博碩士論文 102022603 詳細資訊




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姓名 陶德方(DAO DUC PHUONG)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 利用Landsat 8 OLI 與 MODIS 資料於柬埔寨進行物件導向之洪水區域劃定與影響水稻田範圍評估
(Object-based Flood Mapping and Affected Paddy Rice Estimation in Cambodia using Landsat 8 OLI and MODIS Data)
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摘要(中) 柬埔寨為東南亞常發生洪水災害的國家之一,其地理位置位於湄公河下游地區,地勢上受到高原與高山環繞,中央地區則為低窪的洪氾區。季風季節期間,降於洞里薩湖與湄公河河岸之劇烈強降雨往往導致災難性洪災。由於當地居民以農業生產維生,水稻生態系統中洪災損失評估在此一地區扮演著相當重要的角色。本研究針對2013年10月侵襲柬埔寨中央地區之劇烈颱風個案為研究案例,利用 Landsat 8 OLI 與 MODIS 資料,提出以物件導向之方法用於洪水區域劃定與影響水稻田範圍評估。在洪水區域劃定部分,Landsat 8 OLI Level 1產品被用於偵測洪水區域,然而使用之衛星影像西北部分被雲層遮蔽,因此選用增強時空適應反射率融合模式(ESTARFM)產製具Landsat空間解析度之無雲合成影像以進行洪水範圍偵測。於水稻田辨識部分,依據植被與水體指標於插秧時期之季節間變化,使用Terra(MOD13Q1)和Aqua(MYD13Q1)MODIS植被指數產品以辨識水稻田範圍。在此方法中,以物件之局部方差變化用於估計最佳尺度參數以進行影像分割處理。結果顯示,洪水範圍偵測部分以高空間解析度之影像作為驗證,其整體精度高於95%;而水稻田辨識部分,辨識成果與統計資料呈現良好相關性(R2 = 0.675)。洪水範圍與水稻田位置圖則進行圖層套疊進一步分析受洪水災害影響之水稻田範圍。洪水區域劃定與影響水稻田範圍成果將有助於提供地方政府寶貴之資訊,以利洪水減災及災害補償與重建。本研究提出之方法將可應用於其他研究區域,或針對無現地洪水觀測或雷達遙測資料之區域,以進行大尺度之即時觀測與洪水災害評估。
摘要(英) Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake’s and Mekong River’s banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia after the flood event induced by a severse typhoon occurred in October 2013 using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data. For flood mapping, the Landsat 8 OLI Level 1 products were used to detect inundation regions. However, the images over northwestern region was covered by cloud; therefore, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was appied to generate cloud-free Landsat-scale synthetic data for flood detection in this area. For rice identification, Terra (MOD13Q1) and Aqua (MYD13Q1) MODIS vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. In this approach, image segmentation process was conducted with optimal scale parameter estimation based on the variation of objects’ local variances. The inundated area was identified with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. The rice classification result was well correlated with the statistical data at a commune level (R2 = 0.675). The inundation and paddy rice maps were overlaid and further analyzed to estimate rice area impacted by the disaster. The flood mapping and affected rice estimation result is really useful as it provides local governments with valuable information for flooding mitigation and post-flooding compensation and restoration. The success and findings of this procedure could be promisingly applied in other areas to timely observe and assess the impacts of flood disasters at a large scale and in the areas where in situ flooding observation is inoperable or radar remotely sensed data is unavailable.
關鍵字(中) ★ 物件導向影像分析
★ 影像分割
★ 尺度參數估計
★ 洪水區域劃定
★ 水稻
★ Landsat
★ MODIS
★ 影像融合
★ 增強時空適應反射率融合模式
★ 柬埔寨
關鍵字(英) ★ Object-based image analysis
★ segmentation
★ scale parameter estimation
★ flood mapping
★ paddy rice
★ Landsat
★ MODIS
★ image fusion
★ ESTARFM
★ Cambodia
論文目次 Chinese Abstract i
English Abstract ii
Acknowledgments iii
Table of contents iv
List of Figures vi
List of Tables viii
Explanation of Symbols ix

1. INTRODUCTION 1
1.1. Global Warming and Climate Change 1
1.2. Flooding Status and Socioeconomic Impacts in Cambodia 3
1.3. Research Objectives 4
1.4. Research Procedure 6
2. METHODOLOGY AND MATERIALS 8
2.1. Study Area 8
2.1.1. Geographic Location 8
2.1.2. Socioeconomic Status 9
2.1.3. Flood Inundation Status 9
2.2. Methodology 10
2.2.1. Overall Design 10
2.2.2. Image Fusion Model 11
2.2.3. Object-based Image Analysis 14
2.2.4. Image segmentation 16
2.2.5. Flood Inundation Mapping 18
2.2.6. Paddy Rice Detection 20
2.3. Materials 22
2.3.1. Landsat 8 OLI Imagery 22
2.3.2. MODIS Sensed Data 23
2.3.3. MODIS Land Surface Reflectance 24
2.3.4. MODIS Vegetation Index Product 24
2.3.5. Ancillary Data 26
3. DATA ANALYSIS 28
3.1. Data Preprocessing 28
3.1.1. Landsat 8 Radiometric Calibration 28
3.1.2. Georeferencing 28
3.1.3. Image Mosaicking and Subsetting 29
3.1.4. Data Resampling 29
3.1.5. Ancillary Data Normalization 30
3.2. ESTARFM Image Fusion 31
3.3. Image Segmentation 32
3.3.1. Estimation of Optimal Scale Parameter 32
3.3.2. Segmentation 32
3.4. Classification 33
3.4.1. Inundation Water Detection 33
3.4.2. Paddy Rice Identification from MODIS Data 34
3.4.3. Affected Rice Estimation 35
4. RESULTS AND DISCUSSION 37
4.1. Image Fusion Result 37
4.2. Flood Inundation Map 38
4.3. Paddy Rice Area Map 38
4.4. Affected Rice Field Estimation 39
4.5. Validation 39
4.5.1. Validation of Flood Detection Result 39
4.5.2. Validation of Paddy Rice Identification 40
4.5.3. Evaluation of Affected Rice Area 40
5. CONCLUSION 42

Bibliographies 43
Appendices 48
參考文獻 Baatz, M., Schäpe, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation, Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium Salzburg 2000, Strobl, J., Blaschke, T., Griesebner, G., Eds.; Wichmann: Heidelberg, Germany, pp. 12-23.
Bertin, X. et al., 2014. A modeling-based analysis of the flooding associated with Xynthia, central Bay of Biscay. Coastal Engineering, 94: 80-89.
Bhatt, R.M. et al., 2015. Interspecific grafting to enhance physiological resilience to flooding stress in tomato (Solanum lycopersicum L.). Scientia Horticulturae, 182: 8-17.
Bisquert, M., Bégué, A., Deshayes, M., 2015. Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series. International Journal of Applied Earth Observation and Geoinformation, 37(0): 72-82.
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1): 2-16.
Chau, V.N., Holland, J., Cassells, S., Tuohy, M., 2013. Using GIS to map impacts upon agriculture from extreme floods in Vietnam. Applied Geography, 41: 65-74.
Dao, P.D., Liou, Y.-A., 2015. Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data. Remote Sensing, 7(5): 5077-5097.
Dao, P.D., Liou, Y.-A., Chou, C.-W., 2015. Detection of flood inundation regions with Landsat/MODIS synthetic data, International Symposium on Remote Sensing 2015, Tainan, Taiwan.
Definiens, 2007. Definiens Developer 7: User Guide. Definiens AG, Munich, Germany.
Devadas, R., Denham, R.J., Pringle, M., 2012. Support Vector Machine classification of object-based data for crop mapping, using multi-temporal Landsat imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7(2012 XXII ISPRS Congress, Melbourne, Australia).
Didan, K., Huete, A., 2006. MODIS vegetation index product series collection 5 change summary, Terrestrial Biophysics and Remote Sensing (TBRS) laboratory, The University of Arizona.
Drăguţ, L., Csillik, O., Eisank, C., Tiede, D., 2014. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 119-127.
Drǎguţ, L., Tiede, D., Levick, S.R., 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871.
Dronova, I., Gong, P., Wang, L., 2011. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sensing of Environment, 115(12): 3220-3236.
Dronova, I., Gong, P., Wang, L., Zhong, L., 2015. Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158: 193-206.
Evans, T.L., Costa, M., Telmer, K., Silva, T.S., 2010. Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(4): 560-575.
Frohn, R., Autrey, B., Lane, C., Reif, M., 2011. Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery. International Journal of Remote Sensing, 32(5): 1471-1489.
Gao, F., Masek, J., Schwaller, M., Hall, F., 2006. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218.
Gao, Y., Mas, J., Niemeyer, I., Marpu, P., Palacio, J., 2007. Object-based image analysis for mapping land-cover in a forest area, 5th International Symposium: Spatial Data Quality, Enschede, The Netherlands, pp. 13-15.
Gao, Y., Mas, J.F., Kerle, N., Navarrete Pacheco, J.A., 2011. Optimal region growing segmentation and its effect on classification accuracy. International Journal of Remote Sensing, 32(13): 3747-3763.
Gianinetto, M., Villa, P., Lechi, G., 2006. Postflood damage evaluation using Landsat TM and ETM+ data integrated with DEM. IEEE Transactions on Geoscience and Remote Sensing, 44(1): 236-243.
Gusso, A., Formaggio, A.R., Rizzi, R., Adami, M., Rudorff, B.F.T., 2012. Soybean crop area estimation by Modis/Evi data. Pesquisa Agropecuária Brasileira, 47(3): 425-435.
Hall, D., Bouapao, L., 2010. Social Impact Monitoring and Vulnerability Assessment. 1683-1489, Vientiane, Lao.
Hansen, J., Ruedy, R., Sato, M., Lo, K., 2010. Global surface temperature change. Reviews of Geophysics, 48(4).
Happ, P., Ferreira, R., Bentes, C., Costa, G., Feitosa, R., 2010. Multiresolution segmentation: a parallel approach for high resolution image segmentation in multicore architectures, Proceeding of the 3rd International Conference on Geographic Object-Based Image Analysis, Ghent, Belgium.
Henry, J.B., Chastanet, P., Fellah, K., Desnos, Y.L., 2006. Envisat multi‐polarized ASAR data for flood mapping. International Journal of Remote Sensing, 27(10): 1921-1929.
Heumann, B.W., 2011. An object-based classification of mangroves using a hybrid decision tree—Support vector machine approach. Remote Sensing, 3(11): 2440-2460.
Hussaina, E., Urala, S., Malikb, A., Shana, J., 2011. Mapping Pakistan 2010 floods using remote sensing data, Proceeding of ASPRS Annual Conference, Milwaukee, Wisconsin, USA.
ITT Visual Information Solutions, 2009. Atmospheric Correction Module: QUAC and FLAASH User’s Guide, Version 4.7. ITT Visual Information Solutions, Boulder, CO, USA.
Kamal, A.H.M., Rashid, H., Sakata, K., Komatsu, S., 2015. Gel-free quantitative proteomic approach to identify cotyledon proteins in soybean under flooding stress. Journal of proteomics, 112: 1-13.
Knight, E.J., Kvaran, G., 2014. Landsat-8 operational land imager design, characterization and performance. Remote Sensing, 6(11): 10286-10305.
le Maire, G., Dupuy, S., Nouvellon, Y., Loos, R.A., Hakamada, R., 2014. Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil. Remote Sensing of Environment, 152: 136-149.
Liou, Y.-A. et al., 2012. Assessment of Disaster Losses in Rice Paddy Field and Yield after Tsunami Induced by the 2011 Great East Japan Earthquake. Journal of Marine Science and Technology, 20(6): 618-623.
Mallinis, G., Gitas, I.Z., Giannakopoulos, V., Maris, F., Tsakiri-Strati, M., 2011. An object-based approach for flood area delineation in a transboundary area using ENVISAT ASAR and LANDSAT TM data. International Journal of Digital Earth(ahead-of-print): 1-13.
Matinfar, H., Sarmadian, F., AlaviPanah, S., Heck, R., 2007. Comparisons of object-oriented and pixel-based classification of land use/land cover types based on Lansadsat7, ETM+ spectral bands (case study: arid region of Iran). American-Eurasian Journal of Agricultural & Environmental Sciences, 2(4): 448-456.
Men, S. et al., 2001. Description of rice varieties released by the varietal recommendation committee of Cambodia (1999–2000), Cambodian Agricultural Research and Development Institute, Phnom Penh, Cambodia.
Metz, B., Davidson, O., Bosch, P., Dave, R., Meyer, L., 2007. Climate Change 2007: Mitigation: Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change: Summary for Policymakers and Technical Summary. Cambridge University Press.
Nesbitt, H.J., 1997. Rice production in Cambodia. International Rice Research Institute, Laguna, Philippines.
Nussbaum, S., Menz, G., 2008. Object-based image analysis and treaty verification, 1. Springer, Berlin, Germany.
Oh, M., Komatsu, S., 2015. Characterization of proteins in soybean roots under flooding and drought stresses. Journal of proteomics, 114(0): 161-181.
Oruc, M., Marangoz, A., Buyuksalih, G., 2004. Comparison of pixel-based and object-oriented classification approaches using Landsat-7 ETM spectral bands, Proceedings of the ISRPS 2004 Annual Conference, Istabul, Turkey, pp. 19-23.
Peng, D., Huete, A.R., Huang, J., Wang, F., Sun, H., 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. International Journal of Applied Earth Observation and Geoinformation, 13(1): 13-23.
Rogger, M. et al., 2012. Runoff models and flood frequency statistics for design flood estimation in Austria–Do they tell a consistent story? Journal of Hydrology, 456: 30-43.
Royal Government of Cambodia, 2014. Post-flood early recovery need assessment report, Royal Government of Cambodia report. Royal Government of Cambodia, Phnom Penh, Cambodia.
Rudari, R., Gabellani, S., Delogu, F., 2014. A simple model to map areas prone to surface water flooding. International Journal of Disaster Risk Reduction, 10(0): 428-441.
Sakamoto, T. et al., 2007. Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote sensing of environment, 109(3): 295-313.
Sakamoto, T., Van Nguyen, N., Ohno, H., Ishitsuka, N., Yokozawa, M., 2006. Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sensing of Environment, 100(1): 1-16.
Sanyal, J., Lu, X., 2004. Application of remote sensing in flood management with special reference to monsoon Asia: a review. Natural Hazards, 33(2): 283-301.
Solano, R., Didan, K., Jacobson, A., Huete, A., 2010. MODIS vegetation index user’s guide (MOD13 series). The University of Arizona.
Tapia-Silva, F.-O., Itzerott, S., Foerster, S., Kuhlmann, B., Kreibich, H., 2011. Estimation of flood losses to agricultural crops using remote sensing. Physics and Chemistry of the Earth, Parts A/B/C, 36(7): 253-265.
Thompson, J.A., Lees, B.G., 2014. Applying object-based segmentation in the temporal domain to characterise snow seasonality. ISPRS Journal of Photogrammetry and Remote Sensing, 97: 98-110.
Trimble, 2011. eCognition Developer 8.7 user guide, Trimble Documentation, München, Germany, pp. 258.
Uddin, K., Gurung, D.R., Giriraj, A., Shrestha, B., 2013. Application of Remote Sensing and GIS for Flood Hazard Management: A Case Study from Sindh Province, Pakistan. American Journal of Geographic Information System, 2(1): 1-5.
USGS, 2013. Landsat 8 Fact Sheet. 2013-3060, Earth Resources Observation and Science (EROS) Center, Reston, VA.
Vang, S., 2011. Country report on rice cultivation pratice, Proceedings of Expert meeting, Bandkok, Thailand.
Vermote, E., Kotchenova, S., Ray, J., 2011. MODIS surface reflectance user’s guide, version 1.3, MODIS Land Surface Reflectance Science Computing Facility.
Vintrou, E. et al., 2012. Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products. International journal of applied earth observation and geoinformation, 14(1): 83-93.
Williams, D., 2008. Landsat 7 science data users handbook, General Interest Publication. NASA, pp. 186.
Woodcock, C.E., Strahler, A.H., 1987. The factor of scale in remote sensing. Remote sensing of Environment, 21(3): 311-332.
Xiao, X. et al., 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95(4): 480-492.
Zhang, F., Zhu, X., Liu, D., 2014. Blending MODIS and Landsat images for urban flood mapping. International Journal of Remote Sensing, 35(9): 3237-3253.
Zhu, X., Chen, J., Gao, F., Chen, X., Masek, J.G., 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623.
指導教授 劉說安(Liou, Yuei-An) 審核日期 2015-6-26
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