spectral reduction of dimensions in order to produce thematic indices (as an input data) instead of continuous images. Finding: Final result showed a high accuracy of built up areas compared to other spectral indices like normalized difference built-up index - NDBI and index -based built-up index - IBI, more than 10% compared to classic NDBI and 6% compared to modified index (IBI).
Landsat OLI, PCA, Remote Sensing, Spatial Enhancement, Spectral Index, Urban Heat Island Effect.
- Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment. 2004; 93(4):480–92.
- Smith AJ. Subpixel estimates of impervious surface cover using Landsat TM imagery. [MS Scholarly Paper]. College Park: Department of Geography, University of Maryland; 2000.
- Zhao H, Xiaoling C. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Proceedings of 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS '05; 2005 Jul 25-29. p. 1666–8.
- Weng Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods and trends. Remote Sensing of Environment. 2012; 117:34–49.
- Ridd MK. Exploring a VIS (Vegetation-Impervious surface-Soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. International Journal of Remote Sensing. 1995; 16(12):2165–85.
- Quackenbush LJ. A review of techniques for extracting linear features from imagery. Photogrammetric Engineering and Remote Sensing. 2004; 70(12):1383–92.
- Richards J, Landgrebe D, Swain P. A means for utilizing ancillary information in multispectral classification. Remote Sensing of Environment. 1982; 12(6):463–77.
- Vogelmann J, Sohl T, Campbell P, Shaw D. Regional land cover characterization using Landsat Thematic mapper data and ancillary data sources. Environmental Monitoring and Assessment. 1998; 51(1-2):415–28.
- Wu C, Murray AT. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment. 2003; 84(4):493–505.
- Turner DP, Cohen WB, Kennedy RE, Fassnacht KS, Briggs JM. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment. 1999; 70(1):5268.
- Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing. 2003; 24(3):583–94.
- Rogers A, Kearney M. Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. International Journal of Remote Sensing. 2004; 25(12):2317–35.
- Chen J, Li M, Liu Y, Shen C, Wei W. Extract residential areas automatically by new built-up index. 18th International Conference on Geoinformatics; 2010 Jun 18-20.
- Zhang Q, Wang J, Peng X, Gong P, Shi P. Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. International Journal of Remote Sensing. 2002; 23(15):3057–78.
- Guindon B, Zhang Y, Dillabaugh C. Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sensing of Environment. 2004; 92(2):218–32.
- Al-Sharif AA, Pradhan B, Shafri HZM, Mansor S. Spatio-temporal analysis of urban and population growths in Tripoli using remotely sensed data and GIS. Indian Journal of Science and Technology. 2013; 6(8):5134–42.
- Atif I, Mahboob MA, Waheed A. Spatio-temporal mapping and multi-sector damage assessment of 2014 flood in Pakistan using Remote Sensing and GIS. Indian Journal of Science and Technology. 2016; 9(1).
- Xu H. Extraction of urban built-up land features from Landsat imagery using a thematic oriented index combination technique. Photogrammetric Engineering and Remote Sensing. 2007; 73(12):1381–91.
- Xu H. A new index for delineating built‐up land features in satellite imagery. International Journal of Remote Sensing. 2008; 29(14):4269–76.
- McFeeters S. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 1996; 17(7):1425–32.
- Wen CY, Chen JK. Multi-resolution image fusion technique and its application to forensic science. Forensic Science International. 2004; 140(2):217–32.
- Saraf A. IRS-1C-LISS-III and PAN data fusion: An approach to improve remote sensing based mapping techniques. International Journal of Remote Sensing. 1999; 20(10):1929–34.
- Lwin KK, Murayama Y. Evaluation of land cover classification based on multispectral versus pansharpened landsat ETM+ imagery. GIScience and Remote Sensing. 2013; 50(4):458–72.
- Pohl C, Van Genderen JL. Review article multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing. 1998; 19(5):823–54.
- Amolins K, Zhang Y, Dare P. Wavelet based image fusion techniques - An introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing. 2007; 62(4):249–63.
- Zhang Y. Problems in the fusion of commercial high-resolution satelitte as well as Landsat 7 images and initial solutions. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences. 2002; 34(4):587–92.
- Shah VP, Younan NH, King RL. An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Transactions on Geoscience and Remote Sensing. 2008; 46(5):1323–35.
- Tu TM, Su SC, Shyu HC, Huang PS. A new look at IHS-like image fusion methods. Information Fusion. 2001; 2(3):177–86.
- Jung HS, Park SW. Multi-sensor fusion of Landsat 8 Thermal Infrared (TIR) and Panchromatic (PAN) images. Sensors. 2014; 14(12):24425–40.
- Jawak SD, Luis AJ. A comprehensive evaluation of PAN-sharpening algorithms coupled with resampling methods for image synthesis of very high resolution remotely sensed satellite data. Advances in Remote Sensing. 2013; 2(4):332–44.
- Oke TR. The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society. 1982; 108(455):1-24.
- Zhangyan J, Yunhao C, Jing L. On urban heat island of Beijing based on Landsat TM data. Geo-Spatial Information Science. 2006; 9(4):293–7.
- Tran H, Uchihama D, Ochi S, Yasuoka Y. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation. 2006; 8(1):34–48.
- Azmi R, Saadane A, Kacimi I. Estimation of spatial distribution and temporal variability of land surface temperature over Casablanca and the surroundings of the city using different Landat satellite sensor type (TM, ETM+ and OLI). International Journal of Innovation and Applied Studies. 2015; 11(1):49–57.
- Zhang Y, Odeh IOA, Han C. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation. 2009; 11(4):256–64.
- Falahatkar S, Hosseini SM, Soffianian AR. The relationship between land cover changes and spatial-temporal dynamics of land surface temperature. Indian Journal of Science and Technology. 2011; 4(2):76–81.
- Kumar D, Shekhar S. Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicology and environmental safety. 2015; 121:39–44.
- Plan HCa. RGPH 2014. Haut commissariat au plan 2014.Available from: http://www.hcp.ma/downloads/RGPH-2014_t17441.html
- Chavez PS. Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing. 1996; 62(9):1025–35.
- Sobrino JA, Jimenez-Munoz JC, Paolini L. Land surface temperature retrieval from LandsaT TM 5. Remote Sensing of environment. 2004; 90(4):434–40.
- Johnson B. Effects of pansharpening on vegetation indices. ISPRS International Journal of Geo-Information. 2014; 3(2):507–22.
- Matsuoka M. Comparison of the spectral properties of pansharpened images generated AVNIR-2 and PRISM Onboard ALOS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2012; 7:291–6.
- Vrabel J. Multispectral imagery band sharpening study. Photogrammetric Engineering and Remote Sensing. 1996; 62(9):1075–84.
- Laben CA, Brower BV. Process for enhancing the spatial resolution of multispectral imagery using pansharpening. Google Patents; 2000.
- Siddiqui Y, The modified IHS method for fusing satellite imagery. ASPRS 2003 Annual Conference Proceedings; Anchorage, Alaska. 2003..
- Sun W, Chen B, Messinger DW. Nearest-neighbor diffusion-based pansharpening algorithm for spectral images. Optical Engineering. 2014; 53(1):013107.
- Hong'an W, Jianjun J, Jie Z. Dynamics of urban expansion in Xi'an City using Landsat TM/ETM+ data. Acta Geographica Sinica. 2005; 60(1):143–50.
- Otsu N. A threshold selection method from gray-level histograms. Automatica. 1975; 11(285-296):23–7.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.