Establishment of the watershed image classified rule-set and feasibility assessment of its application

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1 Establishment of the watershed classified rule-set and feasibility assessment of its application Cheng-Han Lin 1,*, Hsin-Kai Chuang 2 and Ming-Lang Lin 3, Wen-Chao Huang Department of Civil Engineer, National Taiwan University, Taipei, Taiwan. 4 Department of Civil Engineer, National Central University, Taoyuan, Taiwan. * Corresponding author, address: r @ntu.edu.tw (Cheng-Han Lin). Abstract Extreme weather events result in catastrophic disasters around the world recently. More commonly, these disasters occurred due to multiple reasons that coupled together. To develop disaster prevention strategies, application of satellite s can be effective because of its promptness and vast coverage area. This study established an classified rule-set which used the object-based analysis methodology. There are several surface features in the watershed that were classified based on the proposed rule set, including main channels, secondary channels, sandbars, alluvial fans, landslides and place of the geotechnical damage. The study applied this rule-set in different watersheds and different-resolution satellite ry, and assessed the feasibility by comparing in-situ data and calculating the error matrix. The results showed that the rule-set logic can be flexible in different watersheds and different s. The classification of the rule-set is reproducible and accurate. As this result we can apply the rule-set to disaster management and land use planning in the future work. Keywords: Watershed satellite, Object-based analysis, Rule-set, Geotechnical damage. 1. Introduction More than 2,000 mm of cumulative rainfall was recorded during Typhoon Morakot, which happened on August 7 to 9 in 2009 in Taiwan. Typhoon Morakot caused severe damages in central and southern Taiwan. The long duration and high intensity rainfall caused serious damage such as landslides along river banks, the silted up of the channel, the debris dams and the basin flooding. These natural disasters have thus made field investigation difficult because of the interruption of main roads mountain roads. Limited by available man power, resorting to remote sensing techniques for disaster prevention is an urgent need. In recent years the availability of the remote sensing technology has been continuously growing, the number of band has increased, and cost has been reduced. With high-resolution and multi-band satellite s, more ground information is able to be identified. Using remote sensing data and techniques for land surface change after natural disaster has gained increasing attentions (Gamanya et al., 2007; Hung, 2009; Huang, 2010; Chuang, 2012). Methodologically, most approaches are based on the analysis of object-based classification analysis. The application of object-based analysis with high-resolution satellite data has solved the existing survey in the problem of lack of space and timing, and the results also similar to artificial interpretation. This research establishes a rule-set for classifying disaster-related surface features from watershed satellite, and discusses the applicability of different resolution satellite

2 s. Based on literature review, previous researches do not pay attention to the practicability of results derived from different datasets. Therefore, the objective of this study is to identify disaster sites with high correctness using an object-based classification rule-set, and suggest the evaluation of suitability for different satellite and different watershed. 2. Methods 2.1. Study area and Image data The focus of this study was the Chenyoulan river watershed and the Lao-Nong river watershed in the central and southern Taiwan. The study site centered over lower stream in the Chenyoulan River and upper stream in the Lao-Nong River. During Typhoon Morakot, landslides, mudslides, floods and damaged public facilities occurred in these regions. The field investigations had highlighted the importance of watercourse alteration, surface features of flood plain area and artificial structures (Lin et. al., 2009; Chen et. al., 2009). The satellite s which were used in this study were all acquired after Typhoon Morakot, comprising four multi-spectral bands and a panchromatic band. The detailed information is shown as in Figure 2-1. In terms of satellite identification, the spatial resolution is related to the identification of the surface feature and bands provided extraction of spectral features. Revisit frequency expressed the period of time required when taking a photo to a given specific area. Fig. 2-1 Basic information of Satellite s. (From RiChi Technology Inc.). Comparing individual differences of the s, although the spatial resolution of WorldView-2 is higher than those of QuickBird and Formosat-2, however, Formosat-2, which is a satellite system from Taiwan, is relatively convenience to use due to its price and timeliness Research procedures The classification in the first step is to segment the satellite into the object segments. The second step is to assign the object segments into classes it belong. The research flow chart is shown in Figure 2-2. Fig. 2-2 Research flow chart In addition to Blue, Green, Red, NIR and Pan layers used in this study, Slope and Landform Index which were produced by the high-resolution digital terrain model (DTM) were also added in the layers. The layer features in the classification was set on the basis of both spectral reflection and the topography relationship. Firstly, this study referred to the rule-set that was established by Lao-Nong river watershed QuickBird (Chuang, 2012). Secondly, this rule-set was applied in the Chenyoulan river watershed WorldView-2 s and the Lao-Nong river watershed Formosat-2, respectively.

3 The feasibility is assessed by comparing with investigation photos and error matrices. In the conclusion section, the applicability and the subsequent application of the rules-set will be suggested. The main methodology of the study is described in the following sub-section. structure, which is the smallest category, will be in classification (Fig. 2-4). The parameters are decided in the segmentation according to the spatial size of each category after the determination of the surface features and the hierarchy Object-based analysis This study uses the object-based analysis by the software ecognition, which has the segmentation algorithm of the Multi-resolution Segmentation. The procedure for the Multi-resolution Segmentation can be described as a region merging methodology, and the merging decision is based on heterogeneity criteria. The resulting object segments contain more features, including shape, texture, size, area and spectral value. These features are basic input parameters to build up the spatial relationship, and the following rule-set can be used more consistently with human knowledge. The Multi-resolution Segmentation in addition to the size of the objects controlled by the heterogeneity criteria can also be given different weights according to different layers. For instance, the water body has strong reflection in the blue band, and the identification of the artificial structures require high resolution panchromatic band. Multi-resolution Segmentation can give a higher weight for the particular layer and compute the heterogeneity criteria as threshold. Therefore, object-based analysis not only provides abundant features that are closer to human interpretation results, but also identifies boundary of the segment by choosing proper parameters such that it fits the surface feature Establishment of the rule-set The rule-set is to integrate analysis at every step from the segmentation to the classification, and to translate the human logic and expert knowledge into algorithms. In this study the hierarchy of the rule-set is divided into four stages sequentially. First of all, a large area of vegetation and shadow in the are classified, and the segments that are not classified are moved into the next step. Finally the artificial Fig. 2-4 Hierarchy of the classification rule-set section Establishment of the rule set for the first time requires try and error to find the most efficient classification threshold. For example, the first stage is to find out the vegetation. According to common experiences in Remote Sensing, it has confirmed that the Normalized Difference Vegetation Index (NDVI) can be used to identify the vegetation area. Therefore, the Assign Class was selected for the vegetation classification. On the other hand, the fourth stage of the category is not easy to classify with single feature and threshold, therefore the Nearest Neighbor, which performs selective sample training before executive supervised classification, was selected. The result of the rule-set in Lao-Nong river watershed is shown in figure 2-5. Statistics of error matrix showed the Kappa value of and the overall accuracy of in Baolai, and the Kappa value of and the overall accuracy of in Liouguei. This study visually presented the rule-set by using the decision tree. The tree structure and the flow component help application of the rule-set methodology for non-experts and the beginners (Fig. 2-6, 2-7).

4 domain is also helpful for the analysis, such as the case that the landslide is in steep slope and the high relative height, while the artificial structure is in the steep slope and the flat region. Through the modification of the rule-set, it spent 10 hours in the WorldView-2 analysis and only 70 minutes in the Formosat-2 analysis. For both the WorldView-2 and Formosat-2 classification results, Kappa value of the error matrix were all greater than In the conclusion organizes the assessment results and made a description of the Fig. 2-5 Classification result in Baolai using the Lao-Nong reflection. river watershed QuickBird 2.5. Feasibility assessment of its application In this study there are two methodologies for checking the application of the rule-set. One was using the rule-set for the classification in the different watershed. It can be seen clearly that distinct land use between lower stream in the Fig. 2-6 Definition of the Rule-set tree component Chenyoulan River and upper stream in the Lao-Nong River. The other one evaluated the rule-set in the same watershed but different resolution satellite s. The spatial resolution of Formosat-2 is relatively lower comparing to the QuickBird. Formosat-2 only provides 2m panchromatic and 8m multispectral that cannot distinguish detailed surface features. With the resolution of the Formosat-2, the layers of Slope and Landform Index also produced by lower resolution DTM (40m). The spectral distribution is influenced by both the satellite sensor and weather condition. Therefore it must modified classification algorithm in applied the rule-set in Chenyoulan river watershed. Adaption by adjusting the classification threshold and the Multi-resolution Segmentation parameters. Fig. 2-7 Rule-set tree Also, it is necessary to re-select the surface features because the land use in Chenyoulan river watershed is more complicated than in the Lao-Nong river watershed. For example the distribution of the agricultural area and the artificial structure are more abundant in Chenyoulan river watershed. Using the Landform Index layer to specify the 3. Results and discussion To evaluate the classification results, manual selection of ground true information and comparison with object-based analysis results is a qualitative criterion. Calculating the

5 error matrix can clearly determine the mutual confusion is unable to comply with the surface features boundary, between the surface features. Based on its statistical for example, bridges, roads and villages can only get its parameters, it was observed that all classification results have location. Considering large-scale disaster events, such as overall accuracy up to 80%, and Kappa value greater than landslides, the alluvial fan caused by the mudslides, river 75%. Joseph (2003) purposed that Kappa value up to 75% is channel accumulation, it is able to accurately evaluate disaster an acceptable accuracy. However, higher misjudgment location, size and their possible impact ranges (Fig. 3-4). Even between the river bed and artificial structures occurred though the result does not guarantee perfect accuracy, it occasionally. This misjudgment was caused by the spatial site proved beneficial for the disaster prevention and planning of of the surface features, but more obviously it was caused by the post-disaster mitigation. spectral reflection. Usually the disaster triggered by extreme weather is in a larger scale, and not easy to investigate the disaster areas after damages were made. The comparison of the classification results and investigation photos after Typhoon Morakot provides the approach when using different spatial resolution satellite in objet-based classification process. The rule-set established in this study can be an automatic and accurate disaster location identification tool in disaster mitigation planning. The WorldView-2 in Chenyoulan river watershed has 0.5 meters spatial resolution of the pan band. The higher spatial resolution is more applicable to recognition detail Fig. 3-1 Comparison of the Chenyoulan river watershed with site photos in Xin-Shan (photos taken by Lin et. al., 2009) landforms. The result of this classification, which zooms into Xin-Shan and Jun-Keng, is shown in Fig. 3-1 and Fig.3-2. Many asphalt roads and concrete bridges were destroyed after Typhoon Morakot in this region. The assessment of this comparison show that this rule-set distinguish between bridges and roads are pretty efficient, and for mountain roads, which is commonly hidden by the forest, can still be traced by its linear structure of the roads, while the villages and agricultural areas can also get its distribution range. As for the application of the rule-set by using Lao-Nong river watershed but with different resolution, Figure 3-3 can be seen that the spatial resolution influenced the object segments. The coarse object segments led to the results that the interpretation of the smaller landform in the Formosat-2 Fig. 3-2 Comparison of the Chenyoulan river watershed with site photos in Jun-Keng (photos taken by Lin et. al., 2009)

6 to modify the rule-set in this study, including the hierarchy from large to small spatial scale, determination of the scale parameter of the object-based segmentation, setting of layer weights, building the knowledge base of the classes, and selection of algorithms and threshold. As error matrix is used to calculate the overall accuracy and the Kappa value with qualitative statistics. The feasibility was also assessed by comparing the classification result to the site photos. By using two methods for checking the accuracy, the watershed classification rule-set was proved to be Fig. 3-3 Classification results in Baolai using different resolution reproducible and applicable to non-experienced users. One (left: QuickBird; right: Formosat-2) important variable is different watershed region. The rule-set logic is flexible for adoption that surface features should be re-selected because the land use in different watershed is not the same. Another variable is different satellite of a wide or narrow spectral distribution. Modification of the classification threshold or increase the other algorithms will be able to improve the classification accuracy. Using the rule-set for analysis in the software ecognition, spend about 10 hours on a high-resolution computing and only close to 70 minutes on Formosat-2. The enhancement of the proposed approach is the reduction of human errors, manual Fig. 3-4 Comparison of the Lao-Nong river watershed with classification process and the reduction in processing time. After site photos (photos taken by Chen et. al., 2009) 4. Most research approaches aim for the extraction of surface feature, such as landslide and artificial structures by focusing on a single satellite. Even with high spatial satellite s, such as QuickBird and WorldView-2, it is still incapable satisfying both aspects in classification accuracy and the program run time. This study applies the rule-set to watershed classification by using QuickBird, WorldView-2 and Formosat-2 data respectively. The result provides an automatic and applicable process of the classification by the software ecognition, the segments can output both shape file and Conclusion resolution the watershed classification, and demonstrates that the rule-set logicality is suitable to apply to different s and different watersheds. It is also proposed Raster data. Those data can be used to estimate the land cover changes by using the software ERDAS Image or ArcGIS. The WorldView-2 and the QuickBird are both foreign commercial satellites. The s are too expensive and not prompt enough to response to the damages that happened here in Taiwan. Previous research of object-based classification in Taiwan had used Formosat-2 multi-period, and automated the analysis of landslide and artificial facilities (Huang, 2010). If the proposed approach is to apply to vast s more than two watersheds, we suggest to clip the satellite s by using the software ERDAS Image, or select the targeted surface features.

7 As is known form the literature (Chuang, 2012), the landform index layer combined with slope and relative elevation is used to describe the topography for the spatial relationship. Because the flat area around flood plain is with high priority in this study, the slope grading only simply divided into two grades. Future research can involve aspect and curvature as a data layer within the software ecognition, these parameters will result in effective interpretation for topology features. For establishing an object-based classification rule-set in the new location, the most rapid operation is through the high-resolution to low-resolution, and the modification in the same watershed will be simpler than different watershed. Refer to the Figure 4-1. Subsequent application to establishment of rule-set, user can follow the real path, which is in the same location and from high-resolution to low-resolution. Disasters caused by extreme weather events in the future must be discussed by the disaster historical perspective. Analysis of the land cover changes by using this methodology in each object segment is a priority in coupled-disaster assessment, land use change, environmental monitoring. Thus it is expected to avoid the recurrence of similar disasters in the future. Fig. 4-1 Suggestion for establishing the rule-set (No.1 and No.2 are the assess methodology in this study) 5. Reference [1] Baatz, M., A. SCHÄPE. Multiresolution Segmentation: An optimization approach for high quality multi-scale segmentation. Angewandte Geographische Informations- Verarbeitung, Vol. XII., 12 23, [2] T.J. Chen, J.J. Wu, M.J. Weng, K.H. Xie, J.Z. Wang. Slope failure of Lawnon basin induced by Typhoon Morakot. Sino-Geotechnics, Vol. 122, 13-20, [3] Gamanya, R., Philippe De Maeyer, Morgan De Dapper. An Automated Satellite Image Classification Design Using Object-Oriented Segmentation Algorithms- A Move towards Standardization. Expert System with Applications, Vol. 32(2), , [4] H.K. Chuang. Combining landform thematic layer and object-based analysis to map the surface features of mountainous flood plain and surrounding areas. Master s dissertation, National Taiwan University, [5] Joseph L. F., B. L., and M. C. P. Statistical Methods for Rates and Proportions. 3rd Ed, [6] J.Y. Lin, S.Q. Xu, P.H. Cai, S.C. Hui, C.Y. Lai, M.F. Lai, M.T. Yang, K.J. Shou, F.G. Huang, K.C. Chan, S.Y. Xu. Slope disaster inducement in the Chenyoulan river watershed. Sino-Geotechnics, Vol. 122, 41-50, [7] K.C. Hung. Landslide detection using various features from multispectral ry. Master s dissertation, National Cheng Kung University, [8] National Science and Technology Center for Disaster Reduction. Typhoon Morakot principal investigation plan: landslide (2). Typhoon Morakot disaster investigation and analyzed, pp , [9] W.K. Huang. Applying object-oriented analysis to segmentation and classification of landslide and artificial facilities with remote sensing s. Master s dissertation, National Taiwan University, 2010.