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Subpixel Estimation of Impervious Surface Using Regression Tree Model: Accuracy of The Estimation at Different Spatial Scales
M. Rafee Majid
Dept. of Urban and Regional Planning
Universiti Teknologi Malaysia
81310 UTM, Skudai
Johor, MALAYSIA
rafee@utm.my
ABSTRACT
As a key indicator of the level of urbanization, impervious surface has widely been used as an environmental indicator linking development to its impact on the environment. Many have for example suggested that there is a direct correlation between the amount of impervious surface and the environmental characteristics of an urban area such water quality and local climate. Quantification of impervious surface, nevertheless, remains tedious if not difficult and lately more efforts have been focused on the methods employing remote sensing and GIS technologies for this purpose. One of the methods used is the regression tree model that has generated promising results at various pixel levels. This paper discusses an attempt at applying the regression tree model in estimating the amount of impervious surface based on the Landsat ETM+ images. Using data from the remote sensing images and high-resolution aerial photography, a regression tree model is first developed for estimating subpixel impervious surface. The accuracy of the estimated impervious surface using the model is then evaluated at different spatial scales, from pixel to regional scale. While the results reveal that the estimated impervious surface may not be as accurate at pixel scale, it shows encouraging accuracy at subdivision and regional scales. Thus there is a potential for the use of such method at a larger spatial scale. A discussion on the advantages and pitfalls of using the estimation method then concludes the paper.
INTRODUCTION
Impervious surface is not a single homogeneous quantity but when used as a landscape indicator it is typically presented as a percentage of the land that is covered with impervious materials. Arnold & Gibbons (1996) defined impervious surfaces as any material covering the ground that prevents infiltration of water into the soil. While the most prevalent impervious surfaces are man-made materials such as pavement and building rooftops, there are also natural surfaces that are so heavily compacted as to be functionally impervious. Examples of these are compacted soil in construction areas, dirt roads and, even to a certain extent, grass turf in residential areas (Arnold & Gibbons, 1996; Schueler, 1995). In this study, impervious surface is defined as any fixed man-made materials in a residential subdivision that has the potential to prevent infiltration of water into the soil. These include rooftops and patios; transportation-related impervious surface such as roads, parking lots and sidewalks; recreational surface such as tennis court and swimming pool; and infrastructure-related impervious surface such as water tank and the likes.
This paper discusses the application of a regression tree model on remote sensing images for estimating the amount of impervious surface. Specifically, the paper investigates the accuracy of using the model on moderate-resolution Landsat ETM+ images in estimating impervious surface aggregated at three spatial scales; pixel, subdivision (local) and regional. Even though there have been studies reporting the accuracy of the method at the pixel level (see Smith, 2000; Ward et al., 2000; Wu & Murray, 2000; Yang et al., 2003), no accuracies have so far been reported at the subdivision and regional scales. In an effort to fill the gap, this paper attempts to investigate the accuracy of the method across the three spatial scales. Accuracies are assessed by the degree of mean absolute errors(MAE) when comparing the predicted amount of impervious surface to the actual amount. The amount of impervious surface obtained through visual interpretation of 0.3m orthophotos is adopted in this study as the actual amount of impervious surface. Due to its relatively high accuracy, imperviousness estimated through visual interpretation of the high-resolution orthophotos can be considered as the actual value of imperviousness in the field (Lee, 1987; Harvey, 1985; Kienegger, 1992).
ESTIMATING IMPERVIOUS SURFACE FROM ABOVE
Early impervious surface mapping efforts using remotely sensed data were mainly conducted through visual interpretation of aerial photography which were both time consuming and prohibitively expensive when performed over a large area. In addition, available aerial photographs were collected at differing scales and on different dates, thus requiring time-consuming rectification, digitization, and interpretation. Digital satellite imagery later began to provide a synoptic view of the Earth’s surface capable of producing regular, repeatable spatially-intensive land cover maps. Significant reductions in the amount of labor necessary for impervious surface delineation came with computer-automated spectral analysis of satellite data. These methods were capable of obtaining results comparable to aerial photo interpretation in considerably less time and with a significant reduction in cost (Ragan & Jackson, 1975).
Another advantage of satellite imagery over aerial photographs is that satellite sensors have spectral bands that match the spectral reflectance properties of certain land covers. The Landsat TM (Thematic Mapper) sensor, for example, has six reflective bands whereas color and color-infrared aerial photography is limited to three spectral bands, and black and white photographs have only one band. There are, however, some limitations to satellite imagery depending on the resolution (pixel size) of the image. Landsat TM images, for instance, have a pixel size of 30m x 30m which is large enough to encompass a diversity of land-cover conditions of differing imperviousness. On the other resolution scale, however, finer-resolution imagery such as IKONOS (4m x 4m for multispectral image, 1m x 1m for panchromatic image) is cost prohibitive for a large study area requiring multiple scenes.
Many of the earlier methods using spectral information from satellite sensors are based on supervised and unsupervised classification techniques and other forms of spectral clustering, thresholding, and modeling. Products are often presented as maps portraying the presence or absence of impervious features at the single pixel scale. Other estimates of impervious cover, meanwhile, rely on lookup tables (conversion factors) derived from surrogate measures of parcel size (Monday et al., 1994) and land use and land cover information (Deguchi & Sugio, 1994; Williams & Norton, 2000; Ward et al., 2000). Forster (1985), however, warned against classifying MSS and TM pixels found in the urban settings as one specific land cover class due to a mismatch in resolutions; the sensor resolution being too coarse compared to the fine spatial resolution of features in the urban environment.
More recent studies adopted advanced machine learning algorithms and spectral mixture analysis that allowed the derivation of imperviousness at the subpixel level. Flanagan & Civco (2001), for example, conducted a subpixel impervious surface mapping using artificial neural network and an ERDAS Imagine® subpixel classifier. The overall accuracy at the binary impervious-non-impervious detection level varied from 71 to 94% with a root mean square error (RMSE) of 0.66 to 5.97. Ji and Jenson (1999), Wu and Murray (2002), Ward et al. (2000), and Rashed et al. (2003) meanwhile have experimented with spectral mixture modeling to derive information about the amount of impervious cover in a single pixel. Wu and Murray reported an overall estimation RMSE of 10.6 percent imperviousness. In another approach, modeling techniques using decision tree models have been successfully implemented in subpixel quantification of impervious surface. A decision tree model dealing with discrete data is known as a classification tree model and that dealing with continuous data is referred to as a regression tree model. Smith (2000), for instance, used classification tree with the overall within-class accuracy of about 84%. Yang et al. (2003) went a step further by using regression tree, thus modeling the impervious surface output as a continuous rather than discrete variable.
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