融合光學衛星影像及地形資訊進行崩塌地之判釋

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姓名

賈絲汀(Justine Douglas) 查詢紙本館藏

畢業系所

國際永續發展碩士在職專班

論文名稱

融合光學衛星影像及地形資訊進行崩塌地之判釋
(An Optical-Topographic Fusion Approach for Landslide Detection)

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摘要(中)

在台灣，因多山的地形、頻繁的地震、暴雨及颱風的侵襲，導致崩塌災害經常發生。因此建立一個有效、並能產出合理精度成果的崩塌偵測方法顯得相當重要。物件式影像分析法為一新的影像分析技術，首先為依據影像中物件的均質性進行分割，之後則針對分割好的影像物件進行分析。不同於傳統的影像分析方法，影像切割能有效地整合各類資料並產製更精確的結果。本研究在方法上嘗試(1)偵測並繪製2009年莫拉克颱風所發生的崩塌地；(2)將已知對崩塌發生具有直接影響的地形參數 (包含坡度、坡向、高程、曲率與凸性) 透過於物件式影像分析法進行整合；(3) 透過比較研究展現此方法在崩塌偵測成果上的優勢，主要以總體精度 (overall accuracy)以及Kappa統計值檢驗之。為此，本研究以台灣南部高屏溪內花果山盆地作為研究區，並使用8公尺地面解析度的福衛二號多光譜影像(可見光以及近紅外光)，並整合10公尺地面解析度之數值高程模型所產製的地形參數進行研究。為了證明地形參數在崩塌偵測上的有效性，本研究比較了加入地形參數以及未加入地形參數的影像分類成果，具體來說，本研究針對6種不同的地表型態進行影像分類 – 裸露地、河道、森林、崩塌堆積區與崩塌侵蝕區，並使用隨機抽樣來評估分類精度，結果顯示當加入地型屬性資料，總體精度為81.8%，Kappa為0.64；沒有加入地形屬性資料的總體精度則為77.5%，Kappa為0. 55。從本研究的案例發現，加入地形參數後可降低崩塌的在陰影森林地區中所發生的分類錯誤，並可將建物、崩塌地以及裸露進行較好的區分。總體來說，研究成果顯示了以物件式影像分析方法將光譜資訊及地形參數進行整合後能有效提升崩塌判釋之正確性。

摘要(英)

Landslide hazards are common in Taiwan due to its mountainous topography and high number of earthquakes and typhoons experienced yearly. It is essential to develop a method of landslide detection that is capable of providing results with a reasonable level of accuracy, and may also be integrated into an early warning or monitoring system. Object Based Image Analysis (OBIA) is a new method of geographical image investigation which uses segmentation to analyze and process images. Unlike traditional methods, segmentation allows for easy integration of ancillary data during the research process, allowing for the creation of more accurate results. This research attempts to (1) detect and map landslides which occurred following Typhoon Morakot in 2009, (2) incorporate topographic attributes with known effects on the landsliding process (slope, aspect, elevation, curvature, and convexity) directly into the classification process using OBIA and (3) determine the benefits of using segmentation in the landslide mapping process by. A very high resolution 8m FORMOSAT-2 image of the Huaguoshan Basin was used in combination with topographic attributes derived from a 10m digital elevation model for the study area. In order to prove the effectiveness of the topographic attributes in the classification process, the segmentation and classification were first performed with topographic attributes before repeating the process after their removal. The results were subsequently classified based on 6 land use types present within the study area – bare soil, channel, forest, landslide runout and landslide source. Once the classification the accuracy of the process assessed using a random point sampling method. Landslide areas were detected with an overall accuracy of 81.8% and kappa value of 0.64 when using topographic attributes and with an overall accuracy of 77.5% and kappa value of 0.55 without them. The addition of topographic attributes assisted in reducing the amount of misclassifications that occurred in shadowed forest areas and helped separate small urban areas from the surrounding landslide and bare soil areas. This indicates that the integration of topographic attributes is a good means of improving classification accuracy in mountainous areas such as the Huaguoshan basin.

關鍵字(中)

★ 崩塌判釋
★ 物件式影像分析
★ 福衛二號衛星
★ 颱風
★ 地形參數

關鍵字(英)

★ Landslide detection
★ OBIA
★ Formorsat-2
★ Typhoon
★ Topographic Attributes

論文目次

中文摘要 i
ABSTRACT ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
ACRONYMS viii
NOTATIONS xi
CHAPTER 1 - INTRODUCTION 1
1.1 Research Background 1
1.2 Landslide Mapping 1
1.3 Traditional vs Modern Landslide Mapping 2
1.4 Optical Imagery 2
1.5 Object Based Image Analysis 4
1.5.1 Incorporation of Topographic Attributes 5
1.6 Research Objective . 6
CHAPTER 2 – LITERATURE REVIEW 7
2.1 Landslide Mapping with Optical Imagery 7
2.2 Advances in Landslide Mapping Techniques 8
2.3 Current Landslide Mapping Techniques 11
2.4 Landslide Mapping with VHR Imagery . 14
CHAPTER 3 – STUDY AREA 16
CHAPTER 4 – METHODS AND MATERIALS 19
4.1 Data Pre-processing 19
4.1.1 Geospatial Data 20
4.2 Satellite and Satellite Derived Data 20
4.3 DEM and DEM Derived Data 24
v
4.5 Object Based Image Assessment 31
4.6 Accuracy Assessment and Validation 34
CHAPTER 5 – RESULTS AND DISCUSSION 38
5.1 Classification with Topographic Features 40
5.1.1 Southern Urban Area 46
5.1.2 Central Urban Area 49
5.1.3 North Eastern Stream Area 51
5.1.4 Source and Runout Areas 53
5.2 Classification without Topographic Features 57
5.2.1 Southern Urban Area 58
5.2.2 Central Urban Area 59
5.2.3 North East Stream Area 61
5.2.4 Source and Runout Areas 63
CHAPTER 6 – CONCLUSION 66
6.1 Recommendations 67
REFERENCES 68

參考文獻

Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment: summary review and new perspectives. Bull. Eng. Geol. Environ. 58, 21–44. doi:10.1007/s100640050066
Altman, D.G., 1991. Practical Statistics for Medical Research, Chapman & Hall/CRC Texts in Statistical Science. Chapman and Hall.
Barlow, J., Franklin, S., Martin, Y., 2006. High Spatial Resolution Satellite Imagery, DEM Derivatives, and Image Segmentation for the Detection of Mass Wasting Processes. Photogramm. Eng. Remote Sens. 72, 687–692. doi:10.14358/PERS.72.6.687
Barsi, J.A., Lee, K., Kvaran, G., Markham, B.L., Pedelty, J.A., 2014. The spectral response of the Landsat-8 operational land imager. Remote Sens. 6, 10232–10251. doi:10.3390/rs61010232
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58, 239–258. doi:10.1016/j.isprsjprs.2003.10.002
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16. doi:10.1016/j.isprsjprs.2009.06.004
Blaschke, T., Strobl, J., 2001. What’s wrong with pixels ? Some recent developments interfacing remote sensing and GIS. Interfacing Remote Sens. GIS 1–7.
BritishGeologicalSurvey, 2000. Landslide hazard mapping: summary report.
Burrough, P.A., McDonnell, R.A., 1998. Principles of Geographical Information Systems. Oxford University Press.
Cardinali, M., Galli, M., Guzzetti, F., Ardizzone, F., Reichenbach, P., Bartoccini, P., 2006. Rainfall induced landslides in December 2004 in south-western Umbria, central Italy: types, extent, damage and risk assessment. Nat. Hazards Earth Syst. Sci. 6, 237–260. doi:10.5194/nhess-6-237-2006
Chang, K.-T., Chiang, S.-H., 2009. An integrated model for predicting rainfall-induced
69
landslides. Geomorphology 105, 366–373. doi:10.1016/j.geomorph.2008.10.012
Chang, L.W., Hsieh, P.F., Lin, C.W., 2006. Landslide identification based on FORMOSAT-2 multispectral imagery by wavelet-based texture feature extraction. Int. Geosci. Remote Sens. Symp. 3317–3320. doi:10.1109/IGARSS.2006.852
Chen, A.S., Hsu, M.H., Teng, W.H., Huang, C.J., Yeh, S.H., Lien, W.Y., 2006. Establishing the database of inundation potential in Taiwan. Nat. Hazards 37, 107–132. doi:10.1007/s11069-005-4659-7
Cheng, K.S., Wei, C., Chang, S.C., 2004. Locating landslides using multi-temporal satellite images. Adv. Sp. Res. 33, 296–301. doi:10.1016/S0273-1177(03)00471-X
Chiang, S.-H., Chang, K.-T., 2011. The potential impact of climate change on typhoon-triggered landslides in Taiwan, 2010?2099. Geomorphology 133, 143–151. doi:10.1016/j.geomorph.2010.12.028
Chiang, S.H., Chang, K.T., Mondini, A.C., Tsai, B.W., Chen, C.Y., 2012. Simulation of event-based landslides and debris flows at watershed level. Geomorphology 138, 306–318. doi:10.1016/j.geomorph.2011.09.016
Chung, C.-J.F., Fabbri, A.G., 2012. Systematic Procedures of Landslide Hazard Mapping for Risk Assessment Using Spatial Prediction Models, in: Landslide Hazard and Risk. John Wiley & Sons, Ltd, Chichester, West Sussex, England, pp. 139–174. doi:10.1002/9780470012659.ch4
Claessens, L., Knapen, A., Kitutu, M.G., Poesen, J., Deckers, J.A., 2007a. Modelling landslide hazard, soil redistribution and sediment yield of landslides on the Ugandan footslopes of Mount Elgon. Geomorphology 90, 23–35. doi:10.1016/j.geomorph.2007.01.007
Claessens, L., Schoorl, J.M., Veldkamp, A., 2007b. Modelling the location of shallow landslides and their effects on landscape dynamics in large watersheds: An application for Northern New Zealand. Geomorphology 87, 16–27. doi:10.1016/j.geomorph.2006.06.039
Dai, F.., Lee, C.., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42, 213–228. doi:10.1016/S0169-
70
555X(01)00087-3
Dhakal, A.S., Sidle, R.C., 2004. Pore water pressure assessment in a forest watershed: Simulations and distributed field measurements related to forest practices. Water Resour. Res. 40, n/a-n/a. doi:10.1029/2003WR002017
Gao, B., 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266. doi:http://dx.doi.org/10.1016/S0034-4257(96)00067-3
Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31, 181–216. doi:10.1016/S0169-555X(99)00078-1
Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M., Chang, K.-T., 2012. Landslide inventory maps: New tools for an old problem. Earth-Science Rev. 112, 42–66. doi:10.1016/j.earscirev.2012.02.001
Hölbling, D., Füreder, P., Antolini, F., Cigna, F., Casagli, N., Lang, S., 2012. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories. Remote Sens. 4, 1310–1336. doi:10.3390/rs4051310
Joyce, K.E., Samsonov, S. V., Levick, S.R., Engelbrecht, J., Belliss, S., 2014. Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data. Nat. Hazards 73, 137–163. doi:10.1007/s11069-014-1122-7
Kurtz, C., Passat, N., Gançarski, P., Puissant, A., 2012. Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology. Pattern Recognit. 45, 685–706. doi:10.1016/j.patcog.2011.07.017
Kurtz, C., Stumpf, A., Malet, J.P., Gançarski, P., Puissant, A., Passat, N., 2014. Hierarchical extraction of landslides from multiresolution remotely sensed optical images. ISPRS J. Photogramm. Remote Sens. 87, 122–136. doi:10.1016/j.isprsjprs.2013.11.003
Lin, T.-H., Liu, G.-R., 2009. In-Orbit Radiometric Calibration of the FORMOSAT-2 Remote
71
Sensing Instrument. Terr. Atmos. Ocean. Sci. 20, 833. doi:10.3319/TAO.2008.12.31.01(a)
Liu, D., Xia, F., 2010. Assessing object-based classification: advantages and limitations. Remote Sens. Lett. 1, 187–194. doi:10.1080/01431161003743173
Liu, J.G., Mason, P.J., 2016. Representing and exploiting surfaces, in: Image Processing and GIS for Remote Sensing. John Wiley & Sons, Ltd, Chichester, UK, pp. 227–246. doi:10.1002/9781118724194.ch17
Longoni, L., Papini, M., Arosio, D., Zanzi, L., Brambilla, D., 2014. A new geological model for Spriana landslide. Bull. Eng. Geol. Environ. 73, 959–970. doi:10.1007/s10064-014-0610-z
Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870. doi:10.1080/01431160600746456
Lu, P., Stumpf, A., Kerle, N., Casagli, N., 2011. Object-Oriented Change Detection for Landslide Rapid Mapping. IEEE Geosci. Remote Sens. Lett. 8, 701–705. doi:10.1109/LGRS.2010.2101045
Mantovani, F., Soeters, R., Van Westen, C.J., 1996. Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology 15, 213–225. doi:10.1016/0169-555X(95)00071-C
Martha, T.R., Kerle, N., Jetten, V., van Westen, C.J., Kumar, K.V., 2010. Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116, 24–36. doi:10.1016/j.geomorph.2009.10.004
Moine, M., Puissant, A., Malet, J.-P., 2009. Detection of landslides from aerial and satellite images with a semi-automatic method. Application to the Barcelonette basin (Alpes-de-Haute-Provence, France). Int. Conf. ’Landslide Process. From Geomorphol. Mapp. to Dyn. Model. 63–68. doi:halshs-00467545
Möller, M., Lymburner, L., Volk, M., 2007. The comparison index: A tool for assessing the accuracy of image segmentation. Int. J. Appl. Earth Obs. Geoinf. 9, 311–321.
72
doi:10.1016/j.jag.2006.10.002
Mondini, A.C., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., Ardizzone, F., 2011. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens. Environ. 115, 1743–1757. doi:10.1016/j.rse.2011.03.006
Mondini, A.C., Marchesini, I., Rossi, M., Chang, K.T., Pasquariello, G., Guzzetti, F., 2013. Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data. Geomorphology 201, 135–147. doi:10.1016/j.geomorph.2013.06.015
Montgomery, D.R., Dietrich, W.E., 1994. A physically based model for the topographic control on shallow landsliding. Water Resour. Res. 30, 1153–1171. doi:10.1029/93WR02979
Navulur, K., 2007. Multiespectral Image Analysis Using the Object-Oriented Paradigm, Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki. Taylor and Francis Group LLC.
Nichol, J., Wong, M.S., 2005. Detection and interpretation of landslides using satellite images. L. Degrad. Dev. 16, 243–255. doi:10.1002/ldr.648
Otukei, J.R., Blaschke, T., 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int. J. Appl. Earth Obs. Geoinf. 12, S27–S31. doi:10.1016/j.jag.2009.11.002
Reichenbach, P., Galli, M., Cardinali, M., Guzzetti, F., Ardizzone, F., 2012. Geomorphological Mapping to Assess Landslide Risk: Concepts, Methods and Applications in the Umbria Region of Central Italy, in: Landslide Hazard and Risk. John Wiley & Sons, Ltd, Chichester, West Sussex, England, pp. 429–468. doi:10.1002/9780470012659.ch15
Sassa, K., Canuti, P., 2009. Landslides - Disaster Risk Reduction, Thomson Reuters Foundation. Springer Berlin Heidelberg, Berlin, Heidelberg. doi:10.1007/978-3-540-69970-5
Scaioni, M., Longoni, L., Melillo, V., Papini, M., 2014. Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives. Remote Sens. 6, 9600–9652. doi:10.3390/rs6109600
73
Schiewe, J., 2002. SEGMENTATION OF HIGH-RESOLUTION REMOTELY SENSED DATA - CONCEPTS, APPLICATIONS AND PROBLEMS. Jt. ISPRS Comm. IV Symp. Geospatial Theory, Process. Appl.
Soeters, R.;van Westen, C.J., 1996. Slope instability recognition, analysis, and zonation. Landslides Investig. Mitig.
Stehman, S. V., Czaplewski, R.L., 1998. Design and Analysis for Thematic Map Accuracy Assessment. Remote Sens. Environ. 64, 331–344. doi:10.1016/S0034-4257(98)00010-8
Stumpf, A., Kerle, N., 2011. Object-oriented mapping of landslides using Random Forests. Remote Sens. Environ. 115, 2564–2577. doi:10.1016/j.rse.2011.05.013
Tseng, C.M., Lin, C.W., Hsieh, W.D., 2015. Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories. Nat. Hazards Earth Syst. Sci. Discuss. 3, 1137–1173. doi:10.5194/nhessd-3-1137-2015
van Westen, C.J., van Asch, T.W.J., Soeters, R., 2006. Landslide hazard and risk zonation—why is it still so difficult? Bull. Eng. Geol. Environ. 65, 167–184. doi:10.1007/s10064-005-0023-0
Varnes, D.J., 1978. Slope Movement Types and Processes. Transp. Res. Board Spec. Rep. 11–33.
Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D., 2006. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery. Photogramm. Eng. Remote Sens. 72, 799–811. doi:10.14358/PERS.72.7.799
Zhou, C.., Lee, C.., Li, J., Xu, Z.., 2002. On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology 43, 197–207. doi:10.1016/S0169-555X(01)00130-1

指導教授

姜壽浩(Shou-Hao Chiang)

審核日期

2017-7-19

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