Preparation of LULC map from GE images for GIS based Urban Hydrological Modeling

Transcription

1 International Conference on Modeling Tools for Sustainable Water Resources Management Department of Civil Engineering, Indian Institute of Technology Hyderabad: December 2014 Abstract Preparation of LULC map from GE images for GIS based Urban Hydrological Modeling Conference Theme: Remote Sensing and GIS in Hydrology Agilan V 1, Umamahesh N V 2 1 Project Associate, Department of Civil Engineering, NIT Warangal, agilan1991@gmail.com 2 Professor, Department of Civil Engineering, NIT Warangal, mahesh@nitw.ac.in Land Use/ Land Cover (LULC) map is an important input in Geographical Information System (GIS) based hydrological modeling such as rainfall runoff modeling. Preparation of LULC map by satellite image or field visit is expensive in terms of money and time. The spectral characteristics in Google Earth (GE) images are very poor compare to satellite images which having Near Infra-Red (NIR) band such as Quick-Bird images. In this study LULC map has been prepared from freely available satellite images such as GE images. Part of Hyderabad city which is having area of around km 2 is chosen as study area. The high resolution GE images have been downloaded and pre-processed for removing texture variations. Different supervised and unsupervised classification procedures have been applied to get LULC map. In addition to standard classification procedures, the object based classification procedure is also applied. The accuracy of each method is assessed by kappa coefficient and overall accuracy. From the results it is observed that the all conventional standard classification procedures produced poor performance compared to object based classification. The less spectral variations within features caused poor performance of standard classification procedures. However, due to object based analysis, the object based classification method has given satisfactory result. The prepared LULC map can be used for urban hydrological modeling, climate modeling, and weather forecasting. Keywords: Image classification, LULC, Accuracy assessment, GE images

2 1. Introduction In Geographical Information System (GIS) based hydrological models such as rainfall runoff models the Land Use/ Land Cover (LULC) map is an important and essential input. Remote sensing plays an important role in generating land use/cover information from regional to global scales, not only due to its spatially-explicit representation of the earth surface, but also due to its frequent temporal coverage and relatively low observation costs (Wu, Shibasaki, Yang, Zhou, & Tang, 2008) (Bargiel & Herrmann, 2011). From literature survey its observed that the most previous studies on land use/cover mapping at large scale commonly used the low and medium spatial resolution imagery, such as NOAA/AVHRR, TERRA/MODIS and Landsat TM or ETM+ (Zhou, Aizen, & Aizen, 2013) (Wijedasa, Sloan, Michelakis, & Clements, 2012). Because of low and medium spatial resolution several global and regional land use/cover products such as GlobCover (Tchuente, Roujean, & De, 2011) were derived from remotely sensed data and made available to the public. But, due to the relatively low spatial resolution, they are insufficient for detailed land cover mapping for those areas with complex and high heterogeneous landscapes such as the urban environment. In recent decade, with the development of new satellite sensors, a variety of high spatial resolution imageries, i.e., QuickBird, IKONOS and RapidEye, have been made possible. These satellite imageries provide detailed information about the size and shape of surface targets, as well as clear spatial relationships among the neighboring objects. But, because of the narrow spatial coverage it is high economic costs. Fortunately, the high spatial resolution images released from GE, as a free and open data source. However, the spectral characteristics in Google Earth (GE) images are very poor compare to satellite images which having Near Infra- Red (NIR) band such as Quick-Bird images. Moreover, the richness in texture, tone and geometric characteristics makes the spectral characteristics of GE image more complex and variable (Dragut, Tiede, & Levick, 2010). Due to these reasons, the standard classification procedures of remote sensing data such as supervised and unsupervised classification algorithms will give poor results for GE images. In this study, the standard classification algorithms and object based classification algorithm are tested on GE images for selecting best classification procedure for GE images.

3 2. Study Area and Data Pre-Processing 2.1 Study area For this study, part of Hyderabad city which is having area of around km 2 has been chosen as study area. The location map of the study area is shown in Figure 1 and it lies between latitude of 17 o 14ꞌ 7.8ꞌꞌ N and 17 o 23ꞌ 17.38ꞌꞌ N and longitude of 78 o 23ꞌ 55.64ꞌꞌ E and 78 o 32ꞌ 50.77ꞌꞌ E. The study area have four level-1 classes i.e settlements, water bodies, vegetation and open land. Figure 1: Study area Map 2.2 Data Pre-Processing The GE images are downloaded using Google Earth software. The high resolution scenes are downloaded separately and mosaicked using Erdas Imagine software. But the mosaicked scene has different tones and textures. Image dodging, image color balancing, and image histogram matching processes have been carried on mosaicked image for removing texture and tonal variation. Figure 2 shows the difference between raw mosaicked images and pre-processed images. All the classification algorithms are applied to the pre-processed image. The pre-

4 processing removes the variations in texture and tone and the pre-processed image will have nearly same texture and tone for same feature. (a) (b) Figure 2: a) Raw Image. b) Pre-Processed image 3 Methodology After pre-processing the raw image, the pre-processed image is classified using different image classification algorithms such as Maximum likelihood classification, Minimum distance to mean classification, K-means automatic clustering algorithm, Isodata automatic clustering algorithm and object based classification. After classifying the pre-processed image, the accuracy assessment is carried out for each classification algorithms. In accuracy assessment each method is evaluated based on overall accuracy and kappa coefficient. The entire methodology flowchart adopted in this study is shown in Figure3.

5 4 Result and Discussion Figure 3: Methodology Flowchart of the study The LU/LC map of the study area is prepared with five different classification procedures. In supervised classification method, the maximum likelihood classifier and minimum distance to mean classifier is used. For these two classification algorithm same training data is given as signature file and no parametric rule is defined. In unsupervised classification method, the K- means and Isodata automatic clustering algorithms are evaluated. For both algorithm, the classification is carried for 4 classes and only 20 iterations are carried out. In object based classification method, initially object and pixel training data sets are defined and then the segmentation is carried out. The generalization, probability filters, size filters, and shape filters are used to extract each features in object based classification. Each classification algorithms are evaluated based on overall accuracy (Congalton & Green, 2008) value and kappa coefficient (Congalton & Green, 2008) value. The overall accuracy and kappa coefficients ( ^ k ) are given in equation 1 and 2 respectively.

6 a Overall Accuracy= ii 100% N (1) (a) (b) (c) (d) (e) Figure 4: Classification result (a) Object Based Classification, (b) Isodata (c) Maximum likelihood (d) Min. distance to mean, (e) K-means

7 k k N a a a ^ k ii i i i1 i1 k 2 N aiai i1 Here, N is the total number of pixels, aii is entry (i,i) of the confusion matrix, ai+ and a+i are the marginal totals of row i and column i respectively, and k is number of classes. Table1: Confusion Matrix of all classification procedure Minimum distance to mean Water Settlement Vegetation Open land Maximum Likelihood Water Settlement Vegetation Open land K-means Clustering Water Settlement Vegetation Open land Isodata Water Settlement Vegetation Open land Object Based Classification Water Settlement Vegetation Open land Overall accuracy (2) Kappa Coefficient

8 The output LU/LC map of each classification algorithms is shown in Figure 4 and the confusion matrix of accuracy assessment, overall accuracy, and kappa coefficient values are given in Table 1. From confusion matrix, overall accuracy and kappa coefficient it is observed that the object based classification method produced highest overall accuracy of 78% with 0.70 kappa coefficient. Furthermore, except maximum likelihood method, all other standard classification algorithms are produced very poor results due to less spectral variation in GE images. The maximum likelihood method produced overall accuracy of 71% and kappa coefficient of Conclusions In this study, the urban area GE images are classified using maximum likelihood, minimum distance to mean, K-means, Isodata and object based classification algorithms for preparation of LU/LC map. Based on the study results, it is concluded the GE images can be used for preparation high resolution LU/LC maps in urban area. This GE images can be classified using object based classification method or maximum likelihood classification algorithms for satisfactory results. And prepared LU/LC maps can be used for urban hydrological modeling, climate modeling and weather forecasting. Acknowledgements Authors thank ITRA for supporting this work. We also thank Google Company for providing free images for research purpose. References Bargiel, D., & Herrmann, S. (2011). Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution spotlight TerraSAR-X data. Remote Sens., Congalton, R. G., & Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press. Dragut, L., Tiede, D., & Levick, S. R. (2010). A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci., Tchuente, A. T., Roujean, J. L., & De, J. S. (2011). Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. Int. J. Appl. Earth Obs. Geoinf., Wijedasa, L. S., Sloan, S., Michelakis, D. G., & Clements, G. R. (2012). Overcoming limitations with Landsat imagery for mapping of peat swamp forests in Sundaland. Remote Sens.,

9 Wu, W., Shibasaki, R., Yang, P., Zhou, Q., & Tang, H. (2008). Remotely sensed estimation of cropland in China: A comparison of the maps derived from four global land cover datasets. Can. J. Remote Sens., Zhou, H., Aizen, E., & Aizen, V. (2013). Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study. Remote Sens. Environ.,