ISSN 1346-7328
国総研資料 第 1110 号
令和２年４月

国土技術政策総合研究所資料
TECHNICAL NOTE of
National Institute for Land and Infrastructure Management

No.1110

April 2020

Guideline for the interpretation of sediment-related disasters
by synthetic aperture radar（SAR）images

SUZUKI Yamato
MATSUDA Masayuki
TAKIGUCHI Shigetaka
NOMURA Yasuhiro
YAMASHITA Kumiko
NAKAYA Hiroaki

国土交通省 国土技術政策総合研究所
National Institute for Land and Infrastructure Management
Ministry of Land, Infrastructure, Transport and Tourism, Japan

Technical Note of NILIM

国土技術政策総合研究所資料
2020年4月

第1110号

No.1110

April 2020

Guideline for the interpretation of sediment-related disasters
by synthetic aperture radar（SAR）images

SUZUKI Yamato*,

MATSUDA Masayuki**,

NOMURA Yasuhiro ****,TAKIGUCHI Shigetaka***,YAMASHITA Kumiko*****,NAKAYA Hiroaki******

Synopsis
This guideline describes synthetic aperture radar images interpretation of sediment-related disasters
that can be used even at night or under adverse weather conditions, in order to enable to identify
disaster-stricken area.
Key Words: SAR Image, Slope Collapse, Sediment-related Disaster, Landslide Dam,
Image Interpretation, Backscatter Coefficient*
土砂災害研究室研究官**
土砂災害研究室交流研究員***
土砂災害研究室主任研究官****
前土砂災害研究室主任研究官***** 前土砂災害研究室交流研究員****** 土砂災害研究室長

Researcher, Sabo Risk-Management Division
Guest Research Engineer, Sabo Risk-Management Division
Senior Researcher, Sabo Risk-Management Division
Former Senior Researcher, Sabo Risk-Management Division
Former Guest Research Engineer, Sabo Risk-Management Division
Head, Sabo Risk-Management Division

[Introduction]
When Sediment deposition occurs in a channel as a result of occurrence of a large-scale slope
failure or deep failure, an inundated area may be formed by the damming up of the river. The
sediment that has blocked the channel is often unstable, and if the inundated area has expanded
rapidly, a dam failure or the like may occur, thereby causing serious damage to the lower reaches,
and therefore a quick grasping of the situation after the disaster is required. In addition, it is also
important to grasp the place where the slope failure or deep failure occurred in a concentrated
manner as well as the range of such occurrence, similarly, in order to contribute to earlycommencement of first-aid measures.
The National Institute for Land and Infrastructure Management has been engaging heretofore in astudy of techniques for quickly grasping the location of channel blockage, by focusing on reading byusing images of Synthetic Aperture Radar (hereinafter called "SAR") that enables observation to bemade day and night in any weather condition. The results of such study were summarized as
Technical Note of National Institute for Land and Infrastructure Management No. 760 of November
2013 "Method of emergency search for the location of landslide dams using high-resolution singlepolarization SAR image interpretation" (hereinafter called "NILIM Technical Note No. 760"). After
that, it was developed into an extraction technique using high-resolution dual-polarized SAR images,
in order to grasp the location of channel blockage more quickly and reliably, and the results of suchdevelopment were summarized as Technical Note of National Institute for Land and InfrastructureManagement No. 791 of June 2014 "Method of emergency search for the location of landslide dams
and collapses using high-resolution dual-polarization SAR image interpretation" (hereinafter called"NILIM Technical Note No. 791").
In recent years, opportunities are increasing in which SAR images before and after a disaster canbe utilized by making use of data that are continually accumulated by steady state observation.
NILIM has devised a method for utilizing its findings heretofore and at the same time for engaging
in image interpretation survey of a sediment disaster by using SAR images before and after thedisaster, and has applied the method to response to the disasters in recent years. Consequently, it has
been clarified that the method can extract not only large-scale slope failures that have been
researched until the present, but also smaller failure phenomena as well as locations where thetopography and land cover have undergone changes, and that the situation of damage can be grasped
in a short period of time regarding the sediment disasters where slope failures occur in a wide range
and with a high density as well. This material explains the method for the interpretation of sedimentdisasters by means of SAR images, and at the same time has summarized the conditions ofapplication and the precautions. Now that further utilization of satellite images, etc. is required inorder to improve the efficiency of the Regional Development Bureaus, etc. during response todisasters, it is our hope that this material will serve as a reference, as one of the techniques forsupporting an appropriate initial investigation by narrowing down the range of damage at an earlystage.
This material is part of the results of a joint research with the Japan Aerospace Exploration
Agency, a National Research and Development Agency, (hereinafter called the "JAXA"), i.e. the"Joint research on the development of methods to monitor landslide/mudflow using Daichi 2, theadvanced land observing satellite" (FY 2017-).
April 2020 Sediment Disaster Risk Management Division, Sediment Disaster Research Department,
NILIM

[Purpose and composition of this material]
This material shows the procedures and technical guidelines for the image interpretation surveyof sediment disasters using SAR images to be carried out by the staff of a Regional Development
Bureau and others at the time of occurrence of a sediment disaster. Also, since the material explains
the basics and technical precautions concerning SAR images, we believe that the material can beutilized not only by persons in charge of disaster preparedness but by other technical personnel
involved in the image interpretation survey of sediment disasters, etc. as well.
This material consists of the following 3 parts. See the Table of Contents for details.
Part 1

Basics concerning Synthetic Aperture Radar (SAR) images

It explains basics such as observation by means of SAR and the features of SAR images as wellas precautions for use.
Part 2

Explanation of the techniques for the image interpretation survey of sediment disasters bymeans of SAR images

It explains the procedures for the interpretation of sediment disasters using SAR images.
Part 3Results of application of the image interpretation survey of sediment disasters by meansof intensity difference SAR images

It shows the results of application of the image interpretation survey of sediment disasters using
intensity difference SAR images to actual disasters.
By utilizing this material together with the "Guide Book for Artificial Satellite Utilization during
Disasters - Sediment Disaster Edition" (JAXA Satellite Applications and Operations Center, andSabo (Erosion and Sediment Control) Planning Division, Sabo (Erosion and Sediment Control)
Department, Water and Disaster Management Bureau, Ministry of Land, Infrastructure, Transport
and Tourism), it is expected that the image interpretation survey of sediment disasters by means ofmore effective SAR images will be realized.

Guide for the Image Interpretation of Sediment Disasters by Using Synthetic
Aperture Radar (SAR) images
Table of Contents
Part 1 Basics concerning Synthetic Aperture Radar (SAR) images ...........1
1．Synthetic Aperture Radar (SAR) ..................................................................................... 1
1.1 Principles of SAR observation ........................................................................................ 1
1.2 Features of SAR .............................................................................................................. 3
1.3 Wavelength (band) .......................................................................................................... 5
1.4 Scattering characteristics ................................................................................................. 6
1.5 Polarization characteristics .............................................................................................. 9
2. SAR images ...................................................................................................................... 10
2.1 Generation of SAR images ............................................................................................ 10
2.2 Features of SAR images ................................................................................................ 12
2.3 Precautions for SAR images ......................................................................................... 13
2.4 Resolution of SAR images ............................................................................................ 16
2.5 Analysis and types of SAR images ............................................................................... 17
3. Utilization of SAR images in the image interpretation survey of sediment disasters................................................................................................................................................. 20
3.1 Conditions of observation of SAR images suitable for the image interpretation surveyof sediment disasters ........................................................................................................... 20
3.2 Precautions when utilizing SAR images in the image interpretation survey of sedimentdisasters ............................................................................................................................... 21

Part 2 Explanations of the techniques for the image interpretation survey of
sediment disasters by means of SAR images ............................24
1. Selection of the techniques for the image interpretation survey of sedimentdisasters by means of SAR images ................................................................................... 24
1.1 Selection according to the conditions of available SAR images ................................... 24
1.2 Selection according to the scale of a sediment disaster to which the image interpretation
survey is applicable ............................................................................................................. 26
2. Image interpretation survey of sediment disasters by means of dual-polarized SAR
images .................................................................................................................................... 27
3. Image interpretation survey of sediment disasters by means of intensity difference
SAR images........................................................................................................................... 28
3.1 Generation of intensity difference SAR images ............................................................ 28
3.2 Interpretation of intensity difference SAR images ........................................................ 29
3.3 How to display and view intensity difference SAR images .......................................... 32
3.4 Flow of the image interpretation survey of sediment disasters by means of intensity
difference SAR images........................................................................................................ 33
3.5 Checklist of the image interpretation survey of sediment disasters by means of intensity
difference SAR images........................................................................................................ 37
4. Application of the image interpretation survey of sediment disasters by means of
SAR images........................................................................................................................... 41
4.1 Efficient method of image interpretation survey ........................................................... 41
4.2 Method of image interpretation survey that increases the reliability of results............. 43

5. Precautions for the techniques for the image interpretation survey of sedimentdisasters by means of SAR images ............................................................................... 45
5.1 Effects of artificial modification ................................................................................... 45
5.2 Orientation of a slope relative to the direction of microwave irradiation ..................... 47

Part 3 Results of application of the image interpretation survey of sedimentdisasters by means of intensity difference SAR images ....................49
1. Time required for the image interpretation survey of sediment disasters by meansof intensity difference SAR images .................................................................................... 49
2. Accuracy of the image interpretation survey of sediment disasters by means ofintensity difference SAR images ........................................................................................ 50
2.1 Conditions of verification of accuracy .......................................................................... 50
2.2 Results of verification of accuracy ................................................................................ 52

References ..........................................................56
Parameters of the SAR images in the text ...............................57
Reference material 1. Time required for the image interpretation survey of
sediment disasters by means of intensity difference SAR images of eachdisaster .............................................................58
Reference material 2. Accuracy of the image interpretation survey of sedimentdisasters by means of intensity difference SAR images in each disaster .....66
Reference material 3. Checklist of the image interpretation survey of sedimentdisasters by means of intensity difference SAR images ....................71

Part 1 Basics concerning Synthetic Aperture Radar (SAR) images
1．Synthetic Aperture Radar (SAR)
1.1 Principles of SAR observation
The "Synthetic Aperture Radar" mounted on a satellite irradiates microwaves obliquely downward
in the direction perpendicular to the traveling direction (azimuth direction) of the satellite (range
direction) (Fig.-1.1.1).
The angle formed by the vertically downward direction of the satellite and the direction of
microwave irradiation of the satellite is called the "off nadir angle," and the angle formed by the
direction of microwave irradiation of the satellite when viewed from the direction of the apex of thetarget is the "incidence angle" (Fig.-1.1.1). Many satellites make the off nadir angle duringobservation variable, and on a inclined land, as shown in Fig.-1.1.2, the angle formed by the normal
to the slope and the direction of microwave irradiation is called the "local incidence angle." Notethat the relationship of "Off nadir angle < Incidence angle" holds even on a flat land because theEarth is a globe.

a. Azimuth direction）
c. Off nadir angle

d. Incidence angle）
b. Range direction

Fig.-1.1.1 Terms and a schematic diagram related to SAR observation1

Normal to the slope

Local incidence angle
Direction of microwave irradiation

Fig.-1.1.2 Local incidence angle
The synthetic aperture technique realizes effects equivalent to those of the results of observation
with a virtual antenna (synthetic aperture length (Ds)) having a length greater than the physicallength of the antenna (actual aperture length (Dr)), by performing continuous observations thatutilize the stable high-speed traveling of a satellite, etc. (Fig.-1.1.3). Also, theoretically the azimuth
space resolution of SAR has a value half of the actual aperture length (Dr), and high-resolution
observation data can be obtained.
Also, SAR is an image radar, which is a sensor for imaging (generating the image of) the target ofobservation.

2

Synthetic aperture length (Ds)Azimuth direction
Actual aperture length(Dr)

Resolution of SAR

Fig.-1.1.3 Principles of the synthetic aperture technique

1.2 Features of SAR
Observation with SAR uses an active sensor that irradiates microwaves from the antenna itself ofthe radar to the target of observation, and receives the electromagnetic energy scattered by the targetof observation with the antenna. Since it is not a passive sensor that utilizes the electromagnetic
energy radiated by the sun, observation can be made day and night. Also, SAR is unlikely to beaffected by cloud coverage, etc. because it irradiates microwaves (Fig.-1.1.4) having a wavelength
greater than the size of a particle in the atmosphere such as cloud or rain.
Thus, SAR has an advantage of "all weather capability," thereby providing the merits ofperforming observation without depending on the time zone or meteorological condition.

3

Names, wavelengths, and frequencies of microwaves
Band name
VisibleMid-infrared Thermal infrared

Wavelength (mm)Frequency (GHz)

Microwave band

Ultraviolet Near Infrared

Transmittance, %Wavelength bands and names of electromagnetic waves

Wavelength
Atmospheric transmittance of each wavelength band

Source: NASA Instrument panel report, VolumeI If, SAR earth
observing system

Fig.-1.1.4 Relationships between microwaves and atmosphere/cloud/rain
Source: Remote Sensing Technology Training Textbook "Microwave Remote Sensing"From 1. Features of microwaves Relationships between microwaves and atmosphere/cloud/rain (created/edited by RESTEC)

4

1.3 Wavelength (band)
As SAR, microwaves (wavelength: approx. 0.1 to 100 cm) are used among the electromagnetic
waves. They are subdivided into the band names shown in Fig.-1.1.5 according to the wavelength.
Microwaves with a relatively short wavelength such as those of the X band are scattered on a
grassland, tree canopy, or on the surface of a leaf of a tree. On the other hand, if the wavelength isgreat such as that of the L band, the microwaves are transmitted through the grass land or tree
canopy, and are scattered on the tree trunk or ground surface. Differences in the scatteringcharacteristics depending on the wavelength will be described in "Part 1, 1.4 Scatteringcharacteristics."

W

Band nameWavelength
[cm]

V

0.3

Q

Ka

K

2.4

0.8

X

L

Ku

L

X

C

S

L

P

3.8

7.5

15.0

30.0

100.0

X

3cm
24cm

ForestGrassland

Fig.-1.1.5 Band names as well as classification and features of wavelengths

5

1.4 Scattering characteristics
The intensity of microwaves returning to the irradiation direction side after the irradiatedmicrowaves have been scattered on the ground surface, etc. is called the "backscatter intensity,"
which is recorded as the "backscatter coefficient" in the SAR observation data. The backscatter
coefficient has dependency on the incidence angle, according to the roughness of the scattering
surface.
The backscatter coefficient is described as follows.
Let Pt be the transmitted power, λ the wavelength, R the distance from the antenna, G the antenna
gain, and σ the cross-sectional area of the radar. Since SAR performs the transmission and receivingof radio waves by means of a single antenna, the monostatic radar equation is applied, and thereceived power Pr is represented by Equation-1.1.1.

𝑃

𝑃 𝐺 2 𝜆24𝜋

3Equation-1.1.1

𝜎𝑅4

Since the backscatter coefficient (σ0) is the cross-sectional area (σ) of the radar per unit area (A)on the scattering surface, it is represented by Equation-1.1.2.
3

𝜎0

𝑃 4𝜋 𝑅4

Equation-1.1.2

𝑃 𝐺 2 𝜆2 𝐴

1.4.1 Surface scattering
The scattering of microwaves irradiated from SAR on the border surface such as the groundsurface is called "surface scattering." Surface scattering is affected by the roughness on the bordersurface. There are characteristics such that, when the microwaves are irradiated to a smooth surface,scattering toward the side opposite to the direction of irradiation (forward scatter) increases, andwhen irradiated to a rough border surface, scattering toward the side of the direction of irradiation(backscatter) increases (Fig.-1.1.6).

6

Backscatter

Surface scattering

Mirror surface

Surface scattering

Fig.-1.1.6 Schematic diagram of surface scattering

7

Rough
surface1.4.2 Volume scattering
The occurrence of scattering caused by unevenness of the internal inductive capacities as a resultof the propagation of microwaves irradiated from SAR by penetrating the border part is called"volume scattering." Since it is a phenomenon caused by penetration of the microwaves to theinside, attention should be paid to it when handling microwaves having a great wavelength and whenobserving a target with a smooth border surface having a low inductive capacity where surface
scattering is unlikely to occur.
In the case of L-band, a typical example is volume scattering inside a forest, and volume
scattering in the inside of a tree and surface scattering on the ground surface need to be consideredsimultaneously (Fig.-1.1.7; on the left).
1.4.3 Double-bounce scattering
As shown in Fig.-1.1.7 on the right, there is a phenomenon called "double-bounce scattering" inwhich backscatter increases as a result of recurrence of scattering after surface scattering has
occurred. It may occur strongly in cases such as those where the direction of microwave irradiation
comes face-to-face with a building, structure, etc.

Azimuth direction

Azimuth direction

Volumescattering

Surfacescattering

Double-bounce
scattering

Surfacescattering

Ground surface

Fig.-1.1.7 Schematic diagrams of volume scattering and double-bounce scattering81.5 Polarization characteristics
Electromagnetic waves are classified by the surface containing the direction of the electric field(polarization surface) into horizontal polarization (of which amplitude has a horizontal surface
relative to the direction of propagation: H) and vertical polarization (of which amplitude has avertical surface relative to the direction of propagation: V) (Fig.-1.1.8), and are expressed as HH,
HV, VH, and VV by the combination of transmission and receiving (1st letter: irradiation, 2nd letter:receiving).
When electromagnetic waves are reflected or scattered, the state of polarization may varydepending on the geometrical shape of the target. SAR that uses microwaves have features such thatimages generated by horizontal polarization are different from those generated by verticalpolarization.
The characteristics are such that, when horizontal polarization (H) has been irradiated to a target
having a cubic structure like a forest, part of it rotates and is reflected as vertical polarization (V),and in the case of vertical polarization (V) having being irradiated as well, part of it is reflected ashorizontal polarization (H). HH and VV are called "like-polarization," and these polarization
characteristics reduce backscatter of the observation target whose volume scattering is dominantsuch as a forest. On the other hand, HV and VH are called cross-polarization, which increases
backscatter of a forest, etc.
Electric
field
Magnetic
field

Verticalpolarization

Horizontal
polarization

Fig.-1.1.8 Schematic diagrams of horizontal polarization and vertical polarization
Source: Remote Sensing Technology Training Textbook "Microwave Remote Sensing"From 3-2. Features of SAR images Polarimetry (created/edited by RESTEC)

92. SAR images
2.1 Generation of SAR images"SAR images" are the data of the darkness levels according to the backscatter intensity at eachpoint on the ground surface that has been observed by SAR that have been visualized as each pixel.
SAR images that show the backscatter intensity of certain polarization are called "single-polarized
SAR images."
Fig.-1.2.1 shows single-polarized SAR images. From the single-polarized SAR images,
topographical features can be identified that are like a scarp having a horseshoe shape. Also, in thechannel of the river flowing in the northern and southern directions that is located right under the
scarp, light colors are dominant signifying that backscatter is intense, where Sediment deposition
may have occurred. Moreover, a range can slightly be identified in the upstream side (north side) of
the colluvial soil that is considered to be an inundated area, where backscatter is shown with a darkcolor to be considerably low, and it is conjectured that there is a situation where a natural dam has
been formed by channel blockage.
As stated above, locations of channel blockage that have been caused by a large-scale slope
failure, etc. of roughly 10,000 m2 or more in planar projection area can often be extracted by theimage interpretation of topography using single-polarized SAR images having a high spatial
resolution. For details, refer to NILIM Technical Note No. 760.

10

ScarpInundation

ChannelColluvial soil

Channel©JAXA

Fig.-1.2.1 Example of a single-polarized SAR image (Hita City, Oita Prefecture)11

2.2 Features of SAR images
Due to the factors described above in "Part 1, 1.1 Principles of SAR observation," SAR images
provides a look like a bird's-eye view having distortion (projecting the topography as though it has
fallen down). Also, the backscatter intensities visualized in SAR images are shown in darkness
corresponding to the land cover that becomes a scattering surface or its texture, and therefore
intuitive interpretation of the target of observation is difficult. In the single-polarized SAR images inFig.-1.2.2, the bent part of the Iya River is conspicuously distorted, and it is difficult to distinguishforests from farmland, etc.

Dozan River

Yoshino River

Iya River

Dozan
River
Yoshino River

Iya RiverFig.-1.2.2 Differences in appearance between an optical image (top) and a single-polarized SAR
image (bottom) (Miyoshi City, Tokushima Prefecture)122.3 Precautions for SAR images
Due to the fact that the direction of microwave irradiation that was described above in "Part 1, 1.1
Principles of SAR observation" is obliquely downward and due to geometrical relationships with the
topography, in the SAR images, distortion may occur or an invisible range resulting from such
distortion may occur. This effect is collectively called the "geometric image modulation"; thefollowing 3 points will be explained.
2.3.1 Foreshortening
The position of the target to be observed on an image in SAR is determined by the time ofreciprocation required for the transmission and receiving of microwaves. If the target to be observed
has a height, it is projected toward the front side according to the shortened portion of thetransmission and receiving time of microwaves, and the target having an altitude on the SAR image
appears to have fallen down toward the side of irradiation direction of the microwaves. Thisphenomenon is called "foreshortening."
When an explanation is given by taking Mt. Fuji shown in Fig.-1.2.3 as an example, Point A thatshows the mountain top of a mounting having a high altitude is projected as though falling down tothe position of Point A' at the side of the direction of microwave irradiation (west side) as comparedwith the actual position. On the other hand, a place with a low altitude is affected little byforeshortening, and the actual position of Point B is almost the same as the position of the projected
Point B'.

𝑨

𝑨F

Foreshortening𝒍Ground surface

𝑩

Position ofprojection on
SAR image

𝑩′ 𝒍′ 𝑨′
©JAXA𝒍 𝒍
→ Foreshortening

Fig.-1.2.3 Schematic diagram of foreshortening and an example by means of a single-polarized
SAR image (around Mt. Fuji)

13

2.3.2 Layover
If the degree of inclination of a slope increases, the effects of foreshortening become greater, andthe positional relationships between the top end and bottom end of the slope may be projected in areversed manner. This phenomenon is called "layover."When an explanation is given by taking Mt. Fuji shown in Fig.-1.2.4 as an example, Point A at themountain top of Mt. Fuji is greatly subjected to foreshortening toward the west side, and is projected
to Point B on the slope at the west side as though covering Point B, and the slope at the west side
(section AB-BC) becomes an invisible range widely. Also, since scattering is dense on the slope at
the west side where layover occurs, SAR images are featured by being displayed in conspicuously
light colors as though there are great backscatter intensities.

𝑨

𝑨

𝑨′
Layover

Ground surface
Position ofprojection on
SAR image

𝑪
𝑪′ 𝑨

𝑩
𝑩′
©JAXA

Section AB-BC invisible range→ LayoverFig.-1.2.4 Schematic diagram of layover and an example by means of a single-polarized SAR
image (around Mt. Fuji)

14

2.3.3 Radar shadow
In a place where microwaves are not irradiated shadowed by the slope, scattering does not occur
at all, and projection is done on SAR images as if shadowing effects have been produced. This iscalled "radar shadow," and this range is invisible. When the off nadir angle increases, the range
where radar shadow occurs also is expanded.
When an explanation is given by taking Mt. Fuji shown in Fig.-1.2.5 as an example, on the slope
at the east side from mountain top A of Mt. Fuji, microwaves do not reach the ground surface, andradar shadow occurs. Since there are no scattered microwaves in the radar shadowed part, on SAR
images, backscatter is shown in dark colors where the backscatter is considerably low. Since its
visibility is similar to surface scattering that occurs on a smooth, flat land area such as water surface,attention should be paid to its distinction.

Radar shadow

𝑨
Volcanic
sand

Ground surface
Position ofprojection on
SAR image

𝑩
𝑨′

𝑩′

Section AB invisible range©JAXA→ Radar shadow

Fig.-1.2.5 Schematic diagram of layover and an example by means of a single-polarized SAR
image (around Mt. Fuji)

15

2.4 Resolution of SAR images
When the distance between 2 points, Point A and Point B, is sufficient, scattered microwaves canbe distinguished at each point, but when the positional relationship between Point A and Point B iscloser, each scattering situation cannot be distinguished (Fig.-1.2.6). The limit distance between 2
points that enables this distinction is called resolution.
However, the resolution of SAR images and the size of the target of observation that can be
distinguished are different from each other, and generally it is considered that in order to visually
identify a specific target, its size needs to be 10 - 20 times the resolution.

Ground surface

Intensity

𝑨

𝑨

𝑩

Time

Fig.-1.2.6 Schematic diagram of resolution
The resolution R [m] of SAR images is shown by Equation 1.2.1, and theoretically the greater theincidence angle, the higher the resolution.

R𝑐
2𝐵 sin 𝜃

Equation-1.2.1

c [m/s]: Speed of microwaves in air (speed of light), B [Hz]: Bandwidth of chirp signals, θ [°]: Incidence angle

162.5 Analysis and types of SAR images
By synthesizing multiple polarized SAR images or by comparing SAR images that have been
observed in different periods, SAR images are generated that enable the situation of ground objects
and ground surface cover to be distinguished at a higher level. This material handles the 3 types of
SAR images shown in Table-1.2.1.
Table-1.2.1 Types and features of SAR images to be handled in this material
Type

Features

Single-polarized SAR
images• Visualize the backscatter intensity of single-polarization in 1
period• Image expressed in darkness

Dual-polarized SAR
images• Visualize the differences in scattering characteristics of eachpolarization in 1 period• Spatial resolution is lower than that of single-polarized SAR
imagesIntensity difference SAR
images• Visualize the changes in backscatter intensity of singlepolarization in different periods• Single-polarized SAR images under the same conditions except
for the observation period are required

2.5.1 Single-polarized SAR images
Single-polarized SAR images are the basic darkness images that express the scattering intensity of
microwaves. Aside from HH, single-polarized SAR images of each of the polarization components,
HV, VV, and VH can be generated. Note that the single-polarized SAR images of HH polarization
are shown above in Fig.-1.2.1.
Single-polarized SAR images enable the rough situation of topography and ground surface cover
to be grasped from the darkness that shows the backscatter intensity. In particular, since water areas
have features such that the backscatter intensity becomes considerably low, an inundated area in achannel area caused by the formation of a natural dam may be interpreted.

172.5.2 Dual-polarized SAR images
Dual-polarized SAR images are images in which the anisotropy of the scattering characteristics of2 types of polarization is visualized. They can be created by RGB color synthesis processing of
single-polarized SAR images of like-polarization and single-polarized SAR images of crosspolarization.
Fig.-1.2.7 shows dual-polarized SAR images that have been created by RGB color synthesis
processing with R (red): HH polarization, G (green): HV polarization, and B (blue): HH
polarization, according to NILIM Technical Note No. 791. The forest areas are expressed in green,
and bare land without forests is expressed in purple red, thereby making it possible to distinguishforests and bare land including failure areas based on the image color.

©JAXA

Fig.-1.2.7 Example of a dual-polarized SAR image (Aoi Ward, Shizuoka City, Shizuoka Prefecture)18

2.5.3 Intensity difference SAR images
Intensity difference SAR images are images in which changes in the backscatter intensity between
single-polarized SAR images that have been observed in different periods are visualized. They canbe created by RGB color synthesis processing of 2 single-polarized SAR images under the same
conditions of observation except for the observation period.
In this material, intensity difference SAR images are created by assigning R (red): archive images,
G (green): new observation images, and B (blue): new observation images (Fig.-1.2.8). In the 2
periods, the place where the backscatter intensity decreased is expressed in red, and the place where
the backscatter intensity increased is expressed in cyan (greenish-blue close to light blue), thereby
making it possible to distinguish the situation of occurrence of a sediment disaster based on thechange in image color.

ChannelScarp

Inundation

Colluvial soil
Channel©JAXA

Fig.-1.2.8 Example of an intensity difference SAR image (Hita City, Oita Prefecture)19

3. Utilization of SAR images in the image interpretation survey of sedimentdisasters
3.1 Conditions of observation of SAR images suitable for the image interpretation
survey of sediment disasters
As described above in "Part 1, 2.2 Features of SAR images, 2.3 Precautions for SAR images," inSAR images, there exists an invisible range caused by characteristic distortion resulting fromobservation principles and the falling down of topography. The effects of this invisible range can bepartially reduced by considering the conditions of observation. Table-1.3.1 shows the conditions ofobservation of SAR images suitable for the image interpretation survey of sediment disasters.
Table-1.3.1 Conditions of observation of SAR images suitable for the image interpretation surveyof sediment disasters
Conditions of observation

Recommended value

Off nadir angle

About 30° - 45°

Resolution

to about 3 m

Wavelength (band)

About 3 - 30 cm (X/C/L)

When the off nadir angle decreases, the effects of layover increase, and when the off nadir angle
increases, the effects of radar shadow increase. For this reason, it is appropriate to use SAR images
that have been observed at an off nadir angle of about 30° - 45° in a mountainous area to which theimage interpretation survey of sediment disasters is applicable.
In general, it is desirable to utilize SAR images with higher resolution in the image interpretation
survey of sediment disasters, because the distinction of ground objects and ground surface cover will
be easier. This material explains techniques for the image interpretation survey of sediment disasters
that have been verified by using SAR images of about 3 m in resolution.
When distinguishing the occurrence of a sediment disaster, the difference in visibility in the X- to
L-band is small. Therefore, the techniques for the image interpretation survey of sediment disasters
to be explained in this material assume that SAR images that have been observed in the X- to L-band
can be utilized.

20

3.2 Precautions when utilizing SAR images in the image interpretation survey of
sediment disasters
3.2.1 Invisible ranges in SAR images
As described above in "Part 1, 2.3 Precautions for SAR images," there exists in SAR images aninvisible range due to the occurrence of falling down, etc. of topography. In a mountainous area towhich the image interpretation survey of sediment disasters is applicable, this effect appears more
conspicuously.
Fig.-1.3.1 shows the situation of occurrence of a sediment disaster in contrast to the visibility ofintensity difference SAR images. Thus, it is necessary to handle the results of image interpretation
survey, with an awareness of the direction of microwave irradiation and the slope direction, by
paying attention to the fact that there is a range where the image interpretation of failure areas isdisabled in the image interpretation survey of sediment disasters by means of SAR images.
Note that this invisible range occurs irrespective of the type of SAR images described in "Part 1,
2.5 Analysis and types of SAR images."West-facing slope

East-facing slope

Invisible ranges in SAR images

Geospatial Information
Authority of Japan©JAXA

Fig.-1.3.1 An example of invisible ranges in SAR images (Asakura City, Fukuoka Prefecture)21

3.2.2 Problems in ground object identification by means of SAR images
When implementing the image interpretation survey of sediment disasters for mountainous areas,some polarization characteristics obtained from SAR images and changes in the backscatter intensity
show places with aspects that are similar to those of the occurrence of sediment disasters, andinterpretation may be done by misreading.
With dual-polarized SAR images, it is difficult to distinguish the places with sediment disasters
from bare land such as deforested area or land where a failure already occurred (Fig.-1.3.2), and withintensity difference SAR images, attention needs to be paid to artificial modification (deforestation,
topography modification, etc.) and seasonal changes (inundation of farmland, etc.) in the 2 periods(Fig.-1.3.3). Interpretation of changes in intensity difference SAR images will be described in detailin Part 2.

Bare land
Bare land
Bare land

Bare land©JAXA©Sentinel-2

Fig.-1.3.2 Appearance of bare land in dual-polarized SAR images(Aoi Ward, Shizuoka City, Shizuoka Prefecture)

22

Rice paddy

Rice paddy©Sentinel-2©JAXA

Fig.-1.3.3 Appearance of rice paddies in intensity difference SAR images(Nantan City, Kyoto Prefecture)

23

Part 2 Explanations of the techniques for the image interpretation
survey of sediment disasters by means of SAR images
1. Selection of the techniques for the image interpretation survey of sedimentdisasters by means of SAR images
1.1 Selection according to the conditions of available SAR images
When selecting the techniques for the image interpretation survey of sediment disasters by meansof SAR images, judgment is made as to which will be used, dual-polarized SAR images or intensity
difference SAR images, according to the conditions of observation, etc. of available single-polarized
SAR images. This SAR image selection flow is shown in Fig.-2.1.1.
In the first place, the range of implementation of the image interpretation survey of sedimentdisasters is selected. Next, the results of observation of SAR that include the range of survey aresearched, and it shall be checked whether new observation under the same conditions of observation
as those in the archive is possible.
If possible, a request for observation is made under the same conditions of observation, andsingle-polarized SAR images by means of the archive and new observation are obtained. By using
these, intensity difference SAR images are generated and the image interpretation survey of
sediment disasters is implemented.
If new observation cannot be made under the same conditions of observation as those of thearchive, a request for observation by means of dual-polarization is made, and the SAR data of suchobservation are obtained. From the SAR observation data of dual-polarization, dual-polarized SAR
images are generated, and the image interpretation survey of sediment disasters is implemented.
Note that search of the archive and the request for observation need to be checked with theoperator, data handling personnel, etc. of the applicable SAR.

24

Selection of the rangeof survey

Observation can be madeunder the same conditions ofobservation as those of thearchivenoRequest for observation bymeans of dual-polarization
Obtain data of dualpolarization

yes
Request for observation
under the same conditions
Obtain single-polarized
SAR images

Implementation of the image
interpretation survey bymeans of intensity difference
SAR images

Implementation of the image
interpretation survey bymeans of dual-polarized SAR
imagesFig.-2.1.1 Selection flow of the techniques for the image interpretation survey of sediment disasters
according to the conditions of available SAR images

25

1.2 Selection according to the scale of a sediment disaster to which the image interpretation
survey is applicable
Selection is possible according to the scale of a sediment disaster to which the image
interpretation survey is applicable (Table-2.1.1).
According to NILIM Technical Note No. 791, it is considered that dual-polarized SAR images canbe applied to the interpretation of a large-scale failure and channel blockage of roughly 10,000 m2 or
more. With intensity difference SAR images, results have been obtained in which a large-scale
failure and channel blockage of 10,000 m2 or more can be detected with a high degree of certainty,
and a relatively small-scale failure of 2,000 m2 or more can also be detected at a rate of around 70%(to be described in detail in Part 3). They can sufficiently be applied, when a surface layer failure,etc. has occurred with a high density, if the accuracy is such that the range and external edges ofsuch failure are specified.
Therefore, if the conditions shown in "Part 2, 1.1 Selection according to the conditions ofavailable SAR images" are satisfied, the image interpretation survey by means of intensity difference
SAR images is recommended.
Table-2.1.1 Types of SAR images to be handled in this material
Techniques for the image interpretation
Large-scale failure
Small-scale failure
survey of sediment disasters by means of
Channel blockage
SAR images
Image interpretation survey by means of dualpolarized SAR images○

×(Applicable)(Not applicable)

Image interpretation survey by means ofintensity difference SAR images○○(Detection of about 80%)

(A majority can be detected)* Large-scale: area in orthographic projection 10,000 m2 or more, small-scale: area in orthographic projection 2,000 m2 or more
* The invisible range is not included in the scope of verification.

26

2. Image interpretation survey of sediment disasters by means of dual-polarized
SAR images
By the interpretation of dual-polarized SAR images having a high spatial resolution, a large-scaleslope failure or areas of channel blockage resulting from such failure may be extracted. For details,
refer to NILIM Technical Note No. 791.

27

3. Image interpretation survey of sediment disasters by means of intensity
difference SAR images
The image interpretation survey of sediment disasters by means of intensity difference SAR
images is explained below, using examples of the 2018 Hokkaido Eastern Iburi Earthquake. Notethat the image interpretation survey of sediment disasters hereafter assumes that the standard
functions provided in GIS software and image processing software are utilized.

3.1 Generation of intensity difference SAR images
As for the intensity difference SAR images, as described above in Part 1, single-polarized SAR
images that were observed before and after the disaster, respectively, are prepared, and then changes
in the backscatter intensity are visualized. Two types of images have been obtained: the singlepolarized SAR images that were observed before the occurrence of the earthquake (August 23, 2018)(archive) and the single-polarized SAR images that were newly observed right after the occurrenceof the earthquake (September 6, 2018). By assigning R (red): August 23, 2018, G (green): September
6, 2018, and B (blue): September 6, 2018 to these images, intensity difference SAR images are
generated by the RGB color synthesis processing (Fig.-2.3.1).

28

©JAXA©JAXA

Single-polarized SAR image(August 23, 2018, before the disaster)Single-polarized SAR image(September 6, 2018, after the disaster)©JAXA

Intensity difference SAR image

Fig.-2.3.1 Generation of intensity difference SAR images(Atsuma Town, Mukawa Town, Hokkaido Prefecture)

3.2 Interpretation of intensity difference SAR images
In the image interpretation survey of sediment disasters, places with inflow of failure sedimentand with a high probability of slope failure are extracted from the color changes in the intensity
difference SAR images that show changes in the backscatter intensity before and after the disaster.
The relationships between changes in color and changes in the backscatter intensity on the intensity
difference SAR images are shown in Table-2.3.1.
When there is no change in the backscatter before and after the disaster in a place where thebackscatter is great in ordinary times such as a forest or farmland, it is displayed in white on the29intensity difference SAR images. Also, when there is no change in the backscatter before and after
the disaster in a place where the backscatter is considerably small such as water areas on a sea
surface, etc., it is displayed in black on the intensity difference SAR images. As shown in Fig.-2.3.2,
if sediment flows in a rice paddy, etc. due to the occurrence of a disaster, the backscatter intensity
increases after the disaster, and therefore the place is shown in cyan on the intensity difference SAR
images, and in a place where outflow of a forest was caused by a slope failure, the backscatter
intensity decreases after the disaster, and therefore the place is shown in red on the intensity
difference SAR images.
Table-2.3.1 Changes in color and in backscatter intensity on intensity difference SAR images
Color

Changes in backscatter intensity before andafter the disaster

ExampleWhite

Backscatter intensity exists, and there is nochangeForest, farmland

Black

Backscatter intensity is small, and there is nochangeSea surface, lake surface

Cyan

Backscatter intensity increases after thedisaster

Sediment outflow, floating
objectRed

Backscatter intensity decreases after thedisasterSlope failure, water
seepage/inundation

Backscatter

Surfacescattering

V

Backscatter

V

V

Volumescattering

V

Before the disaster

V

V

After the disasterFig.-2.3.2 Image of changes in backscatter intensity caused by the occurrence of a sedimentdisaster30

When comparing the intensity difference SAR image with the optical image in Fig.-2.3.3, it isfound that the red range on the intensity difference SAR image corresponds very well with theposition of the failure area on the optical image. Due to the occurrence of the slope failure, forests,
etc. have been lost and smooth failure surfaces have been exposed, with the result that the situation
where the backscatter intensity has decreased as compared with that before the disaster has appeared
distinctly. Also, the change in cyan that appears linearly on the intensity difference SAR image
corresponds to the valley bottom plain area on the optical image. It is considered that, as a result ofoutflow of sediment, etc. from the failure onto the smooth, flat land area such as farmland,
appearance is shown in which the backscatter intensity has increased as compared to that before thedisaster.

©JAXA©Airbus DS / Spot Image

Fig.-2.3.3 Intensity difference SAR image (top) and optical image (bottom)(Atsuma Town, Mukawa Town, Hokkaido Prefecture)31

3.3 How to display and view intensity difference SAR images
To display intensity difference SAR images when carrying out the image interpretation survey of
sediment disasters, it is considered that use of the scale shown in Table-2.3.2 is appropriate.
First, the images are displayed in scale of about 1:50,000, and rough survey is carried out thathave a bird's eye view of the entire intensity difference SAR image. In the rough survey, whether
there are changes in red, cyan, etc. is checked, and the range where detailed image interpretation will
be implemented is set as shown on the left in Fig.-2.3.4.
Next, detailed survey is carried out by displaying the image in scale of about 1:25,000. As a ruleof thumb, check whether there are changes for each valley, and check whether there is the range ofcyan that appears when outflow of sediment occurred to the valley outlet or plain area such as avalley bottom plain and whether there is the range of red that may appear on a slope that has turned
into bare land due to failure in the upstream side of such plain area (Fig.-2.3.4, center). It is desirable
that image interpretation be done, being aware of the continuity of phenomena, such as estimating aslope failure in the invisible range from the changes shown in cyan in the plain area.
Lastly, an intensity difference SAR image is displayed in scale of about 1:10,000, and judgment ismade on each slope unit (Fig.-2.3.4, right). It is recommended to use a judgment method to be
described later.
Table-2.3.2 Rule of thumb of scale when displaying intensity difference SAR images
Survey stage

Scale

Rough survey(Bird's eye view of the entire image)

1:50,000

Detailed survey(Image interpretation of each valley)

1:25,000

Judgment(Image interpretation of each slope)

1:10,00032

Slope failure

Sediment outflow

©JAXA

©JAXA

1 : 50,000

1 : 25,000©JAXA

1 : 10,000

Fig.-2.3.4 Intensity difference SAR images displayed in each scale and how to view the images(Atsuma Town, Hokkaido Prefecture)

3.4 Flow of the image interpretation survey of sediment disasters by means ofintensity difference SAR images
It is desirable to proceed with the image interpretation survey of sediment disasters by means ofintensity difference SAR images by taking Steps 1 to 3 below.
In Step 1, judgment is made based on the minimum necessary items by placing emphasis on quick
reportability of the survey (Fig.-2.3.5). First, check the changes around the slopes in the intensity
difference SAR image, and when there is a change in red or pair-like change in red/cyan, compare
the single-polarized SAR images before and after the disaster by visual checks. If the singlepolarized SAR image after the disaster appears to be dented as though concave topography has been
formed, the possibility of occurrence of a slope failure is suspected. Also, if changes in cyan are
dominant, the single-polarized SAR image should be checked visually in the same manner, and ifchanges such as those with swollen topography can be identified, sediment may have been depositedby a failure. If changes in red or cyan cannot be identified on the intensity difference SAR image, or
if changes in topography (dents and projections) cannot be identified from comparison of the singlepolarized SAR images before and after the disaster, it is determined that judgment by means ofintensity difference SAR images is difficult.

33

Start of the image
interpretation
surveyCheck changes onslopes in intensity
difference SAR images(1) Red or red/cyan paircan be seenno(2) Cyan can be
seen

no

yes
yes
Check changes in singlepolarized SAR images
before and after thedisaster(3) Appears to bedentedCheck changes in singlepolarized SAR images
before and after thedisasterno(4) Appears tohave swollen

yes
A slope failure may
have occurred.

noChanges related to aslope failure or sedimentdeposition cannot be
identified

yes
Sediment may havebeen deposited by a
failure.

Fig.-2.3.5 Flow of the image interpretation survey of sediment disasters by means of intensity
difference SAR images (Step 1)
In Step 2, the place extracted in Step 1 is evaluated as to its certainty based on the materials otherthan the SAR images (Fig.-2.3.6). In the case of initial response to a disaster having high emergency,
this step may be omitted, but if there are few places to be extracted, implementation of this step isdesirable.
In Step 1, when falling under "A slope failure may have occurred.", check that the place is an
inclined land based on a topographical map. Its standard is that a slope of 20° or more in gradientshould be included, and the like. Next, by referring to the topographical map and optical image(satellite photo, aerial photo, etc.) before the disaster, check that there is a low possibility of artificial
modification of the slope in question, and that it was a forest before the disaster. If all the conditions
stated above are met, it is determined that "It is highly likely that a slope failure has occurred."Also, when it has been determined that "Sediment may have been deposited by a failure.", in thesame manner, check that the place is a flatland based on the topographical map. We consider that, asfor the standard for determining that the place is a flatland, determination from the topography suchas valley outlet or valley bottom plain and the land utilization such as residential land or farmland
would suffice. After that, by referring to the topographical map and optical image before the disaster,34

check that there is a low possibility of artificial modification in the applicable range, and that theplace is adjacent to changes in red. If all the conditions stated above are met, it is determined that "Itis highly likely that sediment has been deposited by a failure."
A slope failure may have occurred.

Sediment may have been deposited by a
failure.

Refer to a topographical map

Refer to a topographical mapno(6) Can be determined
to be an inclined land

yes

yes
Refer to the topographical map and theoptical image before the disaster

Refer to the topographical map and the opticalimage before the disasters(7) Not an artificiallymodified landno

Cannot be determined
to be a change related
to a slope failure.

(10) Not an artificiallymodified landyes(8) Is a forestno(9) Can be determined
to be a flatland

Cannot be determined to
be a change related tosediment deposition.

no

yes
no

A slope failure may
have occurred.

(11) Is adjacent to changes in red

yes

no

Sediment may havebeen deposited by a
failure.

yes

It is highly likely that a slope failure has
occurred.

It is highly likely that sediment has been
deposited by a failure.

Fig.-2.3.6 Flow of the image interpretation survey of sediment disasters by means of intensity
difference SAR images (Step 2)
In Step 3, the possibility of channel blockage formation is checked for all the places falling under"A slope failure may have occurred." in Step 1 (Fig.-2.3.7). In consideration of emergency ofidentifying the place of channel blockage, in the same manner as in Step 1, it is desirable to carry out
mandatory checks and use the results for an initial report.
From the intensity difference SAR images, check whether changes in cyan can be seen in thechannel, and whether ranges in red can be seen in the upstream side of the channel, and when fallingunder both of these, refer to the topographical map and the optical images before the disaster, toensure that such changes or ranges are not existing dams, reservoirs, etc. that have been
misinterpreted. If all the conditions stated above are met, it is determined that "It is highly likely thatchannel blockage may have been formed." Also, when SAR images that were observed in the initial
response period are used, it is expected that the scale of inundation is small as a result of channel
blockage, and therefore it is desirable that monitoring and additional survey be carried out for placesin which it has been determined that "Channel blockage may have been formed." as well.

35

Sediment may have been deposited by a
failure.

Refer to intensity difference SAR images(12) Cyan can be seen in thechannel.

Changes related tochannel blockage
cannot be identified.

no

yes
no

(13) Red can be seen in the upstream side of
the channel

yesRefer to the topographical map and theoptical image before the disasterno(14) No inundated facility

Channel blockage
may have been
formed.

yes
It is highly likely that channel
blockage has been formed.

Fig.-2.3.7 Viewpoints of whether channel blockage has been formed by means of intensity
difference SAR images (Step 3)36

3.5 Checklist of the image interpretation survey of sediment disasters by means ofintensity difference SAR images
In the image interpretation survey of sediment disasters by means of intensity difference SAR
images, since the evaluation viewpoints, experiences, etc. of the surveyor may affect the results ofevaluation, it is necessary to secure a certain level of accuracy as the results of generalized image
interpretation survey of sediment disasters. Therefore, it is recommended to utilize the Checklist of
the image interpretation survey of sediment disasters by means of intensity difference SAR images(hereinafter called the "Checklist") shown in Fig.-2.3.8. For each item in the Checklist, input the
information and evaluation shown in Table-2.3.3.
In actual practice, the survey time will be prolonged if the judgment results are obtained byapplying the Checklist for all the slope failures that occurred frequently like those in the 2018
Hokkaido Eastern Iburi Earthquake, and therefore it is desirable that, individual judgment be madeby using the Checklist for representative places, and then comprehensive judgment be made basedon the results of such judgment.

37

Image Interpretation Survey of Sediment Disasters by Means of Intensity Difference SAR Images

Checklist

No.

LatitudeID

Location
Longitude

Intensity difference SAR image

Single-polarized SAR image (before disaster) Single-polarized SAR image (after disaster)

Topographical map

Step

1

Images, etc. to bereferred toRange to be checked

Check item

Intensity difference SAR
image

Coloring range of intensity
difference SAR image

Dominant color

Single-polarized SAR
image

Changes in single-polarized
SAR image before andafter the disaster

Shape change

Intensity difference SAR
image

Coloring range of intensity
difference SAR image

Clarity(1).(3): ○ => 2 (failure) (2), (4):2
(failure)2(deposition)

3

Optical image before disaster

Judgment criteria

Evaluation(○×)(1) Red or red/cyan pairs can be seen
(2) Cyan can be seen(3) Appears to be dented(4) Appears to have swollen(5) Is clear○ => 2 (deposition)

Topographical map

Topography

Slope gradient

Topographical map
Optical image before disaster

Surroundings

Land cover(6) Is an inclined land(7) Not an artificially modified land

Topographical map
Optical image before disaster

Surroundings

Land cover(8) Is a forest

Topographical map

Topography

Slope gradient

Topographical map
Optical image before disaster

Surroundings

Land cover(10) Not an artificially modified land

Topographical map
Optical image before disaster

Surroundings

Positional relationship(11) Is adjacent to changes in red

Intensity difference SAR
image
Topographical map
Optical image before disasterIn the channel
Upstream side of thechannelUpstream side of thechannel(12) Cyan can be seen
Dominant color(13) Red can be seen
Inundated facility

a. Occurrence of a slope failure

Judgment results(9) Is a flat land(14) No inundated facility

May have occurredb. Sediment deposition caused by a failurec. Formation of channel blockage/

Changes cannot be verified

May have occurred

May have occurred//

Changes cannot be verified

Changes cannot be verified

d. Image interpretation cannot be made sufficiently from SAR images

Fig.-2.3.8 Checklist of the image interpretation survey of sediment disasters by means of intensity
difference SAR images

38

Table-2.3.3 Changes in color and backscatter intensity on intensity difference SAR images
Item

Input

No

ArbitraryID

Arbitrary

Location

Arbitrary

Latitude/Longitude

Mandatory

Intensity difference SAR
image
Single-polarized SAR
image (before disaster)Single-polarized SAR
image (after disaster)

Mandatory
Mandatory
Mandatory

Contents to be input
Assign an arbitrary number for handling with the results of image
interpretation survey.
Assign an arbitrary code for managing the place of image
interpretation survey.
Enter the address of the place of image interpretation survey.
Input the latitude and longitude of the place of image interpretation
survey.
Paste the intensity difference SAR image of the place of image
interpretation survey.
Paste the single-polarized SAR image (before disaster) of the placeof image interpretation survey.
Paste the single-polarized SAR image (after disaster) of the place ofimage interpretation survey.
Paste the topographical map of the place of image interpretation
survey.
Paste the optical image before disaster of the place of image
interpretation survey.
Carry out evaluation according to Fig.-2.3.5.
Note that for (5), carry out relative evaluation in the range of survey,
arbitrarily.

Topographical map

Arbitrary

Optical image before
disaster

Arbitrary

Step 1

Mandatory

Step 2(failure/deposition)

Arbitrary

Carry out evaluation according to Fig.-2.3.6.

Step 3

Mandatory

Carry out evaluation according to Fig.-2.3.7.

Judgment results

Mandatory

Based on the results of evaluation, enter a to d so that the applicable
place can be understood.

An example of utilization of flow of the image interpretation survey and the Checklist in the 2018
Hokkaido Eastern Iburi Earthquake is shown (Fig.-2.3.9).
The place of image interpretation survey on the intensity difference SAR image (the valley shownin the blue circle in Fig.-2.3.9) has changes in red in the slope part, and all the check items in Step 1
from the valley outlet to changes in cyan, etc. have been verified, and it has been determined that aslope failure may have occurred and sediment deposition by the failure may have occurred. In
addition, any of the failure and deposition in Step 2 is also applicable. Therefore, it can be estimatedthat the occurrence of a slope failure and the deposition of sediment caused by the failure haveoccurred with a high degree of certainty. And in Step 3, cyan could be identified in the channel, but
no changes in red can be seen in the upstream part of the channel, and therefore it has been
determined that no changes can be verified concerning the formation of channel blockage.
Based on the above results of evaluation, it has been determined that there is a sufficientpossibility that in the place in question, a slope failure has occurred and sediment deposition causedby the failure has occurred, but changes of the formation of channel blockage cannot be verified.

39

Image Interpretation Survey of Sediment Disasters by Means of Intensity Difference SAR Images

ID

Example of entry

Aza Horonai, Atsuma Town, Yufutsu County,
Hokkaido Prefecture

Location

Intensity difference SAR image2(failure)2(deposition)

3

Longitude141.985929©JAXA

Optical image before disaster

Geospatial Information
Authority of Japan

Geospatial Information
Authority of Japan

Images, etc. to bereferred toRange to be checked

Check item

Intensity difference SAR
image

Coloring range of intensity
difference SAR image

Dominant color

Single-polarized SAR
image

Changes in single-polarized
SAR image before andafter the disaster

Shape change

Intensity difference SAR
image

Coloring range of intensity
difference SAR image

Clarity(1).(3): ○ => 2 (failure) (2), (4):42.753956©JAXA

Topographical map

1

LatitudeSingle-polarized SAR image (before disaster) Single-polarized SAR image (after disaster)

©JAXA

Step

1

No.

Checklist

Judgment criteria

Evaluation(○×)(1) Red or red/cyan pairs can be seen○

(2) Cyan can be seen○(3) Appears to be dented○(4) Appears to have swollen○(5) Is clear○(6) Is an inclined land○(7) Not an artificially modified land○

○ => 2 (deposition)

Topographical map

Topography

Slope gradient

Topographical map
Optical image before disaster

Surroundings

Land cover

Topographical map
Optical image before disaster

Surroundings

Land cover(8) Is a forest○

Topographical map

Topography

Slope gradient(9) Is a flat land○

Topographical map
Optical image before disaster

Surroundings

Land cover(10) Not an artificially modified land○

Topographical map
Optical image before disaster

Surroundings

Positional relationship(11) Is adjacent to changes in red○

Intensity difference SAR
image
Topographical map
Optical image before disasterIn the channel
Upstream side of thechannelUpstream side of thechannelInundated facility

a. Occurrence of a slope failure

Judgment results(12) Cyan can be seen○(13) Red can be seen

×(14) No inundated facility○

Dominant color

May have occurredb. Sediment deposition caused by a failurec. Formation of channel blockage/

Changes cannot be verified

May have occurred

May have occurred//

Changes cannot be verified

Changes cannot be verified

d. Image interpretation cannot be made sufficiently from SAR images

Fig.-2.3.9 Checklist of the image interpretation survey of sediment disasters by means of intensity
difference SAR images (example of entry)

40

4. Application of the image interpretation survey of sediment disasters by meansof SAR images
4.1 Efficient method of image interpretation survey
4.1.1 Utilization of the display functions of GIS software, etc.
"Image permeation display" and "image swipe display" with which GIS software is often provided
as standard are the functions for increasing the efficiency of image interpretation survey whenreferring to multiple image data and when handling both ground range images and ortho images.
4.1.2 Utilization of GIS data
Collect GIS data such as those of administrative boundaries, rivers, roads, and sediment disasterwarning areas, and use them as a reference during image interpretation. At this time, since theground range images and GIS data are not overlapped in the same place, attention should be paid tothis during image interpretation, and the position should be specified by referring to a topographicalmap, etc.
4.1.3 Utilization of ground range images
Use SAR images in the ground range in which the distance from the target object has been
projected by SAR to the ground surface (Fig.-2.4.1). By performing ortho processing (orthographic
projection) of the image, it can be overlapped accurately with GIS data such as topography oradministrative boundaries, but as a result of distortion of the image, the visibility of a failure area,channel, etc. decreases. On the other hand, since a ground object is photographed as an image likethat of an oblique photo, its intuitive interpretation can be performed easily. Moreover, since ortho
processing takes a lot of time, when speedy image interpretation is required, the use of ground range
images is recommended that provides data quickly.

©JAXA

Ground range image

Ortho image
41
Fig.-2.4.1 Comparison of ground range image and ortho image(Sakurajima, Kagoshima City, Kagoshima Prefecture)©JAXA

4.1.4 Image erecting
Since a SAR image has dependency on the direction, image interpretation is performed byerecting the image, visibility increases. When erecting a SAR image, the stereoscopic feeling of the
topography is emphasized by making the direction of microwave irradiation (direction in which the
topography falls down due to layover) face upward (Fig.-2.4.2).

Rotatedrightward by
100°

Nagatonodani
ValleyAkatani Valley

Failure area
Failure area

Totsukawa
River
©JAXA
©JAXA

Fig.-2.4.2 Visibility when a SAR image has been erected(Gojo City/Totsukawa Village, Nara Prefecture)42

4.2 Method of image interpretation survey that increases the reliability of results
4.2.1 Filtering of SAR images
SAR images contain spot-like noise called "speckle noise," and this noise becomes a factor of
decreasing the visibility of the image and the accuracy of synthetic processing of the image, etc. For
this reason, it is desirable to perform filtering of the SAR images to be used for image interpretation
survey. As a general rule of thumb, filtering is often used in which the average and median of 3×3
pixels are obtained.
Fig.-2.4.3 shows intensity difference SAR images with which a comparison is made between withfiltering and without filtering. Even if filtering is done, changes in red or cyan are not subject to theeffects of filtering, and visibility increases as a result of reduction of only the effects of the spots inthe form of black and white in the SAR image. Note that when intensity difference SAR images, etc.
are to be generated, single-polarized SAR images before color synthesis processing need to be
filtered.
Note that the time required for filtering varies with the size of the SAR image and the type of thefilter, it takes about several minutes to perform processing with which ordinary software is provided
as standard.

©JAXA

©JAXA

Unfiltered intensity difference SAR image

Filtered intensity difference SAR image(average of 3×3 pixels)

Fig.-2.4.3 Comparison of intensity difference SAR images between filtered and unfiltered ones
(Abira Town, Hokkaido Prefecture)43

4.2.2 Additional survey of invisible ranges
As will be described later in "Part 2, 5.2 Orientation of a slope relative to the direction of
microwave irradiation", the image interpretation survey of sediment disasters by means of SAR
images is subject to deviation in detectability due to the relationships between the direction of
microwave irradiation and the slope direction, with many invisible ranges occurring on slopes in
specific directions. For this reason, it is desirable to wait for the opportunity of satellite observationand obtain SAR images produced by irradiating microwaves from the reverse direction, and carryout image interpretation survey of sediment disasters additionally.

44

5. Precautions for the techniques for the image interpretation survey of
sediment disasters by means of SAR images
5.1 Effects of artificial modification
In a mountainous region where the occurrence of a slope failure is expected, deforested area or aquarry shows changes in the backscatter intensity that are similar to those of a slope failure (Fig.2.5.1). When a forest is lost by deforestation, volume scattering that was dominant before thedisaster disappears, and consequently the backscatter also decreases greatly, which appears on theintensity difference images as changes in red or of red-cyan pairs (Fig.-2.5.2). Also, similar changes
appear as a result of the retreat of a slope caused by quarrying changing the position of the scattering
surface, or as a result of a change in local incidence angle caused by a change in the slope gradient(Fig.-2.5.3).
These are difficult to be distinguished from changes in the backscatter intensity of a slope failure
in image interpretation survey by visual checks, and the following can be considered as the measures
for avoiding erroneous image interpretation.
(1) Check whether there are changes in cyan caused by outflow of sediment to a plain area, etc.
(2) Use the most recent archive SAR images within the available range.
(3) Make judgment based on the shape of changes such as artificial rectangular shape, regularity,etc.
(4) Check the effects of artificial modification based on the optical image before the disaster.

SAR image (newobservation)

SAR image (archive)(Apr. 29, 2016)(July 17, 2017)

Deforestation, etc.

Slope failure

Slope's backscatterdecreases

Slope's backscatterdecreases

Indistinguishable
Fig.-2.5.1 Case where distinction between slope failure and artificial modification becomes difficultin intensity difference SAR images

45

Geospatial Information Authority of Japan©JAXA

Fig.-2.5.2 Example in which changes in backscatter caused by deforestation appeared(Toho Village, Fukuoka Prefecture)©JAXA

Geospatial Information Authority of Japan

Fig.-2.5.3 Example in which changes in backscatter caused by quarrying appeared(Asakura City/Toho Village, Fukuoka Prefecture)46

5.2 Orientation of a slope relative to the direction of microwave irradiation
SAR images are projected by being strongly affected by the geometrical conditions due to theirradiation angle of microwaves, etc., and an invisible range is generated by the effects of thegeometric image modulation described in "Part 1, 2.3 Precautions for SAR images."
Fig.-2.5.4, left shows the invisible range derived by the geometrical calculations of the irradiation
angle of microwaves with a topographical model (10 m numerical value altitude model). In places
where many slope failures occurred with various slope orientations, when the aerial photo taken after
the disaster (Fig.-2.5.4, center) is compared with the intensity difference SAR image (Fig.-2.5.4,
right), it can be seen that visibility on the intensity difference SAR image varies greatly with theslope orientation, and it is estimated that this effect is related to the accuracy of the image
interpretation survey.
Namely, if the off nadir angle set as the recommended condition in this material is around 30° to
45°, the slope facing the direction of microwave irradiation (west-facing slope in Fig.-2.5.4) is theinvisible range caused by layover, and therefore sufficient results of survey by image interpretation
cannot be obtained. As shown in Fig.-2.5.5, even in the case of a large-scale slope failure of about
76,600 m2 in area, image interpretation becomes difficult when being strongly subjected to the effectof layover at the slope orientation facing the direction of microwave irradiation (east-facing slope in
Fig.-2.5.5).
Since this effect cannot be reduced when handling SAR images, practically attention needs to be
paid to the following points.
(1) Make judgment after checking changes in cyan on a flatland, etc. that is unlikely to be affectedby layover.
(2) Implement image interpretation survey and handle the results of survey by bearing in mind theoccurrence of an invisible range.

Geospatial Information Authority of Japan

Geospatial Information Authority of Japan

Invisible range

Fig.-2.5.4 Comparison of visibility in invisible ranges(Higashihiroshima City/Yasuura Town, Hiroshima Prefecture)

47

©JAXA

Geospatial Information Authority of
Japan

©JAXA

©JAXA

Fig.-2.5.5 Comparison of visibility depending on the direction of microwave irradiation(Hita City, Oita Prefecture)
Center: direction of microwave irradiation [east → west]

48

Right: direction of microwave irradiation [west → east]Part 3 Results of application of the image interpretation survey of
sediment disasters by means of intensity difference SAR
images
1. Time required for the image interpretation survey of sediment disasters bymeans of intensity difference SAR images
Table-3.1.1 shows a rule of thumb for the time required for the image interpretation survey of
sediment disasters by means of intensity difference SAR images by the confirmation of response to 7
cases of disasters. In the disasters caused by rainfall, the time was about 35 hours from theoccurrence of the disaster (announcement of sediment disaster warning information), and in thedisasters caused by an earthquake, it was about 15 hours from the occurrence of the earthquake.
Although some parts greatly depend on the observation by the satellite, it takes about 7 hours tocarry out the image interpretation survey of sediment disasters after observation by means of SAR.
Based on the above results, it can be said that the image interpretation survey of sedimentdisasters by means of intensity difference SAR images is a method that enables the results of survey
to be obtained in about 0.5 to 1.5 days from the occurrence of danger of the occurrence of sedimentdisasters.
Table-3.1.1 Time required for the image interpretation survey of sediment disasters by means ofintensity difference SAR images
Required time [h]Occurrence of disaster SAR observation

SAR observation Image interpretation
surveyOccurrence of disaster Image interpretation
survey

4.9

8.7

13.5

11.9

7.1

19.1

46.7

7.1

53.8

19.5

7.3

26.8

2018 Northern Osaka Earthquake

16.0

9.2

25.2

Torrential Rainfall in July 20182018 Hokkaido Eastern Iburi
Earthquake

33.9

7.9

41.8

8.6

3.8

12.4

Average (all)

20.2

7.3

27.5

Average (rainfall disaster)

28.0

7.4

35.4

Average (earthquake disaster)

9.8

7.2

17.0

Median (all)

16.0

7.3

25.2

Median (rainfall disaster)

26.7

7.2

34.3

Median (earthquake disaster)

8.6

8.7

13.5

Disaster name2017 Southern Nagano
Earthquake
Torrential Rainfall in Shimane
Prefecture in July 2017
Northern Kyushu Torrential
Rainfall in July 20172017 Typhoon No. 21

49

2. Accuracy of the image interpretation survey of sediment disasters by meansof intensity difference SAR images
2.1 Conditions of verification of accuracy
For the 4 cases of sediment disasters that occurred on a large-scale and in a wide area, "2016
Kumamoto Earthquake," "Northern Kyushu Torrential Rainfall in July 2017," "Torrential Rainfall in
July 2018" and "2018 Hokkaido Eastern Iburi Earthquake," the accuracy of the image interpretation
survey of sediment disasters by means of intensity difference SAR images was checked.
The verification of accuracy was made for the places in SAR images excluding the invisible
ranges, carrying out evaluation by using the percentage of the places where the occurrence of asediment disaster is determined to have a high probability from the intensity difference SAR images(hereinafter called the "detection target") in the places where a sediment disaster occurred selectedfrom the results of image interpretation of aerial photos, etc. taken after the disaster (hereinafter
called the "verification target") as the detection rate [%].
The verification target was selected so that the targets are roughly uniform in relation to thecriteria shown in Fig.-3.2.1: "disaster trigger factor," "area (area in orthographic projection), "slope
gradient," "slope direction (clockwise angle with the north set to 0°)." Also, as for the detection
target, the number of cases was used that were determined as "A slope failure may have occurred."or "Sediment may have been deposited by a failure." from the evaluation in Step 1 in "Part 2, 3.4
Flow of the image interpretation survey of sediment disasters by means of intensity difference SAR
images", which is equivalent to the accuracy during the initial disaster investigation. The detection
rate was checked for each of the items: "area," "slope gradient," "slope direction," "local incidence
angle," and "relative direction angle (angle expressed by ±180° with the direction facing thedirection of microwave irradiation being set to 0°)."Note that this verification gives the results of using the places of a scale or shape with which it canbe judged that a sediment disaster could have surely occurred as the verification target, and smallscale failures of which image interpretation cannot be made from aerial photos, etc. and failures inthe shadow of a forest, etc. have not been checked due to the nature of the data used for verification.

50

2Area [m ]Disaster trigger factor

120
339

309

143

117
143

2016 Kumamoto Earthquake [failure]2016 Kumamoto Earthquake [deposition]2018 Hokkaido Eastern Iburi Earthquake

109 points
113 points
117 points

Disaster causedby rainfall

Northern Kyushu Torrential Rainfall in July 2017 100 points
Torrential Rainfall in July 2018 [failure]
107 points
Torrential Rainfall in July 2018 [deposition]
102 points

Disaster caused
by earthquake

Slope direction angle [°]Slope gradient [°]

153

125

104

141

99
111

112
178

176
107

115

Fig.-3.2.1 Number of verification targets for each selection criterion (N = 648)512.2 Results of verification of accuracy
The accuracy of the image interpretation survey of sediment disasters by means of intensity
difference SAR images is shown below.
As shown in Fig.-3.2.2, the area and accuracy have correlations, and it can be seen that the greater
the scale of changes, the higher the detection rate. When a slope failure or outflow/deposition of
sediment of 2000 m2 or more in area has occurred, it can be detected with a probability of about
70%.
The slope gradient and slope direction are not the factors that directly affect the detection rate
(Fig.-3.2.3, Fig.-3.2.4). On the other hand, the local incidence angle and relative direction angle
greatly affects the detection rate. As for the local incidence angle shown in Fig.-3.2.5, as the angle
increases, the detection rate improves, and the detection rate reaches its maximum, being about 50%,
when the angle is 45 to 60°. When the angle reaches to a higher value of 75 to 90°, the tendency ofthe detection rate decrease can be seen, but it is a result of a small number of verifications, and suchtendency is uncertain. By referring to "Reference material 2. Accuracy of the image interpretation
survey of sediment disasters by means of intensity difference SAR images in each disaster,"
unevenness can be seen in the relationships between local incidence angle and detection rate in eachdisaster, but in any of the disasters, the detection rate is very low when the local incidence angle isextremely small or extremely large. Also, as for the relative direction angle in Fig.-3.2.6, thedetection rate has a tendency such that it reaches the lowest value, being about 15 to 25% when theangle is 0 to ±60°, and the detection rate becomes higher, being about 50 to 60% when the angle is±120 to ±180°. It is considered that this is due to the fact that, as described in "Part 2, 5.2 Orientationof a slope relative to the direction of microwave irradiation," since the slope facing the direction of
microwave irradiation (slope having a small relative direction angle) is an invisible range caused by
the effect of layover, the detection rate becomes extremely low.

52

100%

200

80%

150

60%

100

40%

50

20%

0

Detectionrate
検出率

Frequency
度数

250

0%
0 - 500

500 - 1,000

Number
検証数ofverifications

1,000 - 1,500 1,500 - 2,000

Area [m2]面積［㎡］Number of
検出数
detections

2,000 -

検出率 rate
Detection

Fig.-3.2.2 Detection rate in the image interpretation survey of sediment disasters by means ofintensity difference SAR images (area)

53

100%

200

80%

150

60%

100

40%

50

20%

検出率
Detection
rate

Frequency
度数

250

0%

0
0 - 1515 - 25

25 - 35

35 -Slope gradient [°]
斜面勾配［°］
Number of
検証数
verifications

Number of
検出数

検出率
Detection rate

detections

Fig.-3.2.3 Detection rate in the image interpretation survey of sediment disasters by means of

250

100%

200

80%

150

60%

100

40%

50

20%

0

Detectionrate
検出率

Frequency
度数

intensity difference SAR images (slope gradient)

0%0 - 6060 - 120

120 - 180

180 - 240

240 - 300

300 - 360

Slope direction angle [°]斜面方位角［°］Number of
検証数
verifications

Number of
検出数
detections

検出率 rate
Detection

Fig.-3.2.4 Detection rate in the image interpretation survey of sediment disasters by means ofintensity difference SAR images (slope direction angle)

54

100%

200

80%

150

60%

100

40%

50

20%

0

検出率
Detection
rate

Frequency
度数

250

0%
-180 - -120 -120 - -60-60 - 00 - 6060 - 120

120 - 180

Relative
direction angle [°]相対方位角［°］Number of
検証数
verifications

Number of
検出数

検出率
Detection rate

detections

Fig.-3.2.5 Detection rate in the image interpretation survey of sediment disasters by means of

250

100%

200

80%

150

60%

100

40%

50

20%

検出率
Detection
rate

Frequency
度数

intensity difference SAR images (relative direction angle)

0%

0
- 15

15 - 30

30 - 45

45 - 60

60 - 75

75 - 90

Local
incidence angle [°]局所入射角［°］Number of
検証数
verifications

Number of
検出数
detections

Detection rate
検出率

Fig.-3.2.6 Detection rate in the image interpretation survey of sediment disasters by means ofintensity difference SAR images (local incidence angle）

55

References
1) Masaki Mizuno, Joko Kamiyama, Masafumi Ekawa, Takumi Satou, Junichi Kanbara, Shin-ichiro
Hayashi (2013): Method of emergency search for the location of landslide dams using high-resolution
single-polarization SAR image interpretation, Technical Note of National Institute for Land andInfrastructure Management No. 760
2) Masaki Mizuno, Joko Kamiyama, Masafumi Ekawa, Takumi Satou, Junichi Kanbara (2014): Methodof emergency search for the location of landslide dams and collapses using high-resolution dualpolarization SAR image interpretation, Technical Note of National Institute for Land and InfrastructureManagement No. 791
3) Yamato Suzuki, Masayuki Matsuda, Yasuhiro Nomura, Hiroaki Nakaya (2019): Application of
Sediment Disaster Survey Method by Difference of Backscattering Intensity over SAR Images, Civil
Engineering Journal, Vol. 61, No. 12, pp.16-19
4) Kumiko Yamashita, Joko Kamiyama, Yamato Suzuki, Tomoyuki Noro, Jun Sugimoto, Takashi
Shibayama, Toshiaki Udono (2019): Validation of landslides detection capability using multi-temporal
SAR images: A case study on the heavy rainfall in northern Kyushu in July, 2017, Journal of the Japan
Society of Erosion Control Engineering, Vol. 71, No. 6, pp. 21-27
5) (National Research and Development Agency) Japan Aerospace Exploration Agency (2018): Satellite
for Disaster Monitoring from Space - Land Observing Satellite No. 2 "Daichi 2" Disaster Case Collection
2014 to 2017 6) (National Research and Development Agency) Japan Aerospace Exploration Agency/Ministry of Land,
Infrastructure, Transport and Tourism (2018): Guide Book for Artificial Satellite Utilization during
Disasters - Sediment Disaster Edition http://www.mlit.go.jp/river/sabo/satellite/manual_180327.pdf
7) Kazuo Ouchi (2004): Basics of Synthetic Aperture Radar for Remote Sensing, Tokyo Denki University
Press8) Japan Association of Remote Sensing (2001): Graphic Explanation - Remote Sensing, Japan
Association of Surveyors

56

Parameters of the SAR images in the text
Drawing
numberDate & time of observation

Resolution[m]

Off nadir angle[°]

Polarization

Orbit
direction

Direction of
microwave
irradiation

Fig.-1.2.1

2017/07/07

3

29.1

HH

SouthboundSatellite traveling
direction, left

Fig.-1.2.2

2018/04/14

3

38.2

HH

NorthboundSatellite traveling
direction, rightFig.-1.2.3

2019/06/21

3

38.2

HH

NorthboundSatellite traveling
direction, rightFig.-1.2.4

2019/06/21

3

38.2

HH

NorthboundSatellite traveling
direction, rightFig.-1.2.5

2019/06/21

3

38.2

HH

NorthboundSatellite traveling
direction, rightFig.-1.2.7

2019/09/09

10

52.4

HH HV

SouthboundSatellite traveling
direction, rightFig.-1.2.8

2017/04/29 2017/07/07

3

29.1

HH

SouthboundSatellite traveling
direction, left

Fig.-1.3.1

2017/04/29 2017/07/07

3

29.1

HH

SouthboundSatellite traveling
direction, left

Fig.-1.3.2

2019/09/09

10

52.4

HH HV

SouthboundSatellite traveling
direction, rightFig.-1.3.3

2018/06/18

3

38.2

HH

NorthboundSatellite traveling
direction, rightFig.-2.3.1

2018/08/23 2018/09/06

3

32.4

HH

SouthboundSatellite traveling
direction, rightFig.-2.3.3

2018/08/23 2018/09/06

3

32.4

HH

SouthboundSatellite traveling
direction, rightFig.-2.3.4

2018/08/23 2018/09/06

3

32.4

HH

SouthboundSatellite traveling
direction, rightFig.-2.3.9

2018/08/23 2018/09/06

3

32.4

HH

SouthboundSatellite traveling
direction, rightFig.-2.4.1

2020/03/02

3

32.4

HH

SouthboundSatellite traveling
direction, rightFig.-2.4.2

2017/10/23

3

38.2

HH

NorthboundSatellite traveling
direction, rightFig.-2.4.3

2018/08/23 2018/09/06

3

32.4

HH

SouthboundSatellite traveling
direction, rightFig.-2.5.2

2017/04/29 2017/07/07

3

29.1

HH

SouthboundSatellite traveling
direction, left

Fig.-2.5.3

2017/04/29 2017/07/07

3

29.1

HH

SouthboundSatellite traveling
direction, left

Fig.-2.5.4

2018/03/17 2018/07/21

3

32.4

HH

NorthboundSatellite traveling
direction, rightFig.-2.5.5,
Center2017/04/29 2017/07/07

3

29.1

HH

SouthboundSatellite traveling
direction, left

Fig.-2.5.5,
Right2017/06/12 2017/07/10

3

32.4

HH

SouthboundSatellite traveling
direction, right57

Reference material 1. Time required for the image interpretation survey of
sediment disasters by means of intensity difference SAR images of eachdisaster2017 Southern Nagano Earthquake■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2]

Earthquake

2017/6/25
07:02

Rainy - Cloudy

About 3,500[A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disaster- Imageoccurrence - Image
interpretation surv ey interpretation surv ey

4hours54minutes 8hours39minutes 13hours33minutes■ Image interpretation survey timeline
Date & time

Transmitter

ReceiverResponse, etc.

--

An earthquake occurred in the southern part of Nagao Pref ecture(a maximum seismic intensity of 5 upper was observ ed in Otaki
Village and Kiso Town).

Around 09:00

MLIT Headquarters

JAXA

Around 11:56--

Around 17:00

JAXA

NILIM

20:01

JAXA

MLIT Headquarters・
NILIM

Reported the results of image interpretation.

20:35

NILIM

MLIT Headquarters

Reported the findings in the results of image
interpretation.

20:35

MLIT Headquarters

2017/6/25 07:02

A

ALOS-2 Emergency Observation Request
ALOS-2 observation

B

ALOS-2 observation data was provided; started
image interpretation.

Chugoku Regional
Dev elopment Bureau Provided the results of image interpretation.

Starting at around 7:24 on June 26, the Regional Developm ent Bureau started helicopter survey after
w aiting for the w eather to get better. => No large-scale failure w as identified.

■ Parameters of the SAR images used for the image interpretation survey

ArchiveNew
observ ation

Observation date & time

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenesMay 28, 2017 Around
11:56
June 25, 2017 Around
11:56

3

29.1
（U2-6）

HH

Descending(Southbound)

Traveling
direction, right2

58

Torrential Rainfall in Shimane Prefecture in July 2017
■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2]

Rainfall2017/7/5
00:15

Rainy

About 2,000[A] Disasteroccurrence - SAR
observ ation[A+B] Disaster[B] SAR observ ation
occurrence - Image- Imageinterpretation surv ey interpretation surv ey

11hours56minutes 7hours07minutes

19hours03minutes■ Image interpretation survey timeline
Date & time

Transmitter

ReceiverResponse, etc.

2017/7/5 00:15--

Sediment disaster warning information wasannounced in Hamada City and Masuda City,
Shimane Prefecture.

05:55

-

-

Special heavy rain warning was announced in
Hamada City, Masuda City, Ohnan Town, andTsuwano Town, Shimane Prefecture.

Around 12:11--

ALOS-2 observation

15:08

MLIT Headquarters

NILIM

17:51

NILIM

MLIT Headquarters

Reported the results of image interpretation(quick report).

19:03

JAXA

NILIM

Reported the results of image interpretation.

19:18

NILIM

MLIT Headquarters

Reported the findings in the results of image
interpretation.

20:04

MLIT Headquarters

A

B

ALOS-2 observation data was provided; started
image interpretation.

Chugoku Regional Communicated the results of image
Dev elopment Bureau interpretation.

■ Parameters of the SAR images used for the image interpretation survey
Observation date & timeArchiveNew
observ ation

March 29, 2017 Around
12:11
July 5, 2017 Around 12:11

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes3

38.6（U2-9）

HH

Descending(Southbound)

Traveling
direction, right159

Northern Kyushu Torrential Rainfall in July 2017
■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2]

Rainfall2017/7/5
14:10

Rainy
Clear f rom themorning, next day

About 3,500[A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disaster- Imageoccurrence - Image
interpretation surv ey interpretation surv ey

46hours42minutes 7hours08minutes

53hours50minutes■ Image interpretation survey timeline
Date & time

Transmitter

ReceiverResponse, etc.

2017/7/5 14:10--

Sediment disaster w arning information w as announcedin Asakura City and Toho Village, Fukuoka Prefecture.

2017/7/5 17:51--

Special heavy rain w arning w as announced in Fukuoka
Prefecture.

MLIT Headquarters

JAXA

Around 20:00

A

ALOS-2 Emergency Observation Request

Starting at around 6:00 on July 6, the Regional Development Bureau started helicopter survey after waiting for the weather toget better. => Frequent occurrences of sediment disasters were identified in Asakura City.

Around 2017/7/7 12:52

-

14:16

JAXA

17:21

NILIM

18:01

NILIM

Around 20:00

NILIM

-

ALOS-2 observation

B

ALOS-2 observation data w as provided; started image
interpretation
Reported the results of image interpretation (quick
MLIT Headquarters report) in the major places hit by the disaster that w ere
reported in new s media.
Reported the results of image interpretation in major
MLIT Headquarters
extracted places on the north side of Toho Village.

NILIM

Reported the results of image interpretation (the entire image interpretation

MLIT Headquarters range).
Provided information to the Kyushu Regional Development Bureau.

Photo taken by the National Institute for Landand Infrastructure Management(Left) Intensity difference SAR im age
around Asakura City

(Right) Area w here failure occurred
frequently that w as identified by the■ Parameters of the SAR images used for the image interpretation survey
Observation date & timeArchive

April 29, 2016 Around 12:52

New
observ ation

July 7, 2017 Around 12:52

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes3

29.1（U2-6 ）

HH

Descending(Southbound)

Traveling
direction, left

1

60

2017 Typhoon No. 21■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2]

Rainfall2017/10/22
16:00

Rainy

About 6,000[A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disaster- Imageoccurrence - Image
interpretation surv ey interpretation surv ey

19hours29minutes 7hours20minutes

26hours49minutes■ Image interpretation survey timeline
Date & time

Transmitter

ReceiverResponse, etc.

2017/10/22 16:00--

Sediment disaster w arning information w as announcedin Shingu City, Wakayama Prefecture, and others.

2017/10/23 09:33

Kinki Regional
Development
BureauMLIT Headquarters

-

-

14:04

JAXA

NILIM

18:35

JAXA

MLIT Headquarters・
NILIM

Reported the results of image interpretation.

18:49

NILIM

MLIT Headquarters

Reported the findings in the results of image
interpretation.

Around 11:29

A

Request for SAR image observation
ALOS-2 observation

B

ALOS-2 observation data was provided; started
image interpretation.

■ Parameters of the SAR images used for the image interpretation survey

New
observ ation

Observation date & time

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes

October 23, 2017 Around 11:29

6

56.2（U5-21）

HV

Descending(Southbound)

Traveling
direction, left

3

61

2018 Northern Osaka Earthquake■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2]

Earthquake

2018/6/18
07:58

Cloudy - Rainy
Bad weather
conditions continued
until around 21st.

About 3,500[A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disaster- Imageoccurrence - Image
interpretation surv ey interpretation surv ey

16hours00minutes 9hours10minutes

25hours10minutes■ Image interpretation survey timeline
Date & time

Transmitter

ReceiverResponse, etc.

--

An earthquake occurred in the northern part of Osaka Pref ecture(a maximum seismic intensity of 6 lower was observ ed in Kita
Ward, Osaka City , Takatsuki City , Hirakata City , Ibaraki City ,and Minoh City ).

MLIT Headquarters

JAXA

-

-

2018/6/19 Around 01:00

JAXA

NILIM

Around 07:00

NILIM

-

08:06

JAXA

NILIM

09:08

NILIM

MLIT Headquarters

2018/6/18 07:58

08:34
Around 23:58

A

ALOS-2 Emergency Observation Request
ALOS-2 observation

B

ALOS-2 observation data was provided.
Started image interpretation.
Sent the results of image interpretation.
Reported the findings in the results of image
interpretation.

Starting at around 9:30 on June 19, the Regional Development Bureau started helicopter
survey. => No major damage was identified.
■ Parameters of the SAR images used for the image interpretation survey
Observation date & timeArchive

April 9, 2018 Around 23:58

New
observ ation

June 18, 2018 Around
23:58

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes3

38.2（U2-9）

HH

Ascending(Northbound)

Traveling
direction, right1

62

Torrential Rainfall in July 2018
■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2][A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disaster- Imageoccurrence - Image
interpretation surv ey interpretation surv ey

Rainfall2018/07/06
14:10

Rainy

About 10,000

33hours54minutes 7hours56minutes

41hours50minutes■ Image interpretation survey timeline
Date & time

ReceiverResponse, etc.

--

Sediment disaster w arning information w as announcedin Hiroshima City, Hiroshima Prefecture, and others.

MLIT Headquarters

JAXA

ALOS-2 Emergency Observation Request

19:40

-

-

Special heavy rain w arning w as announced in
Hiroshima, Okayama, and Tottori Prefectures.

2018/07/08 Around 00:04--

ALOS-2 observation

Around 03:50

JAXA

NILIM

Around 08:00

NILIM

Chugoku/Shikoku Regional
Development Bureaus

2018/07/06 14:05
Around 17:00

Transmitter

A

B

ALOS-2 observation data was provided; started
image interpretation.
Reported the results of image interpretation.

Photo tak en by Shik ok u Regional
Dev elopment Bureau(Left) Place of sediment movement reported
during SAR image interpretation(Right) Large-scale failure identified at the site

■ Parameters of the SAR images used for the image interpretation survey
Observation date & timeArchiveNew
observ ation

April 14, 2018 Around
00:04
July 8, 2018 Around 00:04

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes3

38.2（U2-9）

HH

Ascending(Northbound)

Traveling
direction, right4

63

2018 Hokkaido Eastern Iburi Earthquake■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2]

Earthquake

2018/9/6
03:07

Cloudy - Rainy
Bad weather conditions
continued for a while after
occurrence of the disaster.

About 3,000[A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disasteroccurrence - Image- Imageinterpretation surv ey interpretation surv ey

8hours34minutes 3hours49minutes 12hours23minutes■ Image interpretation survey timeline
Date & time

Transmitter

Receiver--Around 04:00

MLIT Headquarters

JAXA

Around 11:41--

2018/9/6

03:07

Response, etc.

Occurrence of the disasterA

ALOS-2 Emergency Observation Request
ALOS-2 observation

B

ALOS-2 observation data w as provided; started image
Around 14:12
JAXA
NILIM
interpretation.
Starting at around 15:00, the Regional Developm ent Bureau started helicopter survey after w aiting for thew eather to get better.

Around 15:30

Hokkaido Regional Provided the results of image interpretation.
Development
Reported the range w here slope failures concentrate
Bureauestimated by SAR images speedily.

NILIM

Much tim e w as required to grasp the situation of dam age by m eans of optical im ages due to bad w eather
conditions.
2018/9/11 Around 09:42-

Observation by means of optical satellites
Confirmed that the range of failure concentration roughly
coincides w ith the range obtained by image
interpretation of SAR images.

-

Range of failure
concentration(Left) Range of failure concentration reported
during image interpretation of SAR images(Right) Range of failure concentration confirmedby optical satellites■ Parameters of the SAR images used for the image interpretation survey
Observation date & timeArchive

August 23, 2018 Around 11:41

New
observ ation

Saptember 6, 2018 Around 11:41

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes3

32.4（U2-7 ）

HH

Descending(Southbound)

Traveling
direction, right2

64

2017 Torrential Rain in southwestern Sri Lanka■ Conditions of/time required for image interpretation survey
Disasterf actorDate & time ofdisaster occurrenceMeteorologicalsituation during
disaster occurrence

Image interpretation
area [km 2][A] Disasteroccurrence - SAR
observ ation[B] SAR observ ation[A+B] Disaster- Imageoccurrence - Image
interpretation surv ey interpretation surv ey

Rainfall2017/5/24 ～26

Rainy

About 5,600

144hours00minutes

29hours00minutes

173hours00minutes■ Image interpretation survey timeline
Date & time

Transmitter

ReceiverResponse, etc.

--

Sediment disasters occurred in southw estern Sri Lanka
due to record-breaking torrential rainfalls.

2017/5/29

MLIT Headquarters

JAXA

2017/5/30--

2017/5/31 Around 08:30

JAXA

NILIM

17:00

NILIM

MLIT Headquarters

2017/6/1

NILIM

JICA

2017/5/24～26

A

ALOS-2 Emergency Observation Request
ALOS-2 observation

B

ALOS-2 observation data was provided; started
image interpretation
Reported the findings in the results of image
interpretation.
Provided the results of image interpretation.

June 6 The Japan Disaster Relief Team conducted site survey based on the results of image
interpretation. => Confirmed a large-scale failure.

(Lef t) Place where sediment mov ement occurred reported during
the image interpretation of SAR images(Right) Large-scale failure confirmed at the site.

■ Parameters of the SAR images used for the image interpretation survey

New
observ ation

Observation date & time

Resolution[m]

Off nadirangle [°]

Polariz ation

Orbit direction

Direction of
microwav e irradiation

Number ofscenes2017/10/23 Daytime path

10

39.3

HV

Descending(Southbound)

Traveling
direction, left

3

65

Reference material 2. Accuracy of the image interpretation survey of sedimentdisasters by means of intensity difference SAR images in each disaster
Disaster地震による災害
caused by earthquake
100%

150

100%

120

80%

120

80%

90

60%

90

60%

60

40%

60

40%

30

20%

30

20%

0%

0

0
1,000 - 1,500 1,500 - 2,000

Frequency
度数2,000 -

0%0 - 500

2

Area [m ]面積［㎡］Number of
Number
検証数
検出数 ofverifications
detections

Detection
検出率
rate

Detection
検出率
rate

100%

50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0 - 500

500 - 1,000

Number of
検証数
verifications

1,000 - 1,500 1,500 - 2,000

Frequency
度数

Detection
検出率 rate50

2,000 -

0%0 - 500

Area
[m2]
面積［㎡］
Number
検出数 ofdetections

500 - 1,000

1,000 - 1,500 1,500 - 2,000

2,000 -

2

Area [m ]面積［㎡］Number of
Number
検証数
検出数 ofverifications
detections

Detection
検出率
rate

Detection
検出率
rate

2016 Kumamoto Earthquake

Torrential
Rainfall in July 2018
平成30年7月豪雨（崩壊）(failure)

平成28年熊本地震（堆積）(deposition)
50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0
500 - 1,000

Number of
検証数
verifications

1,000 - 1,500 1,500 - 2,000

Frequency
度数

Detection
検出率 rate

100%

度数

50

0 - 500

2,000 -

0%0 - 500

Area
[m2]
面積［㎡］
Number of
検出数
detections

Detection
検出率 rate

2016
Kumamoto Earthquake (failure)
平成28年熊本地震（崩壊）

度数

Frequency

2,000 -

20Frequency

1,000 - 1,500 1,500 - 2,000

Area [m ]面積［㎡］NumberNumber
検証数 of
検出数 ofverifications
detections

Northern Kyushu Torrential Rainfall in
平成29年7月九州北部豪雨
July 2017

500 - 1,000

1,000 - 1,500 1,500 - 2,000

Number of
検証数
verifications

Detection
検出率
rate

Area [m2]面積［㎡］Number of
検出数
detections

2,000 -

Detection
検出率
rate

2018 Hokkaido Eastern Iburi
平成30年北海道胆振東部地震

Torrential Rainfall in July 2018
平成30年7月豪雨（堆積）Earthquake(deposition)

50

100%

50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0
0 - 500

500 - 1,000

Number of
検証数
verifications

1,000 - 1,500 1,500 - 2,000

Frequency
度数

Detection
検出率 rate

Frequency
度数500 - 1,000

Detection
検出率 rate

500 - 1,000

2,000 -

0%0 - 500

500 - 1,000

Area [m2]面積［㎡］Number of
検出数
detections

1,000 - 1,500 1,500 - 2,000

2,000 -

2

Detection
検出率
rate

Number of
検証数
verifications

66

Area [m ]面積［㎡］Number
検出数 ofdetections

Detection
検出率
rate

Detection
検出率 rate

0 - 500

Detection
検出率 rate

150

Detection
検出率 rate

Frequency
度数

降雨による災害
Disastercaused by rainfall

150

100%

150

100%

120

80%

120

80%

90

60%

90

60%

60

40%

60

40%

30

20%

30

20%

0%

0

0
25 - 35

度数
Frequency

35 -0%
0 - 15Slope
gradient [°]
斜面勾配［°］
Number of
検出数
detections

Number of
検証数
verifications

Detection
検出率
rate

Number
検出数 ofdetections

Detection
検出率
rate

100%

50
50

100%
100%

40

80%

40
40

80%
80%

30

60%

30
30

60%
60%

20

40%

20
20

40%
40%

10

20%

10
10

20%
20%

0%

00

0 - 1515 - 25

25 - 35

35 -

Slope
gradient [°]
斜面勾配［°］

0 - 150 - 1515 - 25
15 - 25

25 - 35
25 - 35

35 35 -

Slope
gradient [°]
斜面勾配［°］

Number of 斜面勾配［°］
Number of
検証数
検出数
verifications
検証数
検出数
detections

Detection
検出率
rate

Number of
検出数
detections

Number
検証数 ofverifications

Frequency
度数
度数

Detection
検出率 rate

50

0

0%
0%

Detection
検出率
検出率
rate

2016 Kumamoto Earthquake

Torrential Rainfall in July 2018
平成30年7月豪雨（崩壊）(failure)

平成28年熊本地震（堆積）(deposition)
100%

50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0
0 - 1515 - 25

25 - 35

Slope
gradient [°]
斜面勾配［°］

Number of
検証数
verifications

Number of
検出数
detections

Frequency
度数

Detection
検出率 rate

50

度数Frequency

35 -

2016
Kumamoto Earthquake (failure)
平成28年熊本地震（崩壊）

度数

Frequency

Northern Kyushu Torrential Rainfall in
平成29年7月九州北部豪雨
July 20170%
0 - 1535 -

15 - 25

25 - 35

Slope
gradient [°]
斜面勾配［°］

Detection
検出率
rate

Number of
検証数
verifications

Number of
検出数
detections

35 -Detection
検出率
rate

2018 Hokkaido Eastern Iburi
平成30年北海道胆振東部地震

Torrential
Rainfall in July 2018
平成30年7月豪雨（堆積）(deposition)

Earthquake

50
50

100%
100%

50

100%

40
40

80%
80%

40

80%

30
30

60%
60%

30

60%

20
20

40%
40%

20

40%

10
10

20%
20%

10

20%

0%
0%

0

00

15
00 -- 15

15 -- 25
25
15

25 -- 35
35
25

Slope
gradient [°]
斜面勾配［°］

Number of
検証数
検証数
verifications

斜面勾配［°］
Number of
検出数
検出数
detections

35 -35Frequency
度数

Detection
検出率
検出率 rate

Frequency
度数
度数

25 - 35

Slope
gradient [°]
斜面勾配［°］

検出率
検出率 rate
Detection

Number of
検証数
verifications

15 - 25

検出率 rate
Detection

15 - 250%
0 - 1515 - 25

25 - 35

Slope
gradient [°]
斜面勾配［°］

Detection
検出率
検出率
rate

Number of
検証数
verifications

67

Number of
検出数
detections

35 -Detection
検出率
rate

検出率 rate
Detection

0 - 15

検出率 rate
Detection

地震による災害
Disastercaused by earthquake

Detection
検出率 rate

Frequency
度数

降雨による災害
Disastercaused by rainfall降雨による災害
Disastercaused by rainfall
150

100%

120

80%

120

80%

90

60%

90

60%

60

40%

60

40%

30

20%

30

20%

0%

0

120 - 180

180 - 240

240 - 300

Slope斜面方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

度数
Frequency

300 - 360

0%0 - 60Detection
検出率
rate

240 - 300

300 - 360

Detection
検出率
rate

平成28年熊本地震（崩壊）2016
Kumamoto Earthquake (failure)
50

100%
100%

40

80%

40

80%
80%

30

60%

30

60%
60%

20

40%

20

40%
40%

10

20%

10

20%
20%

0%

0

0 - 6060 - 120

120 - 180

180 - 240

240 - 300

Slope 斜面方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

Frequency
度数
度数

Detection
検出率 rate

100%

度数

Frequency

180 - 24050

0
300 - 360

500
00 -- 60

- 1,000
1,500 240
- 2,000
60500- 120
120 1,000- 180 - 1,500
180 - 240
- 300

Slope面積［㎡］direction angle [°]斜面方位角［°］Number of
Number of
検証数
検出数
verifications
detections

Detection
検出率
rate

2,000
300 --360

0%0%Detection
検出率
検出率
rate

2016 Kumamoto Earthquake
平成28年熊本地震（堆積）Torrential
Rainfall in July 2018
平成30年7月豪雨（崩壊）(failure)(deposition)

100%

50

100%
100%

40

80%

40

80%
80%

30

60%

30

60%
60%

20

40%

20

40%
40%

10

20%

10

20%
20%

0%

0

0
0 - 6060 - 120

120 - 180

180 - 240

240 - 300

Slope direction
angle [°]
斜面方位角［°］NumberNumber
検証数 of
検出数 ofverifications
detections

Frequency
度数

Detection
検出率 rate

50

度数Frequency

120 - 180

Slope斜面方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

Northern
Kyushu Torrential Rainfall in
平成29年7月九州北部豪雨
July 2017

300 - 3600%0%00 -- 60
500

60500
- 120
120 1,000- 180 - 1,500
180 - 240
- 300
- 1,000
1,500 240
- 2,000

Slope面積［㎡］direction angle [°]斜面方位角［°］Number of
Number of
検証数
検出数
verifications
detections

Detection
検出率
rate

300 - -360
2,000

Detection
検出率
rate

2018 Hokkaido Eastern Iburi
平成30年北海道胆振東部地震

Torrential Rainfall in July 2018
平成30年7月豪雨（堆積）Earthquake(deposition)

50
50

100%

50

100%

40
40

80%

40

80%

30
30

60%

30

60%

20
20

40%

20

40%

10
10

20%

10

20%

0%

0

00
00 -- 60
500

60500
- 120
120 1,000- 180 - 1,500
180 - 1,500
240 240- 300- 1,000- 2,000

Slope 面積［㎡］direction angle [°]斜面方位角［°］NumberNumber
検証数 of
検出数 of
検証数
検出数
verifications
detections

Frequency
度数

検出率 rate
Detection

Frequency
度数60 - 120

検出率 rate
Detection

60 - 120

300 - -360
2,000

0%
0 - 6060 - 120

120 - 180

180 - 240

240 - 300

Slope 斜面方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

Detection
検出率
rate

68

300 - 360

Detection
検出率
rate

Detection
検出率 rate

0 - 60

検出率 rate
検出率
Detection0

検出率 rate
Detection

100%

度数
Frequency150

検出率 rate
Detection

地震による災害
Disastercaused by earthquake

150

100%

150

100%

120

80%

120

80%

90

60%

90

60%

60

40%

60

40%

30

20%

30

20%

0%

0

0 - 6060 - 120

度数
Frequency

Detection
検出率
rate

60 - 120

120 - 180

Detection
検出率
rate

平成28年熊本地震（崩壊）2016
Kumamoto Earthquake (failure)
50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

-180 - -120 -120 - -60-60 - 00 - 6060 - 120

Relative相対方位角［°］direction angle [°]NumberNumber
検証数 of
検出数 ofverifications
detections

Frequency
度数

Detection
検出率 rate

100%

度数

Frequency

0 - 6050

00%-180 - -120 -120 - -60

120 - 180

-60 - 00 - 6060 - 120相対方位角［°］Relative
direction angle [°]Number of
Number of
検証数
検出数
verifications
detections

Detection
検出率
rate

120 - 180

Detection
検出率
rate

2016 Kumamoto Earthquake
平成28年熊本地震（堆積）Torrential
Rainfall in July 2018
平成30年7月豪雨（崩壊）(failure)(deposition)

50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

-180 - -120 -120 - -60-60 - 00 - 6060 - 120

Relative相対方位角［°］direction angle [°]NumberNumber
検証数 of
検出数 ofverifications
detections

Frequency
度数

Detection
検出率 rate

100%

度数

50

0

0%
-180 - -120 -120 - -60

120 - 180

-60 - 00 - 6060 - 120

Relative
direction angle [°]相対方位角［°］Number of
Number
検証数
検出数 ofverifications
detections

Detection
検出率
rate

120 - 180

Detection
検出率
rate

2018 Hokkaido Eastern Iburi
平成30年北海道胆振東部地震

Torrential Rainfall in July 2018
平成30年7月豪雨（堆積）Earthquake(deposition)
50

100%

50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0
-180 - -120 -120 - -60-60 - 00 - 6060 - 120

Relative相対方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

Frequency
度数

検出率 rate
Detection

Frequency
度数-60 - 0相対方位角［°］Relative
direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

Northern
Kyushu Torrential Rainfall in
平成29年7月九州北部豪雨
July 2017

Frequency

0%-180 - -120 -120 - -60

120 - 180

検出率 rate
Detection-60 - 0Relative相対方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

120 - 1800%-180 - -120 -120 - -60-60 - 00 - 6060 - 120

Relative相対方位角［°］direction angle [°]Number of
Number
検証数
検出数 ofverifications
detections

Detection
検出率
rate

69

120 - 180

Detection
検出率
rate

Detection
検出率 rate-180 - -120 -120 - -60

検出率
Detectionrate

0

検出率 rate
Detection

地震による災害
Disaster caused by earthquake

検出率 rate
Detection

度数
Frequency

降雨による災害
Disaster caused by rainfall

地震による災害
Disaster caused by earthquake
150

100%

120

80%

120

80%

90

60%

90

60%

60

40%

60

40%

30

20%

30

20%

0%

0

0
- 15

15 - 30

30 - 45

45 - 60

60 - 75

局所入射角［°］Local incidence
angle [°]
NumberNumber
検証数 of
検出数 ofverifications
detections

75 - 900%- 15

Detection
検出率
rate

45 - 60

60 - 75

75 - 90

Detection
検出率
rate

50

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

- 15

15 - 30

30 - 45

45 - 60

60 - 75

Local incidence
angle [°]
局所入射角［°］NumberNumber
検証数 of
検出数of
verifications
detections

Frequency
度数75 - 90

検出率 rate
Detection100%

Detection
検出率 rate

50

度数

0%
- 15

15 - 30

30 - 45

45 - 60

60 - 75

Local incidence
angle [°]
局所入射角［°］NumberNumber
検証数 of
検出数 ofverifications
detections

Detection
検出率
rate

75 - 90

Detection
検出率
rate

2016 Kumamoto Earthquake
平成28年熊本地震（堆積）Torrential
Rainfall in July 2018
平成30年7月豪雨（崩壊）(failure)(deposition)

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0
- 15

15 - 30

30 - 45

45 - 60

60 - 75

Local incidence
angle [°]
局所入射角［°］NumberNumber
検証数 of
検出数of
verifications
detections

75 - 90

Detection
検出率 rate

100%

Frequency
度数50

Detection
検出率 rate

100%

度数

50

0%
- 15

15 - 30

30 - 45

45 - 60

60 - 75

Local incidence
angle [°]
局所入射角［°］NumberNumber
検証数 of
検出数 ofverifications
detections

Detection
検出率
rate

75 - 90

Detection
検出率
rate

2018 Hokkaido Eastern Iburi
平成30年北海道胆振東部地震

Torrential
Rainfall in July 2018
平成30年7月豪雨（堆積）(deposition)

Earthquake

100%

40

80%

40

80%

30

60%

30

60%

20

40%

20

40%

10

20%

10

20%

0%

0

0
- 15

15 - 30

30 - 45

45 - 60

60 - 75

Local incidence
angle [°]
局所入射角［°］Number of
Number
検証数
検出数 ofverifications
detections

度数

Frequency

50

Detection
検出率 rate

100%

度数

50

75 - 900%- 15

15 - 30

30 - 45

45 - 60

60 - 75

Local incidence
angle [°]
局所入射角［°］Number of
Number of
検証数
検出数
verifications
detections

Detection
検出率
rate

70

75 - 90

Detection
検出率
rate

Detection
検出率 rate

Frequency

30 - 45

平成28年熊本地震（崩壊）2016
Kumamoto Earthquake (failure)0

Frequency

15 - 30

局所入射角［°］Local incidence
angle [°]
NumberNumber
検証数 of
検出数 ofverifications
detections

Northern
Kyushu Torrential Rainfall in
平成29年7月九州北部豪雨
July 2017

Frequency

Detection
検出率 rate

100%

Frequency
度数150

Detection
検出率 rate

Frequency
度数

降雨による災害
Disaster caused by rainfallReference material 3. Checklist of the image interpretation survey of sedimentdisasters by means of intensity difference SAR images
Intensity difference SAR image

Single-polarized SAR image (before disaster) Single-polarized SAR image (after disaster)

Topographical map

Step

1

Images, etc. to bereferred toRange to be checked

Check item

Intensity difference SAR
image

Coloring range of intensity
difference SAR image

Dominant color

Single-polarized SAR
image

Changes in single-polarized
SAR image before andafter the disaster

Shape change

Intensity difference SAR
image

Coloring range of intensity
difference SAR image

Clarity(1).(3): ○ => 2 (failure) (2), (4):2
(failure)2(deposition)

3

Optical image before disaster

Judgment criteria

Evaluation(○×)(1) Red or red/cyan pairs can be seen
(2) Cyan can be seen(3) Appears to be dented(4) Appears to have swollen(5) Is clear○ => 2 (deposition)

Topographical map

Topography

Slope gradient

Topographical map
Optical image before disaster

Surroundings

Land cover(7) Not an artificially modified land

Topographical map
Optical image before disaster

Surroundings

Land cover(8) Is a forest

Topographical map

Topography

Slope gradient(9) Is a flat land

Topographical map
Optical image before disaster

Surroundings

Land cover(10) Not an artificially modified land

Topographical map
Optical image before disaster

Surroundings

Positional relationship(11) Is adjacent to changes in red

Intensity difference SAR
image
Topographical map
Optical image before disasterIn the channel
Upstream side of thechannelUpstream side of thechannel(12) Cyan can be seen
Dominant color(13) Red can be seen
Inundated facility

a. Occurrence of a slope failure

Judgment results(6) Is an inclined land(14) No inundated facility

May have occurredb. Sediment deposition caused by a failurec. Formation of channel blockage/

Changes cannot be verified

May have occurred

May have occurred//

Changes cannot be verified

Changes cannot be verified

d. Image interpretation cannot be made sufficiently from SAR images

71

TECHNICAL NOTE of NILIM
No.1110

April 2020

© National Institute for Land and Infrastructure Management
National Institute for Land and Infrastructure Management
Ministry of Land, Infrastructure, Transport and Tourism
1, Asahi, Tsukuba, Ibaraki, 305-0804 Japan
Phone:+81-(0)29-864-2675