BUILT-UP AREA DEMARCATION USING NDBI

Siva Subramanian M

Introduction

» Remote sensing images are useful for monitoring the spatial distribution and growth of urban built -up areas.

» Rates of urban population growth are higher than the overall growth in most countries.

» Urban areas are dominated by built-up lands with impervious surfaces.

» Timely availability of the data is of great importance for urban planners and decision makers. Fortunately, satellite remote sensing technology offers considerable promise to meet this requirement.

Literature review

» Masek et al. (2000) • Washington D.C. metropolitan area • NDVI-differencing approach• Overall accuracy of 85 percent.» Zhang et al. (2002) • change detection of Beijing, China.• Integrated a road density layer with spectral bands for

the post-classification • This greatly reduced spectral confusion and increased

accuracy of the change detection.

Literature review

» Xu (2002)• Fuqing City in southeastern China• Combination of signature analysis and supervised

classification.• Based on the analysis of spectral response differences

between built land and various non-built classes within multispectral bands

» Xian and Crane (2005) • The Tampa Bay watershed of Florida by using a regression

tree algorithm to map urban impervious surfaces and an unsupervised classification to reveal related land cover

• Accuracy greater than 85 percent.

Methods

» Normalized Differential Built-up Index (NDBI)

• The development of the index was based on the unique spectral response of built-up lands that have higher reflectance in MIR wavelength range than in NIR wavelength range. This index highlights urban areas where there is typically a higher reflectance in the shortwave-infrared (SWIR) region, compared to the near-infrared (NIR) region.

• Applications include watershed runoff predictions and land-use planning.

𝑵𝑫𝑩𝑰= ( − )/( + )𝑴𝑰𝑹 𝑵𝑰𝑹 𝑴𝑰𝑹 𝑵𝑰𝑹

Methods

» Built-up Index• Built-up index is the binary image with only

higher positive value indicates the built-up and barren thus, allows the BU to map the built-up area automatically.

BU = NDBI - NDVI

Case Study

» Mapping Built-up area in West Chennai:

The study area is located in west Chennai, was selected as the study area to evaluate the performance of NDBI. The study area covers 12.15 Sq.Km. The satellite image for acquired from Landsat TM on 23th may, 1991.

Fig.1 Landsat TM image of Study area acquired on 23th May, 1991 –

Standard false color composite.

Result and Discussion

Fig.2 NDBI processed image of Study area.

Fig.3 BU processed image of Study area.

Result and Discussion

» From fig.2 it is easily identified that the higher value in the range indicates the Built-up area where as very low or negative value indicates the barren land or forest area.

» The NDBI approach is an effective method for automatically mapping urban built-up areas using the Landsat TM imagery, offering easy operation and independence of sample selection by manual operation. However, this Index has some limitations associated with recoding the NDBI imagery.

Result and Discussion

» Because of this recoding process, the NDBI approach was unable to separate urban areas from barren and open land.

» NDBI also suffered from commission error, showing some vegetation as built- up area.

» From fig.3 it is observed that range of the resultant image is wide than the fig.2, which provides large classification.

» BU index are used in the metro cities where the urbanization so much congested.

References

» Bhatta, B. 2009. Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. International Journal of Remote Sensing, 30: 4733–4746.

» Congalton, R. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35–46.

» Guindon, B., Zhang, Y. and Dillabaugh, C. 2004. Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sensing of Environment, 92: 218–232.