Urban Land Cover and Land Use Classification Using High Spatial Resolution Images and Spatial Metrics

In this paper, the author evaluates the results of applying two evolving classification techniques, decision trees (DT) and artificial neural networks’ back-propagation (ANN-BP), to obtain land cover and land use classes from Quickbird XS data combined with spatial metrics, in a selected urban zone in Bogota, Colombia. In order to improve the quality of the land cover classification, additional bands of texture and edges were added to the spectral bands and tested. In summary, results using the new methods did not surpass those obtained using the traditional maximum likelihood (ML) classifier. Nevertheless, the addition of one edge band rose the thematic accuracy of the classification compared to merely using the original spectral bands, no matter which classifier was used. The author also reports the attainment of good quality results when inferring urban land use from land cover units’ composition and diversity bands derived from the previous classification. In the test, the application of ANN-BP and DT algorithms did lead to get more accurate urban land use classes than using the ML classifier. Spatial metrics seems to be an appropriate framework to describe and better classify urban landscapes. Emergent classification techniques are very promising and need to be further investigated. INTRODUCTION The new generation of optical multispectral sensors like IKONOS, QuickBird and Orbview-3 provide raw data with a spatial resolution suitable for urban land cover and land use classification. But higher spatial detail does not mean higher spectral richness and some limitations arise to get accurate classes. On the classification of urban land cover a major problem is to deal with the spectral mixture of similar physical materials present in different land cover types (i). On the classification of urban land use the challenge is to find the adequate contextual data needed to infer classes that represent a functional concept –the human activity that happens on the land(ii). In the work reported here a solution to both challenges was proposed and tested by: (1) adding an edge image to Figure 1: Study zone as seen from QuickBird satellite (color composite RGB321). Center for Remote Sensing of Land Surfaces, Bonn, 28-30 September 2006 293 the original image bands in order to separate mixed land cover classes, and 2) obtaining and using spatial metrics to infer land use types. Spatial metrics, also known as landscape metrics, is a methodology suitable for describing land use structures (iii, iv). In this study, two alternative supervised classification algorithms -back propagation (BP) and decision trees (DT) were evaluated and compared with the traditional maximum likelihood (ML). METHODS The source data are four spectral bands composing a QuickBird-XS image – spanning the visible and near-infrared wavelengthstaken in February 2005. In Table 1 the main characteristics of the image are indicated. The study area is located in the northwest of Bogota, the capital of Colombia, delimited between longitudes φ1=74o07'20.77''W to φ2=74o04'41.18''W and latitudes λ1=4o40'19.14''N to λ2=4o37'38.92''N, and covers about 2416 hectares. In Figure 1 a RGB321 colour composition of the image is shown. It is apparent the variety of land cover and land use units existing in the zone. In Table 2, the land cover classification scheme is shown. In Table 3, land use types can be seen. Both were defined based on the Anderson’s classification schema (vi). In Table 2, the graphic samples show that some land cover classes have similar spectral signature and that additional data is needed to be able to differentiate between them. In Table 3, it is apparent that, in most cases, spectral information is useless to classify land use. Therefore, a spatial metrics approach is a suitable way to accomplish that task. In Figure 2, the workflow used in this work is depicted. This decomposition of the land use classification process in three stages has proven to be very useful in similar studies as reported in the recent literature (vii). Table 1: QuickBird XS image specifications (v). Band Wavelength (n m) Spatial Resolution (m) Blue 450 – 520 2.4 Green 520 – 600 2.4 Red 630 – 690 2.4 Near IR 760 – 900 2.4 Table 2: Land cover classification schema Code Description Sample