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Effectiveness of using very high resolution imagery (IKONOS) for land use mapping
4. Results and discussion 4.1. Detection of land use classes The identification (or on-screen digitizing) of different land cover/use classes ramified at level 4 on IKONOS images can be explained as follows giving some concrete examples: 1) The dense urban fabric class indicates 80% urban cover of a given area distributed non linearly (Figure 2a); 2) The medium density urban fabric refers to a mixing of urban (˜ 70% of the total area), vegetation and bare soil (Figure 2b); 3) The low density urban fabric can be extended on a linear or non linear surface (Figure 2c); 4) Diverse equipment regroups schools, universities campus, etc. (Figure 2d). In addition, some classes can be misinterpreted such as railway station class that can be confused with industrial or commercial area class regarding its texture, necessitating a prior knowledge of the area or a topographic map for its identification (Figure 2e). Urban extension class is similar to urban vacant land class (Figure 2f), both sharing a future urbanization. The only distinction is based on the fact that urban extension occupies a more important and better infrastructure that urban vacant lands.  Figure 2. Examples of land use classes as shown on pan-sharpen IKONOS images (1 m). Other classes are difficult to extract such as dumpsites representing variable spectral signature resembling to that characterizing other land cover classes (Figure 3a). While some are easily detected like mineral extraction sites appearing in white patches or land fill sites located always in proximity to the sea with a limited number (Figure 3b). The green colour appearance and the particular spatial identity allow an ease differentiation of olives from other types of vegetation (Figure 3c). Citrus appear in brighter colours than fruit trees and with a more compact density (Figure 3d). The texture of bananas is disorganized because of their big leaves that overlap (Figure 3e). Their colour varies between dark green and yellow. Greenhouses represent the easiest class to identify on IKONOS images because of their remarkable reflection (Figure 3f). The latter appears similar to a mirror once the plastic houses have small dimensions.  Figure 3. Difficulties of interpretation of some land use types on IKONOS imageries. 4.2. Spatial and statistical comparisons between LUC maps The number of polygons equal to 4355 in land use/cover map (LUC) produced from Landsat and IRS imageries has increased to 5934 in the map resulting from the interpretation of IKONOS imageries, within a difference of 1500 entities. Moreover, the statistical comparison achieved at 2 land cover/use levels delineates closer difference percentage values between the 2 maps (1:20,000 and 1:5,000) in coastal areas (varying between -0.74 and +1.04) than in mountainous ones (extending from -2.39 to +2.49) (Table 1). This indicates the importance of using IKONOS imageries in land use mapping of mountainous remote areas, competing aggressively against topography. 
If we compare the concordance (in km2) of major land cover/use classes in the two corresponding maps through considering only the coastal area, we can evaluate the efficiency of using IKONOS images. Therefore, a confusion matrix was established indicating an overall coincidence of 87% (Table 2). Some LUC classes are less matched between the 2 maps than others, e.g., roads, unproductive lands and grasslands. 
4.3. Causes of spatial differences between LUC maps The 2 LUC maps are showing several spatial differences that can be explained as follows: 1) a “5 times” better spatial resolution in the LUC maps produced in the current study (1 m IKONOS images against 5.8 m pan-sharpen Landsat TM-IRS images); 2) a bad positioning of the coastal line in LUC maps resulting from Landsat and IRS images with a shifting attaining in some cases hundred meters (Figure 4). For that, the line of high waters was adopted in the current study to define the maritime national limit since it is easily detected by the colour of the foreshore uncovered by the tide with a mean amplitude of 35 cm (Durand, 1998), but it is sensitive to seasonal evolutions of beaches; 3) photo-interpretation errors (digitalization) that can related to a bad georeferencing, a low accuracy of digital data and a high subjectivity of interpreters.  Figure 4. Coastal line detection in LUC maps. The accuracy has been improved using very high resolution data. This ensures previous results done by Zhou and Li (2000) and Devriendt et al. (2005); and 4) an incompatibility of codification between land use maps produced from different satellite types. 4.4. Field verification of LUC maps The use of IKONOS pan-sharpen imagery resulted in a more accurate LUC map (total precision 88.5%) as compared to previous LUC maps (total precision 82%) produced from other lower resolution remotely sensed data. The improved accuracy of the IKONOS imagery is most apparent in an urban environment where there is a large proportion of impervious surfaces.  
The user’s accuracy, which is the percentage of sites in a class derived from visual interpretation, correctly classified vis-à-vis the reference data (field), is ranging between 74 and 100%, with relatively low errors of commission (excesses), varying between 0 and 26% in the case of IKONOS images. This accuracy is much lower, once we consider merged Landsat TM with IRS images ranging from 70 to 92% with higher errors of commission (8-30%). The producer’s accuracy, corresponding to the percentage of sites of a reference class correctly classified by the images, is ranging between 74 and 95%, with similarly relatively low errors of omission (deficits) depending on the class into consideration (5-26%) for IKONOS images. Similarly to user’s accuracy, Landsat TM merged with IRS show higher errors of omission (12-33%) than IKONOS images. Therefore, IKONOS imagery adds additional quantifiable spatial and spectral advantages over lower resolution spatial data in land use mapping. 5. Conclusion This study has shown that IKONOS imagery shows potential as a source of data within a national mapping agency, being more accurate than other pan-sharpen images. The created LUC map at 1:5,000 through the visual interpretation of IKONOS images serves the diverse needs and applications of the agricultural and urban development, policy making, risk assessment and planning. The applied methodology can be extrapolated to the whole country, being homogeneous, smooth, easy to obtain and updated. The time required for the visual interpretation of 2.3 km2 is equal to 1 hour and a half, therefore we need 6533 hours for the treatment of the whole country (studied areas excluded). References
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