應用NDVI時序軌跡分析於產製多時序崩塌圖資—以台灣南投地區為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：79

、訪客IP：3.145.63.131

姓名

阮成惟(Nguyen Thanh Duy) 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

應用NDVI時序軌跡分析於產製多時序崩塌圖資—以台灣南投地區為例
(Generating multi-temporal landslide inventory using the NDVI temporal trajectory in Nantou County, Taiwan)

相關論文

★ 評估不同數值地型資料於降雨型崩塌作用模式之應用性-以小尺度坡面之崩塌事件為例	★ 應用最大熵法於蒙古山區進行森林樹種分類
★ 利用Landsat衛星影像監測並預測中美洲瓜地馬拉首都–瓜地馬拉市之都市發展	★ 都市化與發展：對海地永續發展之意涵
★ 客家文化重點發展區之客家政策研究：以龍潭大池整體環境規劃與營造計畫為例	★ 利用多時期Landsat衛星影像進行森林砍伐之評估 -以尼加拉瓜波沙瓦生態保護區為例
★ 融合光學衛星影像及地形資訊進行崩塌地之判釋	★ 應用Sentinel-1 SAR影像進行水稻監測-以泰國中部大城府省為例
★ 都市三維結構變遷之分析-以臺灣臺北市為例	★ 應用 Sentinel-1 合成孔徑雷達資料進行地層下陷監測 - 以 2017 年泰國曼谷都會區為例
★ 利用人工神經網絡模型建立多事件為基礎之崩塌模型-以台灣玉山國家公園為例	★ 應用衛星影像於都市發展之監測與預測 ─以台灣桃園為例
★ 分析降雨及不透水面對台南水患發生之影響	★ 應用Google Earth Engine與影像分類技術於巴拉圭查科地區進行森林砍伐評估
★ 應用多時期Sentinel-1 合成孔徑雷達影像進行崩塌及淹水偵測-以印尼爪哇島Pacitan地區為例	★ 母岩裸露指標之建立並應用於崩塌判釋與監測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

邊坡災害是許多國家必須面對的一個重要災害問題，而完整、準確和即時的崩塌目錄以及崩塌圖資對於崩塌相關的災害管理和預防可說是相當重要的資料。近年來，地球觀測衛星（EOS）已廣泛用於土地監測的工作上，包括崩塌的監測以及崩塌圖資之產出。現今應用遙測技術所產製之崩塌目錄以及圖資多為事件型的，也就是針對單一崩塌事件，並且主要是針對災害地區域進行崩塌圖資繪製，這使得崩塌圖資在多時、廣域的應用上時必須進行繁複的整合工作。本研究認為，使用衛星資料，並透過分析地表光譜軌跡的時間序列來檢視地表變遷可有效地、經濟地針對大範圍區域建立多時序的崩塌圖資。具體來說，本研究將基於Landtrendr（Landsat-based detection of Trends in Disturbance and Recovery）技術，使用包括 Landsat 和 SPOT 在內的多重衛星、多時序資料來檢測地震和颱風發生前後的地表標準化差異植生指數(Normalized Difference Vegetation Index, NDVI)，並應用NDVI之時間變化來檢視其角變值(Angular Chang, AC)之多時序軌跡用以進行崩塌判釋。本研究區位於台灣中部南投縣，該地區於1999年的集集地震中遭受嚴重的破壞，產生大量、大規模的崩塌事件，並且其後數年也遭受數個颱風襲擊，使得崩塌地在時間及空間上均有較大的變動性。本研究針對台灣中部南投縣一帶，1999年至2004年地震和颱風事件誘發的崩塌進行試驗，進行多時序崩塌目錄的建構，期望本研究提出之方法可以更有效、更快速地產制大範圍多時序之崩塌圖資，以精進邊坡災害之防治工作。

摘要(英)

The landslide hazard is a crucial issue in many countries. A complete, accurate, and timely-provided landslide inventory is important to generate necessary information for hazard management and prevention. In recent years, the Earth Observation Satellites (EOS) have been widely used for land monitoring practice, including landslide detection and inventory preparation. However, most landslide mapping tasks are mainly event-based and focus on particular areas. In recent years, Landtrendr (Landsat-based detection of Trends in Disturbance and Recovery) is an approach process to perform the change of a land surface through time-series spectral trajectories. This study applies the analysis concept of the Landtrendr method in detecting temporal changes of earthquake- and typhoon-triggered landslides by using multiple satellite images, including Landsat and SPOT. The study area, Nantou County, located in central Taiwan, experienced high consequences of the Chi-Chi earthquake in 1999 and also suffered from seasonal typhoons year by year. Earthquake events with a magnitude over 0.6g of peak ground acceleration (PGA) and typhoon events with precipitation higher than 200mm/day are the main triggers that caused landslides in this area. The multi-temporal landslide mapping method, the angular change (AC) analysis, proposed in this study is based on NDVI (Normalized Difference Vegetation Index) temporal trajectory which uses EOS time-series data to identify the location, extent, and timing of landslides induced by the Chi-Chi earthquake and four typhoon events in Nantou County from 1999 to 2004. This study expects that the proposed method can contribute to the generation of multi-temporal landslide inventory more efficiently.

關鍵字(中)

★ 地震型崩塌
★ 颱風型崩塌
★ 時序軌跡
★ 角變值分析

關鍵字(英)

論文目次

Table of contents
中文摘要 i
Abstract iii
Acknowledgments v
Table of contents vi
List of figures viii
List of tables x
List of symbols xi
List of abbreviations xi
CHAPTER 1: INTRODUCTION 1
1.1. Research background 1
1.2. Research objectives 2
CHAPTER 2: LITERATURE REVIEW 3
2.1. Remote Sensing and landslide studies 3
2.2. Remote sensing techniques on landslide mapping 5
2.3. Change detection using Landtrendr 7
CHAPTER 3: STUDY AREA AND DATA 10
3.1. Study area 10
3.2. Earthquake events 12
3.3. Typhoon events 13
3.4. SPOT Images 15
3.5. LANDSAT images 16
3.6. Image collection 18
CHAPTER 4: RESEARCH METHODS 21
4.1. Image pre-processing 21
4.1.1. Geometric correction 21
4.1.2. Radiometric Correction 22
4.2. Normalized different vegetation index (NDVI) 23
4.3. Image normalization 25
4.4. Landslide detection using AC analysis 28
4.5. Accuracy assessment 29
CHAPTER 5: RESEARCH RESULTS 33
5.1. AC images 33
5.2. Manual delineation landslide by using satellite images 38
5.2.1. Subset 1 of manual landslide delineation based on satellite images 40
5.2.2. Subset 2 of manual landslide delineation based on satellite images 41
5.2.3. Subset 3 of manual landslide delineation based on satellite images 44
5.2.4. Subset 4 of manual landslide delineation based on satellite images 46
5.2.5. Subset 5 of manual landslide delineation based on satellite images 48
5.3. Threshold determination of image classification 50
5.4. Multi-Temporal Landslide Maps based on the threshold of AC values 54
5.5. Accuracy assessment 58
CHAPTER 6: DISCUSSION 72
6.1. Problems in the application of satellite imagery in image classification 72
6.2. Limitation of satellite images’ quality 73
6.3. Landslide detection by using AC analysis 74
CHAPTER 7: CONCLUSION 76
REFERENCES 78

參考文獻

A. Nemčok, J. Pašek and J. Rybář. (1972). Classification of landslides and other mass movements. Rock mechanics, 4, 2, 71-78.
D.J. Varnes. (1978). Slope movement types and processes. Special report 176, 11- 33.
D.M. Cruden. (1991). A simple definition of a landslide. Bulletin of the International Association of Engineering Geology-Bulletin de l′Association Internationale de Géologie de l′Ingénieur. 43, 1, 27-29.
D.J. Varnes, D.J. Cruden. (1996). Landslide types and processes. 247.
A. Tibaldi, L. Ferrari and G. Pasquarè. (1995). Landslides triggered by earthquakes and their relations with faults and mountain slope geometry: an example from Ecuador. Geomorphology, 11, 3, 215- 226.
C.-T. Lee,(2013). Re-evaluation of factors controlling landslides triggered by the 1999 Chi–Chi earthquake. Springer. 213-224.
P. Meunier, N. Hovius and J.A. Haines. (2008). Topographic site effects and the location of earthquake induced landslides. Earth and Planetary Science Letters, 275, 3-4, 221-232.
C.-H. Wu and S.-C. Chen. (2009). Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology, 112, 3-4, 190-204.
J.C. Huang, J.D. Milliman, T.Y. Lee, Y.C. Chen, J.F. Lee, C.C. Liu, J.C. Lin, and S.J. Kao. (2017). Terrain attributes of earthquake‐and rainstorm‐induced landslides in orogenic mountain Belt, Taiwan. Earth Surface Processes and Landforms. 42, 10, 1549-1559.
R. Kennedy, Z. Yang and Cohen. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment. 114, 12, 2897-2910.
T. Lillesand, R.W. Kiefer and J. Chipman,(2015). Remote sensing and image interpretation. Remote sensing and image interpretation.
A.J. Tatem, S.J. Goetz and S.J. Hay. (2008). Fifty years of earth observation satellites: Views from above have lead to countless advances on the ground in both scientific knowledge and daily life. American scientist. 96, 5, 390.
J.R. Jensen,(1996). Introductory digital image processing: a remote sensing perspective. Prentice-Hall Inc.
F. Mantovani, R. Soeters and C.J.G. Van Westen. (1996). Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology. 15, 3-4, 213-225.
D. Brunsden. (1993). Mass movement; the research frontier and beyond: a geomorphological approach. Geomorphology. 7, 1-3, 85-128.
H.T. Rib and T.J.. Liang. (1978). Recognition and identification. Transportation Research Board Special Report. 176.
A. Turner and R.J.N.R.C. Schuster, Special Report. (1996). Landslides–Investigation and mitigation: Transportation Research Board. 247, 672.
V.J. Singhroy and risk. (2005). Remote sensing of landslides. Landslide hazard and risk. 469-492.
F. Ardizzone, M. Cardinali, M. Galli, F. Guzzetti, P. Reichenbach. (2007). Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar. Natural Hazards and Earth System Sciences. 7, 6, 637-650.
J.A. Zinck, J. López, G.I. Metternicht, D.P. Shrestha, L.J. Vázquez-Selem and Geoinformation. (2001). Mapping and modelling mass movements and gullies in mountainous areas using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation. 3, 1, 43-53.
K.S. Cheng, C. Wei and S. Chang. (2004). Locating landslides using multi-temporal satellite images. Advances in Space Research. 33, 3, 296-301.
A.J. Kääb. (2002). Monitoring high-mountain terrain deformation from repeated air-and spaceborne optical data: examples using digital aerial imagery and ASTER data. ISPRS Journal of Photogrammetry and remote sensing. 57, 1-2, 39-52.
J. McKean and J. Roering. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology. 57, 3-4, 331-351.
F. Catani, P. Farina, S. Moretti, G. Nico and T. Strozzi. (2005). On the application of SAR interferometry to geomorphological studies: estimation of landform attributes and mass movements. Geomorphology. 66, 1-4, 119-131.
P. Reichenbach, M. Cardinali, P. De Vita and F. Guzzetti. (1998). Regional hydrological thresholds for landslides and floods in the Tiber River Basin (central Italy). Environmental Geology. 35, 2-3, 146-159.
P. Salvati, F. Guzzetti, P. Reichenbach, M. Cardinali and C.P. Stark. (2003). Map of landslides and floods with human consequences in Italy. CNR Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche Publication. 2822.
F. Guzzetti, A.C. Mondini, M. Cardinali, F. Fiorucci, M. Santangelo and K.-T. Chang. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews. 112, 1-2, 42-66.
Z. Li, W. Shi, S.W. Myint, P. Lu and Q. Wang. (2016). Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sensing of Environment. 175, 215-230.
J. Hervás, J.I. Barredo, P.L. Rosin, A. Pasuto, F. Mantovani and S.J.Silvano. (2003). Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy. Geomorphology. 54, 1-2, 63-75.
J. Nichol and M.S. Wong. (2005). Satellite remote sensing for detailed landslide inventories using change detection and image fusion. International journal of remote sensing. 26, 9, 1913-1926.
P.L. Rosin and J. Hervás. (2005). Remote sensing image thresholding methods for determining landslide activity. International Journal of Remote Sensing. 26, 6, 1075-1092.
J.Y. Rau, L.C. Chen, J.K. Liu, T.H. Wu. (2007). Dynamics monitoring and disaster assessment for watershed management using time-series satellite images. IEEE transactions on geoscience and remote sensing. 45, 6, 1641-1649.
W. Zhao, A. Li, X. Nan, Z. Zhang, G. Lei. (2017). Postearthquake landslides mapping from Landsat-8 data for the 2015 Nepal earthquake using a pixel-based change detection method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10, 5, 1758-1768.
Singh. (1989). Review article digital change detection techniques using remotely-sensed data. International journal of remote sensing. 10, 6, 989-1003.
R. Kennedy, Z. Yang, W.B. Cohen, E. Pfaff, J. Braaten and P. Nelson. (2012). Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sensing of Environment. 122, 117-133.
A.E. Cocke, P.Z. Fulé and J.E. Crouse. (2005). Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. International Journal of Wildland Fire. 14, 2, 189-198.
S. Escuin, R. Navarro and P.J. Fernandez. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Journal of Remote Sensing. 29, 4, 1053-1073.
X. Li and G. Shao. (2014). Object-based land-cover mapping with high resolution aerial photography at a county scale in midwestern USA. Remote Sensing. 6, 11, 11372-11390.
W.B. Cohen, Z. Yang and R. Kennedy. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—Tools for calibration and validation. Remote Sensing of Environment .114, 12, 2911-2924.
R. Headley, DC: US Department of the Interior. (2010). Landsat: A global land-imaging project. US Geological Survey Fact Sheet. 4, 3026.
P.M. Mather and M. Koch,(2011). Computer processing of remotely-sensed images: an introduction. John Wiley & Sons.
J.R. Jensen. (2005). Introductory digital image processing. In: Digital Change Detection Prentice Education, Inc, New Jersey, 471-487.
S. Huang, L. Tang, J.P. Hupy, Y. Wang and G. Shao. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research. 32, 1-6.
G.M. Foody. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment. 80, 1, 185-201.
D. Lu, P. Mausel, E. Brondizio and E. Moran. (2004). Change detection techniques. International journal of remote sensing. 25, 12, 2365-2401.

指導教授

姜壽浩(Shou-Hao Chiang)

審核日期

2021-8-12

推文