Doctoral Dissertation
Application of Remote Sensing Technique for Assessment of Natural
Disasters in Afghanistan㸦࢔ࣇ࢞ࢽࢫࢱࣥ࡟࠾ࡅࡿ⮬↛⅏ᐖᢕᥱࡢࡓࡵࡢ
࣮ࣜࣔࢺࢭࣥࢩࣥࢢᢏ⾡ࡢᛂ⏝࡟㛵ࡍࡿ◊✲㸧

Department of Architecture
Graduate School of Engineering
Hiroshima University

Mujeeb Rahman Atefi
Sep 2022

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Dedication“To my family”

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Declaration
I hereby declare that I have completed the present work, entitled " Application of Remote
Sensing Technique for Assessment of Natural Disasters in Afghanistan” solely and without
illegal aid from outside sources. There are no further sources and aids besides those listed,which have indicated sections borrowed verbatim or thematically from the utilizedreferences.
Hiroshima, August 10, 2022
Mujeeb Rahman Atefi

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Acknowledgments
First and foremost, I would like to express my sincere gratitude to my advisor assoc.
Prof. Hiroyuki Miura for the continuous support of my Ph.D. studies and research, as well as
for his patience, motivation, enthusiasm, and immense knowledge. His office door was always
open whenever I ran into a trouble spot or had a query regarding my study or writing. Moreover,he consistently steered me in the right direction whenever he believed it was necessary, and his
dynamism, vision, sincerity, and motivation have deeply inspired me. He has instructed me on
the process for conducting research and presenting it as plainly as feasible. In a word, I can saythat he is more than a supervisor, and thankfully, it was a wonderful privilege and honor towork and study under his supervision, and I hope that our professional relationship may
continue.
Second, my most profound appreciation goes to Prof. N. Nakamura and Assoc. Prof. T.
Mori for being my sub-supervision and taking their valuable time for reading this dissertation.
I would also like to thank Prof. Hiroshi Tagawa, for serving as my committee member despite
his busy schedules and provide me his insightful feedback during the preliminary exam.
During my studies, I was grateful for the MEXT (Ministry of Education, Culture,
Sports, Science, and Technology) scholarship. I am extremely grateful to everyone who has
been supportive of my professional objectives and who has worked hard to give me with theprotected academic time to achieve those ambitions, including the Engineering support officeand other Hiroshima University employees for their kind demeanor and unending support.
Nobody has been more important to me in the pursuit of this project than the membersof my family, so this is the opportunity to express my profound and sincere gratitude to myparents, sister, and brothers, whose love, guidance, and unending inspiration words are withme in whatever I pursue.
Mujeeb Rahman Atefi
Hiroshima University, Higashihiroshima City, Japan
Sept 2022

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Acknowledgments
The dissertation may share some identical sentence or similar formulations from mypublications:” Volumetric Analysis of the Landslide in Abe Barek, Afghanistan Based on Nonlinear
Mapping of Stereo Satellite Imagery-Derived DEMs”,” Detection and Volume Estimation of Large-scale Landslide in Abe Barek, Afghanistan Using
Nonlinear Mapping of DEMs” and” Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan”.

For the purpose of improving readability, I remove a clear identification and utilize theparagraphs of the current dissertation from the articles with the publisher's courteous
permission. The readers are referred to the journals through the DOI URLs that are provided
in the metadata tables in order to access the final versions of the articles.

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Abstract
Afghanistan is a country that is exposed to numerous geomorphological hazards suchas earthquakes, landslides, floods, droughts, avalanches, and man-made disasters. Natural
disasters such as landslides and floods are among the most common hazards in various parts ofthe country, causing extensive damage to buildings and killing hundreds of people each year.
Safety and economic problems, instability, ongoing war, and poverty have put Afghanistan
among the countries that have paid less attention to research. Therefore, it is challenging tofind a published map or find reports after a disaster to show you the details of a natural disasteralong with its exact geographical location. Assessing the degree of catastrophes is vital for
post-disaster efforts and for structural engineers planning and constructing appropriate
structures to prevent future disasters. Based on this, people would choose to reside in secure
places instead of vulnerable ones. Because most structures in Afghanistan are created by people
without any engineering, designs, assessments, or ideas owing to a lack of total government
oversight and regular urban planning. Considering the concerns described, the relevance of theissue, and the scarcity of research in the specified areas in Afghanistan, an attempt was madein this study to perform a study on floods and landslides. It was planned to conduct current
study to investigate without traveling to the impacted region for on-site data collection due toacute security issues. As a result, this research focuses on assessing two natural disasters (theAbe-Barek landslide and the Charikar flood) utilizing remote sensing analyses.
Chapter 1 discussed how natural catastrophes, notably floods and landslides, arecommon in many places of Afghanistan. In the background and literature review part, it has
been demonstrated that, despite being classified as a natural disaster-prone country,
Afghanistan has less research and knowledge on natural disasters. It has also been discussed
that how catastrophe assessments are crucial for post-disaster operations and for policymakers
seeking to prevent future disasters. This chapter also discussed the purpose and research
problem.
Chapter 2 discussed the specifics of natural disaster risk in Afghanistan, such as
earthquakes, floods, landslides, avalanches, and droughts. The availability of risk informationis critical for effective catastrophe and climate risk management. The risk profile summarizes
and visualizes the national multi-hazard evaluations. Such data and information will be criticalfor politicians, decision-makers, development planners, and infrastructure investors to create avimore resilient future for Afghans. As a result, this chapter addressed the susceptibility map,lack of coping skills, lack of adaptive capacities, vulnerability map, population exposure tonatural disasters, and world risk index as a result of exposure and vulnerability. Furthermore,
it was also presented that which natural disasters have the greatest impact in terms ofagricultural losses, property losses, and human losses.
Chapter 3 presented on the assessment of the Abe-Barek landslide using remotesensing analysis. The Abe-Barek landslide occurred on May 2, 2014 in northern parts of
Afghanistan. Pre-and post-event Digital Elevation Models (DEMs) created from stereo pairsof high-resolution satellite images was used to evaluate the extent and the volume of displaced
material. For obtaining such objectives a nonlinear technique was proposed to accurately alignthe multi-temporal DEMs to decrease the undesired artifacts from identified landslide affectedareas and finally increase the accuracy of the estimation. The landslide volume was estimatedas 1.05 × 106 m3 from the corrected DEM of difference (DoD) by the nonlinear method and therelationship between the area and volume compared to those of the previous studies werediscussed. Furthermore, in order to map the damaged region and determine the number ofimpacted people, the remote sensing-based damage assessment was also conducted.
Chapter 4 proposed a method for identifying and assessment of the Charikar flood.
Since the Charikar flash flood occurred on August 26, 2020, there have been no high-resolutionstereo pair satellite data available to use the same approach utilized for the landslide
identification. A pixel-based spectral index approach which is suitable for assessing flash
floods using lower resolution data were utilized. Relative Difference Normalized Difference
Vegetation Index (NDVI) was used in Google Earth Engine with freely accessible medium
resolution images (Sentinel-2). Also, pre- and post-event NDVIs and NDVI-time series wereapplied to track changes in the study region, and it is found that the NDVI dropped considerably
after the incident and remained low for two years. The fact that the NDVI levels have notchanged after two years is clear indication that there is no recovery process, neither given by
the government nor by the people themselves. A DEM-based flow simulation was also
conducted to examine the simulation's usefulness in detecting unsafe locations for future flood
disasters The accuracy was also assessed using the official survey reports and other on-site
data, such as satellite images. Based on the available data, it is concluded that bothmethodologies were suitable for monitoring, analyzing, and mapping almost all types ofdisasters in the future.
Chapter 5 concluded the overall result of this study as well as the study's strengths,
shortcomings, recommendations and future directions.

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TABLE OF CONTENTS
Pages
Dedication........................................................................................................................ i

Declaration.......................................................................................................................ii

Acknowledgments ....................................................................................................................iii
Abstract ..................................................................................................................................... vi
Table of Contents ................................................................................................................... viii
List of Acronyms ...................................................................................................................... xi
List of Notations ......................................................................................................................xii
List of Equations .................................................................................................................... xiii
List of Tables .......................................................................................................................... xiv
List of Figures .......................................................................................................................... xv
CHAPTER I....................................................................................................................... 1

Introduction....................................................................................................................... 1

1.1 Background ........................................................................................................................ 1
1.2 Literature Review ............................................................................................................... 5
1.2.1 Landslide Studies in Remote Sensing ................................................................... 5
1.2.2 Flood Studies in Remote Sensing ....................................................................... 11
1.3 Problem Statement ........................................................................................................... 15
1.4 Research Objectives ......................................................................................................... 17
1.5 Organization of the Dissertation....................................................................................... 18

CHAPTER II..................................................................................................................... 20

Natural Disaster in Afghanistan ............................................................................................... 20
2.1 Introduction ...................................................................................................................... 20
2.2. Floods ........................................................................................................................... 24
2.2.1 Flood Risk and its impact on assets in Afghanistan............................................ 26
2.2.2. Recommendations .............................................................................................. 28
2.3. Earthquake ....................................................................................................................... 29
2.3.1 Earthquake Risk and its impact on assets in Afghanistan ................................... 33

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2.3.2. Recommendations .............................................................................................. 36
2.4. Landslide ......................................................................................................................... 37
2.4.1 Landslide Risk and its impact on assets in Afghanistan ..................................... 38
2.4.2. Recommendations .............................................................................................. 39
2.5. Snow Avalanches ............................................................................................................ 40
2.5.1 Snow avalanches’ risk and its impact on assets in Afghanistan ......................... 42
2.5.2. Recommendations .............................................................................................. 43
2.6. Droughts .......................................................................................................................... 44
2.6.1 Drought risk and its impact on assets in Afghanistan ......................................... 47
2.6.2. Recommendations .............................................................................................. 49

CHAPTER III ..................................................................................................................... 50
Assessment of the Abe-Barek Landslide using Remote Sensing Analysis ............................. 50
3.1. Introduction ..................................................................................................................... 50
3.2. Satellite imagery used in landslide analysis .................................................................... 55
3.3. Nonlinear Mapping Method ............................................................................................ 60
3.4. Evaluation and Applicability of the Method ................................................................... 65
3.4.1. Statistical Assessment ........................................................................................ 66
3.4.2. Shifting Vectors Assessment ............................................................................. 68
3.5. Quality Assessment ......................................................................................................... 70
3.6. Landslide based nonlinear method’s results .................................................................... 72
3.6.1. Corrected and uncorrected DEMs profile cross-sections ................................... 72
3.7. Volume Estimation .......................................................................................................... 75
3.7.1 Previous landslide’s volume versus current estimated one ................................. 79
3.8. Advantages and Shortcoming of the Method .................................................................. 82
3.9. Future Studies .................................................................................................................. 83
3.10. Summary of this chapter................................................................................................ 84

CHAPTER VI ..................................................................................................................... 86
Assessment of the Charikar Flood Using Remote Sensing Analysis ...................................... 86
4.1 Introduction ...................................................................................................................... 86
4.2 Study Area and the Flash Flood in Charikar .................................................................... 87
4.3. The flash flood in Charikar ............................................................................................. 89

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4.4. Materials .......................................................................................................................... 93
4.4.1. Sentinel-2 imagery ............................................................................................ 93
4.5. Methodology .................................................................................................................. 95
4.5.1. NDVI and Spectral water indices ....................................................................... 96
4.5.2. Normalized Difference vegetation Index (NDVI) ............................................. 99
4.5.3. Normalized Difference Water Index (NDWI) ................................................. 100
4.5.4. Normalized Difference Moisture Index (NDMI) ............................................. 101
4.5.5. Relative Difference NDVI (rdNDVI) .............................................................. 104
4.5.6. Histogram-based segmentation ........................................................................ 106
4.5.7. Accuracy assessment procedures using confusion matrix ............................... 107
4.5.8. Flood susceptibility mapping model based on Flow-R .................................... 108
4.6. Extraction of flood inundated areas............................................................................... 109
4.7. Inundation map by visual interpretation ........................................................................ 114
4.8. Accuracy assessment ..................................................................................................... 116
4.9. Monitoring the changes in the affected area in two years ............................................. 118
4.10. Flow propagation analysis by Flow-R......................................................................... 120
4.11. Significance of the method .......................................................................................... 122
4.12. Urban planners and flood managers’ expectations from current study ....................... 123
4.13. Applications and future aspects ................................................................................... 125
4.14. Summary of this chapter.............................................................................................. 127

CHAPTER V................................................................................................................... 129

CONCLUSIONS ................................................................................................................... 129
References................................................................................................................... 134

List of Publications ................................................................................................................ 155

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LIST OF ACRONYMS

ABBREVIATIONSDESCRIPTIONS

DEM

Digital Elevation Model

GCP

Ground Control Point

DoD

Difference of Difference

LiDAR

Light Detection and Ranging

St. deviation

Standard deviation

RPC

Rational Polynomial Equation

NDVI

Normalized Difference Vegetation Index

rdNDVI

relative difference in Normalized Difference Vegetation Index

NDWI

Normalized Difference Water Index

MNDWI

Modified Normalized Difference Water Index

NDMI

Normalized Difference Moisture Index

AWEI

Automated Water Extraction Index

EWI

Enhanced Water Index

GEE

Google Earth Engine

ESA

European Space Agency

MSI

Multispectral Instrument

SAR

Synthetic Aperture Radar

ASTER-GDEM

ASTER Global Digital Elevation Model

ROI

Regions of Interest

NSIA

National Statistic, and Information Authority

NIR

The near infrared

ASTER

Advanced Spaceborne Thermal Emission and Reflection Radiometer

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LIST OF UNIT NOTATIONSNOTATIONSDESCRIPTIONS

m

meter

%

Percentage

m3

Cubic meterAL

Landslide Area

VL

Landslide Volume

Z

Elevation

Nw

Window size

Ns

Search area

Nc

Consensus area

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LIST OF EQUATIONS

Equation 3-1

Elevations’ difference (S-Value). ................................................................ 60

Equation 3-2

The first scalars of the shifting vector ......................................................... 61

Equation 3-3

The threshold value of the vectors(dth) ........................................................ 61

Equation 3-3

The ratio of inter-class variance .................................................................. 61

Equation 3-4

The total variance(στ) .................................................................................. 61

Equation 4-1

Normalized Difference vegetation Index (NDVI) formula ......................... 99

Equation 4-2

Normalized Difference Water Index (NDWI) formula ............................. 100

Equation 4-3

Modified Normalized Difference Water Index (MNDWI) ....................... 101

Equation 4-4

Normalized Difference Moisture Index (NDMI) ...................................... 102

Equation 4-5

Relative Difference NDVI (rdNDVI) ........................................................ 105

Equation 4-6

Binarization method .................................................................................. 106

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LIST OF TABLES

Table 2-1

recorded number of earthquakes, magnitude from 1971 to 2017 ................ 31

Table 3-1

Distinguished features of the satellite imagery used in this study ............... 55

Table 3-2

Statistical parameters of the DoDs .............................................................. 67

Table 3-3

Summarized erosion and deposition volumes for the corrected anduncorrected DoD.......................................................................................... 79

Table 3-4

Empirical relationships linking the landslide area (AL) to the landslidevolume (VL) obtained by various researchers ............................................. 81

Table 4-1

Distinguished features of the satellite imagery used in this study ............... 95

Table 4-2

Accuracy assessment result using the confusion matrix. .......................... 117

Table 4-3

Debris-flood parameters used in Flow-R. ................................................. 120

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LIST OF FIGURES

Figure 1-1

location of Afghanistan and neighboring countries. .................................... 02

Figure 1-2

Outline of the dissertation............................................................................ 19

Figure 2-1

World disaster-related maps ........................................................................ 22

Figure 2-2

number of affected people from different types of natural disaster from2012 to 2022 ................................................................................................ 23

Figure 2-3

Flood prone areas across all Afghanistan .................................................... 24

Figure 2-4

GDP and Population exposure to floods ...................................................... 25

Figure 2-5

distribution of assets and its exposure to the flood ...................................... 26

Figure 2-6

geotectonic plates inside and beyond Afghanistan’s border........................ 29

Figure 2-7

active faults inside Afghanistan ................................................................... 30

Figure 2-8

Historical earthquake between 11971-2017 in Afghanistan ........................ 31

Figure 2-9recorded earthquake’s magnitudes and depths from 2015 – 2022 .............. 32

Figure 2-10

Population and GDP exposure to earthquake .............................................. 33

Figure 2-11

distribution of assets and its exposure to the flood ...................................... 35

Figure 2-12

Population and GDP exposure to landslide ................................................. 37

Figure 2-13

distribution of assets and its exposure to the landslides .............................. 38

Figure 2-14

Population and GDP exposure to snow avalanches ..................................... 40

Figure 2-15

long line of vehicles half-buried in snow avalanches .................................. 41

Figure 2-16

distribution of assets and its exposure to the avalanches ............................. 42

Figure 2-17

Time series of mean temperature anomalies from 1981-2020 .................... 44

Figure 2-18

Population and GDP exposure to Droughts ................................................. 46

Figure 2-19

Current and future impact of avalanche on assets ....................................... 47

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Figure 3-1

Location of the Abe-barek ........................................................................... 51

Figure 3-2

front and top view of landslide affected area............................................... 52

Figure 3-3

Flow chart of the data acquisition................................................................ 56

Figure 3-4

DEM, Hillshades, true image, and point cloud images .............................. 58

Figure 3-5

Uncorrected DEMs of difference................................................................. 59

Figure 3-6

Flowchart of the nonlinear mapping method ............................................... 63

Figure 3-7

Segmentation of DEMs, matching segments, shifting vector generation .... 63

Figure 3-8

histogram of shifting vectors thresholding, and consensus operation ......... 64

Figure 3-9

Schematic plan and profile before and after nonlinear mapping ................. 64

Figure 3-10

DEM presents the selected test area ............................................................ 66

Figure 3-11

Generated shifting vector for nine scenarios. .............................................. 69

Figure 3-12

Comparison of histograms of the corrected and uncorrected DoD ............. 71

Figure 3-13

Profile cross-section of uncorrected DOD ................................................... 74

Figure 3-14

Profile cross-section of corrected DOD ....................................................... 74

Figure 3-15

Cross-section profile presents the pre-and post-event surface difference ... 77

Figure 3-16

Elevation difference along the cross-section line A-A’ obtained from (prepost) DEMs .................................................................................................. 78

Figure 3-17

Comparison of the estimated landslide volume from current study andprevious studies ........................................................................................... 81

Figure 4-1

Topographical map Charikar study area ...................................................... 87

Figure 4-2

Photographs of the flashflood affected area. ............................................... 90

Figure 4-3

Compounds in Charikar city are at risk of flood due to a lack of regularurban planning ............................................................................................. 91

Figure 4-4

Flowchart of the methodology used in this study. ....................................... 96

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Figure 4-5

Observation of changes in the study area using pre- and post-event Sentinel2 satellite images.......................................................................................... 98

Figure 4-6

Observation of changes in the study area using......................................... 100

Figure 4-7

Observation of changes in the study area using pre- and post-event
MNDWIs. .................................................................................................. 101

Figure 4-8

Observation of changes in the study area using NDMIs. .......................... 102

Figure 4-9

Observation of changes using NDVI, MNDWI, and NDMI time series .. 103

Figure 4-10

Correlation graphs between NDVI and NDMI and MNDWI. .................. 104

Figure 4-11

Result of rdNDVI map for five scenarios. ................................................. 110

Figure 4-12

rdNDVI-based change detection image and greenest pixel composites .... 111

Figure 4-13

Histogram of the rdNDVI image with the chosen thresholds.................... 112

Figure 4-14

Extraction of inundated and non-inundated areas from the rdNDVI result
based on binarization ................................................................................. 113

Figure 4-15

Visual interpretation of the flood inundated area ...................................... 114

Figure 4-16

NDVI time series over the study area; the blue line indicates the raw NDVI
from Feb. 2019 to Apr. 2022. .................................................................... 119

Figure 4-17

rdNDVI changes in Charikar after one year period. .................................. 119

Figure 4-18

Flow-R flood -debris susceptibility map using different travel angles...... 121

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CHAPTER I
Introduction
1.1 BackgroundAfghanistan is completely a landlocked, dry, multiethnic country located in the heart ofsouth-central Asia bounded to the east by Pakistan, to the west by Iran, to the north by thecentral Asian countries of Turkmenistan, Uzbekistan, and Tajikistan (Fig. 1-1). It has also ashort border with China to the northeast side. The majority of the land (i.e., more than 70 %)in Afghanistan is mountains. Most of Afghanistan's land is situated between 600 to 3000 metersin elevation (Dupree et al, 2022). The central mountains such as the Hindu Kush mountains
with 414000 square km are a part of the Himalayan chain that is mainly situated in Afghanistan.
Some peaks of the Hindu Kush Mountain rise above 6400 meters. Soil quality varies indifferent parts of the country. The central highlands have desert-steppe or meadow-steppe typesof soil.
The northern plains have extremely rich, fertile, loess-like soils, while the southwesternplateau has infertile desert soils except along the rivers, where alluvial deposits can be found.
Erosion is much in evidence in the central highlands, especially in the regions affected byseasonal monsoons and heavy precipitation. In general, the climate ranges from arid to semiarid, with a large temperature difference between altitude and between warm and cold seasons.
In the mountains the annual mean precipitation increases from west to east; there, as in thesoutheastern monsoon region, it averages about 400 mm. National precipitation extremes havebeen recorded in the Salang Pass of the Hindu Kush, with the highest annual precipitation of
53 inches 1,350 mm, and in the arid region of Farah in the west, with only 75 mm per year.
Because of the geophysical location, types of soil, and weather extreme in the country, almost

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all parts of Afghanistan are very susceptible nearly to all kinds of natural hazards, including
earthquakes, floods, drought, sandstorms, snow avalanches, and landslides (Shroder, 2014).

Figure 1-1. Presents the location of Afghanistan and neighboring countries.

About 30 % of the population in Afghanistan is living in Urban areas (Dupree et al,2022). The majority of urban settlements chose to live along the roads from Kabul
southwestward to Kandahar and Herat, northwest to Mazar-i-Sharif, and northeast to
Badakhshan. The rural population which is the majority (i.e., 70 %) of the population are living
as farmers and nomads over the rest of the country which is distributed unevenly along therivers. The most heavily populated part of the country is between the cities of Kabul toCharikar, Kabul to Kandahar, and Herat (Dupree et al, 2022). Typical types of buildings inurban areas are made of Masonry, precast (i.e., made during the Soviet invasion), RC and RCwith shear walls. On the other hand, Rural settlements (i.e., most of them farmers) usuallysparsely live along valleys of major rivers. The majority of their houses are built from local
materials (i.e., stone, mud. etc.) by themselves without the help of professionals or any
engineering designs. Even in most of the cases government or local government does not3involve in the whole process of building particularly in rural areas. Because of the poor
economy, continuous war, and unstable situation, Afghanistan remains among the country withfewer surveys, and research about any aspects particularly natural disasters. There is no proper
urban or rural settlement map or micro zonation map, that shows the natural disaster-prone
area. Therefore, usually, they don’t have the knowledge to choose a safe place regarding natural
disasters. Most of the time they are building their houses along the path of floods, or inlandslide-prone areas. Another reason for unlawful, non-engineered urban sprawl is the rapid
increase of population after 2001 as a result of mass returning to the country. The rapid increaseof population in urban areas like Kabul and other major cities was a challenge for thegovernment in terms of building shortages. The landlords and local government start selling
land without a proper urban master plan. As a result of this rapid increase in building
construction, most of the settlements build their houses in risky areas such as flood andlandslide-prone areas.
Landslides (Zhang et al., 2015) and frequent impactful floods (World bank, 2017) areamong the most widespread natural hazards. Most of the time the so-called hazards pose threat
to the unlawful weak settlements built from local materials without any proper design andhazard assessments. Since 1980, disasters caused by natural hazards have affected 9 million
people and caused over 20,000 fatalities in the country (World Bank, 2017). Providing natural
disaster assessments such as landslide assessments and flood mapping assessments is the mostcritical risk information that paves the ground for the government, policymakers, and evenpeople themselves. Because having access to risk maps makes the public decide to build theirhouse in the proper place and more importantly increases the resilience of future and evenexisting reconstruction to neutral hazards which can save lives and livelihoods.
It has been clear that the risk related assessment is very advantageous, howevercollecting data in an insecure country like Afghanistan is almost impossible to conduct the on-

4site survey. Hence, taking advantage of remote sensing information is crucial for monitoring
land use, and land cover change. The advantages of remote sensing include the ability to collectinformation over large spatial areas; characterize concerned features and monitor their changes
over time, and the ability to integrate this data with other information to aid decision making
without putting the life of the surveyor at risk.

5
1.2 Literature Review
1.2.1 Landslide Studies in Remote Sensing
Landslides induced by heavy rainfalls and earthquakes are among the most common
geohazard phenomena that frequently occur in mountainous regions, constitute one of the
major hazards responsible for Interruption and destruction of transport-related structures,industrial and hydropower plants, and cause loss of lives worldwide. Recent studies show thatthe number of locations, activity, severity, frequency of landslides, and their social andeconomic impacts are increasing (Dai et al., 2002; Haque et al., 2019; Petley et al., 2005;
Schuster et al., 2001). As an example, between 2004 and 2010, 2,620 deadly landslides
occurred, bringing the lives of 32,322 people to an end. Likewise, the result of a publishedreport by the International Federation of Red Cross and Red Crescent Societies (IFRCRCS,
2001) shows that 42 % of the natural hazards related incidents are triggered by landslides,
which annually account for millions to billions of U.S. dollars in economic losses in three
countries like Canada, Nepal, and Sweden alone. Wenchuan landslides occurred in Sichuan,
China, and were one of the deadliest landslides which led 2,000 people to death (Yin et al.,2009).
Afghanistan is considered a country prone to several geomorphological hazards suchas earthquakes, landslides, flooding, drought, avalanches, and man-made disasters.
Geomorphic hazards are a notable feature in Afghanistan that kills dozens every year alongwith other burdens coming from deadly insecurity, unsustainable economy, crippling poverty,
and chronic health problems. Safety and economic problems made Afghanistan among thecountries with only a little research on landslides. Therefore, the exact location and number oflandslides prone areas all over the country have not been specified yet, but as per records of
the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA, 2020) Of
all-natural disasters reported in Afghanistan between 2012 and 2020, landslides accounts for6only 6.77% of total natural hazard events which 30.33 % of all Geohazards related deaths being
attributed to landslides. Of all recorded (UNOCHA, 2020) landslides in the country between
2012 to 2020, 41.8% occurred in Badakhshan, and surprisingly, landslide events in Badakhshan
account for 16.25% of all-natural disaster incidents (UNOCHA, 2020). It accounts for 53.6%
of all recorded landslide-related deaths in Afghanistan. As reported by (Gupta, 2010)
Badakhshan province is Identified as a high-priority province due to its greater susceptibility
to landslides.
Furthermore, (Shroder, 2011a) examined 22 mass movements in northeast Afghanistan andrecognized them as loess landslides after due consideration and analysis. A similar study(Shroder, 2011b) found 34 large-scale loess slope failures on the north, west, and northwest
slopes of Badakhshan. According to (UNCHA, 2020) From 2012 to 2020, more than 14
thousand individuals were affected by Landslide hazards in Badakhshan province alone, andas reported by (Gupta, 2010) Badakhshan province is a high priority province due to its greater
susceptibility to the landslide. The majority of Afghanistan's land is covered by mountains.
Hence, due to the insufficient flat area in most of the cities, people constructed their houses
along the hills. For instance, Abi-Barek is one of the districts, located in a suburb area that canbe counted as a landslide-prone area surrounded by steep slope mountains. A massive landslide
occurred on May 2, 2014, in the Abe-Barek area buried all houses with their owners along its
path and affected thousands of people and had a considerable impact on the fragile economyof Afghanistan.
Badakhshan province's high susceptibility to landslides led us to conduct a study on oneof the occurred landslide events areas using remote sensing. In this research, the attention
spotlight is on the Abe-Barek landslide incident which occurred on May 2, 2014, in
Badakhshan, Afghanistan, and bring great international attention due to its large dimensions
and catastrophic damage. In accordance with United Nations Institute for Training and7
Research (UNITAR/UNOSAT, 2014; Zhang et al., 2015) the death toll ranges from 300 to
more than 2700. Based on an initial comparison of high-resolution worldView-2 satellite
imagery of pre-and post-event a total of 87 structures were buried by a mass of soil
(UNITAR/UNOSAT, 2014; Zhang et al., 2015). Moreover (UNITAR/UNOSAT, 2014) afterinvestigation and comparison of different areal images conclude that in the same area
previously mass movements occurred and are highly likely to occur again. Zhang et al., (2015)
conducted landslide susceptibility assessments in Abe-Barek landslide affected area using a
low-resolution of 90m SRTM elevation data and DEM derivatives with great limitation oflacking landslide inventory data.
In order to prepare a suitable response to landslides, various slope reinforcement
structures, such as retaining walls, anti-sliding piles, and anchor cables have been commonly
used. Knowing the volume of deformed soil is a key index to knowing the magnitude of alandslide, conducting accurate design of slope reinforcement structures, analyzing and evaluate
accurately the risk assessments, and predicting the secondary risks triggered by landslides.
Compared to calculating the area of a landslide, which is usually easily determined by aerial
imagery, quantifying the volume of specified landslides is a challenging task and requires
detailed information on the surface and sub-surface geometry profile of the deformed soil
(Malamud et al., 2004; Chen et al., 2014; Chen et al., 2019)
There are several methods available that can be used to quantify the displaced soilvolume. In some studies, the landslide volume was calculated using combined algebraicalapproaches (e.g., Integral approximation methods,) in situ profile surveying approaches, andgeophysical or drilling techniques (Chen et al., 2014; Chen et al., 2019). In some other studies,
the volume of landslides was obtained using statistical approaches (Brunetti et al., 2009;
Kovacs et al., 2019; Xu et al., 2016; Zekkos et al., 2017; Larsen et al., 2010). Several studies
thus far have claimed that there is a strong relationship between total failure area with the8relevant volume of landslides (Malamud et al., 2004; Guzzetti et al., 2009; Larsen et al., 2010)
conventional surveying methods are not suitable for large landslide areas ( Kovacs et al., 2019)because in case of urgent hazard assessments it takes considerable time and is not budgetfriendly. Another method of quantifying the volume of a landslide material that is the preferred
method for this study is remote sensing (e.g. aerial photo surveying, LiDAR, and InSAR
techniques) which can provide large-scale data, covering extensive areas. It gives the scientist
ability to collect information and images of the Earth's land to characterize the natural featuresor physical objects on the ground; to observe surface areas and objects on a systematic basis
and monitor their changes over time; and the ability to integrate this data with other information
to ease the decision-making with urgent support and rescue operation. A general overview ofremote sensing techniques for landslide studies and their beneficial service to society has been
widely presented (Scaioni et al., 2014). The increasing availability of satellite data, highefficiency, enhanced performance, great accuracy of the computational process, andappropriate output of remote sensing tools led researchers to take advantage of it with greater
reason. Thus, every day there is the development of fresh methodologies in numerous sectors,
particularly in the study of landslides. Furthermore, Remote sensing techniques provide a
potentially low-cost alternative to field-based assessment, monitoring, early investigation, andremedial work planning for different disasters, especially landslides.
In order to quantify the volumes of a displaced material, identifying the location ofslope failure is very crucial. identification of a landslide area is commonly performed byutilizing change detection techniques on remote sensing images. Identifying the significant
change in a set of images of the same location captured at different time frames is defined as achange detection technique (Singh, 1989; Asokan et al., 2019). Numerous change detection
techniques have been introduced, among all, the image differencing (Sohl, 1999), DEMs ofdifference (Williams, 2012), Normalized difference vegetation index (NDVI) (Ghandhi et al.,92015; Miura, 2019), principal component analysis (Fauvel et al., 2009; Sabokbar et al., 2014),
post-classification comparison (Rogan et al., 2004), spectral mixture analysis (Somers et al.,2011) and artificial neural networks (ANN) (Lei et al., 2019; Ermini et al., 2005; Yilmaz et al,
2009; Mas et al., 2008) become more prominent techniques. A difference of elevation between
pre-and post-event DEMs (DEMs of difference: DoD) has been used to identify the location of
the affected areas and quantify the displaced soil volume (Tsutsui et al., 2007; Martha et al.,2010; Miura, 2019; Lin et al., 2020). Among various remote sensing data sources, the lightdetection and ranging (LiDAR) technique can produce a reliable estimate of morphologicalchange (Jaboyedoff et al., 2012). However, LiDAR observations have been rarely conducted
in remote areas such as the Abe Barek area. The satellite image-based approach is more feasible
to assess the natural disasters in such remote areas. DEM can be created from a stereo analysis
of multiple satellite images with different off-nadir angles. In the case that a set of highresolution stereo pair satellite images is available before and after the disaster, the DoD analysis
can be performed by developing pre-and post-event DEMs.
In order to quantify landslide volumes from DoD, the geometries of the obtained DEMs
need to be registered accurately. However, the DEM errors of height values are found in manycases, even when the data is geometrically registered. Previous studies revealed that theuncertainty, including registration-noise or locational errors in multi-temporal DEMs, would
cause the miscalculation of soil volumes(Chen et al., 2014; Hsieh et al., 2016). These errors
arise from many factors such as the quality of original data, interpolation algorithm, projection
method during the DEM creation process, etc. (Ostrowski et al., 1989). The horizontal
misregistration between DEMs would be one of the major causes of the uncertainty in the DoD
analysis.
Various researches have given special attention to the development of advanced
registration techniques not only for aerial images (Foseca et al., 1996; Le Moigne et al., 2002;

10
Sarvaiya et al., 2011) but also for multi-temporal DEMs(Chen et al., 2014; Hirakawa, 2006;
Miura, 2015; Jaboyedoff et al., 2012) addressed the use of ground control points (GCPs) to
refine the generic sensor’s orientations. The collection of GCPs is time-consuming in an urgent
time of disaster. Chen et al. (2014) proposed a mass balance model to accurately correct two
DEMs and quantify the landslide’s accumulated volume. However, the mass balancecalculation would be difficult in affected areas where collapsed soils flowed into buildings andthe downstream rivers. Hirakawa (2006) also proposed an optimization-based method toautomatically correct the geometrical errors in multi-temporal LiDAR-derived DEMs by
assuming unique linear errors throughout the area of concern. It would be difficult to correct
the geometries of the DEMs when substantial nonlinear distortions are found in the area ofinterest. Those previous studies indicate that misregistration caused by nonlinear distortions in
multi-temporal DEMs still remains an open problem. Miura, (2015) presented a nonlinear
mapping technique for reducing horizontal locational errors between LiDAR-derived DEMs
and estimating the displaced volumes of debris flows. However, the applicability of thetechnique for a landslide in satellite image-derived DEMs, and the parameter selections in thetechnique were not fully discussed in the previous study (Miura, 2015).
In current study, the proposed methodology from (Miura, 2015) has been followed topresent the nonlinear mapping technique’s applicability in reducing the registration noise of
the multi-temporal DEMs to estimate the volume of the Abe Barek landslide. A set of highresolution DEMs generated from stereo pair satellite images was analyzed. The nonlinear
mapping technique was applied for reducing the nonlinear distortions in the DEMs. Unlike theprevious work (Miura, 2015), here in this study, particular attention was given to illustrating
the efficacy of the method using both statistical approaches and profile cross-sections. The
volume of the landslide was estimated from the DoD corrected by the nonlinear technique.

11
1.2.2 Flood Studies in Remote Sensing
Flooding is one of the most destructive and recurring natural hazards on the planet. It
causes catastrophic damage and threats to life, property, and cultural heritage. Floods affectmore people than any other sort of weather-related disaster and are a leading cause of natural
disaster fatalities worldwide (UNISDR, 2015). It is triggered by natural factors such as heavy
rainfall, overflowing of water onto dry land in the floodplain, rapid melting of snow, and thefailure of man-made structures such as dams and levees. Floods have had significant impacts
on the human economy, as well as indirect effects on economic growth and societal well-being.
Floods disproportionately have high impacts on the poorest and most vulnerable people(Freebairn et al, 2020). Afghanistan is vulnerable to a variety of hazards, including earthquakes,
flooding, drought, avalanches, and man-made calamities (Atefi and Miura, 2021). Due to safety
and economic concerns, there is virtually little emphasis on research. As a result, very little
research has been conducted on natural catastrophes, particularly flood disasters. Of all thereported natural catastrophes in 2020, floods directly affected about 50,000 individuals in
Afghanistan (IFRCa, 2021) More than 3,000 people were severely affected by the flood in
Charikar, Parwan province, during the same year (IFRCb, 2021). A destructive flash flood
ravaged Charikar city on August 26, 2020, severely damaging the residential area and killinghundreds of people. Nevertheless, very few studies have been conducted to explore and identify
the flood-affected areas in Charikar. Consequently, the precise location of flood-inundated
places, as well as the degree of flood damage area, remains unknown to this day.
Although flood control is a tough task, minimizing the consequences is a viable,
necessary, and smart option (Khosravi et al, 2019; Mius et al, 2015; Wu et al, 2017; Zaji et al,2018). It is never easy to figure out and choose the ideal strategy and policy to deal with floods.
However, it is possible to reduce these losses and dangers by notifying the public ahead of timeand providing credible information, as well as providing proper flood mapping of flood-prone

12
areas that are experiencing the most severe floods. Flood maps prepared in flood-prone areas
utilizing remotely sensed data are very safe, economical, and timesaving, and they play asignificant role in predicting, preparing, preventing, and mitigating flood damage.
Furthermore, flood monitoring with satellite imagery has the advantage of covering a vast areaand being reasonably quick to collect and analyze data. Researchers often benefit from theavailability of both optical and radar remotely sensed imagery data for flood monitoring andmitigation methods. The Charikar event was a flash flood caused by severe rainfall that swept
downward from the top of the mountain. Since no water has accumulated downstream for along period, employing optical satellite photos is considered ideal. As a result, in this study,
uses optical satellite data to assess flood inundated sites in Charikar.
Several approaches and methodologies based on pixel and object-oriented methods canbe used to detect water using optical satellite data, including supervised and unsupervisedclassification methods; transformation of spectral channels; texture analysis; visualinterpretation; single channel thresholding; and channel ratio use. In this approach, water
indicators are extremely beneficial. Their implementation is contingent on the formulation ofa threshold value that differentiates water pixels from those representing other forms of land
cover.
Several approaches for extracting water bodies from remotely sensed data have beendeveloped by researchers. (McFreeters, 1996) proposed the Normalized Difference Water
Index (NDWI) to detect water features from Landsat TM using band 2 and band 4. (Rogers etal, 2004) introduced a new NDWI for extracting water from Landsat TM utilizing bands 3 and5. To extract surface water bodies from raw digital Landsat data, (McFreeters, 1996) proposeda threshold value of zero, with all positive NDWI values categorized as water and negativevalues classed as non-water. This threshold, on the other hand, makes it impossible todistinguish between built-up surfaces and water pixels. As a result, (Xu, 2006) developed the13
Modified Normalized Difference Water (MNDWI) using Landsat TM bands 2 and 5. When
compared to other indicators, the MNDWI indicator has a stronger ability to reduce
disturbances generated by buildings, vegetation, and soils (Xu, 2006). (Feyisa et al, 2014)
developed the Automated Water Extraction Index (AWEI) to increase the accuracy of water
extraction in locations containing shadows and dark surfaces. The MNDWI has been used tocreate a simple Enhanced Water Index (EWI) by (Wang et al, 2014). Water surfaces can be
identified from background information such as deserts, soil, and vegetation. For the extractionof surface water from Landsat data, (Rokni et al, 2014) examined the characteristics of NDWI,MNDWI, NDMI, WRI, NDVI, and AWEI in applying a novel surface water change detection
process based on the principal components of multi-temporal NDWI. Surface water was
retrieved from the indices using a trial-and-error thresholding methodology. The overall
accuracy and kappa coefficient were used to examine the efficacy of each water body extraction
procedure, and NDWI was found to outperform other measures. These indices have also beenused in a multitude of scenarios, such as surface water mapping (Feyisa et al, 2014; Duan et al,2013), land use and land cover change assessments (Dayranche et al, 2010), and ecologicalresearch (Poulin et al, 2010).
Numerous researchers have utilized the Normalized Difference Vegetation Index(NDVI) to detect land cover change since the NDVI shows positive values for vegetation,
values close to zero for bare soil, and negative values for water (Huete et al, 1997; Escuin etal, 2008; Sidi et al, 2020). Many studies show that AWEI and MNDWI achieved better results
and more stable thresholds than the NDVI index (Huang et al, 2018). However, these indices
are frequently used by the researchers to extract water bodies such as lakes, canals, waterways,
ponds, and so on. Unlike current study, they never employed the so-called spectral indices inflash flood scenarios where there is no standing water except the damaged area as a result of
the flood.

14
Although modern satellites have provided a superior solution over traditional methods inmonitoring water surfaces, manually downloading and processing imagery associated withlarge study areas or long-term time scales is time-consuming. Most of the traditional methods
are not straightforward, which makes them technically challenging for the user to apply
because the extra procedures like preprocessing (DeVries et al, 2020).
Google Earth Engine (GEE), a planetary-scale platform for earth science data and analysis
developed by Google, has been utilized in this study for applying the NDVI techniquedeveloped in (Scheip et al, 2021). The GEE has enabled the development of global-scale
products, tools, and services using temporal earth observation data such as Sentinel-2. The GEE
platform provides a fast-forward solution for these kinds of big data problems. The GEE has
been used to conduct various global and regional scale studies, including regional land cover
mapping (Saah et al, 2019), surface water mapping (Phongsapan et al, 2019), accessing foodsecurity situations (Poortinga et al, 2019), settlement and population mapping (Patel et al,2014), and other applications (Mutanga et al, 2019). Hazard Mapper is an open-accessapplication developed in the GEE (Scheip et al, 2021) and has been used in many case studies
and in different countries to delineate different kinds of natural hazards, including floods.
Although the model's original developer (Scheip et al, 2021) never used it to estimate floods in
non-vegetated environments such as high altitudes or semi-arid to arid regions, this research is
looking into it to see if it can be used to locate flood inundated areas in non-vegetated, datascarce regions in Afghanistan. The flood in Charikar was most likely caused by torrential rain,
which caused enormous stones and debris to slide along the path of the stream channel, which
was blocked by residential residences, resulting in devastating debris flows that ruined homes,
infrastructure, and properties. There were no reports of water accumulation during or after theflood event, indicating that the water was moving extremely quickly along the valley fromuphill to the flat areas of the mountain. In this study, the GEE was used to automatically identify

15Charikar flash flood flooded areas using a change detection method based on pre- and postevent NDVIs in optical satellite Sentinel-2 pictures. The detected inundation area's accuracy
has been evaluated and compared with flood maps derived from visual image interpretations
and government reports. Furthermore, the NDVI time series has also been used to measurechanges in NDVIs two years after the event date to follow the recovery process in the affected
area. Moreover, a digital elevation model (DEM) has been also used to examine flow pathassessment of gravitational hazards at a regional scale (Horton et al, 2013) in order to assess
the feasibility of the simulation technique for finding and predicting vulnerable regions infuture flood occurrences.
1.3 Problem Statement
Slope instability and flood issues are the problems that have to be prioritized for finding
a solution to decrease the risk at a tolerable level either by using effective engineering
techniques or other effective measures. A budget-friendly way to help decrease the impact oflandslides and floods is to identify, assess and develop methods to rapidly can detect in an
emergency period of hazards. Assessments of floods and landslides can be possible throughon-site surveys and remote sensing. However, remote sensing analysis is safer in terms of
securing and faster in terms of efficiency. The assessments of floods and landslides using
efficient remote sensing techniques has also been presented. However, there is the following
problem applying remotely sensed data:
1. In order to make sure that, landslide and flood assessment analysis is being done
properly, it needs to be precisely compared and confirm with field data. In order to carryout such validation analysis; it is necessary to have both precise and sufficient GCP(Ground control points) of the study area. GCP collection can be done by in-situ
surveying. Surveying in an unsafe country like Afghanistan is not recommendable. To
solve this problem, a method has been proposed that answers the problem very well.

16
2. Availability of data in remote sensing analysis for a specific study area is crucial. Thereis very less high-resolution satellite imagery available during the landslide time which
occurred in Abe-Barek. The available data was also not observed by one satellite. Thisis also a challenge because two satellite images with different sensors and resolutions
cannot be directly used for the analysis.
3. As stated earlier, for landslide analysis a methodology is proposed that only could beapplied with the high-resolution stereo pair satellite imagery. On the other hand, forflood analysis, high-resolution satellite imagery was unavailable to carry out the same
methodology as conducted in landslide analysis. Therefore, for the freely available
medium resolution satellite image, different methodology was proposed.
Concerning all the mentioned problems, it becomes the purpose to come up with methods that
fit very well by performing remote sensing analysis. To do this, understanding knowledge ofthe engineering characteristic of each problem is vital.

17
1.4 Research Objectives
The aim of the study is set to fulfill the following objectives from two case studies:1. The objectives of Abe-Barek landslide assessment analysis are as follows:x

Applicability of nonlinear mapping technique on minimizing geometric errorsx

Validation of nonlinear mapping algorithm by comparison of several
descriptive and graphical parameters.

x

Landslide detection and volume estimation by using corrected DEM based on anonlinear mapping method.

x

Validation and comparison of the obtained volume of displaced material withprevious studies.

x

Landslide hazard assessments to determine the number of affected houses.

2. The objectives of the Charikar flood assessment analysis are as follows:x

Using Sentinel-2 images in Google Earth Engine to automatically identify theCharikar flash flood inundated area.

x

Rapid mapping of the flood soon after the event with fewer post-event images.

x

To detect and estimate the area of flood inundated area with freely available lowresolution satellite imagery.

x

Evaluate the performance of the relative normalized vegetation index using thegoogle earth engine on flash floods that occurred in non-vegetated environments.

x

Accuracy assessment of the method

To fulfill the above objectives following computer-based software is used:x

Fortran 77 code-oriented program.

x

Google Earth Engine (GEE), a JavaScript API-based program.

x

QGIS

x

ENVI

18
1.5 Organization of the Dissertation
This dissertation is comprised of 5 chapters. The contents of each chapter are briefly
summarized as follows:
1. Chapter 1 provides a general context of the study including background, problem
statement, and research objectives.
2. Chapter 2 is covering an overview of the natural disaster such as floods,
earthquake, ladslide, snow avalanches, and droughts in Afghanistan.
3. Chapter 3 this chapter is focusing on the assessment of landslide using nonlinear
mapping technique. This chapter contains a detailed explanation regarding,acquiring satellite data, features of the obtained data, DEM extraction, alignment of
the DEMs, detailed explanation of non-linear mapping methodology, analysis of
Abe-barek landslide, its applicability, estimation of the landslide area and volumeusing remotely sensed data. Moreover, it covers a comparison between previously
landslide volume estimation and the current study.
4. Chapter 4 This chapter covers the assessment of flood using rdNDVI. This chapter
covers the details regarding, acquiring satellite data, features of the obtained data,
DEM acquisition, detailed explanation of spectral indices, relative Normalized
difference Vegetation Index methodology using Google Earth Engine, its
applicability, propagation of flood debris mapping in the study area, estimation of
the landslide area using remotely sensed data. Moreover, it covers a comparison
accuracy assessment using visual interpretation and government survey reports.
5.

Chapter 5 gives information about the general conclusion and future work of thisstudy.

19

Figure 1-2. Outline of the dissertation.

20

CHAPTER II
Natural Disaster in Afghanistan
2.1 Introduction
Afghanistan with complex topography, and varying climate conditions with highly
fluctuating temperatures between warm and cold in all seasons, is highly prone to a variety ofnatural hazards (UNDRR, 2020). Exposure of people and assets to natural disasters has been
increased due to political instability, war, weak institution, less control of the government on
rapid urbanization, and poor planning in the country along with the Covid-19 pandemic andinsufficient investment in disaster risk reduction strategies further contributed to increasing thefrequency and severity of various natural disasters. Based on World Risk Report (2021)
Afghanistan is ranked third for the lowest coping capacity, among highest vulnerability, andstands among the countries with a high lack of adaptive capacity (Fig.2-1). It indicates that any
small shock or disaster can easily cause loss of life and properties due to high vulnerability andlow coping capacity. Also, Afghanistan ranked second place in terms of natural disaster-related
fatalities between 1980-2015. Moreover, in Afghanistan, about 9 million people have been
affected and 20000 has been lost their life since 1980 by natural disasters from which floods
are the most frequent and damage-causing hazard in the country (World Bank, 2017). Intense
rainfall, rapid snow melting, and less vegetation coverage of land in mountainous areas mainly
contribute to flood inundation, and urban flooding is mostly a problem in major cities such as
Kabul, Mazar-i-Sharif, and Herat due to poor drainage systems (World Bank, 2017).
Afghanistan is also highly vulnerable to earthquakes. It is among the 10 % of countries withthe highest expected annual earthquake mortality risk. (Shi, et al, 2015). The history ofdevastating earthquakes in the country ranges more than 4000 years (Boyed et al, 2007). Of
all-natural disasters, historically most of the fatalities occurred in the earthquake. Since 1980

21
more than 10000 people have lost their lives because of earthquakes. (World Bank, 2017).
From 1997 to 2007 earthquake-related death has been reported for 7000 people, from which
4000 people died in the Nahrin earthquake in May 1998 (Boyed et al, 2007). As per reports of
the world bank, 2017 on average earthquakes cause 80 million US dollars in damages on
average annually in Afghanistan.
Furthermore, in Afghanistan with a mountain dominant landscape, landslide
susceptibility is classified as high. Having heavy rainfall patterns, high slope terrain, type of
geology, soil, land cover, and more importantly earthquakes make the landslide event afrequent hazard phenomenon. Based on (DRR & World Bank, 2017) over 3 million people in
Afghanistan are under high exposure to landslide hazards. As per records of the United Nations
Office for the Coordination of Humanitarian Affairs (OCHA, 2020) from all reported natural
disasters in Afghanistan between 2010 and 2017, landslides only account for 3.6% whichmakes 16.8% of all Geohazards related deaths. As reported by (Gupta, M.,2010) Badakhshan
and with other three northern provinces are identified as high priority provinces due to theirgreater susceptibility to landslides. Drought is also threatening the lives of nearly three million
Afghans (NRC, 2021). Severe weather, including heavy rain and snow falls, has been affecting
the central, northern, and northeastern provinces of Afghanistan causing casualties anddamage. Avalanches frequently happen almost every year and are triggered after a heavysnowstorm and long-term snowfalls. According to the (World Bank, 2017) reports over two
million people are under high threat of avalanches in Afghanistan. Avalanches most often
happen in northern parts of the country. The 2017 avalanches were a series of avalanches that
occurred in different parts of the country and reportedly killed 100 people (BBC News, 2017).
The DRRR estimates show that more than 10000 km of roads which is more than 15 % of allroads in Afghanistan are under high exposure to avalanches, it contains the key transport route22such as the Salang pass which connect the northern province to Capital Kabul. (DRP & worldbank, 2017).

Figure 2-1. World disaster-related maps, a) susceptibility map b) lack of coping capacities c) lack ofadaptive capacities d) vulnerability map e) exposure of the population to the natural hazards; f) world
risk index as a result of exposure and vulnerability (WorldRiskReport, 2021)

Based on report of (OCHA, 2022), from January 2012 to May 2022, 1.6 million people wereaffected by natural disasters throughout Afghanistan. A total of 13 provinces out of 34
experienced some kind of natural disaster during the period (Fig. 2-2). Among all the natural
disaster flood and flash flood seems to be more frequent. (Fig.2-2). On average the so-called
disasters affect 40000 people every year.

23This chapter will cover four main frequent natural disasters in Afghanistan such as floods,
landslides, earthquakes, avalanches, and droughts in detail:Figure 2-2. Presents number of affected people from different types of natural disaster from 2012 to2022 (UNDATA).

24
2.2. Floods
Flood and flash flood are the most frequent types of natural disaster which happening
in different parts of Afghanistan and causing loss of life and properties. As can be seen in Fig.
2-3 almost all parts of the country including urban areas and countryside areas are flood prone
areas. However, flood impact on population is comparatively low on flat areas compared to themountainous areas. The main cause of flood in rivers mainly occurs due to the heavy rainfall
and snow and glaciers melt. It is noteworthy to mention that lack of vegetation and contributes
to the occurrence of flooding. Flooding in urban areas is typically triggered by intense local
rainfall and lack of drainage systems. Floods in Afghanistan cause more deaths than othercountries due to low socio-economic development, lack of awareness, lack of coping
mechanisms and lack of protection capacities.

Figure 2-3. Presents the flood prone areas across all Afghanistan. (World Bank, 2017).

25
Figure 2-4 presents the flood hazard map of all Afghanistan. As can be seen in Fig 2-4 Kabul
with high density of population has most affected population because of floods. In Kunar
Province flood has high impact on population and GDP.

Figure 2-4. GDP and Population exposure to floods. (Data from World Bank, 2017)26
2.2.1 Flood Risk and its impact on assets in Afghanistan
The exposure assessment of assets for floods provides essential information about theassets, which are located in the flood hazard prone areas. The information created from theassessment are important and useful to and policy makers as basis for plans and interventions
on preparedness, early warning, response recovery, and mitigation. In Afghanistan floods,
primarily affecting people, private buildings, roads, healthcare facilities, education relatedbuildings and facilities, and energy related facilities (Fig. 2-5). In Afghanistan floods, primarily
affecting people, private buildings, roads, healthcare facilities, education related buildings andfacilities, and energy related facilities. Moreover, from Fig. 2-5 it clearly can be noticed thatthe cities with largest densities of any assets having more exposure to the flood.

Figure 2-5. Presents the distribution of assets and its exposure to the flood across all Afghanistan.
(World Bank, 2017)27
For example, Kabul province with highest population density has the highest exposure of the
population to the flood. The percentage of population exposed to flood ranges from 8 to 15
percent. Likewise, private buildings, roads healthcare and education related buildings arefound to be exposed in flood prone areas.

28
2.2.2. Recommendations
As discussed, floods frequently occurring in different parts of the country causing
significant damage and has the potential to disrupt the livelihood throughout the country.
There are various flood risk management methods that can reduce the impacts they can cause.
x

Flood risk-reduction measures particularly building infrastructure like strengthening
dikes and retrofitting buildings would help improve the flood protection in cities andrural areas.

x

Avoid constructing houses in floodplains and flood prone areas as much as possible.

x

Because of lack of government control on urban planning and building construction
majority of the buildings are being built in riverbanks and flood plains. Increasing thenatural space along the riverbanks would help reduce the occurring of flood fromrising of the river water.

x

Lack of drainage system in all Afghanistan including big cities like Kabul, Mazar-iSharif. etc., cause blockage of rainwater.

x

Preparing flood inundation maps will prevent people not to construct public andprivate property in flood prone areas.

29
2.3. Earthquake
Afghanistan is located in a tectonically active region of the world. Each year
Afghanistan experience moderate to strong earthquakes, causing massive human casualties,
loss of life and damages to homes and infrastructure because many dwellings are not as stableor well-built. Afghanistan situated between Indian and Eurasian plates Fig 2-6, which area
moving upward and downward direction respectively.

Figure 2-6. Presents the geotectonic plates inside and beyond Afghanistan’s border, which as a result
may cause major earthquake in Afghanistan and surrounding countries.

30
If a modern deformation continues in the so-called region, moderate- to- large magnitude areoccurring. As a result, the earthquake will cause massive damage, not just from ground shaking
and ground surface fault rupture, but also triggering liquefaction and extensive landslide as
occurred in Kashmir (Harp et al. 2006). Among all active faults in Afghanistan Hindu KushPamir region the most active because it is intersected by network of faults that have different
orientation and direction of movement. Fig 2-7.

Figure 2-7. Presents the active faults inside Afghanistan (USGS) Modified from Wheeler et al.
(2005).

Large number of earthquakes in Afghanistan is triggered by Indian plates which ismoving northward beneath Eurasian plate at speed of 3-4 cm/year (Fig. 2-6). This collision
make the Himalayan, Hindu Kush, and Pamir Mountains uplifted. As a result of this activity
majority of the historical earthquakes happing northern parts of the country. Figure 2-8 clearly
illustrates that the distribution of historical earthquakes mostly occurred in Hindu Kush-Pamir31region and gives and insight that future earthquake most likely to happen more and morebecause those areas under large stresses that cause slip on faults.

Figure 2-8. This map indicates all the earthquakes recorded in the period between 1971 until first
Oct. 2017(IMMAP - E. Hagen, 2017).
Table 2-1. Shows the recorded number of earthquakes, magnitude in Afghanistan from 1971 to 2017
(U.S Geological Survey earthquake program, 2017).

Number of Earthquakes (magnitude)
Period1971-1979
1980-1989
1990-1999
2000-20092010-2017
Total

3-4.9

5-5.9

6-6.9

7-7.9

Total

763
1258
2,181
3,800
1,750
9,752

144
140
178
176
122
760

17
14
26
21
5
83

1
2
3
1
3
10

925
1,414
2,388
3,998
1,880
10,605

Historically many earthquakes struck Afghanistan with magnitude larger than 6 asshown in Table 2-1 which caused huge injuries, loss of life and damages to the properties.

32
Figure 2-9 presents the earthquakes’ magnitude and depth between 2015 to 2022. In 2015 an
earthquake which is recorded in USGS with magnitude 7.5 Mw (Fig 2-9) caused nearly loss of
400 people, more than 2500 injuries, and more than 36000 houses were damage, and 7000
houses were totally destroyed. (IFRC, 2015). The shake impact was felt beyond Afghanistan’sborder as damages and casualties have been reported in the western part of Pakistan while
minor damage occurred in India. (OCHA, 2015).

Figure 2-9. Illustrates the recorded magnitudes and depth of the earthquake from 2015 to 2022
(USGS, 2022).

33
2.3.1 Earthquake Risk and its impact on assets in Afghanistan
Afghanistan is struck by moderate to strong earthquakes, causing huge damage and lossof life. Based on report (World Bank, 2017) earthquake has been caused the greatest numbersof deaths since 1900. As discussed north and north-eastern parts of the country is under highrisk of earthquake hazards. Particularly most of the earthquake record was observed along theborders with Tajikistan, Uzbekistan, and Pakistan. Based on reports (World Bank, 2017)
10,000 people were killed since 1990 as a result of earthquake. As shown in Fig. 2-10 Kabul,
capital of Afghanistan is under high risk due to its location in earthquake prone area and theconcentration of assets. So far Kabul has the highest average estimated damage compared to
all regions in Afghanistan. (World Bank, 2017). Figure 2-10 presents the people and GDP
exposure to the earthquakes.

Figure 2-10. Population and GDP exposure to earthquake in Afghanistan. (World Bank, 2017)

34
Based on a study conducted by (World Bank, 2017) an earthquake with high magnitudein Kabul highly likely cause approximately more than 8000 deaths and over $500 million indamages, due to high concentration of population and GDP.
The exposure assessment of essential assets is presented in Fig 2-11, it shows thelocation where each asset intersects with these hazards, and the vulnerability of those assets.
This map is produced based on historical records of earthquakes which on the other hand
illustrates the location of earthquake hazard prone areas. The information created from theassessment are important and useful for policy makers as basis for plans and interventions on
preparedness, early warning, response recovery, and mitigation. In Afghanistan earthquakes,
primarily affecting people, private buildings, roads, healthcare facilities, education relatedbuildings and facilities, and energy related facilities as shown in (Fig. 2-11). The impacts ofearthquake on each asset are different, it is mostly depending on the characteristics of the assets.
Moreover, from Fig. 2-11 it clearly can be noticed that the cities which is susceptible to theearthquake and having high concentration of any assets having more exposure to theearthquake.

35

Figure 2-11. Presents the distribution of assets and its exposure to the Earthquake across all
Afghanistan. (World bank, 2017).

As can be seen in Fig 2-11, north and north-eastern parts of the country’s assets are underhigh exposure. Particularly, Kabul province with highest population density has the highest
exposure of the population to the earthquakes. The percentage of population exposed toearthquake ranges from 0 to 0.15 percent. Likewise, private buildings, roads healthcare andeducation related buildings are found to be exposed to earthquake and percentage of exposureis shown in Figure 2-11.

36
2.3.2. Recommendations
As mentioned north and north-eastern part of Afghanistan suffers from one of thehighest earthquake rates in Afghanistan. The proximity of the boundary between the Indianand Eurasian tectonic plates means that there is a lot of seismic activity going on almost
constantly. Most of these earthquakes go unnoticed, but every now and then a larger scalequake hits the area and the impacts of it can be felt for a long time afterwards. Constructing
new structures based on updated seismic design code, retrofitting existing building can reducethe building collapse and fatalities from earthquake significantly. Moreover, lack ofconsciousness also play an important role in high rate of fatalities due to earthquake. Therefore,
learning some basic earthquake safety tips can help reduce the death tolls considerably.

37
2.4. Landslide
Due the mountainous terrain, unstable soil, heavy rainfall, frequent earthquakes morethan 60 % of land in Afghanistan, from the northeast to the western region are susceptible tolandslides. Lack of landslide hazard maps and lack of consciousness of the people make themto build their houses in susceptible places which leads loss of life and properties. In Fig 2-12
exposure of two categories of assets are illustrated. The intersection of hazard and exposed
datasets has been carried out by using landslide susceptibility maps produced at the national
level (Afghanistan-MHRA, 2017). As can be seen in Fig 2-12 in province such as Badakhshan,
Nuristan, Daikundi, and Ghor large number of people exposed to landslide. Similarly, in
Badakhshan, Takhar, and Ghor province large numbers of assets values under exposure oflandslides. Based on reports (World Bank, 2017) deforestation, land-use change and increasein heavy rainfall increase the severity of the landslide hazard to the houses and settlements.

Figure 2-12. Population and GDP exposure to landslide in Afghanistan. (World bank, 2017).

38
2.4.1 Landslide Risk and its impact on assets in Afghanistan
This study evaluates the significance and socioeconomic value of assets (people,
infrastructures, schools, health facilities, highways, etc.) in relation to their exposure level.
Such an approach provides a comprehensive overview of the general exposure at the national
level. The available exposure data layer for each category (population, government buildings,
commercial and residential buildings, agricultural, infrastructure, and GDP) are shown in Fig.
2-13.

Figure 2-13. Presents the distribution of assets and its exposure to the landslides across all
Afghanistan. (World bank, 2017)

As can be seen in Fig 2-13 highest number of assets exposed to landslide are located innorth, north-eastern, and central parts of the country. Based on findings of (World Bank, 2017)
over 3 million people area exposed to landslides in Afghanistan. Badakhshan has more than
800 million dollars’ worth of assets and more than 280,000 people exposed to landslide. an39example of a devastative landslide in Afghanistan would be Abe-Barek landslide which caused
death hundreds to thousands of people.
2.4.2. Recommendations
It is stressed that landslide is a common natural disaster causing deaths and economic
losses each year. The risk of landslide can be reduced by increasing consciousness of the people
in the national and local level avoid building their houses landslide prone areas. Moreover,
government also prepare the landslide hazard maps in local level to better understand the risk
in each area. Furter, it is government job to recognize the landslide susceptible area and avoid
the construction of transport networks or service lines and other important infrastructure. In
most of the landslide prone countries local monitoring of slopes is very effective and it canhelp identify the landslide hazard early. It is also noteworthy that if a landslide susceptible area
identified then strengthening of the slope would reduce the potential risk of landslide to someextent. Passive measures such as gathering of boulders in the foot of hillside, geosynthetic andgeotextile can also reduce the impact of small landslide from reaching to the assets.

40
2.5. Snow Avalanches
Avalanches are a common natural disaster found in most mountain snowy regions of
the world. Avalanches are rapid mass movement of snow caused by gravity and are considered
meteorologically induced dangers. There are various types of avalanches such as wet type, dry
type. All avalanches from starting point to the stopping point has three stages: origin, transition,and runout. Typically avalanches occur in steep terrain of 30° to 45° having large accumulationof snow (Fuchs et al., 2019). On terrain below 15°, the avalanche slows down and stops. The
flow velocity of avalanches is ranges between 50-200 km/h, whereas wet avalanches are denser
and slower (20-100 km/h). (Fuchs et al., 2019) Avalanches can trigger naturally or artificially.
Whereas spontaneous natural avalanches pose a major threat to buildings and infrastructure.

Figure 2-14. Population and GDP exposure to snow avalanches in Afghanistan (World bank,2017).

41
Figure 2-14. presents the avalanche prone province in Afghanistan. As can be seen from Fig.
2-14 the majority of north-eastern to western, and central parts of the country are very
susceptible to avalanches due to the availability of snow in most of the seasons in highmountains Hindu Kush.
Snow avalanches affecting the people living in those hazardous areas. Avalanche deaths
continue to increase in Afghanistan. The latest avalanche in 2022 in Afghanistan killed at least
eight people, including three children, have been killed and 10 others wounded in three separate
incidents of landslide and avalanche in Afghanistan (Xinhua, 2022). The worst avalanche wasoccurred in Salang Pass (Fig 2-15) on Feb 2010 which is the only road connecting Kabul to thenorthern provinces and more than 180 people have been killed there which was the worst
avalanche in a past 30 years. (World bulletin, 2010).

Figure 2-15. Long line of traffic jam including different vehicles are half-buried in snow after
avalanches killed at least 180 people at the Salang tunnel in Parwan province, Afghanistan on February
10, 2010. (Photo: Shah Marai)42
Accessing and providing assistance to in emergency time to such remote places is the biggest
challenge with the avalanche’s events particularly in unstable country like Afghanistan. As
illustrated in Fig 2-14 Badakhshan, Kabul, and Daikundi have the greatest number of people
and GDP affected by avalanches.

2.5.1 Snow avalanches’ risk and its impact on assets in Afghanistan
In order to illustrate the population, infrastructures, schools, health facilities, roads, etc.
level of exposure to the avalanches, this study consider their relevance and socioeconomic
value accordingly. Such an approach very helpful for overviewing of the general exposure at
the national level. The available exposure data layer for each sector (population, governmentbuildings, commercial and residential buildings, agricultural, infrastructure, and GDP) is
shown in Fig. 2-16.

Figure 2-16. Presents the distribution of assets and its exposure to the avalanches across all
Afghanistan. (World Bank, 2017).

43
As can be seen in Fig 2-16 highest number of assets exposed to landslide are located innorth, north-eastern, and central parts of the country. Based on findings of (World Bank, 2017)
over 2 million people area exposed to landslides in Afghanistan. Badakhshan has more than
990 million dollars’ worth of assets. From 2000 to 2015, over 153,000 people were affected byavalanches in Afghanistan (World Bank, 2017).
2.5.2. Recommendations
As discussed in this section avalanches are a common natural disaster causing deaths
and economic losses each year in Afghanistan. The risk of snow avalanches similar to othernatural disasters can be reduced by increasing consciousness of the people in the national andlocal level avoid building their houses in landslide prone areas. Building retention structures
such as snow supporting structures, artificial release of avalanches in susceptible areas,building dam avalanches, and avalanches galleries can reduce the avalanches risk of exposure
to different assets.

44
2.6. Droughts
Afghanistan has arid and semi-arid climate condition suffers from recurring droughts
with varying lengths and severity. Agriculture in Afghanistan contributes to 80 % of
Afghanistan’s total income. Frequent occurrence of droughts in Afghanistan affects millions
of people and causing large economic damages to agriculture, households, and other lifeline
structures. Its annual water budget relies heavily on winter rainfall in its mountains, whichaccumulate in the form of glaciers, snow, and ice. The amount of water available depends on
the amount of rainfall and stable weather patterns, which causes snow and ice to melt
continuously during the planting season. The Hindu Kush Mountains in Afghanistan are thesource of 80% of the country's renewable water. Afghanistan experienced severe drought in
the past (e.g., 1970, 1996 and 2001), but in the southern provinces of Kandahar, Helmand,
Zabul, Uruzgan and Nimroz, it was so severe that in 2000, the United Nations estimated thatnearly three million people needed emergency assistance (OCHA, 2001).
5
Mean Temperature Anamoly (C°)

4
3
2
1
0
-1
-2
-3
-4

-5

2020

2000

1980

1960

1940

1920

1900

1880

-6

Time (in year)

Figure 2-17. Time series of annual values of Afghanistan mean temperature anomalies and its trends(Base period 1981-2010) (NOAA data).

45
Figure 2-17 presets the Time series of annual values of Afghanistan mean temperature
anomalies and its trends from 1880 to 2022. A positive anomaly means that the temperature
was warmer than normal; a negative anomaly indicates that the temperature was cooler thannormal. As can be seen the year recorded as warmer temperature or low negative winter often
corresponds to the driest year or drought.
Droughts causes further threat to livelihoods, income, poverty of the people which 80% of their livelihood rely on agricultural. Lack of water resources led to excessive reduction ofwheat yields and drying of fruit trees, plants, grasses, and the loss of 50 to 60 percent of
livestock in those provinces. The drought caused so much famine that the United Nations
named the worst-hit areas, from northwestern Afghanistan to the central Hazaras, the "Hunger
Belt."
Afghanistan's drought cycle lasts from three to five years once a decade (Arthur et al,1966). In the last two decades, Afghanistan has experienced drought twice a decade, in the
2000s in 1996-2001 and 2007-2009, in 2010 in 2011-2013 and 2016.
Afghanistan is expected to be affected by climate change and uncertain rainfall and frequent
droughts, and the increased risk of flooding would come in secondary concern. Annual drought
in many parts of the country is likely to become common by 2030. As can be seen in Fig.2-18
population and assets in southern provinces are under high exposure and threat of droughts

46

Figure 2-18. Population and GDP exposure to Droughts in Afghanistan. (World Bank, 2017).

Based on reports (World Bank, 2017) on average, droughts cause 280 million of
economic loss to agriculture each year. Due to the climate change consequences, thehydropower production will decrease to zero once in every 10 years in Kajaki Hydropower,
and once in Naghlu Hydropower (World Bank, 2017).

47
2.6.1 Drought risk and its impact on assets in Afghanistan

Figure 2-19. Presents the distribution of assets (e.g., agriculture, and population) and the current andfuture impacts of snow avalanches on them in different part of Afghanistan. (World Bank, 2017).

Drought has a direct impact on crops and thus on people. Therefore, the impact of
current and future droughts on agriculture and people will be introduced. Assessing drought
and its potential impact on agriculture in vulnerable areas is very important, especially indrought-prone areas. Quantifying the impact of agricultural drought on food supply may enable
policy makers to make more sustainable agricultural decisions. This requires a thoroughassessment of the relationship between spatiotemporal drought variability, agricultural
systems, irrigation effects and the availability of water resources. To illustrate the population,
and agricultural impacts, level of exposure to the droughts, this study consider their relevanceand socioeconomic value accordingly. Such an approach very helpful for overviewing of thegeneral exposure at the national level. The current and future impacts of droughts in
Afghanistan are shown in Fig. 2-19. The effects of drought on agriculture and population

48
modeled by FAO for Afghanistan are shown in Figure 2-19, which shows that approximately
90% of Afghanistan will be affected by drought by 2050.

49
2.6.2. Recommendations
There is an urgent need to enhance public awareness to improve the possibility of
wastewater sampling and decrease water use in light of the anticipated precipitation over the
next few months. Additionally, farmers should shift to crops that produce greater yields perunit of water applied to irrigable land. This will maintain the water balance and minimize
drought danger to some degree. Beyond these short-term remedies, the government mustprioritize a comprehensive drought risk management plan. By enhancing the agricultural
system's resilience, sustainable development can reduce vulnerabilities to climate change. In
addition, applying a successful model of a nation that has experienced drought along with best
management techniques on farms and in agriculture might reduce the danger of drought tosome extent. Implementing a low water consumption restriction policy during the high-waterseason and conserving that quantity of water in particular areas, such as the southern provinces,might also be an effective method for promoting sustainable living.

50

CHAPTER III
Assessment of the Abe-Barek Landslide using Remote Sensing
Analysis3.1. Introduction
A devastating landslide hit Abe Barek village of Argo District, Badakhshan Province,
Afghanistan (Fig 3-1) in May 2014. It was the worst landslide in 2014, killing 300 to more than
2700 people (Zhang et al., 2015). According to the locals, two landslides occurred within ashort period of time. The villagers may lack the specialized knowledge, training, and necessary
equipment to perform triage and started the search and rescue operations for assistance to thefirst trapped victims shortly after the first incident (New York Times, 2014). Tragically, many
volunteer rescuers become secondary victims themselves (The Guardian, 2014). Considering
the remoteness of the landslide area, rough road conditions, insufficient capacity for fast andwell-timed transfer of machinery, and the necessary equipment, the government quickly
concluded that retrieving the buried bodies is unattainable. Hence, the site was declared a mass
grave (CNN, 2014). Figure 3-2a, taken by (Kohsar, 2014), and outlined with a yellow color,shows the landslide’s downward movement direction in a sufficiently inclined surface rupture
over a considerable distance and buried the settlements along its pathway. Moreover, Figure 32a, b clearly illustrates previous landslides’ boundaries in the same position with red lines.
The study area with a latitude of 37°1′ 18″ N and a longitude of 70°22′01″ E is situated
21 km away from Faizabad, the capital of Badakhshan province. Badakhshan is one of the 34
provinces of Afghanistan, located in the northeastern part of the country along the Pamir and
Hindu Kush Mountains. Some of its mountains lay between 3000–7000 m above sea level andare covered with snow and glaciers throughout the year. Badakhshan is one of the most remote,51
least developed, and disaster-prone regions, which borders Tajikistan to the north, Pakistan tothe south, and China to the easternmost (Figure 3-1a).

Figure 3-1. Location of the study area. (a) Afghanistan, study area, and borders with neighboring
countries, (b) Badakhshan province, (c) Argo district, and Abe Barek village.

The presence of two active plate boundaries (Shroder, 2014), namely “North Afghan
Platform and Transgressional Plate boundary”, and the passing of three major active faults
through the province (Boyed et al., 2007), namely the Central Badakhshan Fault, Darvaz Fault,
and Henjvan Fault, making it the highest seismic prone region in the country and is arguably
one the most vulnerable regions to all kinds of natural disasters. With this regard, most of theresearchers relate the majority of the landslide hazards to earthquakes. Argo district (Figure 31c), of which Abe Barek is one of its villages, with 10% of the whole province population, isthe most populous district in Badakhshan (UNFPA, 2003). According to the previous study(Zhang et al., 2015), 30.2% of the entire land in Badakhshan province is covered by a slope
gradient of 20–30°, and in the area where the landslide occurred, 78% of its land is made up ofa slope gradient of 20 to 40°. This might be considered as one of the reasons why this area isprone to landslides.

52

Figure 3-2. Collapsed area. (a) Front view of the Abe Barek landslide area, the yellow outlines indicate
the boundary line on the current landslide, the red outlines represent the boundary of old landslides, andthe photograph was taken on May 5, 2014, by (Kohsar, 2014). (b) Top bird view of the landslide areashows how steep the slope is, the red outlines reflect the boundaries of the old landslides, the photograph
was taken on May 5, 2014, by (Kohsar, 2014).

53
The Abe Barek landslide area brutally buried houses and killed large numbers ofpeople, which can be categorized as the most life-threatening (Zhang et al., 2015) natural
disasters after two incidents of the 1998 earthquakes (Boyed et al., 2007; Wheeler et al., 2005)
occurred in this area. To date, (Shroder et al., 2011a; Shroder et al., 2011b) has determined 34
large loess landslides that are commonly triggered by saturation and liquefaction in this area.
Knowing the cause of the landslides is extremely important. However, as mentioned, not somany studies have been conducted in the area of interest to figure out the main reason. The
only research conducted in the study area is the susceptibility assessment in the Abe Barek(Zhang et al., 2015). Landslide factors such as topography, climate (rain), geology andseismicity of the area, land cover and land use, and previous earthquakes were reviewed deeply.
Eventually, they identified the following factors as a leading cause of the incident:x

Weakening of the rock-based silt-covered area due to the repeated seismic events.

x

Slope instability further intensifies due to the remaining too loose materials from theprevious landslides at the same position.

x

Slope instability due to the increased infiltration of rainwater into the loose soil.

x

The presence of sensitive material which is very prone to landslides.

x

Increasing the soil water content from the rapid melting of deep snow and spring rains
up to 200 mm.

xLand use and irrigation activities.
Finally, it can be concluded that the cause of the landslide is not one. Rather, a set of

triggering factors (e.g., complex geology, the presence of landslide-prone material (Shroder etal., 2011a; Shroder et al., 2011b), previous earthquake records, and close distance to the fault
line’s intersection (Wheeler et al., 2005), summer rains, and melting snow) or a combinationof any of such mentioned factors possibly lead to this unfortunate situation.

54
Even though this tragic event caused loss of hundreds of people and buried hundreds of houses,
yet there was no research conducted to illustrate the size (area and volume of landslide) andnumber of affected houses.

55
3.2. Satellite imagery used in landslide analysis
In this study, stereo pair high-resolution satellite images were gathered to generate preand post-event DEMs. The pan-sharpened GeoEye-1 images with the spatial resolution of 0.5
m observed in June 2012 were analyzed to create the pre-event DEM. The pan-chromatic
IKONOS-2 images with the spatial resolution of 0.8 m observed 2 months after the event in2014 were analyzed to create the post-event DEM. Table 3-1 shows the characteristics of thesatellite images used in this study and their observation conditions. Since the pre-event images
were observed on the same day with different off-nadir angles, the pair images’ quality can be
judged as very good. On the other hand, since the time interval of the observations of the postevent images is 8 days, and land cover changes were slightly observed between the images, thepair quality of the post-event images are judged as good.
Table 3-1. Distinguished features of the satellite imagery used in this study and their observation
conditions.
Pre-Event
Image ID

Date
Satellite
Band
Spatial resolution (m)Off-nadir (deg.)
Satellite azimuth (deg.)
Sun elevation (deg.)
Sun azimuth (deg.)
Cloud cover (%)
Overlapped area (km2)
Pair quality

Post-Event

105041000005F800 10504100000CBE100 106001000932AC00 1060010009356D00

13 June 2012
GeoEye-1
Pan-sharpened
0.5
18.4
N63.1E
68.7
N124.2E
1.0

13 June 2012
GeoEye-1
Pan-sharpened
0.5
29.6
N36.8E
68.6
N124.0E
5.0

24.5
Very good

7 July 2014
IKONOS-2
Panchromatic
0.8
24.1
N220.1E
70.0
N132.4E
3.0

15 July 2014
IKONOS-2
Panchromatic
0.8
9.6
N169.4E
67.8
N129.0E
0.1
30.5
Good

Figure 3-3 shows the flowchart for the DEM generations from the acquired satellite
im-ages. The DEMs and orthorectified images were generated by a dense stereo matching
technique using commercial software (Agisoft Metashape Professional Ver. 1.6.3). The
technique consists of a photo-alignment, generation of the three-dimensional dense point
cloud, and building of the mesh.

56

Figure 3-3. Flowchart for the data acquisition and digital elevation model (DEM) generations.

Approximately 168 thousand tie points and 34 mil-lion-point clouds were extracted in the pairof the pre-event images, indicating that the mean cloud density is 1.4 points per square meter.
On the other hand, approximately five thousand tie points and 16 million-point clouds wereextracted in the pair of the post-event images, demonstrating that the mean cloud density is 0.5
points per square meter. The images’ geographic locations were coordinated using rational
polynomial co-efficient (RPC) files. The RPC files were provided by the supplier as a standard
positioning assistance dataset, including polynomial equations to relate geographic locations(latitude, longitude, and height) with locations in the image (row and column).
Figure 3-4a, and b illustrate the pre- and post-event DEMs created from the images,
respectively, and Figure 3-4 c, d shows the hillshades effects on the DEMs. Unexpected small
concavities and convexities were found in the post-event DEMs (Figure 3-4d), probably due tothe lower density of the point clouds. Figure 3-4(e), (f) shows the pre- and post-event
orthorectified images, respectively. Figure 3-4 (g), (h) illustrates the close-up of the generated
point clouds in a rectangular area shown in Figure 3-4(e), (f). From the close-up image, theconcentration of the cloud points in the pre-event DEM is substantially higher than that of the
post-event DEM. The pre-event data spatial resolutions were 1.0 m, whereas the post-event

57
data spatial resolutions were 1.5 m due to the different spatial resolutions of the original images.
Since the spatial resolution and the pair quality of the pre-event data are higher, the post-event
DEM and orthorectified image were resampled to a 1.0 m resolution by the cubic convolution
technique for the following change detection operation in order to obtain a detailed
morphological change.

58

Figure 3-4. Data used in this study. (a) Generated 1 m-resolution pre-event DEM from the GeoEye-1 Satellite inJune 2012; (b, c) pre-and post-event hillshades effects to the DEMs overlay with colors showing clear details of
the study area; (d) generated 1 m-resolution post-event DEM from the IKONOS-1 Satellite in July 2014; (e) preevent orthorectified multispectral image derived from GeoEye-1 images (0.5 m-resolution); (f) post-event
orthorectified panchromatic image derived from IKONOS-1 images (0.8 m-resolution); close-up view of thegenerated point clouds of pre-event data (g) and post-event data (h) presents the rectangular area shown in (e, f).

59
The derived DEMs were georeferenced in GIS during the stereo matching operations
based on the RPC files. Then, they were applied to the DoD technique by subtracting the preevent image from the post-event image. The DoD (post-pre) obtained from the pixel-basedchange detection is shown in Figure 3-5. Although the DEMs were geometrically corrected by
the RPC files, significant noises or errors in height values were found even in unaffected areas.
These false detections would probably arise from the locational errors (e.g., horizontal errors)in the DEMs, indicating that two DEM images were not perfectly matched. Therefore, a further
geometric correction would be necessary to accurately enhance the DEM’s matching andaccurately extract the geomorphological change. Here, in this study, a nonlinear mapping
technique would be utilized to diminish the DEM’s locational errors generated in DoD.

Figure 3-5. Uncorrected DEMs of difference (DoD) obtained from the pixel-based change detection
using the pre-and post-event DEMs.

60
3.3. Nonlinear Mapping Method
An adaptive nonlinear mapping method was introduced by Kosugi et al., 2004, andNakamura et al., 2002 to superpose a pair of aerial or satellite images accurately and detect
pixel-by-pixel changes automatically. The method can geometrically correct the images even
if rotational gaps and local skews are obtained in the images. The method was applied not only
to aerial images but also to LiDAR-derived DEMs by one of the authors for quantifying
volumes of debris flows (Miura, 2015). Hence, this technique was applied to the obtained
DEMs in this study.
The flowchart of the nonlinear mapping technique for pre-and post-event DEMs ispresented in Figure 3-6. This method consists of four operations: Shifting vector generation insubareas, consensus operations for shifting vectors, interpolation of consented shifting vectors,
and the repetition of three previous operations until a satisfactory outcome has been achieved.
In this analysis, the pre-and post-event DEMs are defined as the controller (reference) data andfollower data, respectively. First, both DEMs are divided into subareas with the window sizeof NW-by-NW pixels (Figure 3-7). The segmented subarea (NW × NW pixels) in the follower data
looks for the best-matched segment in the given segment’s neighborhood (NS pixels for left,right, upward, and downward) in the corresponding controller data by adopting the smallest
elevations’ difference using the following formula:‫ݏ‬ሺ݅௖ ǡ ݆௖ ሻ ൌଵேೈ మሺே ିଵሻȀଶሺே ିଵሻȀଶೈೈሼ݀ଵ ሺ݅௖ ൅ ݅ǡ ݆௖ ൅ ݆ሻ െ ݀ଶ ሺ݅௖ ൅ ݅ ൅ ݀‫ݔ‬ǡ ݆௖ ൅ ݆ ൅ ݀‫ݕ‬ሻሽଶ (3-1)ටσ௝ୀିሺே
σ௜ୀିሺேೈ ିଵሻȀଶೈ ିଵሻȀଶThe elevations’ difference (s-value) is calculated in each subarea. Here, d1 and d2 are
the elevations in the pre-and post-event DEMs, respectively, and ic and jc indicate the subarea’s
central location in x- and y-directions, respectively. The shifting vectors are determined fromdx and dy in pixels when the minimum s-value is obtained within the given search area in NW-

61
by-NW pixels. It would be difficult to search for the appropriate shifting vectors in landslideaffected areas due to the fact that the ground movement significantly changes the topography
after the event. Since the false shifting vectors would be generated in such changed areas, theshifting vectors need to be corrected considering the vectors in the altered areas’ vicinities.
The consensus operation will be performed to correct the overestimated shifting vectors
or false generated shifting vectors. The first scalars of the shifting vectors are calculated using
Equation (3-2) in all the subareas.

݀ ൌ ඥ݀‫ ݔ‬ଶ ൅ ݀‫ ݕ‬ଶ

(3-2)

Figure 3-8a shows the schematic diagram for the histogram of the vectors and threshold
selection to two classes. The threshold value of the vectors (dth) is determined by maximizing
the ratio of inter-class variance ( V B ) to the total variance ( V T ) calculated Equations (3-3) to (35) (Kosugi et al., 2004; Nakamura et al., 2002; Otsu, 1979)ఙ మ ሺௗሻ݀௧௛ ൌ ݀ s.t. ݉ܽ‫ ݔ‬ቀ ಳమሺௗሻቁ
ௗߪ஻ ଶ ൌ

ఙ೅௡భ ሺ௠భ ି௠బ ሻమ ା௡మ ሺ௠మ ି௠బ ሻమ௠భ ା௠మߪ் ଶ ൌ ߪଵ ଶ ൅ ߪଶ ଶ

(3-3)
(3-4)
(3-5)

Here, m0, m1, and m2 indicate the mean value of d for all the vectors, the mean value inclass 1, and the mean value in class 2, respectively. Additionally, n1 and n2 are the number ofvectors in classes 1 and 2, respectively. In a similar manner, ߪଵ and ߪଶ represent the variancesof the vectors in classes 1 and 2, respectively. When the d-value in a subarea is larger than dth,
the vectors are classified to class 2. In that case, the shifting vectors are corrected by giving
median values of dx and dy obtained within the neighborhood subareas (NC-by-NC subareas),
as shown in Figure 3-8b. The shifting vectors are provided to all the pixels from the subareas’

62
vectors by the bilinear interpolation technique. These operations are repeated by the given
number of iteration (n). Finally, the controller data is geometrically corrected based on theshifting vector in each pixel. Figure 3-9 illustrates the comparison of the schematic plan andprofile before and after the nonlinear mapping-based geometric correction. Four parameters,
NW, NS, NC, and n are required to perform the nonlinear mapping technique.

63

Figure 3-6. Flowchart of the nonlinear mapping technique.

Figure 3-7. The schematic diagram for nonlinear mapping operations presents the segmentation ofboth pre – and post – event DEMs, the matching segments process, generating shifting vectors, andcorrecting false generated shifting vectors using the consensus operation, the figure is modified from
(Miura, 2015).

64

Figure 3-8. Schematic diagrams of (a) the histogram of shifting vectors and thresholding (b) present
how the direction of generated shifting vector (red arrow) is corrected in the same way as theneighboring arrows using the consensus operation, modified from (Miura, 2015).

Figure 3-9. Schematic plan and profile denote a comparison of DEMs before and after the nonlinear
mapping-based geometric correction.

65
3.4. Evaluation and Applicability of the Method
To apply the nonlinear mapping method to the pre-and post-event DEMs, the algorithm
needs a combination of entry parameters. These key input parameters are NW (window size or
size of subarea), NS (search area), NC (consensus area), n (number of iterations). The selectionof the parameters in the method was discussed in a selected testing zone with different
combination values of these parameters. In this study, a set of scenarios were examined withdifferent values of NW, NS, NC, and n. In the following subsections, statistical and shifting vector
assessments was performed. Based on the quality assessment result, the best possiblecombination of parameters (NW, NS, NC, and n) was selected.

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3.4.1. Statistical Assessment
As shown by the dotted rectangle in Figure 3-10, the nonlinear mapping technique wasapplied repeatedly with different scenarios for the selected test area. For instance, in every
process of the analysis, the pre-event DEM subareas were given a different search area (pixel
value) and consensus value to automatically search and match with the best possible
corresponding reference point in the post-event DEM. Typical parameters illustrated in Table
3-2 were selected for the accuracy assessment. It is convenient to use descriptive statistics(mean and standard deviation) to evaluate different categories and identify the differencesamong other scenarios. If the geometric correction is successfully performed to the data, theDoDs mean value would be zero or close to zero in the unaffected areas. As shown in Table 2,
different window sizes (e.g., NW = 5,7,9…31) and search areas of NS × NS (3 × 3, 5 × 5, 7 ×
7…31 × 31) pixels are evaluated through a repetitive iteration procedure. Finally, in this study,
the window size (NW = 5 pixels) and search area (NS = 9 × 9) highlighted in Table 3-2 with boldtext were chosen based on the lowest mean and standard deviations of DoD maps.

Figure 3-10. The selected test area is shown in a rectangle outlined with a dotted line.

67
Table 3-2. Means and the standard deviations of the DoDs for different parameters in the test
area. Bold texts indicate the chosen parameters, which have the lowest mean and standard
deviation.
No
.
1
2
3
4
5
6
7
8
9
10

Size ofSubarea( NW )
0
5
5
5
7
7
7
9
9
9

SearchConsensus
Area
Area
(N
NS, Pixel)
(N
NC)
0
0
5
3
7
3
9
3
5
3
7
3
9
3
5
3
7
3
9
3

No. of
Iterations
(n)
0
3
3
3
3
3
3
3
3
3

Mean
Value
(m)
1.278
0.475
0.221
0.152
0.862
0.492
0.166
0.500
0.247
0.187

Standard
Deviation
(m)
3.337
1.467
1.254
1.152
2.197
1.592
1.308
1.672
1.468
1.436

Sum ofSquareRemarks

2.00 × 107 Uncorrected
3.87 × 106 Corrected
2.83 × 106 Corrected
2.39 × 106 Corrected
8.68 × 106 Corrected
4.56 × 106 Corrected
3.08 × 106 Corrected
5.03 × 106 Corrected
3.87 × 106 Corrected
3.71 × 106 Corrected68
3.4.2. Shifting Vectors Assessment
Figure 3-11(a)-(i) illustrate the generated shifting vectors for the scenario numbers 210 of Table 3-2. The selected test area of the pre-event DEM based on the entry-parametersdescribed above; looked for the best matching segment in the vicinity of the correspondingreference point in the chosen test area of the post-event DEM. As shown in Figure 3-11, shifting
vectors were generated if the best matching point is not the same as the reference point. During
the operation analysis, more than nine profiles of shifting vectors have been generated. Here,in Figure 3-11, nine of them have been shown. A moderate vector movement that brings
reasonable smoothness in the DEMs quality and considerably removes the locational errors in
the unaffected area compared to another set of shifting vectors would be chosen from all thegenerated shifting vectors. After the comparison, in Figure 3-11, shifting vector with label cwere selected, which corresponds to the parametric combination (NW = 5, NS = 9, NC = 3, n = 3).
Since the shifting of the vectors in the selected parameters is smaller than the other shifting
vectors (e.g., scenario number five to ten in Table 3-2 shown by Fig. 3-11(d)-(i), the corrected
DEM eliminated the locational errors in the unaffected area significantly compared to the othershifting vector-related DEMs. Moreover, the selected shifting vector had the smallest mean,
deviation, and sum of squares, as discussed and shown in Table 3-2.

69

Figure 3-11. Generated shifting vector for nine scenarios. (a)-(i) correspond to the parameter
combinations shown by the scenario numbers (2)-(10) in Table 3-2, respectively.

70
3.5. Quality Assessment
The histogram is a very handy way of graphically displaying the information containedin a single band of remotely sensed images (Jensen et al., 2015). In remote sensing, scientists
usually examine histograms for each band to determine the quality of satellite images. The
histogram presents the information by which one can figure out the quality of image data. In
most cases, scientists show the before and after histogram graphs to compare the effectivenessof their image enhancement methods (Abdullah-Al-Wadud, et al., 2007). Hence, thedistribution of the difference of elevations in the corrected DEM has been compared with theuncorrected DEM using histograms.
As shown in Figure 3-12, the uncorrected DoDs histogram (red line) generated anegatively skewed distribution shape histogram indicating the poor quality of the data or thepresence of additional noise in data. However, the corrected DEM histogram (blue line)
produces a symmetrical frequency distribution. The values are distributed around a central
value, and the pixel numbers decline away from this central point. The distribution graphcompletely changed to a bell-shaped and is called a normal distribution. It can be concluded
that the data quality in DoD after applying the nonlinear mapping method dramaticallyincreased. Meanwhile, the incorrectly detected errors have drastically decreased after
conducting the nonlinear mapping method and choosing the parametric combination.

71

Figure 3-12. Comparison of histograms of the corrected and uncorrected DoD.

72
3.6. Landslide based nonlinear method’s results
In this section, the profile cross-sections are evaluated using the corrected DEMs,
including the landslide area to validate the nonlinear mapping method in Section 3.6.1, andpresented the estimation of the soil volume through a comparison of the volume estimated fromthe uncorrected DEMs in Section 3.6.2.
3.6.1. Corrected and uncorrected DEMs profile cross-sections
The profile cross-sections are compared between the corrected DEMs and uncorrected
DEMs to confirm the nonlinear technique’s quality. Figure 13a shows the DoD obtained fromthe uncorrected DEM. Two cross-section lines was selected in the affected and unaffectedareas, as illustrated by the A-A’ and B-B’ lines in Figure 3-13(b), (c), respectively. It wasobserved from the profile cross-sections that significant gaps between the pre-and post-event
elevations are prominent throughout the cross-sections in the uncorrected DEMs. Figure 314(a) illustrates the DoD obtained from the corrected DEMs in this study, and the crosssections in the A-A’ and B-B’ lines are shown in Figure 3-14(b), (c), respectively. It is
confirmed that the locational errors (e.g., horizontal gaps) were significantly decreased, andthe post-event line matches very well with the pre-event topographies.
The model performance was also evaluated in decreasing the locational errors using thequantitative value of the root means square error (RMSE) to confirm the degree of the matchingin both corrected and uncorrected data in the unaffected area (Figures 3-13 and 3-14). This
indicator has been frequently used to evaluate the prediction error quantitatively (Rawat et al.,2019). The RMSE of the corrected profile cross-section was obtained at 0.81 m, whereas the
RMSE for the uncorrected profile cross-section was obtained at 3.81 m, which is more thanfour times larger than that of the corrected one. It suggests that the horizontal locational errors
are the leading cause of the change detection errors not only in the unaffected areas but also in

73
the affected areas. The employed geometric correction model of the current study can beutilized for accurately extracting the geomorphological changes.

74

Figure 3-13. (a) Uncorrected DoD with the A-Aʹ and B-Bʹ cross-section lines. (b) Profile cross-section
line (A-Aʹ) passing through the affected area. (c) Profile cross-section line (B-Bʹ) passing through theunaffected area.

Figure 3-14. (a) Corrected DoD with the A-Aʹ and B-Bʹ cross-section lines. (b) Profile cross-section
line (A-Aʹ) passing through the affected area. (c) Profile cross-section line (B-Bʹ) passing through theunaffected areas.

75
3.7. Volume Estimation
Before quantifying the volume of the displaced soil, the boundary line of erosion anddeposition areas of the landslide need to be delineated. Figure 3-15(a) shows the DoD map(post-pre) obtained from the corrected DEMs with the landslide area delineated by the
combination of the satellite images, the ground photos shown in Figure 3-2, and the crosssections shown in Figure 3-15(b). The colors in the DoD map in Figure 3-15(a) illustrate the
distribution of displaced soil depths in the Abe-Barek landslide disaster area. The DoD map
consists of positive and negative pixel values. The negative values indicate soil erosion, which
is filled in red to orange color pixels, and the pixel values range from (−37 m < DoD < 0 m).
On the other hand, the positive pixel values correspond to the deposition of the displaced soil
as shown in the blue to green pixel colors, and the pixel values vary from (0 m < DoD < +41m).
Referring to the cross-sections drawn in Figure 3-15(b), the landslide moved in acomplicated fashion in the erosion-affected sections. The post-event elevations weresignificantly decreased in the southern areas such as lines C-C', D-D', E-E', F-F', and G-G',
whereas the elevations were increased after the landslide in the northern areas such as lines II', J-J', and K-K', as shown in Figure 3-15(b). Judging from the cross-sections in Figure 3-15(b),it can be claimed that in the upper slope section line C-C' to the line G-G' the erosion occurred,in which the eroded mass of the material is thought to move as a rigid block with a significant
depth. By contrast, from the cross-section H-H' onward, which is the ending boundary of theerosion-affected area, the displaced material′s movements became more complicated. In otherwords, the mass of the material that was moving almost in a rigid block turned into many subslide surfaces at the lower reach of G-G', and each block in a separated fashion, choosing its
direction to reach a remarkable distance.

76
From section I-I' onward, the post-event elevation increases as the movement continues
with a distance towards the northern direction. It means that the landslide started depositingfrom I-I' on to the stopping points in the deposition zone. Finally, the landslide material that
accumulated along the purple boundary polygon filled with a green color in Figure 315(a)caused the severe building damage. The redline polygon and dotted purple line polygon
in Figure 3-15(a) shows the erosion area (upstream) and the deposition area (downstream) of
the displaced materials, respectively.
The volume of the displaced material was estimated using the corrected DoD map by
aggregating the positive and negative pixel values separately in their boundaries andmultiplying them to the corresponding area boundaries. As an example, in Figure 3-16, theelevation change is presented along the cross-section line A-A'. It indicates the elevationdifference between the pre- and post-event DEMs along the mentioned cross-section line
shown in Figure 3-15. The positive values indicate the deposition of the material, whereas
negative values indicate erosion of the materials. The maximum erosion depth along the crosssection line was obtained at 37.7 m, while on the contrary, the maximum deposition depth
throughout the A-A' was estimated at around 12.7 m.

77

Figure 3-15. (a) DoD map obtained from the corrected DEMs presents the delineation of the landslide
affected area and cross-section lines. (b) Cross-section profiles present the pre-and post-event surface
difference throughout the landslide area.

78
The total erosion and deposition soil volumes in the upstream and downstream aresummarized in Table 3-3. As highlighted in Table 3-3, the total volume of the eroded andaccumulated soil in the landslide area by applying the nonlinear method was estimated at 1.05
× 106 m3 and 5.73 × 105 m3, respectively. The total erosion volume was nearly twice larger
than the total deposition volume. This would be due to the fact that the soil in the downstreamzone was dragged into buildings and parts of them flowed into the river.

Figure 3-16. Elevation difference along the cross-section line A-Aʹ obtained from (pre-post) DEMs.

In addition to that, the volumes of the displaced materials were also estimated for theuncorrected DEMs and summarized in Table 3-3. The total erosion volume of 1.61 × 106 m3
was approximately 53% larger than the one estimated from the corrected DEMs. The total
deposition volume in the uncorrected DEM is 54% larger than that of the adjusted DEMs. The
percentage increase between the corrected and uncorrected DoDs in the erosion and deposition
zones was also summarized in Table 3-3. From the percentage increase, clearly, one canappreciate that the estimated parameters (volumes and depths) in the uncorrected DoD weresignificantly more extensive than that of the correct one. This implies that applying the
proposed method considerably decreases the estimated volume of the soil. Furthermore, it
reveals that the direct use of DoD without correcting the errors, leads to the overestimation of
the landslide volume.

79
Table 3-3. Summarized erosion and deposition volumes for the corrected and uncorrected
DoD.
Corrected A

Upstream
Downstream
Total
Area (m2)
Ave. depth

Erosion

Deposition

Total

Erosion

m3

m3

m3

m3

−1.03 ×106

4.78 × 104

1.08
× 106

−1.56 ×106

5.25 × 105

5.41
× 106

−4.26 ×104
−1.61 ×106
1.22 ×105−1.58 ×104
−1.05 ×106
1.22 ×105-8.6

5.73 × 105
1.23 × 105

4.6(࡮ െ ࡭Ȁ࡭)

Uncorrected B

−13.2

%
Total

Erosion

Deposition

m3

%

%

8.74 × 104

1.61
× 106

51

57

7.96 × 105

8.38
× 105

170

52

8.83 × 105

53

54

1.23 × 105

-

-

7.2

53

57

Deposition
m3(V/A), m

Similarly, the average erosion and deposition depths defined as the volume divided by
the area were estimated by aggregating all the positive and negative pixel values within thelandslide boundary line. The average depths were also presented in Table 3-3 for both thecorrected and uncorrected DoDs, respectively. The average erosion and deposition depths forthe corrected DoD were estimated at around 8.6 and 4.6 m, respectively. The averagedeposition depth was considerably lower than that of the erosion in the DoDs. This is due tothe fact that parts of the deposited material were washed away to the downstream river, as
mentioned above.
3.7.1 Previous landslide’s volume versus current estimated one
In order to understand the scale of the Abe Barek landslide, a comparison was madewith the area-volume relationship or power-law relation derived from the previous studies(Simotett, 1967; Rice et al., 1969; Abele, 1974; Innes, 1983; Whitehouse, 1983; Larsen et al.,
1998; Martin et al., 2002; Guthrie et al., 2004; Guzzetti et al., 2008; Imaizumi et al., 2008; Riceet al., 1971; Guzzetti et al., 2009; Zhang et al., 2020) having a high confidence level at locations
in different countries. The number of sample data, the study areas’ limitations, and the obtained

80
relationships were summarized in Table 3-4. As reflected in Figure 3-17, the volume of thelandslide appears to be slightly larger relative to the ground surface area. It reflects that thelandslide occurred in a relatively small area with a significant loess thickness. Overall, Figure
3-17 indicates that the volume estimated by our analysis shows a good agreement with theprevious studies and is located within correlation lines corresponding to the previous research.

81
Table 3-4. Empirical relationships linking the landslide area (AL) to the landslide volume (VL)
obtained by various researchers.
No.
1
2
3
4
5
6
7
8
9
10
11
12
13

Number of
Data
207
29
53
30
45
1019
615
124
539
11
37
677
50

Maximum MinimumALAL
1.9 × 105
2.3 × 100
2 × 102
2.1 × 100
6 × 107
2 × 1052
5 × 103 × 101
3.9 × 106
4 × 1044
1.6 × 10
5 × 101
5.2 × 104
2 × 1025
1/2 × 10
7 × 1021 × 109
1 × 1013
4 × 10
5 × 101
1.5 × 103
1.1 × 101
9
1 × 102 × 100
2.1 × 105
9.6 × 102

Equation

SourceVL = 0.1479AL1.368
VL = 0.234AL1.11
VL = 0.242AL1.250
VL = 0.0329AL1.385
VL = 0.769AL1.250
VL = 1.826AL0.898
VL = 1.0359AL0.880
VL = 0.1549AL1.0905
VL = 0.0844AL1.4324
VL = 0.19AL1.19
VL = 0.328AL1.104
VL = 0.074AL1.450
VL = 0.333AL1.399

Simotett, 1967
Rice et al., 1969
Abele, 1974
Innes, 1983
Whitehouse, 1983
Larsen et al., 1998
Martin et al., 2002
Guthrie et al., 2004
Guzzetti et al., 2008
Imaizumi et al., 2008
Rice et al., 1971
Guzzetti et al., 2009
Zhang et al., 2020

Figure 3-17. Comparison of the estimated landslide volume from current study with the empirical
relationships linking the landslide area (AL) and volume (VL) resulted from previous studies.

82
3.8. Advantages and Shortcoming of the Method
The advantages of the technique presented in this study are summarized as follows: It
can reduce geometric errors in the DoDs output almost automatically by providing adequate
parameters without collecting GCPs; it can be applied to the DEMs, even significant
morphological changes such as a large-scale landslide are observed in the data; andconsequently, it can provide a more accurate volume quantification of the displaced material
by landslides than a simple DoD analysis.
On the contrary, one of the nonlinear mapping method’s main shortcomings is the timespent in scenarios for choosing the ideal input parametric combinations. For choosing such aparametric combination (NW, NS, NC, n), it is needed to compare each scenarios’ results andchoose them based on a thorough evaluation, which will take a relatively long time. With the
exception of that being said, the rest of the method is very fast.
Since in this study no discussion has been made about the absolute geo coordinate
accuracy by using GCPs, the corrected DEM would still inevitably contain errors. A true error
assessment can typically be conducted with enough GCPs (Tsutsui et al., 2007; Martha et al.,2010; Hsieh et al., 2016; Simonett, 1967; Nadi et al., 2020), which provides an actual error
estimation. However, the error estimation using GCP in an insecure place, located in a remotearea with high mountain and poor traffic accessibility, makes it nearly impossible to collectsuch data. Although some studies conducted such an assessment using the LiDAR-derived
DEM as GCP (Tsutsui et al., 2007; Hsieh et al., 2016), the LiDAR observation has never been
conducted in this area. Martha et al., 2010 suggested that aligning images using RPCs alone isenough for volume estimation. However, in this study, the nonlinear mapping technique isemployed to align the DEMs with the maximum extent possible, to decrease the noise in theunchanged area, and to increase the accuracy of the analysis to the extent feasible.

83
3.9. Future Studies
Although the nonlinear mapping method’s application significantly decreased the noisein the DoDs output, it subsequently may increase the accuracy of the landslide volume
estimation result to a certain level. However, the error estimation has not been discussed in thiswork with the actual GCPs. Once such data are collected, much additional work needs to be
done. For example, an error estimation of both depths and volume obtained from the nonlinear
mapping and real GCP can be compared in a similar procedure conducted by (Tsutsui et al.,2007) using the evaluation change analysis technique. The shortcoming addressed in the earlier
section may need further research to find a possible way of automatically selecting thoseparameters to accomplish the analysis faster. A comparative study, together with othertechniques aimed at reducing undesired noises, can be applied to highlight the method’s
efficacy.
As final remarks, it should be emphasized that, in this study, the attention was todecrease the extra accumulated noises in nonaffected areas of DoDs output and to illustrate theimpact of the adopted nonlinear mapping technique in decreasing the estimated landslidevolume compared to the result coming from directly using DoD. The approach applied in thisstudy aimed to estimate the volume of a large-scale landslide after decreasing the accumulated
noises in the DoDs output. In addition, it has the potential to quantify the volume of thelandslide in a critical disaster time with a relatively shorter time, without using any extra actual
GCPs.

84
3.10. Summary of this chapter
The nonlinear mapping technique is utilized to detect and estimate the volume of thelandslide affected area using stereo pair high-resolution satellite imagery. It is being stressedthat the method can successfully be used in the image processing analysis to conduct mappingtasks, diminishing the geometric errors accumulating in the unchanged areas of the DoDs
output, and finally, obtain the volume and average depth of the displaced material in the disasterarea to ease the crisis management support and contribute very well to the more effectiveplanning of the disaster relief and restoration.
The technique presented in this study was employed in a large-scale landslide that
occurred on 2 May 2014, in Abe Bare village, Badakhshan, Afghanistan. Using the DEMs ofthe difference change detection technique, the DEMs of the landslide affected area was
generated from the pre-and post-event satellite stereo-pair images. Although the DEMs werecorrected using the RPC method, registration noises or locational errors were still spotted in
the nonaffected areas. The nonlinear mapping technique was employed to diminish theaforesaid geometric inconsistencies or registration noises and enhance the accuracy of theprocess analysis. The proposed method has been coded in such a way that, in the selected
testing zone with the given combinations of parameters, the post-event DEM (follower data),
superimposes on the pre-event DEM, and pixel by pixel, scanning neighboring vicinities forthe best possible matching points in the pre-event DEM (controller data). In this study, nine
scenarios were examined with different values of NW (window size or size of the subarea), NS(search area), NC (consensus area), n (number of iterations). As the quantitative and graphicalcomparison revealed, the locational gaps significantly reduced, and a bell-shaped histogram,also known as a normal distribution, was obtained. Moreover, statistical values, in particular,mean and standard deviations, decreased more closer to zero and one, respectively.
Additionally, the obtained RMSE values also verified the nonlinear mapping’s applicability in85
decreasing the locational errors quantitatively. The required input components (NW = 5, NS = 9,
NC = 3, and n = 3) for processing the method were chosen after a thorough comparison andobservation of the assessments.
The total eroded soil volume in the landslide area after adopting the nonlinear mapping
method was approximately estimated at about 1.05 × 106 m3, and the volume of the deposited
soil acquired 6.73 × 105 m3. The landslide’s estimated volume was compared with the volumes
obtained from the previous studies. The erosion volume of the soil obtained in this study
showed a good agreement with that achieved in the previous studies. The average depth oferosion, mostly situated on the mountain’s hilly side, was 8.6 m. On the contrary, the average
depth of deposition, which typically situates in a flatted area, was estimated at 4.6 m. Thishighlights that nonlinear mapping has a considerable potential to reduce locational errors inmountainous regions. Therefore, it indicates a good applicability of the nonlinear mapping
technique. It suggests that the method can be used efficiently on a routine basis and runs
whenever a new landslide event has occurred if high-resolution stereo-pair images of the area
become available.

86

CHAPTER VI
Assessment of the Charikar Flood Using Remote Sensing Analysis
4.1 Introduction
Flood is the most frequent and most damaged caused natural disaster which affects
more people than any other types of weather-related natural disaster in Afghanistan. Low
coping capacity and less consciousness of the people in Afghanistan is a prominent reason for
most of the fatalities. Control of flood is very difficult, and, in most cases, it is very
economically unaffordable for country like Afghanistan. However, it is possible to minimize
the effects thorough efficient policy like enhancing the consciousness of the people byproviding reliable information, providing flood susceptible area. To predict, prepare, prevent,and mitigate flood damage, preparing flood maps in flood-prone areas using remotely sensed
data is very safe, affordable, and time-saving that plays an important role. Therefore, a method
is proposed by which can rapidly detect, monitor, and predict the flood inundation area in any
types of land, such as flat, built up, vegetated or even non-vegetated area. Since Normalized
Difference Vegetation Index (NDVI) commonly used to detect land cover changeIn this study,
the Relative Difference in Normalized Difference Vegetation Index (rdNDVI) approach isdesigned to identify and monitor flooding in Charikar based on NDVI values. The intention
was to use same methodology as used in Landslide to detect and estimate the flood inundationarea, but as discussed due the nature of nonlinear mapping techniques pairs of stereo satellite
imagery was needed. Due to the lack of stereotype satellite image in the flooded area, this study
employed the rdNDVI approach.

87
4.2 Study Area and the Flash Flood in Charikar
The study area is located between latitudes 35° 01′ 9.3’’ W and longitude 69° 09′
55.67’’ N, which includes parts of the capital of the Parwan province (Fig. 4-1). The city lieson the Afghan Ring Road, 69 km from Kabul along the route to the northern provinces. It
connects Kabul and western parts of Afghanistan to northern parts of Afghanistan such as
Mazar-i-Sharif, Kunduz, and Puli Khumri. Despite the proximity to Kabul, slightly more thanhalf of the land is not built-up.

Figure 4-1. Topographical map of the study area showing how Charikar city is surrounded by highmountains of Hindukush which the elevation is between 1000 - 4000 m.

The study area includes the central Charikar district which has a total land area of
30,500 hectares that part of the city was affected by flood (Fig. 4-2). Ghorband river is flowing
through Parwan province. It is a tributary of the Panjshir river, then a sub-tributary of the Indus
River, then the Kabul river. The Ghorband runs entirely in Parwan province, where it gave its
name to the Ghorband district. It is born in the eastern Sheybar pass (which connects theprovinces of Parwan and Bamyan, or watersheds of the Ghorband and Kunduz river) and passes

88
in an eastbound direction which it maintains throughout most of its course. It runs along thesouth and the imposing central range of the Hindu Kush, receiving meltwater in the Sheybar
pass area of Salang. It flows from this in a long valley between the high range of the Hindu
Kush (north) and Koh-i-Baba in the south. It then converges with the Panjshir, on its right bank,
10 km east of Charikar. It flows through the districts of Sheikh Ali, Shinwari, Ghorband, andSurkh Parsa.
Parwan is located at a high altitude, 1,800 m (5,900 feet) above sea level. Winter iscold, with an average temperature in January of -1 °C, usually with freezing nights, and withpossible peaks of -20/-25 °C; snowfalls are frequent and sometimes heavy. Summer is hot
during the day, sometimes scorching, but nights remain usually cool. Precipitation in Charikar,
fairly low, amounts to 300 mm per year. The rainiest season is spring. In summer, it rarely
rains. In Charikar, the sun shines quite often even in winter, while in summer, it regularly
shines.

89
4.3. The flash flood in Charikar
A devastating flash flood occurred near the residential areas in Charikar city of Parwan
Province, Afghanistan on Aug. 26, 2020, killing over 100, most of the victims were children
and women. More than 150 were injured (Tolo News, 2020), In the flash flood event, morethan 17,000 people have been affected and nearly 2,000 homes were destroyed (The New YorkTimes, 2020), The midnight floods surprised locals who were asleep. According to local
witnesses, a roar was firstly heard and then a flood was arrived at their houses (The Guardian,2020). Even police soldiers attempted air shooting to wake up people but unfortunately, mostof the people did not notice. Rescue teams, locals and social groups were searching for thebodies of the victims. But many people were missing. Based on a report (Independent Farsi,2020), a person was missing nine members of his family. The substandard mud houses on themountainside near the Opyan village (Fig. 4-3) and some other areas around Charikar turned
into rubble after the flood as shown in Fig. 4-2.

90

Figure 4-2. The affected area in Charikar A) people walking near the damaged houses after heavy
flooding in the Charikar, Parwan province, Wednesday, Aug. 26, 2020. the photograph is taken from(Gul, 2020) accessed via Fox news; B) a man standing in front of what is left of his house after covering
all his properties under heavy debris of flash flood Photograph taken from (Gul, 2020), C) Villagers
soldiers search for victims in a mudslide resulted after a flash flood affected the area at Sayrah-e-Opiyan
in Charikar (Photograph is taken from (Jalali, 2020). D) flash flood brought debris and boulders into
the house and covered the car and injured and killed the people, (Photograph is taken from
(Azadmanish, 2020), E) A general view of a building in Charikar covered with flooded debris offlooding in Parwan, Afghanistan August 26, 2020, this picture obtained in (The Guardian, 2020).

This tragic event, in addition to the intense war and the Covid-19 epidemic, further
affected the health system, which is completely dependent on foreign aid for even some of its

91
most basic services. Charikar city is located in an area where a series of flood channels andvalleys directly can put the residentials of the city under critical threat. Population pressure,flat area shortages, and inappropriate land development lead most of the people in Afghanistan
to live in hilly flood-prone areas such as Charikar which highly likely lead to an increase inflood-induced building damage.

Figure 4-3. Compounds in Charikar city are at risk of flood due to a lack of regular urban planning,
the flood path information is taken from the Surface Water Resources Department of Afghanistan.

As can be seen in Fig. 4-3, flood paths or valleys are shown in red color where each red
area contains hundreds of residential buildings where the life of their residents can be in danger
at any moment by floods. Government control and regular urban planning have been neglectedin the construction of new towns and buildings. Irregular and arbitrary sale of land by landlords
and local bullies, and most importantly, the absence of floods in the city of Charikar in recent
years have encouraged people to feel that these susceptible areas are safe to buy and build thehouses. The building and construction were growing at an unprecedented rate until a

92devastating flash flood occurred and affected the people’s life and the economy as shown in
Fig. 4-2.

93
4.4. Materials

4.4.1. Sentinel-2 imagery
Sentinel-2 satellite imagery is a constellation of two earth observation satellites,
developed by the European Space Agency (ESA) and the European Commission’s ambitious
Copernicus Program, consists of two identical satellites: Sentinel-2A, launched on June 23,
2015, and Sentinel-2B launched on March 7, 2017, available in various processed formats(ESA, 2015). Because the so-called Multispectral Instrument (MSI) products experience
different stages of processing to reach a Level that becomes accessible by the users. From themain stages of Level-0, Level-1A, Level-1B, Level-1C, and Level 2. However, Level-0 andLevel-1A are not accessible by users and are in the instrument source packet (ISP) format,
which is a compressed raw image data format. Level-1B has radiometrically corrected imagery
with Top-Of-Atmosphere (TOA) radiance which is made of granules 25 by 23 km long. Level1C has formed in 100 × 100 km tiles in an orthorectified format in UTM/WGS 84 projection(ESA, 2015). Level 2A can be processed from Level-1C products using Sentinel-2 Toolbox.
Level -1C and Level-2A are the most used product in land cover/ use mapping. This dataset isavailable free to the public domain as part of the free, full, and open data policy of the European
Commission and European Space Agency.
Sentinel-2 data have been utilized in numerous research studies of earth sciences andatmospheric sciences since its launch and are ideal for agriculture, forestry, and other land
management applications. (Szostak et al., 2018; Miranda et al., 2018; Wang et al., 2018;
Taberner et al., 2020), flood mapping (Caballero et al., 2019; Dinh et al., 2019; Rättich et al.,2020), wetland monitoring (Solovey, 2020) and agricultural activities (Zhang et al., 2019;
Kobayashi et al., 2020). The radiometric and geometric quality of Sentinel-2 data is technicallysuperior to data acquired by other low spatial resolution data such as Landsat data. The

94
dominance of the Sentinel-2 is including a 10-day revisit time at the equator and a 5-day revisit
time at mid-latitudes, as well as a spatial resolution of 10 m. Although they have thedisadvantage of being affected by cloudiness, optical imaging satellites would be preferred forflood studies since the surface conditions of the affected areas are easily confirmed from theimages compared to radar satellites.
In this study, Sentinel-2 data with Level 1C processing available in the GEE were used.
These data have been orthorectified and radiometrically corrected providing top-of-atmospherereflectance values. Bands 4 (Red) and 8 (Near Infrared) were used with original spatial
resolutions of 10 meters. An automatic cloud masking procedure was applied on both opaque
clouds and cirrus clouds using a bitmask band QA60 of the S2-1C product which has cloudmask information. As indicated in Table 4-1, two months containing nine photographs are
selected for the pre-event period, and two months containing five images are selected for the
post-event period. Unfortunately, the first post-event image after the flood event was not usedin the analysis because the affected area was heavily covered with clouds in the image.





95
Table 4-1. Distinguished features of the satellite imagery used in this study and their observation
conditions.
No

Image ID

Date
Acquired

Cloud
Cover

Relative
Orbit

Pass
direction

Processing
level



(%)
усусҌсчҌсц

1.10

134

Descending

Level-1C2уҖ уҖусуссчтцсчсчхт

усусҌсчҌтц

0.63

134

Descending

Level-1C3уҖ уҖусуссчуцсчсчхт

усусҌсчҌуц

4.23

134

Descending

Level-1C4уҖ тҖусуссшсцсчсчхт

усусҌсшҌсц

23.75

134

Descending

Level-1C5уҖ уҖусуссштцсчсчхт

усусҌсшҌтц

35.2

134

Descending

Level-1C6уҖ уҖусуссшуцсчсчхтусусҌсшҌуц

8.63

134

Descending

Level-1C7уҖ уҖусуссщсхсчсчхтусусҌсщҌсх

3.06

134

Descending

Level-1C8уҖ уҖусуссщтхсчсчхтусусҌсщҌтх

1.20

134

Descending

Level-1C

9уҖ уҖусуссщухсчсчхт

усусҌсщҌух

9.87

134

Descending

Level-1C10уҖ уҖусуссътфсчсчхт

усусҌсъҌтф

8.7

134

Descending

Level-1C11уҖ уҖусуссъуфсчсчхтусусҌсъҌуф

0.23

134

Descending

Level-1C

12уҖ уҖусустссфсчсшттусусҌтсҌсф

0.82

134

Descending

Level-1C

13

S2A_MSIL2A_20201013T060821усусҌтсҌтф

11.82

134

Descending

Level-1C

14уҖ уҖусустсуфсчсъутусусҌтсҌуф

0.68

134

Descending

Level-1C
Before eventуҖ уҖусуссчсцсчсчхт



After event

1





4.5. Methodology
The GEE cloud platform was used to execute multiple tasks impeccably fromconcurrent Sentinel-2 optical satellite data retrieval for flood mapping and to identify its impact
on land use and land cover, and population on a parallel processing architecture. The Sentinel2 images were retrieved during the pre-flood period (June 5 to Aug. 24, 2020), the event date(Aug. 26, 2020), and the post-event date (Sep. 13 to Oct. 23, 2020). The various pre-processing
techniques have been conducted by the GEE beforehand.

96

Figure 4-4. Flowchart of the methodology used in this study.

4.5.1 NDVI and Spectral water indicesThis study did not attempt extracting or identifying the body of water in the impactedarea because there was no standing water. However, the NDVI index is utilized to assess andidentify the flooded zone because researchers have developed numerous indices for identifying
flooded areas. This study would shortly examine the application of various indicators in theaffected area in order to evaluate their performance in the affected region. To extract water
bodies from remotely sensed data, several spectral water indices have been established, mainly
by computing the normalized difference between two image bands and then applying anappropriate threshold to classify the outputs into two groups (water and non-water features).

97
The MNDWI and NDMI indices are utilized by most researchers to extract water bodies suchas lakes, rivers, ponds, etc. The current study's purpose is to use rdNDVI to delineate theinundated area following a flash flood where there are no leftover water bodies. Then, acorrelation is calculated between NDVI and three spectral water indices, and the output of eachindicator for identifying changes induced by the flash flood in the research area is illustrated.
Figure 4-5 (a) and (b) shows the true color Sentinel-2 images observed before the flash floodon Aug. 24 and after the flood on Sep. 13. Examining the rectangular rectangles depicted inFig. 4-5 reveals a trail of the inundated area that remains mostly due to the flood event. The
color of the soil in the affected area changed as a result of increased soil moisture after the flash
flood. The reflectance of such darken wet soil is significantly low in infrared band such as nearinfrared (NIR) and short wavelength infrared (SWIR) as shown in Fig. 4-5 (c)-(f). It means
spectral indices using NIR and/or SWIR would be effective to detect the flood-inundated areas.

98

Figure 4-5. Observation of changes in the study area using pre- and post-event Sentinel-2 satellite
images a) True-color images acquired on Aug 24, 2020; b) True-color image acquired on Sep 13,
2020. c) NIR band acquired on Aug 24, 2020; d) NIR band acquired on Sep 13, 2020; e) SWIR band
acquired on Aug 24, 2020; f) SWIR band acquired on Sep 13, 2020; Rectangular area is selected on
those areas to analyses the NDVI time series.

99
4.5.2. Normalized Difference vegetation Index (NDVI)The NDVI index uses reflected light in the visible and near-infrared bands to detect andquantify the presence of live green vegetation. Simply put, NDVI is a metric that may be used
to classify land cover in remote sensing areas and can measure the density and health ofvegetation in each pixel. The first formal report of the NDVI was in (Rouse et al, 1973). The
NDVI is expressed by Eq. (4-1):

ࡺࡵࡾ െ ࡾࢋࢊࡺࡰࢂࡵ ൌ ൬൰ሺ૝ െ ૚ሻࡺࡵࡾ ൅ ࡾࢋࢊWhere NIR is the near-infrared band and red is the visible red band. This index ranges
from -1.0 to 1.0, essentially depicting green, with negative values originating from clouds,
water, and snow, and values close to zero originating from rocks and bare soil. The NDVI
function has very low values (0.1 or below) that correlate to vacant expanses of rocks, sand, or
snow. Shrubs and meadows are represented by moderate values (0.2 to 0.3), while temperate
and tropical forests are represented by large values (0.6 to 0.8). The estimated image of the
NDVI index is visually presented in Fig. 4-6 to see the changes in the flash flood inundated
area.

100

Figure 4-6. Observation of changes in the study area using pre- and post-event NDVIs.

4.5.3. Normalized Difference Water Index (NDWI)The normalized difference water index (NDWI) was developed by (McFeeters, 1996)
for detecting surface water in wetland regions and surface water measurement and is definedas:ࡳࡾࡱࡱࡺ െ ࡺࡵࡾࡺࡰࢃࡵ ൌ ൬൰ሺ૝ െ ૛ሻࡳࡾࡱࡱࡺ ൅ ࡺࡵࡾ

Where, for Sentinel-2, green and NIR are the reflectance of green (band 3) and NIR(band 8), respectively. The NDWI value ranges from -1 to 1. (McFeeters, 1996) determined
the threshold value at zero. That is to say, if NDWI is greater than zero, the type of cover iswater; alternatively, if NDWI is less than zero, the type of cover is not water. According to theprevious study (Xu, 2006), NDWI was unable to entirely differentiate built-up features fromwater features. Because the NIR reflectance was lower than the green reflectance, NDWI

101
displayed positive values in built-up features that were comparable to water. Therefore, (Xu,
2006) proposed the modified NDWI (MNDWI), which substituted the SWIR band (Landsat
TM band 5) for the NIR band in McFeeters' NDWI equation (4-3):
ࡳࡾࡱࡱࡺ െ ࡿࢃࡵࡾ൰ሺ૝ െ ૜ሻࡹࡺࡰࢃࡵ ൌ ൬ࡳࡾࡱࡱࡺ ൅ ࡿࢃࡵࡾWhere, for Sentinel-2, SWIR is the reflectance of short-wave infrared bands (Band 11).
Eq. (4-3) is used to enhance open water features that are dominated by built-up areas. Noises
from built-up land, vegetation, and soil is reduced considerably. This study visually presents
the estimated image of the MNDWI index in Fig.4-7. This made it possible to observe thechanges that had occurred in the region inundated by the flash flood.

Figure 4-7. Observation of changes in the study area using pre- and post-event MNDWIs.

4.5.4. Normalized Difference Moisture Index (NDMI)The Normalized Difference Moisture Index (NDMI) was used to determine vegetationwater content. For Sentinel-2, the normalized difference moisture index (NDMI) is derived asa ratio between the NIR (band 8) and the SWIR (band 11) using Eq. (4-4):

102
ࡺࡵࡾ െ ࡿࢃࡵࡾࡺࡰࡹࡵ ൌ ൬൰ሺ૝ െ ૝ሻࡺࡵࡾ ൅ ࡿࢃࡵࡾLike most indices, the NDMI value ranges between -1 and 1. Negative numbers
approaching -1 indicate water stress, whereas positive values suggest waterlogging. NDMI candetect water stress in early stages, before it becomes a serious problem. Furthermore,
employing NDMI to monitor irrigation, particularly in locations where crops require morewater than nature can provide, aids crop growth tremendously. In order to determine whether
or not it is possible to observe the changes in the study area as a result of the impact of the flash
flood using NDMI, the NDMI images in the study area is illustrated in Fig. 4-8. Initially, theperformance of NDVI, MNDWI, and NDMI in the flooded area is evaluated, and then thecorrelation between these indices and NDVI is demonstrated.

Figure 4-8. Observation of changes in the study area using pre- and post-event NDMIs.

The estimated images of the indices in Fig. 6-8 are visually presented to see theapplicability of the index in detecting the changes in the flash flood inundated area. To examine
how the images would alter after the flood, a completely identical color table ranging between

103(-1 to +1) is specified for all of the estimated indices. As can be observed in Fig. 4-6, the color
in the rectangle area of the NDVI-post event shifted to dark red (+1), although in the same areasome changes were noticed in the MNDWI-post event (Fig. 4-7), but it’s not as pronounced as
the NDVI. Additionally, the NDMI, in the selected rectangular area, depicts the changes as aresult of the flood more pronounced (Fig. 4-8), compared to the MNDWI (Fig. 4-8),
particularly in the NDMI-after image, vast areas at the tops of the hills (surrounding therectangular areas), are altered to darker colors (+1), indicating an overestimation of thedetecting the inundation area.
In the inundated area depicted by the rectangular area in Fig. (4-6)-(4-8), a time series
of NDVI, MNDWI, and NDMI is also displayed in Fig. 4-9 for the months of February throughDecember of 2020. The result demonstrates that both NDVI and MNDWI values decreased
just after the event over the flood affected area. However, NDMI does not follow a similar
trend. In order to evaluate the relationship between the indices the correlation between NDVI,MNDWI, and NDMI is illustrated in Fig. 4-10.

Figure 4-9. NDVI, MNDWI, and NDMI time series over the course of one year inaffected area

104

Figure 4-10. Correlation graphs between NDVI and NDMI and MNDWI.

It is clear from Fig. 4-10 that NDMI and NDVI have a weak negative correlation (R2 = 0.27).
NDVI and MNDWI, on the other hand, show a high positive correlation (R2 = 0.82).
It is possible to identify the changes using most of the stated spectral water indices dueto the impact of the flood on the affected area by manipulating the color table to some extent.
These results indicate that the flood inundated areas can be detected not only by MNDWI but
also by NDVI. Furthermore, the NDVI has a higher spatial resolution (10 m) compared to thoseof MNDWI (20 m) and NDMI (20 m) because the spatial resolution of the SWIR band is 20 min the Sentinel-2 images. Higher spatial resolution images are expected to detect the inundation
areas in more detail. For those reasons, this study intends to employ NDVI applicability indetecting flood-inundated areas.
4.5.5. Relative Difference NDVI (rdNDVI)
It is self-evident that the information contained in any given index varies substantiallydepending on the seasons and type of land cover, such as vegetation types. As a result, using a

105single indicator to assess complex landscapes is technically difficult because change does notalways correlate to the degree of effects (Miller et al, 2007). It's difficult to quantify changes
in fractional forest, grass, or shrub cover since an NDVI drop in a low-NDVI drought-sensitive
open forest area can have a greater impact than a similar percent loss in a high-NDVI dense
forest (Norman et al, 2016). As a result, (Norman et al, 2020) proposed the Eq. (4-5):

࢘ࢊࡺࡰࢂࡵ ൌ ቆࡺࡰࢂࡵࡼ࢕࢙࢚ െ ࡺࡰࢂࡵࡼ࢘ࢋ
ඥࡺࡰࢂࡵࡼ࢘ࢋ ൅ ࡺࡰࢂࡵࡼ࢕࢙࢚ቇ ൈ ૚૙૙ሺ૝ െ ૞ሻ

In Eq. (4-5), NDVIPre and NDVIPost present the greenest pixel composites in
multitemporal pre- and post-event satellite imagery, respectively. Eq. (4-5) was originally used
to help not only interpret or quantify the changes in complex landscapes such as fractional
forest, grass, or shrub cover, particularly with low resolution imagery, but also help partiallyovercome problems that arise from NDVI’s nonlinear responsiveness or NDVI “saturation”
effect (Norman et al, 2020). When forest cover is low, a few trees added (or lost) have a higher
impact on NDVI than a few trees lost in a dense forest cell. When both areas experience thesame absolute change in NDVI, taking the square root of the denominator makes cells withhigh and low NDVI values fall more similarly. The result of Eq. (4-5) represents a normalized
percentage of NDVI gained or lost. Later, (Scheip et al, 2021) used the rdNDVI technique todetect surface changes, primarily vegetation area changes, as a result of fire, volcanic eruptions,and landslides. (Scheip et al., 2021) never discuss the feasibility of the method in detecting
flood inundated areas, particularly in non-vegetated areas, unlike the current work. This study
focusses on feasibility of the rdNDVI-based detection, delineation, assessments of inundated
area in non-vegetated arid region in Charikar, Afghanistan.
In this study, two temporal composites are made. One is a stack of all pre-event images,
and the second contains all post-event images. Then, pre- and post -event NDVI is calculated
for the so-called stacks. The greenest-pixel composite is a single composite or tiled image

106
generated from all images within the user-defined pre- and post-event window that records the
pixel with the highest NDVI result as shown in Eq. (4-1). Instead of differencing true-color
composites such as red, green, and blue bands, it exploits changes in surface vegetation by
developing and differencing an NDVI band from the greenest-pixel composite image. Simply,
the algorithm works in a way that from all-composite images it retains only the pixels with thehighest NDVI value from the entire stack. This composite indicates the peak phenological cycleof pre- and post-event conditions. The rdNDVI indicator was derived using the peakphenological cycle calculated from the user input duration (in months) for the pre- and postevent duration.
4.5.6. Histogram-based segmentation
The thresholding-based segmentation algorithm is one of the simplest, most powerful,and most commonly used algorithms for the segmentation process. Segmentation based on
histogram applies a thresholding value. This technique is used to segment the area of interest(AOI) from images with a uniformly bright background. Based on AOI, the threshold might
have a single or multiple value. The entire image is pixel-by-pixel scanned in order to designate
pixels as objects or background according to gray-level value and thresholding function (T). In
the current study, to discriminate between the inundated and non-inundated area pixels, a
histogram-based thresholding technique is used to find a set of thresholds. The method
associates each pixel of the image (f) with a binary number that depends on the intensity of the
pixels and a threshold T:૚࢏܎ࢌሺ࢓ǡ ࢔ሻ ൒ ࢀǡ۵ሺ‫ܕ‬ǡ ‫ܖ‬ሻ ൌ ൜ሺ૝ െ ૟ሻ૙࢏܎ࢌሺ࢓ǡ ࢔ሻ ൏ ࢀ

The above equation simply defines the binarization method. The thresholding process
produces a binary image as its output. In eq. (4-6), pixels with an intensity value of 1 correspondto objects (inundated area), while pixels with a value of 0 correspond to the background (non-107inundated area). In histogram-based thresholding, it is critical to select the optimum threshold,and this process needs to be carried out in a way that produces the least amount of bias possible.
The optimal value was obtained manually by iterating the value and observing the outcome.
This procedure was repeated until it was determined that, with the selected value, the image
contained almost all of the pixels from the original image. The histogram-based methods
typically produce results that contain noise that needs to be removed before further analysis
can be performed. In this particular research area, using sieve analysis in ENVI 5.3 to exclude
each individual pixel that is less than 10. It is possible to eliminate noise by using larger (11 or
12 pixel) values. However, using pixels less than 10 would diminish most of the single pixelof such size in the vicinities of the inundated area, which may underestimate the inundationarea.
4.5.7. Accuracy assessment procedures using confusion matrix
To validate the rdNDVI detection result, the flood inundation map was first createdusing visual interpretations of pre- and post-event Sentinel-2 images, as well as governmental
reports from after the flood, as shown in Fig. 4. Confusion matrix is employed to evaluate theaccuracy of detected inundation compared to the inundation map of the visual interpretation.
Confusion matrices, also known as error matrices, are typically utilized as the quantitative
method for characterizing the accuracy of image classification. These matrices contain
information about actual and predicted classification. It is a table that illustrates thecorrespondence between the classification result and a reference image. The generation of a
confusion matrix requires ground truth data, such as cartographic information, manually
digitized photographs, and GPS-recorded results of field work or surveys. This data is also
referred to as reference images. In this analysis, the reference image is a manually digitizedversion of an image that was compiled from various sources, including photographs taken after

108
the flood, published reports, and government survey reports. It is necessary to make acomparison between the pixels that have been classified and the truth data. The inundated andnon-inundated regions of this study area were generated utilizing binarization thresholding
techniques. This study utilized the results of the visually identified inundated reference map
data to perform the accuracy assessment. The estimated inundated pixels located within theboundary of the reference map (visually identified inundated map) is the number of correctly
identified pixels. On the basis of the confusion matrix, a pixel-by-pixel analysis of the image’s
accuracy was carried out. The accuracy of the outcome is determined by considering the user’s
accuracy, the producer’s accuracy, the overall accuracy, and the kappa coefficient.
4.5.8. Flood susceptibility mapping model based on Flow-R
Remote sensing technology is a very useful tool for detecting and monitoring land-use
changes after natural disasters. However, evaluation and prediction of hazardous areas forflooding are also important issues in considering countermeasures for future events. In orderto talk about how well the simulation method works for figuring out flood inundation areas for
future flash floods, semi-automatic debris flood mapping susceptibility is made using Flow-R
and free DEM from ASTER Global DEM (ASTER-GDEM) (NASA et al, 2018) with 30 m
spatial resolution (see Fig. 4). Flow-R is a freely available simulation software developed by(Horton et al, 2013) that has been used to simulate the flow propagation of debris flows anddebris floods through a DEM. The Flow-R software is a path assessment of gravitational
hazards that provides a substantial basis for a preliminary susceptibility assessment at aregional scale (Horton et al, 2013). It has been successfully applied to different case studies invarious countries with variable data quality and satisfying accuracy. This study utilizes theFlow-R simulation to discuss the applicability of the technique in assessing flood areas for a
future event. Although in this study, Flow-R is applied to a true disaster, such a simulation

109
technique is especially useful to identify vulnerable areas before the sources of an event areprovided. The main input requires two steps to apply the technique. For applying Flow-r first,
it is needed to provide source points using morphological and/or user-defined criteria. Second,debris floods are being propagated from the given source points based on simple frictional laws
and flow spreading algorithms. Debris floods are caused by water moving quickly throughsteep channels with a lot of debris in them. Their peak flow is similar to that of water floods(Hunger et al., 2014).
The DEM used for the simulation of flood susceptible areas were 30 m, which is very
low resolution. However, the developer of the Flow-R (Horton et al., 2013) reported that thequality of the produced map is of major importance for the result accuracy. In addition, Horton
et al. (2013) suggest that a DEM resolution of 10 m is a good compromise between the amountof time it takes to process the data and the quality of the results. In addition, the hazard mapthat is generated by Flow-R is a possible future hazard map, which means that compatibility of
one hundred percent is not required to be achieved. Because it only shows the areas that could
potentially be hazardous. For Flow-R analysis, this study follows the methods described by(Horton et al., 2013).
4.6. Extraction of flood inundated areas
As described in the preceding chapter, the pre-event and post-event periods for thisstudy are two months and two months, respectively. There were 14 photos available for the
given time period. This study considered the total number of pre-event images and thenchanged the number of post-event images during the course of the study in order to evaluate
and determine the shortest time required for the rdNDVI to analyze and identify the affected
area. As illustrated in Figure 4-11, the analysis technique starts by combining all pre-event
composites into a single post-event image (a). In the subsequent stages of analysis, two post-

110
event images are gathered, and in the final iteration, all pre-event images and post-event datain the study area are compared, as depicted in Figure 4-11 (a)- (e).

Figure 4-11. Result of rdNDVI map for five scenarios. Pre-and post-event images were acquired
between June 5, 2020, and October 31, 2020. Figures (a)–(e) show the results of rdNDVI for all preand post-event images that were available and used in this study. (a) presents the result of rdNDVI
which was generated with only one post-image acquired on Sep. 13, 2020. (b), (c), (d) and (e) represents
the results obtained from two, three, four and five post-event images, respectively.

Figure 4-11 presents the rdNDVIsʹ inundation map where blue color illustrates thedecrease of NDVI as a result of the flood event. The colors other than blue show no change(green) and vegetation gain (red color). From Fig. 4-11, it can be clearly seen that byconsidering only one post-event image, a clear inundation map is obtained. By increasing thenumber of the post-event images, a slight change is noticed in the result of the inundation map.

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The changes were so small that they were barely can be noticeable without a close-up of theimage. Therefore, using only one post-event image and the rdNDVI algorithm, the utilized
method can detect the flooded area. This study employs the image shown in Figure 4-11(a) for
the rest of the study.

Figure 4-12. rdNDVI-based change detection image and greenest pixel composites (pre-event time: 2months; post-event time: 16 days).

Figure 4-12 presents the larger map of the rdNDVI vegetation change in percentage.
The blue color represents the loss of vegetation, which here means inundated areas. Whereas

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red colors indicate vegetation gain or non-flooded areas’ detection. As can be seen, the current technique rapidly detects the inundated area using the explained methodology.

Figure 4-13. Histogram of the rdNDVI image with the chosen thresholds.

However, there were falsely detected areas due to the existence of a sharp slope thatreflected as a shadow. Hence, those undesired noise are canceled out using sieve analysis.
Additionally, wrongly detected spots caused by steep slopes (greater than 30 degrees) aremasked out. To undertake an error assessment, it was necessary to first simplify the
representation of the flooded image into an easily recognizable map. As presented in Fig.4-13,
this study used a histogram-based thresholding technique as explained in the methodology tofind a set of thresholds that can discriminate between pixels that are inundated and pixels thatare in the background. Figure 4-13 presents the histogram of the rdNDVI image with theminimum, optimum, and maximum thresholding. The image is segmented based on the
optimum thresholding of -12.90 into two different colors. The optimum value was obtained
manually by iterating the value and observing the result. This process was repeated until it is113
noticed that, with the selected value, the image contained nearly all of the pixels from theoriginal image. For values less than the optimal value, the color blue is selected to indicate theflooded area. Values larger than optimum thresholding were segmented into a non-inundated
area which is presented in white or non-color. The result of binarization is illustrated in Fig. 414 (a), which only consists of two classes of inundated and non-inundated zones. However, theresult of threshold binarization contains unwanted salt-and-pepper noise. As illustrated in
Figure 4-14(b), the so-called undesired noises with a size of 10 or less than 10 pixels aremasked out. All noises with small areas were canceled out using the Sieve analysis in ENVI
5.3 software, as shown in Fig. 4-14 (b).

Figure 4-14. Extraction of inundated and non-inundated areas from the rdNDVI result based on
binarization for two thresholds. a) The left image presents the result of threshold binarization withunwanted salt-and-pepper noise. b) In the left image, the right image presents the inundated area (redlines) is superimposed on the result of theextracted image of the threshold-binarization after removing
salt-and-pepper noise.

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Figure 104-14 (b) shows the final inundation map of Charikar, which occurred on August 26,
2020. A total of 300 hectares of built-up area were extracted as inundations.
4.7. Inundation map by visual interpretation
To draw the true inundation of Charikar, this study collected the reports publishedmainly in the local language by the Disaster Management Authority, the National Statistic andInformation Authority (NSIA) (HashtiSubh, 2020), and the National Water Affairs Regulation
Authority. Moreover, the pre- and post-event satellite images are side by side compared to
identify the changes between the flood events. The Sentinel-2 satellite images acquired before
the event (Aug. 24, 2020) and after the event (Sep. 13, 2020) are used as reference data.

Figure 4-15. Visual interpretation of the flood inundated area. a) Drawn from visual interpretation withgovernment reports, b) Inundation area mapped by (IWMI, 2020).

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The true Charikar inundated vector data is overlaid in red color, which is similar to thetrue impacted site. Because of the resolution of the satellite images, only significant changes
were considered. Therefore, minor damage may not be visible, and it was neglected in thisstudy. Although the inundation area by the Charikar flood was also estimated by visualinterpretation in a previous study (IWMI, 2020), the interpreted area was limited. This study
estimates the inundation map over a larger area. In the process of visual interpretation, theprevious inundation map created by IWMI (2020), and official reports are examined. The
official reports by the government were reflected in the inundation map. For example, whenbuilding damage was reported in some towns in the official documents, inundation areas wereidentified in the town by the visual interpretations. Figure 4-5 (a) presents the result of visualinterpretation carried out in this study. Figure 4-5 (b) shows the result of the previous
inundation map in (IWMI, 2020). Based on the visual comparison of the images, it can be
confirmed that the inundated area obtained in this study has good agreement with the previous
visual interpretation.

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4.8. Accuracy assessment
The accuracy assessment of the results obtained in this study was made through a
confusion matrix explained in the methodology. The result of the rdNDVI-based change
detection shown in Fig. 4-14(b) is overlaid on the visually identified inundation map shown in
Fig. 4-15(a). The error assessment was conducted pixel-by-pixel based on the confusion matrix.
The result of the confusion matrix is presented in Table 4-2. It presents the user’s accuracy, theproducer’s accuracy, the overall accuracy, and the Kappa coefficient. The accuracy assessment
obtained from the analysis of this study shows an overall accuracy of 88.44 % with a Kappa
coefficient of 0.75, which indicates a good agreement between thematic maps generated fromimages and the reference data.
Based on the inundated map obtained in this study, most of the affected houses were inbuilt-up areas, and it was also confirmed by government reports. Another significant point of
this method is the ability to detect changes with a single post image. Increasing the number ofpost-event images does not increase the quality of the detected image. Therefore, the rdNDVI
algorithm has the potential to quickly identify the flood-affected area immediately after an
event on a routine basis, typically with comparable accuracy to on-site reports, to help decision
makers find a quick way to contribute significantly to the post-disaster activities.

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Table 4-2. Accuracy assessment result using the confusion matrix.

Actual change status

Producer’s
accuracy(%)

Inundated

Noninundated

Row
total

(Pixels)(Pixels)(Pixels)

Inundated

2,323

524

2,847

81.59

88.23

Noninundated

310

4057

4,367

92.9

88.56

ClassesModeledchange status

User’saccuracy(%)

Overall
Column total

2,633

4581

7,214

accuracy
Kappa
Coefficient

88.44
0.75

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4.9. Monitoring the changes in the affected area in two years
The spatial resolution of remote sensing data accessible to state, local, and regional
agencies has markedly improved. It is very helpful for the public sector and local governments
to consider the use of new remote sensing technologies to monitor and efficiently act based onneeds. The use of satellite data and remote sensing applications already brings significant
efficiency to the decision-makers and policymakers of developed countries. However,developing countries such as Afghanistan do not have the technical capacity to use remotesensing data and applications for post-disaster activities. In most cases, even having
information about the affected area, the government does not reach out to the people to help
them in the recovery process.
From the NDVI time series (Figure 4-16) in the affected region depicted by therectangle in Figures (4-5) - (4-8), it can be seen that the NDVI decreased following the Charikar
event. As can be seen, even after one year, the NDVI values did not recover. This study
illustrates the rdNDVI changes one year after the event in Fig. 4-17. The rdNDVI image timeseries shows very minor and negligible changes, indicating that the damaged area has not yet
recovered considerably. Probably one factor behind the lack of changes in the NDVIs is thelimitation of economic and human resources in the Afghanistan government, which they failed
to utilize to revitalize and rebuild the affected areas. People also could not rebuild their houses,
probably due to their poverty. This result shows that the NDVI and rdNDVI techniques arevery useful for monitoring the event, and it will particularly be useful to observe how long it
will take for the recovery of the affected area.

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Figure 4-16. NDVI time series over the study area; the blue line indicates the raw NDVI from Feb.
2019 to Apr. 2022.

Figure 4-17. rdNDVI changes in Charikar after one year period.

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4.10. Flow propagation analysis by Flow-R
The freely available ASTER-GDTM with 30 m spatial resolution was used in thesimulation of the Charikar flood event. The initial pixels of the detected inundated areas in themountains were selected as the predefined source points. The parameters for flow spreading in
Flow-R were provided as shown in Table 4-3.
Table 4-3. Debris-flood parameters used in Flow-R.

Types of input

Flow-R Parameters

Value

Directions
algorithm
Inertial
algorithm

Holmgren(1994 modified)

Dh=2,
exponent=4

Weights

Default

Friction loss
function

Travel angle

4, 5, 6 Degrees

Energy limitation

Velocity

15 m/sec

These parameters were also used in the evaluation of the debris flow events in
Hiroshima prefecture, Japan in July 2018 (Miura, 2019). Three different travel angle iterations
for the analysis, as illustrated in Table 4-3 are considered, because the threshold of the travel
angle largely controls the propagation areas. The result of each iteration is shown in Fig. 4-18.
The overlaid red lines are the visually identified inundation maps. The results show theestimated spreading areas well reproduced the inundation areas by the flood. It is noticed that
smaller travel angles such as 4 degrees overestimated the inundation areas as shown in Fig. 418 (a), and larger travel angles such as 6 degrees underestimated the flood inundation areas(Fig. 4-18 (c)). The result with a travel angle of 5 degrees shown in Fig. 4-18 (b) gave moderatespreading areas. Figure 4-18 (b) depicts a spread region that is slightly bigger than theinundation areas that were determined, but the simulation that used a travel angle of 5 degrees
was able to better reproduce these inundation areas. These results indicate that hazardous areas

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for future flood events can be evaluated if appropriate source points are provided in thesimulation. Such simulations would be useful not only for government officials but also forresidents living in natural hazard susceptible areas, particularly flood and debris flow prone
areas, to increase their awareness of mitigating future disasters.

Figure 4-18. Flow-R generated flood -debris susceptibility map using a 4-degree travel angle
illustrated in (a), 5-degree travel angle in (b), and 6-degree travel angle in (c).

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4.11. Significance of the method
The novel aspect of this study is the combination of pre- and post-greenest pixel
compositing with the Google Earth Engine service in order to map hazard-related changes inthe vegetated and non-vegetated areas, as well as flat areas, mountainous areas, and areas witha high concentration of buildings. This work is also an attempt to bridge the gap between theworlds of cloud computing and big data, which enables extremely large satellite datasets to beprocessed quickly in response to a crisis. On the other hand, government agencies and thegeneral public could benefit from the current method even though they lack the expertise to doso directly through the Google Earth Engine utilizing the rdNVI to monitor changes in thevegetation. Schiep et al., 2021, utilized the rdNVI to monitor changes in the vegetation;
However, those researchers did not investigate the usefulness of the system in identifying
landslides, particularly in areas that were devoid of vegetation. In contrast to the previous
research Schiep et al., 2021, which proposed using the method in vegetated regions, this study
successfully applied and map the inundated area by using the rdNDVI approach in a dry
environment without any vegetation. In addition, the accuracy of the rdNDVI methodology
was not investigated in the previous research; however, in this work, the accuracy of the method
is also discussed.
The use of this study in a place with little data, less research, and significant security
challenges would be an additional contribution that would be of use for government andvolunteer organizations. Another important part of the current study is its applicability in aflood-prone area with no standing water when the satellite is passing. There are numerous
methods for extracting water body using spectral water index such as NDWI (McFeeters 1996;
Rogers et al., 2004), MNDWI (Xu, 2006), AWEI (Feyisa et al., 2014), EWI (Wang et al., 2014),
NDMI (Zha et al., 2003) and NDBI (Gao, 1996). However, none of these techniques

123
acknowledged their application in detection of inundated areas with no standing water.
Although it is conceivable to detect the inundated area in a completely identical scenario suchas Charikar city using some of the water spectral indices, but no research has been conductedin this regard using the so-called indices.
4.12. Urban planners and flood managers’ expectations from current study
Rapid urbanization poses a serious threat to existing flood management infrastructure,putting lives and property at risk if it is not appropriately planned. Rivers, coastal floods,
pluvial and groundwater floods, and manmade system failures can all cause flooding in urban
areas. Lands with open soil that can be used for water storage are scarce in metropolitan
settings. Flooding can occur when rainfall intensity is high and drainage systems do not havethe ability to handle the flow. Constant urbanization has diminished the permeability of the soil
in groundwater recharge zones, resulting in increased runoff and a higher risk of flooding. To
avoid catastrophic disasters, it is critical to take preventative and precautionary measures while
dealing with flood threats. As (Abdrabo et al., 2022) suggested, Flood Risk Assessment (FRA)
is intended to improve disaster management efficiency, decrease the terrible social andeconomic effects of flooding, and direct urban growth to a safe place. As a result, FRA is
critical for detecting flood-prone locations in arid urban contexts, reducing flood risks, andassisting in decision-making. (Abdrabo et al., 2022; Amundrud et al., 2015).
Both structural and non-structural flood control measures must be undertaken to reduce
current and future flood damage. The structural measures are engineering works that can beundertaken to remedy and/or prevent flood-related problems. Non-structural measures arethose that aim to prevent or decrease flood damage or effects by introducing rules, regulations,and programs aimed at, for example, regulating land use and occupation, implementing
warning systems, and raising public awareness.

124This approach will significantly contribute to the rapid detection of inundated areasusing a single publicly available satellite image, such as Sentinel-2. The GEE is utilized for the
analysis in order to speed the operation. Rapid identification of the inundated area expedites
the post-crisis management of rescuing the affected individuals, offering additional essential
aid, and assisting the people in rebuilding and reconstructing the affected areas. Afghanistan’s
government lacks the capacity to develop crisis maps quickly after an occurrence. The resultsof this study and the provision of such results can inspire the government to prioritize remotelysensed data for crisis management in the future. In addition, in this study, the detection of
possible future flood utilizing Flow-R is also discussed. If future flood zones are identified,urban planners, policy makers, and flood operators on the ground will be better equipped tomitigate risks by taking the appropriate countermeasures, such as structural and non-structural
measures. In addition, whether contemplating the formation of new cities or formulating plansfor existing ones, it is strongly recommended that the Flow-R software be used to analyze thelikelihood of future flooding. It is also important to note that local and central government mustensure that no new homes are constructed in high-risk regions. For existing settlements inflood-prone areas, the government must either replace homes with safer alternatives orimplement structural measures such as detention/ retention facilities, channel improvements to
reduce the impact of flooding in flood-hazard areas, construction of banks or dams, upstream
storage and diversion works.
According to the results of the present study, the damaged area has not recovered even
two years later. It is evident that the government is inadequate in terms of monitoring andoffering assistance. The government will be able to monitor the damaged area and provide
better aid to the affected area using the current method’s results and applications. Last but notleast, the outcome of transferring the current study’s skills will be an improvement ingovernment and officials’ awareness.

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4.13. Applications and future aspects
This section discusses the applications and future aspects of remote sensing technology
for detecting and monitoring areas affected by natural disasters. The workflow can be easily
applied to different areas, such as built-up areas, vegetated and non-vegetated areas. Moreover,it can be used in different flood scenarios, such as flash floods, coastal floods, fluvial, andpluvial floods. Unlike any traditional method, the GEE used in this study automatically gives
the flood-affected area with little processing time after giving the area of interest and theconcerning event date. It is also worth noting that the current method can quickly identify aflooded area with significant time efficiency in terms of disaster support management for
affected people. In addition, most traditional methods of identifying flood-affected areas inurban, densely vegetated, or non-vegetated areas are challenging. In most cases, the download
and preprocessing of images add extra time to the analysis process. However, the current
method can determine the inundated area in the mentioned areas with considerable speed
without the existence of the mentioned challenges in traditional methods. The GEE allows for
rapid deployment of the algorithm during flood events and gives the user extra time to not
download the numerous temporal images.
Many false positive pixel changes on the land surface in the inundated images also
noticed, which were mostly not caused by flooding. Most of the false-positive pixels detection
was due to the sharp slope, which makes the pixels in those areas darker and, as a result, falsely
leads the algorithm to detect those areas as a flood. To prevent the detection of such false
positive pixels the sharp slope is masked out in the analyses. The current method is not able tocapture the flood peak due to the acquisition frequency of Sentinel-2 or most of the time dueto the clouds. However, the recent increase in launches of satellites with optical sensors willallow users immediate data acquisition just after a natural disaster. Moreover, the shortcoming

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addressed here may need further research to find a possible way of automatically denoising therdNDVI result to accomplish the analysis faster.

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4.14. Summary of this chapter
Due to the recent increase in the frequency and magnitude of natural hazards such as
flooding events, satellite remote sensing has become more valuable for post-disaster activities.
This study presented utilizing the rdNDVI in GEE using Sentinel-2 images to detect andestimate the flood-affected area. The technique presented in this study was employed in a flood
disaster that occurred on Aug 26, 2020, in Charikar, Afghanistan. The method successfully
detected the flood-affected area with only one post-event image which is significant time
efficient in terms of disaster support management for the affected people. An increasing
number of post-event images does not significantly improve the quality of the estimatedinundation map. Moreover, it is being stressed that the method can successfully be used evenin non-vegetated areas such as semi-arid regions like Charikar to quickly identify affectedareas.
The sharp slope of the mountain caused false positive pixels changes on the land surface
in the inundated images which makes the pixels on those areas darker and as a result falsely
leads the algorithm to detect those areas as a flood. The most flood-affected area was in thebuilt-up area, which also can be confirmed by government survey reports. Accuracy assessment
was carried out to verify and assess the accuracy of the inundated map. The accuracy
assessment obtained from the analysis of this study showed an overall accuracy of 88 % witha Kappa coefficient of 0.75, which indicates a good agreement between thematic maps
generated by the visual interpretation. Even after two years, there is no noticeable increase in
NDVI values in the study area, indicating that the damaged areas have not recovered due to thegovernment's lack of financial and human resources and the poverty of the affected population.
Further, the Flow-R simulation is utilized to discuss the applicability of the technique inassessing flood areas for a probable future flood event. The inundated map obtained from theFlow-R was also comparable with the identified inundation areas.

128
Estimations of flooded areas serve as important input information used for determining
the extent of natural disasters and for making decisions regarding the recovery plans for thedamaged areas. The total flood-affected area using the current method was approximately
estimated at 300 ha. With this, it can be concluded that the method utilized in this study has aconsiderable potential to map the inundated area rapidly in both flat and mountainous regions,and it indicates good applicability of this technique. With that being said, it suggests that thismethod can be used efficiently even with the viability of one single post-event image on aroutine basis and runs whenever a new flood event has occurred.

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CHAPTER V
CONCLUSIONSThis dissertation overviewed five major natural disaster and it finally utilize remotesensing analysis to detect, monitor, and predict the landslide and flood which are the leading
cause of hundreds of life’s losses in Afghanistan. Besides the detection of the mentioned natural
disaster, the extent of the landslide, such as volume, depths of erosion, and depositions is also
estimated. To accomplish the objective of this study, two different methodologies is used. For
landslide detection and assessments, nonlinear mapping techniques is employed. However, forflood detection and assessment, the rdNDVI techniques is utilized. The nonlinear mapping
techniques is used for landslides because of lack high resolution stereo satellite imagery in the
study area where the landslides happened. As a result, in the flood-affected area, similar data
could not be obtained. Applying nonlinear mapping techniques is solely dependent on theavailability of stereo types of satellite imagery. The availability of such high-resolution satellite
imagery give user the opportunity to not only detect and assess the changes in the area but also
estimate the volume and depth of those changes with high accuracy. On the contrary, therdNDVI can detect both landslides and floods in any type of land area rapidly by using freely
available satellite imagery. Although rdNDVI satellite imagery can be used to detect and assessnearly all types of natural disaster, it cannot be used to estimate the volume and depth ofchanges.
This dissertation initially discusses the details of the natural disaster risk such as
earthquakes, floods, landslides, avalanches, and droughts in Afghanistan. The availability ofrisk information is a key to the effective management of disaster and climate risks. The risk
profile provides a summary and visualization of the national multi-hazard assessments. Such

130
information will provide essential data and information for policymakers, decision-makers,
development planners, and infrastructure investors to ensure a more resilient future for people
in Afghanistan. Therefore, this study discussed the susceptibility map, lack of coping
capacities, lack of adaptive capacities, vulnerability map, exposure of the population to natural
hazards, and world risk index as a result of exposure and vulnerability. This study also present
which natural disaster is worse in terms of agricultural losses, property losses, and deaths.
The nonlinear mapping technique is utilized to detect and estimate the volume of thelandslide affected area using stereo-paired high-resolution satellite imagery. It is being stressedthat the method can successfully be used in the image processing analysis to conduct mappingtasks, diminishing the geometric errors accumulating in the unchanged areas of the DoDs
output, and finally, obtain the volume and average depth of the displaced material in the disasterarea to ease the crisis management support and contribute very well to the more effectiveplanning of disaster relief and restoration.
The technique presented in this study was employed in a large-scale landslide that
occurred on May 2, 2014, in Abe-Barek village, Badakhshan, Afghanistan. Using the DEMsof the difference change detection technique, the DEMs of landslide affected areas are
generated from the pre-and post-event satellite stereo-pair images. Although the DEMs werecorrected using the RPC method, registration noises or locational errors were still spotted in
the nonaffected areas. The nonlinear mapping technique was utilized to diminish the aforesaid
geometric inconsistencies or registration noises and enhance the accuracy of the process
analysis. The proposed method has been coded in such a way that, in the selected testing zone
with the given combinations of parameters, the post-event DEM (follower data), superimposeson the pre-event DEM, and pixel by pixel, scans neighboring vicinities for the best possible
matching points in the pre-event DEM (controller data). In this study, nine scenarios were
examined with different values of NW (window size or size of the subarea), NS (search area),131
NC (consensus area), and n (number of iterations). As the quantitative and graphicalcomparison revealed, the locational gaps were significantly reduced, and a bell-shaped
histogram, also known as a normal distribution, was obtained. Moreover, statistical values, inparticular, mean and standard deviations, decreased more towards zero and one, respectively.
Additionally, the obtained RMSE values also verified the nonlinear mapping’s applicability indecreasing the locational errors quantitatively. After carefully comparing and observing theassessments, NW = 5, NS = 9, NC = 3, and n = 3; were chosen as the required inputs for the method
to use for the rest of analysis.
The total eroded soil volume in the landslide area after adopting the nonlinear mapping
method was approximately estimated at about 1.05 × 106 m3, and the volume of the deposited
soil acquired was 6.73 × 105 m3. The landslide’s estimated volume was compared with thevolumes obtained from the previous studies. The erosion volume of the soil obtained in thisstudy showed good agreement with that achieved in the previous studies. The average depth oferosion, mostly situated on the mountain’s hilly side, was 8.6 m. On the contrary, the average
depth of deposition, which is typically situated in a flat area, was estimated at 4.6 m. Thishighlights that nonlinear mapping has a considerable potential to reduce locational errors inmountainous regions. Therefore, it indicates a good applicability of the nonlinear mapping
technique. It suggests that the method can be used efficiently on a routine basis and run
whenever a new landslide event has occurred if high-resolution stereo-pair images of the area
become available.
Chapter four of current study presents This study explored the usefulness of the rdNDVI
approach in Sentinel-2 images for estimating flood inundation areas in a semi - arid and arid
region by employing the GEE. The approach of this investigation was applied to the flash floodincident in Charikar, Afghanistan on August 26, 2020. Several spectral indices, including
NDVI, MNDWI, and NDMI, were utilized to detect the flooded areas. A strong relationship132
was found between NDVI and MNDWI, and both indices decreased significantly in theaffected zones soon following the occurrence of the incident. In contrast, NDMI did not change
significantly between before and after the event. From the perspective of spatial resolution, it
is determined that NDVI should be used to detect flooding zones. The NDVI was effective in
defining flood inundation regions in a semi - arid and arid region, which is one of the mostnoteworthy findings of this study. The method successfully detected the flood-affected areawith only one post-event image, which is significantly time-efficient in terms of disastersupport management for the affected people. An increasing number of post-event images does
not significantly improve the quality of the estimated inundation map. Moreover, it is being
stressed that the method can be successfully used even in non-vegetated areas such as semiarid regions like Charikar to quickly identify affected areas.
The sharp slope of the mountain caused false positive pixels on the land surface in theinundated images, which made the pixels in those areas darker and, as a result, falsely led thealgorithm to detect those areas as a flood. The most flood-affected area was in the built-up area,
which can also be confirmed by government survey reports. An accuracy assessment was
carried out to verify and assess the accuracy of the inundated map. The accuracy assessment
obtained from the analysis of this study showed an overall accuracy of 88% with a Kappa
coefficient of 0.75, which indicates a good agreement between thematic maps generated by thevisual interpretation. No significant increase in NDVIs value in the study area is observed evenafter two years, indicating that the affected areas have not recovered, probably due to thegovernment's lack of financial and human resources and the poverty of the affected people.
Furthermore, the Flow-R simulation is utilized to discuss the applicability of the technique inassessing flood areas for a probable future flood event. The inundated map from the Flow-R
was also to some extent similar to the inundated areas that had already been found.

133
Estimations of flooded areas serve as important input information used for determining
the extent of natural disasters and for making decisions regarding the recovery plans for thedamaged areas. The total flood-affected area using the current method was approximately 300
ha. With this, it can conclude the method utilized in this study has considerable potential tomap the inundated area rapidly in both flat and mountainous regions, and it indicates good
applicability of this technique. Finally, it is suggested that this method can be used efficiently
with the viability of one single post-event image on a routine basis and run whenever a new
flood event has occurred.
The error estimation has not been discussed in landslide assessments using actual GCPs.
Once such data are collected, much additional work needs to be done. For example, an error
estimation of both depths and volume obtained from the nonlinear mapping and real GCP canbe compared using the evaluation change analysis technique. Most of the false-positive pixels
detection in flood detected area was due to the sharp slope, which makes the pixels in thoseareas darker and, as a result, falsely leads the algorithm to detect those areas as a flood, amanual denoising method was applied in this study. Moreover, the proposed rdNDVI technique
is not able to capture the flood peak due to the acquisition frequency of Sentinel-2 or most of
the time due to the cloud’s coverage. So further research needs to be done to find a possible
way of automatically denoising the rdNDVI result to accomplish the analysis faster.
As a final remark it is to say that the approaches based on satellite imagery and/or DEM
would be advantageous for pre- and post-disaster efforts, particularly for countries with limited
financial and human resources, such as Afghanistan. Implementation of remote sensing
technologies, including the proposed method in this work, by the government and official
agencies would be crucial for more prompt and effective responses to future natural disasters
and for the development of effective remedies.

134

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List of Publications

Atefi, M. R. and Miura, H.: Volumetric Analysis of the Landslide in Abe Barek, Afghanistan
Based on Nonlinear Mapping of Stereo Satellite Imagery-Derived DEMs, Remote
Sensing, 13(3), 446, 2021, https://doi.org/10.3390/rs13030446.
Atefi, M. R. and Miura, H.: Detection and Volume Estimation of Large-scale Landslide in Abe
Barek, Afghanistan Using Nonlinear Mapping of DEMs, International Geoscience andRemoteSensing

Symposium(IGARSS),3757-3760,

2021,

https://doi.org/10.1109/IGARSS47720.2021.9554504 .
Atefi, M.R.; Miura, H. Detection of Flash Flood Inundated Areas Using Relative Difference in
NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar,
Afghanistan. Remote Sens. 2022, 14, 3647. https://doi.org/10.3390/rs14153647
Atefi, M. R. and Yamamoto, H.: Obtaining the Optimum Inclination Angle of Anchor by Using
Minor Principal Stress, Proceedings of Annual Research Meeting, Chugoku Chapter,
Architectural Institute of Japan (AIJ), 41, 105-108, 2017.
Atefi, M. R. and Yamamoto, H.: Using D-C Model to Evaluate the Stabilization Technique in a
Typical Soil Sample under Tri-Axial Compression Stresses, Proceedings of Annual
Research Meeting, Chugoku Chapter, Architectural Institute of Japan (AIJ), 41, 109-112,
2017.

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