A new index for delineating built-up land features in satellite imagery

Transcription

1 International Journal of Remote Sensing Vol. 29, No. 14, 20 July 2008, Letter A new index for delineating built-up land features in satellite imagery H. XU* College of Environment and Resources, Fuzhou University, Fuzhou, Fujian , China (Received 16 October 2007; in final form 2 February 2008 ) A new index derived from existing indices an index-based built-up index (IBI) is proposed for the rapid extraction of built-up land features in satellite imagery. The IBI is distinguished from conventional indices by its first-time use of thematic index-derived bands to construct an index rather than by using original image bands. The three thematic indices used in constructing the IBI are the soil adjusted vegetation index (SAVI), the modified normalized difference water index (MNDWI) and the normalized difference built-up index (NDBI). Respectively, these represent the three major urban components of vegetation, water and built-up land. The new index has been verified using the Landsat ETM + image of Fuzhou City in southeastern China. The result shows that the IBI can significantly enhance the built-up land feature while effectively suppressing background noise. A statistical analysis indicates that the IBI possesses a positive correlation with land surface temperature, but negative correlations with the NDVI and the MNDWI. 1. Introduction Rapid development of urban areas has witnessed replacement of natural vegetation cover with buildings and paved surfaces. This has brought about many negative environmental repercussions to the world, for example, less precipitation, more dryness and higher temperatures (Kaufmann et al. 2007), which contribute greatly to global warming. The ability to monitor the built-up land dynamics and changes in the urban extent in a timely and cost-effective manner is highly desirable for local communities and decision makers alike. Fortunately, satellite remote sensing technology offers considerable promise to meet this requirement and satellite imagery has been used to discriminate built-up lands from non-built-up lands for the last few decades. A popular method for the definition of built-up land areas began with conventional multi-spectral classification. However, this may not produce satisfactory accuracy, normally less than 80%, due to spectral confusion of the heterogeneous built-up land class. Therefore, many studies have not only used a single classification method to extract the built-up lands, but have also combined different methods to improve the extraction. Masek et al. (2000) identified urban built-up lands in the metropolitan Washington DC area, based on a normalized difference vegetation index (NDVI) differencing approach with the assistance of an unsupervised classification and achieved an overall accuracy of 85%. Xu (2002) and Xian and Crane (2005) used logic tree algorithms to extract built-up land *Corresponding author. fdy@public.fz.fj.cn International Journal of Remote Sensing ISSN print/issn online # 2008 Taylor & Francis DOI: / 转载

2 4270 H. Xu information, but were only able to achieve an accuracy of around 85%. To date, there are few simple processes of automatically mapping built-up lands, such as using indices. The normalized difference built-up index (NDBI) of Zha et al. (2003) is the only one proposed for this purpose. The development of the index was based on the spectral response of built-up lands that have higher reflectance in the middle infrared (MIR) wavelength range (such as TM 5), than in the near infrared (NIR) wavelength range (see equation (1) below). However, studies have shown that the reflectance for certain types of vegetation over the band pass of TM 5 increased as leaf water content decreased (Cibula et al. 1992, Gao 1996), and the drier vegetation can even have a higher reflectance in the MIR wavelength range than in the NIR range (Gao 1996). Therefore, the extracted built-up land information using the NDBI is often mixed with plant noise, and Zha et al. (2003) had to further use the NDVI to filter out the noise. Obviously, simply using the original spectral bands to construct an index is not applicable for the enhancement of built-up land due to its complex spectral features. Therefore, the author proposes an index-based built-up index (IBI) with the aim to provide a simple, but effective, method to enhance builtup land presence in satellite imagery. 2. Construction of the IBI using thematic index-derived bands An urban area is a complex ecosystem composed of heterogeneous materials. Nevertheless, based on some generalizing features, Ridd (1995) could still divide the urban ecosystem into three components, green vegetation, impervious surface material and exposed soil, and accordingly created a V-I-S model. However, ignoring open water in the V-I-S model has led to inconvenience for many later urban studies based on this model, which had to mask out water information beforehand (Wu and Murray 2003). Accordingly, open water is taken into consideration in this study; this is because of not only the inconvenience mentioned above, but also the importance of water in the urban ecosystem. Consequently, the urban land-use was grouped into three other generalizing categories: built-up land, vegetation and open water. Based on these three components, three thematic indices, the normalized difference built-up index (NDBI), the soil adjusted vegetation index (SAVI) and the modified normalized difference water index (MNDWI, Xu 2006), were selected to represent the three major land-use classes respectively. The MNDWI modifies the normalized difference water index (NDWI) of McFeeters (1996) by substitution of a middle infrared band, such as TM 5 for the NIR band used in the NDWI. As a result, the MNDWI has an advantage over the NDWI by removing built-up land noise when applied to open water enhancement. The three selected indices are expressed as: NDBI~ ðmir{nirþ ðmirznirþ, ð1þ and SAVI~ ðnirzredzlþ ð2þ MNDWI~ ðgreenzmirþ, ð3þ

3 Remote Sensing Letters 4271 where MIR is a middle infrared band such as TM 5, NIR is a near infrared band such as TM 4, Red is a red band such as TM 3 and Green is a green band such as TM 2; l is a correction factor ranging from 0 for very high plant densities to 1 for very low plant densities. The selection of the SAVI instead of the NDVI is because the SAVI is more sensitive than the NDVI in detecting vegetation in the low-plant covered areas such as urban areas. The SAVI can work in the area with plant cover as low as 15%, while the NDVI can only work effectively in the area with plant cover above 30% (Ray 2006). Therefore, the SAVI is more suitable for the urban area. However, in the area where the plant cover is more than 30%, the NDVI can be used: NDVI~ ðnir{redþ ðnirzredþ : ð4þ After producing SAVI, MNDWI, and NDBI images, a new image was created, which used these three new images as three bands. The change from the original multi-band image into the three-thematic-band image largely reduces redundancy between original multi-spectral bands, and the three new bands are negatively correlated with each other (see table 1). Consequently, the spectral clusters of the three major urban components are well separated (see figure 1 and table 2). Table 2 shows the mean and standard deviation (std. dev.) values of the three urban land-use classes in the new three-index-derived images. A unique feature is that the mean value of built-up land is greater than those of vegetation and water in the NDBI band. Furthermore, the mean value of built-up land in the NDBI band exceeds its values in the SAVI band and the MNDWI band. According to these distinct features, the IBI can be created as follows: NDBI{ SAVIzMNDWI IBI~ ½ ð Þ=2Š ½NDBIzðSAVIzMNDWIÞ=2Š : ð5þ The index can enhance the built-up land feature easily because the subtraction of the SAVI band and the MNDWI band from the NDBI band will result in positive values for built-up land pixels only. The index takes advantage of the condition where the features with higher NDBI values but lower SAVI and MNDWI values will be enhanced. Obviously, the IBI is a normalized difference index and thus has such features as: (1) a ratio-based index, (2) values ranging from 21 to + 1 and (3) enhanced information has positive values, while the suppressed background noise generally has zero to negative values. Dividing by two in the equation is to avoid getting too small values of IBI. Before calculating the IBI using equation (5), the Table 1. Comparison of correlation values between the three new bands and the three original ETM + bands representing the visual light band group, the near infrared band and the middle infrared band group, respectively. SAVI band NDBI band MNDWI band ETM + band 3 ETM + band 4 ETM + band 5 SAVI band ETM + band NDBI band ETM + band MNDWI band ETM + band

4 4272 H. Xu Figure 1. Scatter plot of the three major urban land-use classes. values of the NDVI, the NDBI and the MNDWI should be added to 1 or rescaled within to convert negative values of the indices into positive values. The IBI is distinguished from conventional indices by its first-time use of thematic index-derived bands, instead of original image bands, to construct an index. When the NDVI is used instead of the SAVI in equation (5), the IBI can be rewritten, based on equations (1), (3), (4) and (5), as: 2MIR= MIRzNIR IBI~ ð Þ{ ½ NIR= ð NIRzRedÞzGreen= ð GreenzMIR ÞŠ 2MIR= ðmirznirþz½nir= ðnirzredþzgreen= ðgreenzmirþš Equation (6) allows the use of the IBI in just one touch without having to make three indices before calculating the IBI. 3. Validation of IBI The IBI has been verified using the Landsat ETM + image of Fuzhou City in southeastern China (see figure 2(a)), acquired on 29 May Even if a raw image can be directly used for computing the IBI, the image was radiometrically corrected before the calculation to avoid a dataset-specific result. The correction employed the ð6þ Figure 2. Landsat ETM + image of Fuzhou City: (a) false colour image (RGB:432), (b) IBI image, enhanced built-up land features are in a light-grey to white tone and suppressed background noise is in a dark-grey to black shade and (c) built-up land extraction image.

5 Remote Sensing Letters 4273 Table 2. Statistics of the three main urban land-use classes of the new three-band image. SAVI NDBI MNDWI Vegetation Mean Std. dev Built-up land Mean Std. dev Water Mean Std. dev method presented by Lowry et al. (2004), which is based on the algorithm of Chander and Markham (2003) with the addition of an atmospheric correction model suggested by Chavez (1996). Figure 2(b) is the IBI image of the city derived from the radiometrically corrected image. In the IBI image, the built-up land features are greatly enhanced with a light grey to white tone, while vegetation and water are considerably suppressed with a dark-grey to black shade. This is owing to the enlargement of the contrast between built-up land and the other two classes by the IBI. Table 3 shows that the built-up land has positive values, while the vegetation and water are all in negative values in the IBI image. The validation of the IBI is carried out by the quantitative assessment of the accuracy for the extracted urban built-up land features. To extract urban built-up land features from the IBI image, a threshold (0.013) was manually determined. The pixels with values greater than the threshold are built-up land and assigned a value of 1, while the pixels with values equal or less than the threshold are non-built-up land and assigned a value of 0. Thus, the resultant image is a binary image, only showing the extracted built-up land information. Non-urban areas were further masked out using a vector polygon defining the urban outline and only the built-up lands inside the urban region were retained as urban built-up lands (see figure 2(c)). A finer spatial resolution SPOT 5 HI image was used as a reference dataset, with which the extracted result was compared. The image is a 10 m multi-spectral image of Fuzhou area acquired on 13 December A random sampling method was used and 310 pixels were sampled. The assessment shows that the overall accuracy is 96.77% with a kappa coefficient of (table 4). The background noise, such as river channels, lakes and vegetations, is excluded (figure 2(c)). Further validation of the IBI is carried out by examining the relationships of the new index with the vegetation index (NDVI), the water index (MNDWI) and the land surface temperature (LST), respectively, by regression analysis. To objectively examine the relationships, a random sampling method with a large sampling rectangle was used ( pixels were sampled). The result shows that the IBI has a positive correlation with the LST, but negative correlations with the NDVI and MNDWI (see figure 3). This suggests that the increase in built-up land is responsible Table 3. Statistics of the three main urban land-use classes of the IBI image. Built-up land Water Vegetation Minimum Maximum Mean Std. dev

6 4274 H. Xu Table 4. Accuracy assessment. Built-up land Non-built-up land Total User s accuracy Built-up land % Non-built-up land % Total Producer s accuracy 95.5% 99.09% Overall accuracy 96.77% kappa for a raise in land surface temperature and a decrease in water and vegetation covers. The scatter plot of the IBI versus the MNDWI shows a characteristic triangularshaped envelope of pixels (figure 3(c)). The correlation between the IBI and the MNDWI is not as strong as the other two because the random sampling procedure sampled not only built-up land and water pixels, but also many vegetation pixels. The vegetation pixels have low values in both IBI and MNDWI. As a result, their Figure 3. MNDWI. Scatter plots showing the relationship of the IBI with the LST, the NDVI and the

7 points accumulate in the lower left part of the scatter plot and thus cause the lowering of the correlation between the IBI and the MNDWI. Nevertheless, a declined and sharply defined upper edge of the triangular envelope can be seen in the figure. This may represent the real relationship between the IBI and the MNDWI, implying a strong negative correlation between the two indices. This upper edge may correspond to the maximum water proportion at a given level of built-up land cover. In figure 3(b), the points scattered in the lower left part of the scatter plot represent pure water or waterdominated pixels, which have low values in both the IBI and the NDVI, and therefore have also lowered the correlation between the IBI and the NDVI to some extent. Finally, the relationship between the IBI, the NDVI and the MNDWI can be further examined in a three-dimensional spectral feature space (figure 3(d)). This reveals a saddle-shaped scatter plot, the top of which is composed of built-up land-dominated pixels, while the two feet represent water and vegetation pixels, respectively. 4. Conclusions Remote Sensing Letters 4275 Many studies have demonstrated that the built-up land class cannot be efficiently enhanced using an index constructed simply of original multi-spectral bands because the class has a heterogeneous characteristic. The proposed IBI is distinguished from conventional indices by the first-time use of thematic index-derived bands to construct an index, rather than by using original image bands. The new image, composed of three thematic-index bands, the SAVI, the NDBI and the MNDWI, can greatly reduce the data dimensionality and redundancy of the original multispectral image and thus substantially avoids the spectral confusion between land-use classes. Consequently, the IBI can effectively suppress background noise while retaining built-up land features in satellite imagery. Acknowledgements This work is supported by the National Natural Science Foundation of China (no ) and the Natural Science Foundation of Fujian Province, China (no. 2007J0132). References CHANDER, G. and MARKHAM, B., 2003, Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, pp CHAVEZ, P.S. Jr., 1996, Image-based atmospheric corrections revisited and revised. Photogrammetric Engineering and Remote Sensing, 62, pp CIBULA, W.G., ZETKA, E.F. and RICKMAN, D.L., 1992, Response of thematic bands to plant water stress. International Journal of Remote Sensing, 13, pp GAO, B.C., 1996, NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, pp KAUFMANN, R.K., SETO, K.C., SCHNEIDER, A., LIU, Z., ZHOU, L. and WANG, W., 2007, Climate response to rapid urban growth: evidence of a human-induced precipitation deficit. Journal of Climate, 20, pp LOWRY, J., KIRBY, J. and LANGS, L., 2004, SWReGAP Land Cover Mapping Methods Documentation, Available online at: ftp.nr.usu.edu/swgap/data/landcover/map_ methods/ut/ut5_methods.pdf (accessed 3 October 2007). MASEK, J.G., LINDSAY, F.E. and GOWARD, S.N., 2000, Dynamics of urban growth in the Washington DC metropolitan area, , from Landsat observations. International Journal of Remote Sensing, 21, pp

8 4276 Remote Sensing Letters MCFEETERS, S.K., 1996, The use of normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, pp RAY, T.W., 2006, Vegetation in remote sensing FAQs. In ER Mapper Applications, pp (Perth, Australia: ER Mapper Ltd.). RIDD, M.K., 1995, Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16, pp WU, C. and MURRAY, A.T., 2003, Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84, pp XIAN, G. and CRANE, M., 2005, Assessment of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 97, pp XU, H., 2002, Spatial expansion of urban/town in Fuqing and its driving force analysis. Remote Sensing Technology and Application, 17, pp XU, H., 2006, Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, pp ZHA, Y., GAO, J. and NI, S., 2003, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, pp