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VIETNAM NATIONAL UNIVERSITY UNIVERSITY OF SCIENCE - - PHẠM VŨ ĐÔNG APPLICATION OF REMOTE SENSING, GIS AND DEEP LEARNING IN OPEN DATA CUBE PLATFORM FOR LANDSLIDE SUSCEPTIBILITY MAPPING FOR LARGE MOUNTAINOUS REGIONS IN VIETNAM (Ứng dụng viễn thám, GIS học sâu liệu mở để thành lập đồ trượt lở đất vùng đồi núi rộng lớn Việt Nam) Field: Cartography, Remote Sensing and Geographic Information System Code: 8440211.01 MASTER THESIS HANOI – 2020 VIETNAM NATIONAL UNIVERSITY UNIVERSITY OF SCIENCE - PHẠM VŨ ĐÔNG APPLICATION OF REMOTE SENSING, GIS AND DEEP LEARNING IN OPEN DATA CUBE PLATFORM FOR LANDSLIDE SUSCEPTIBILITY MAPPING FOR LARGE MOUNTAINOUS REGIONS IN VIETNAM (Ứng dụng viễn thám, GIS học sâu liệu mở để thành lập đồ trượt lở đất vùng đồi núi rộng lớn Việt Nam) Field: Cartography, Remote Sensing and Geographic Information System Code: 8440211.01 MASTER THESIS Người hướng dẫn khoa học: PGS.TS Bùi Quang Thành XÁC NHẬN HỌC VIÊN ĐÃ CHỈNH SỬA THEO GÓP Ý CỦA HỘI ĐỒNG Giáo viên hướng dẫn Chủ tịch hội đồng chấm luận văn thạc sĩ khoa học PGS.TS Bùi Quang Thành PGS.TS Đinh Thị Bảo Hoa Hà Nội, 2020 STATUTORY DECLARATION I herewith formally declare that I myself have written the submitted Master’s Thesis independently I did not use any outside support except for the quoted literature and other sources mentioned at the end of this paper Author Pham Vu Dong i ACKNOWLEDGE This work was supported by the Domestic Master/PhD Scholarship Programme of Vingroup Innovation Foundation This work was supported by the Asia Research Center, Vietnam National University - Hanoi and the Korea Foundation for Advanced Studies under Grant CA.19.8A I also would like to express my thanks to my dissertation supervisor, Associate Professor - Dr Bui Quang Thanh, for creating all the conditions, wholeheartedly guiding and helping me to complete this thesis well My deep understanding of science, as well as my experience, are the prerequisites for me to gain valuable achievements and experiences! Author Pham Vu Dong ii CONTENTS STATUTORY DECLARATION .i ACKNOWLEDGE ii LIST OF TABLES v INTRODUCTION 1 The need of research Research objectives Proposing research procedure Approaches and methods CHAPTER I OVERVIEW OF METHODS USING DEEP LEARNING TO ANALYZE LANDSLIDE WITH REMOTE SENSING DATA 1.1 Landslide research and data overview 1.1.1 Landslide controlling factors 1.1.2 Geospatial data sets and digital elevation model data 1.2 Landslide susceptibility research in the world 1.3 Landslide susceptibility research in Vietnam 1.4 Landslide analysis with remote sensing data and deep learning 10 1.5 Study area 11 1.5.1 Geographic location 11 1.5.2 Terrain characteristics 12 1.5.3 Geology and terrain 12 1.5.4 Provinces in the Northwest region 13 1.5.5 Residential 14 1.6 Landslide susceptibility model 14 1.6.2 Landslide susceptibility data processing pipeline 14 1.6.3 Landsat surface reflectance data 16 CHAPTER II MULTISPECTRAL REMOTE SENSING IMAGERY AND DEEP LEARNING METHOD FOR IMAGE ANALYSIS 17 2.1 Data collection 17 2.1.1 Landsat 17 2.1.2 Landsat 17 iii 2.1.1 Landsat 18 2.2 Deep learning method 18 2.2.1 Supervised machine learning algorithm 18 2.2.2 Convolutional Neural Network Architecture 20 2.2.3 Learning in Convolutional Neural Network 26 2.2.4 Backward run 30 2.2.5 Parameter updates 38 CHAPTER III INTEGRATING REMOTE SENSING, GIS AND DEEP LEARNING IN OPEN DATACUBE PLATFORM FOR LANDSLIDE SUSCEPTIBILITY MAPPING 40 3.1 Building Deep Convolutional Neural Network for land cover classification 40 3.1.1 Landsat imagery normalization 40 3.1.2 Data processing 42 3.1.3 Deep learning architecture 44 3.1.4 Training and testing phase 47 3.2 Landslide susceptibility mapping 50 3.2.1 Open data cube data ingestion 50 3.2.2 Land cover classification 52 3.3 Results analysis 57 3.4 Field surveying and evaluating 58 CHAPTER IV CONCLUSION 61 REFERENCES 62 iv LIST OF TABLES Table 1.1 Landslide factors according to Table 1.2 Provinces in the Northwest region 13 Table Different wave-length (µm) concerning reflectance bands among different Landsat types 41 Table 3.2 Land cover classes by pixel values 43 Table 3.3 Scene accuracy 48 Table 3.4 Landslide susceptible spots by provinces 57 Table 3.5 Landslide levels proportion by provinces 57 Table 2.1 Landsat (TM) reflectance band’s features………………………….17 Table 2.2 Landsat (ETM+) reflectance band’s features 18 Table 2.3 Landsat (OLI and TIRS) reflectance band’s features 18 Table 2.4 Activation functions 24 Table 5.Different types of loss functions 25 Table 3.1 Different wave-length (µm) concerning reflectance bands among different Landsat types 41 Table 3.2 Land cover classes by pixel values 43 Table 3.3 Scene accuracy 48 Table 3.4 Landslide susceptible spots by provinces 57 Table 3.5 Landslide levels proportion by provinces 57 v LIST OF FIGURES Figure 1.1 Geographic location of north-west mountainous region in Vietnam 12 Figure 1.2 Landslide susceptibility areas detection model 15 Figure 1.3 Landsat surface reflectance path-row specification 16 Figure 2.1 Artificial Neural Network (Source: VIASAT) 19 Figure 2.2 A CNN sequence to classify handwritten digits (source: Towardsdatascience.com) 20 Figure 2.3 Convoluting a 5x5x1 image with a 3x3x1 kernel to get a 3x3x1 convolved feature 20 Figure 2.4 Forward run in fully connected layer 28 Figure 2.5 Forward run in a neuron at layer 29 Figure 3.1 Normalization across reflectance bands 42 Figure 3.2 Landsat Surface Reflectance training data with data for training (pink) and testing (green) 43 Figure 3.3 Skip connection in Resnet 44 Figure 3.4 Proposed deep learning architecture 46 Figure 3.5 Loss function graph 47 Figure 3.6 Accuracy graph 48 Figure 3.7 Visualization of land cover classification on test images path-row: 127-051, 128-048 49 Figure 3.8 Visualization of land cover classification on test images path-row: 129-050, 130-049 49 Figure 3.9 Data cube ingesting process 50 Figure 3.10 Data in study area in ODC environment 51 Figure 3.11 Data processing in ODC 51 Figure 3.12 Adding padding pixel value of 52 Figure 3.13 Land cover map of northwest region in Vietnam 2017 53 Figure 3.14 Land cover map of northwest region in Vietnam 2019 54 Figure 3.15 Slope map of northwest region in Vietnam 55 Figure 3.16 Landslide susceptibility map for northwest region in Vietnam 56 Figure 3.18 Susceptible landslide spots distribution by provinces 58 Figure 3.19 Field surveying at Tan Uyen Distric (Lai Chau, Vietnam) 58 Figure 3.20 Field surveying at Sin Ho (Lai Chau, Vietnam) 59 Figure 3.21 Field surveying at Muong Te (Lai Chau, Vietnam) 59 Figure 3.22 Highly susceptible landslide spots in Lao Cai (Vietnam) 60 Figure 3.23 Highly susceptible landslide spots in Hoa Binh (Vietnam) 60 vi LIST OF SYMBOLS DCNN Deep Convolutional Neural Network ODC Open Data Cube RS Remote Sensing UAV Unmanned Aerial Vehicles LS Landslide Susceptibility DEM Digital Elevation Model NASA National Aeronautics and Space Administration SRTM Shuttle Radar Topography Mission GIS Geographic Information System ANN Artificial Neural Network SVM Support Vector Machine AHP Analytical Hierarchy Process vii INTRODUCTION The need of research Landslide is one of the most common hazards in the world and in Vietnam Over 75% of total Vietnam area are mountainous regions with steep slopes Because of the mismanagement in economic and social planning, natural hazards such as landslide, flood have occurred frequently In recent years, these disasters have been becoming more frequent and causing serious damages, typically in some provinces in Vietnam such as Son La, Lai Chau, Dien Bien, Yen Bai, Lao Cai, Ha Giang, Cao Bang, Thanh Hoa, Nghe An, etc In the world, research in geological disaster was developed from the early stage, numbers of advanced scientific technology have been applied for monitoring landslide phenomenon In Vietnam, this kind of study just have gained focuses by researchers for about 15 years when natural hazards have occurred more frequently However, studies about landslide in Vietnam were normally carried out for a small scale with qualitative forecast That is why there has been needs for a research that can be applied for large scale analysis that effectively support the government planning, warning and handling natural hazards in the context climate change over the world One of the popular application is to establish landslide susceptibility mappings for research area That is to say, this technique is effective to monitor landslide processes that occurred in the past and to predict vulnerable areas that is prone to landslide phenomenon in the future Detecting landslide in satellite images can be archived by analyzing features and shapes of object, however, traditional image analysis required expert knowledge and field surveying which can be intensively time consuming In the past, detecting landslide areas on satellite images using traditional methods requires expert knowledge and field surveys that can be intensively time consuming In recent years, machine learning techniques has been widely applied in many automation tasks Especially, deep learning algorithms such as Deep Convolutional Neural Network (DCNN) has gained huge popularity because of it 3.3 Results analysis The landslide susceptibility map can be analyzed by statistics which are illustrated at Table 3.4 and Table 3.5 Looking at the number of susceptible spots in each province, it can be seen that Son La accounts for the largest proportion of spots with around 39% while Hoa Binh makes up only more than 3% This can be explained by the terrain feature of Son La which is mountainous terrain with high slopes while Hoa Binh terrain is near delta area which is more flatten In addition, Son La also has the highest proportion of high susceptible landslide spots compared to other provinces Table 3.4 Landslide susceptible spots by provinces Province Son La Yen Bai Hoa Binh Dien Bien Lai Chau Lao Cai Region Susceptible landslide spots (number of pixels) Low Average High All Proportion 52674 63743 37578 153995 39.04 % 20358 16263 7960 44581 11.30 % 6828 3880 1763 12471 3.16 % 34777 37933 18817 91527 23.20 % 19848 20246 11957 52051 13.19 % 18971 15417 5382 39770 10.08 % 153456 157482 83457 394395 100 % Table 3.5 Landslide levels proportion by provinces Province Son La Yen Bai Hoa Binh Dien Bien Lai Chau Lao Cai Region Susceptible landslide levels proportion Low Average High 34.20 % 41.39 % 24.40 % 45.66 % 36.47 % 17.85 % 54.75 % 31.11 % 14.13 % 37.99 % 41.44 % 20.55 % 38.13 % 38.89 % 22.97 % 47.70 % 38.76 % 13.53 % 38.90 % 39.93 % 21.16 % For better visualization, Figure 3.17 shows that the landslide susceptibility will mainly occur to the west of the studied region However, in general, the whole region has over 21% susceptible landslide spots at high level which is highly to witness landslide phenomenon in the future under extreme weather 57 Figure 3.17 Susceptible landslide spots distribution by provinces 3.4 Field surveying and evaluating Field surveying was performed at Lai Chau province to validate landslide spots In this study, a 3D model was established from DEM data for higher visualization Figure 3.18 Field surveying at Tan Uyen Distric (Lai Chau, Vietnam) 58 Figure 3.19 Field surveying at Sin Ho (Lai Chau, Vietnam) Figure 3.20 Field surveying at Muong Te (Lai Chau, Vietnam) It can be seen that detected spots on the maps was actually landslide events Furthermore, landslide spots are close to roads and houses, which can be extremely dangerous when landslide occurs However, there are also wrongly classified susceptible landslide spots: urban area, small spots that cannot be detects due to the low resolution of the Landsat data 59 For other provinces, field surveying was unable be taken due to traveling limitations, susceptible landslide spots can be illustrated at Figure 3.20 and 3.21 Figure 3.21 Highly susceptible landslide spots in Lao Cai (Vietnam) Figure 3.22 Highly susceptible landslide spots in Hoa Binh (Vietnam) 60 CHAPTER IV CONCLUSION The study has succeeded in integrating remote sensing, GIS and Deep Learning algorithms with Open Data Cube platform to establish landslide susceptibility mapping for large mountainous regions in Vietnam Three main objectives have been archived: Analyzing and evaluating features and properties of landslide phenomenon in research area Collecting and formatting training data to support supervised machine learning algorithms Integrating deep learning model with ODC platform to establish landslide mapping It is shown that the proposed method used in this study are suitable for analyzing landslide processes at multiple scales One of the advantages of the developed model is that it can be easily applied for any interested regions in Vietnam where landslide hazards frequently occur Another advantage is The Landsat imagery and SRTM DEM data are open data set which can be freely acquired for further studies Furthermore, the intermediate product of the study is the land cover model which are highly effective for researcher to implement to different of remote sensing analysis There are, however, some limitations of the study which are needed to be improved in the future Firstly, the 30m spatial resolution of Landsat imagery can only monitor a very wide spots (900 square meter area) This means spots with smaller area will be faded out, and susceptible landslide spots in local area will be not able to be detected Secondly, the deep learning method depends heavily on the availability of training dataset which is very limited in remote sensing field In the future, the study will investigate to new novel deep learning methods combining higher resolution remote sensing data such as aerial or UAV data, synesthetic aperture radar data,… to improve the accuracy susceptibility mapping 61 of landslide REFERENCES Bui, Q.-T., Nguyen, Q.-H., Pham, V M., Pham, V D., Tran, M H., Tran, T T H., … Pham, H M (2019) A Novel Method for Multispectral Image 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MAPPING FOR LARGE MOUNTAINOUS REGIONS IN VIETNAM (Ứng dụng viễn thám, GIS học sâu liệu mở để thành lập đồ trượt lở đất vùng đồi núi rộng lớn Việt Nam) Field: Cartography, Remote Sensing and Geographic... hướng dẫn khoa học: PGS.TS Bùi Quang Thành XÁC NHẬN HỌC VIÊN ĐÃ CHỈNH SỬA THEO GÓP Ý CỦA HỘI ĐỒNG Giáo viên hướng dẫn Chủ tịch hội đồng chấm luận văn thạc sĩ khoa học PGS.TS Bùi Quang Thành PGS.TS... map of northwest region in Vietnam 2017 53 Figure 3.14 Land cover map of northwest region in Vietnam 2019 54 Figure 3.15 Slope map of northwest region in Vietnam 55 Figure 3.16 Landslide
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