Embed Size (px)
Citation preview
Satellite Imagery Interpretation
Guide: Structure Identification in
Displacement and Conflict
December 2018
BACKGROUND
Authors
All research, analysis, writing, editing and layout for Satellite Imagery Interpretation Guide:
Structure Identification in Displacement and Conflict was completed by the Signal Program on
Human Security and Technology at the Harvard Humanitarian Initiative.
Isaac Baker, Imagery Analysis Manager
Rob Baker, Director
Saira Khan, Imagery Analyst
About the Signal Program on Human Security and Technology
The Signal Program on Human Security and Technology (Signal Program) was founded by the
Harvard Humanitarian Initiative in 2012. Signal Program staff, fellows, and partners work to
advance the safe, ethical, and effective use of information technologies by communities of practice
during humanitarian and human rights emergencies.
The program addresses critical gaps in research and practice HHI encountered while designing
and managing the pilot phase of the Satellite Sentinel Project (SSP) from December 2010 to the
summer of 2012. Through the analysis of satellite imagery and open source reports from Sudan,
SSP was a watershed moment in the use of remote sensing to monitor the human security of
civilians during and armed conflict.
The program’s ongoing research and scholarship focuses on the following three areas:
Tools and Methods
Design and scientifically test tools and methods that remotely collect and analyze data about
humanitarian emergencies;
2
Standards and Ethics
Help lead the development of technical standards and professional ethics for the responsible use
of technology to assist disaster-affected populations;
Mass Atrocity Remote Sensing
And conduct retrospective analysis of satellite imagery and other related data to identify
remotely observable forensic evidence of alleged mass atrocities.
About the Harvard Humanitarian Initiative
The Harvard Humanitarian Initiative (HHI) is a university-wide center involving multiple entities
within the Harvard community that provide expertise in public health, medicine, social science,
management, and other disciplines to promote evidence-based approaches to humanitarian
assistance. The mission of HHI is to relieve human suffering in war and disaster by advancing the
science and practice of humanitarian response worldwide.
HHI fosters interdisciplinary collaboration in order to:
Improve the effectiveness of humanitarian strategies for relief, protection and
prevention;
Instill human rights principles and practices in these strategies; and
Educate and train the next generation of humanitarian leaders.
3
TABLE OF CONTENTS
BACKGROUND 1
Authors 1
About the Signal Program on Human Security and Technology 1
About the Harvard Humanitarian Initiative 2
TABLE OF CONTENTS 3
ACKNOWLEDGEMENTS 4
ACRONYMS 5
INTRODUCTION 6
Areas of Interest 7
BACKGROUND RESEARCH 8
VISUAL ANALYSIS AND INTERPRETATION 10
Displacement Camps 18
Impact of Conflict 22
LIMITATIONS 26
CONCLUSIONS 29
REFERENCES 30
4
ACKNOWLEDGEMENTS
This satellite imagery interpretation guide was produced through the successful collaboration
between the World Food Programme and the Harvard Humanitarian Initiative. The authors
would like to thank the following individuals for their valuable contributions, support, and
guidance throughout this project and the creation of this imagery interpretation guide.
Caitlin Howarth
Early Warning Expert
Signal Program on Human Security and Technology
Harvard Humanitarian Initiative
Laure Boudinaud
Earth Observation Analyst
Analysis and Trends Services
World Food Programme
Rogerio Bonifacio
Head of Geospatial Analysis Unit
Analysis and Trends Services
World Food Programme
Sarah Muir
Earth Observation Analyst
Analysis and Trends Services
World Food Programme
5
ACRONYMS
AOI Area of Interest
GE Google Earth
HDX Humanitarian Data Exchange
HHI Harvard Humanitarian Initiative
IOM International Organization for Migration
MENA Middle East and North Africa
NSAG Non-State Armed Group
VAM Vulnerability Analysis and Mapping
VHR Very High resolution
WFP World Food Programme
6
INTRODUCTION
Since 2016, the Vulnerability Analysis and Mapping (VAM) unit at the World Food Programme
(WFP) has collaborated with the Signal Program on Human Security & Technology (Signal) at the
Harvard Humanitarian Initiative (HHI) to conduct food and human security analysis on displaced
populations in conflict areas. VAM’s Geospatial Team has long used remote sensing data for agro-
meteorology monitoring to provide a leading indication of drought and food shortages.
Combining VAM’s agricultural-based analysis in affected areas with the conflict-based satellite
imagery analysis conducted by Signal, this project resulted in the monitoring and analysis of over
60 individual displaced population camps and settlements in multiple locations in Sub-Saharan
Africa. Both teams documented observations including structure increase and decrease, damaged
dwellings from armed conflict, population movements, burned agriculture, and landscapes
affected by floods. The analysis and findings improved WFP’s capacity to inform decision makers
on the ground and improve the operational response in those areas.
How Signal’s collective analysis informed WFP's response, reflects a trend in employing satellite
imagery to craft more effective and efficient responses. Over the past decade, the humanitarian
space has witnessed an exponential increase in the implementation of technology in response,
monitoring, and research. The use of satellite imagery, in particular, has gained prominence in
this space. Used for decades prior as a means of remotely gathering intelligence of military
activity, the early 21st century has since seen a rapid increase in the use of this data in numerous,
non-military applications. In humanitarian response, this includes identifying displaced
populations and monitoring vulnerable communities in conflict and disaster-prone areas,
providing the ability to not only collect more data more quickly, to name a few and in many cases
in underreported regions due to inaccessibility and/or security concerns. The work that Signal
and VAM have completed encompass each of the aforementioned applications of remote sensing
in the humanitarian sector.
This introductory imagery analysis guide provided by Signal shares practical examples and
materials based on the analysis and methodologies developed and implemented during the
course of VAM and Signal's partnership. The guide is geared towards people who are beginning
to explore imagery analysis or simply want to understand how this technology is implemented in
a humanitarian context and the potential benefits to their own work. This document covers many
principal techniques at the foundation of satellite imagery analysis in communities that face
7
displacement and in humanitarian conflict settings. Examples will be drawn from the range of
countries and contexts in order to demonstrate how structures and settlements common to these
regions appear in satellite imagery. In the future, subsequent imagery interpretation guides will
be devised for intermediate and expert analysts.
Areas of Interest
The examples used in this guide are drawn from the locations mapped in figure 1. The images
used from each of these locations are from the historical imagery archive in Google Earth Pro. You
can explore the individual locations and their respective imagery in Google Earth Pro (kml).
Figure 1. This guide draws from examples collected in 12 different AOIs, primarily situated in Central and Eastern Africa
8
Background Research
Literature and photography
To establish contextual understanding of an AOI, it is highly recommended to conduct
background research prior to analysis. The research should capture contextual details including
information about the environment (weather, climate, land cover and land use, seasons,
vegetation, agricultural practices), the population residing there (population dynamics, culture,
ethnicities, common practices, settlement characteristics), the associated socioeconomic
variables (livelihoods, local economies, trade) and the situational circumstances (political
stability, intervention by foreign actors, conflict, natural disasters). This information may be
retrieved from gray literature, white papers, academic articles, news reports, UN agencies,
international organizations, and other sources. The literature should also ideally be
supplemented with ground, drone, aerial photography or videography.
Datasets
In addition to literature and photography, supplemental datasets are also often available online.
The Humanitarian Data Exchange (HDX) for example, is an excellent platform where spatial and
non-spatial data are collected and stored from across agencies and organizations. This may
include survey information relevant to certain disease outbreaks, population figures in displaced
persons camps, settlement location etc. Additionally, Open Street Map (OSM) holds valuable
spatial attributes of locations while WorldPOP has developed gridded population estimates that
may prove useful in areas where there are no other population estimates. The inspection of these
background materials will help familiarize the analyst with their area of study and improve the
confidence and certainty of interpretations.
Image enhancements
Finally, analysts should also consider researching image enhancement techniques. When applied
to imagery, enhancement techniques often allow features to be better distinguished from one
another and the background. This may include the application of stretches to modify brightness
values or even the application of vegetation indices such as the Normalized Difference Vegetation
Index (NDVI).
𝑁𝐷𝑉𝐼 =(𝑁𝐼𝑅 − 𝑅𝑒𝑑)
(𝑁𝐼𝑅 + 𝑅𝑒𝑑)
9
NDVI is commonly used to distinguish vegetation and non-vegetative cover. For additional
information on image enhancement techniques, refer to Natural Resources Canada and A Review
of Vegetation Indices.
The following sections will provide visual analysis techniques and offer examples of structures
and their commonly identified characteristics in settlements, displaced population camps, and in
conflict-affected areas. The examples highlight structure types frequently identified by analysts
working with locations in Sub-Saharan Africa and the Middle East and North Africa (MENA).
These discussed cases may be used as a guidance for the identification and analysis of structures
in these regions.
10
Visual Analysis and Interpretation
Structures detected in a satellite image can vary greatly in appearance across different AOIs.
Describing a structure’s characteristics such as shape, size, tone, pattern, texture, shadow,
arrangement, proximity to other features and categorizing by type, helps the image analyst to
analyze and interpret what they are seeing in the image. The technique of recognizing a feature
to be a structure, describing its characteristics, and extracting information about it from satellite
imagery, is the core of visual image analysis and interpretation.
The following subsection will identify and describe typical structures in communities in Sub-
Saharan Africa not affected by conflict or displacement. Beware that the provided examples do
not reflect all the structure types present in this region. The information provided in this guide
simply highlights common features to assist in the analysis of structure identification.
Structure Type
As previously mentioned, feature characteristics including color, shape, and construction
material greatly vary from structure to structure. When analyzing satellite imagery, we are
presented with a bird’s eye view, meaning that we mainly see rooftops. It is important to
recognize the variation among structure characteristics to accurately identify buildings.
For instance, by describing the visual characteristics of features in an image we can recognize and
distinguish a brightly toned, smooth rectangular surface as a metallic roof from a dark colored,
textured feature, which may blend into the landscape as it may be composed of natural materials
such as dead vegetation (branches, leaves). In figure 2 we can see that most of structures in Gulu,
Uganda and Dolo, Ethiopia are generally angular, constructed with metallic material, and are near
one another. On the contrary, figure 3 shows that many of the structures in Palabek, Uganda and
Umm Dalil, Sudan are circular, composed of natural materials and are somewhat dispersed. From
these observations, we can extract information such as Gulu and Dolo are more affluent
settlements given the presence of metallic roofing material. Palabek and Umm on the other hand,
are less affluent as their main roofing material is composed of organic material.
11
Gulu, Uganda
11 Nov 2017
Dolo, Ethiopia
4 Dec 2013
Figure 2. The roofs of the structures in Gulu, Uganda and Dolo, Ethiopia are primarily constructed with metal
12
Palabek, Uganda
11 Feb 2016
Umm Dalil, Sudan
20 Jan 2018
Figure 3. Structures in Palabek, Uganda and Umm Dalil, Sudan are generally constructed with organic materials
13
Another example is that of Gulu, Uganda in figure 4 in which most structures are U-shaped. The
buildings have a conjoined roof facing the street but the structure splits into two separate
branches as you move away from the street. Given the construction material and dimensions of
these structures, they are likely used for commercial purposes and are not residential. Be aware,
however, that through the interpretation of satellite imagery you cannot always conclude what
the purpose of the visible structure is. However, you can make inferences based on prior
knowledge of what homes, warehouses, or any other structures look like in the AOI in
consideration. Remember that OSM, Google Maps, Google Earth Pro and other resources may
exist that have information about the structures in the AOI that you are analyzing.
In the southernmost part of figure 4, a clearly defined roof
is visible, which is recognizable by the roof’s geometry.
From this image, we can also conclude that this is a tall
structure, given the size of the building’s shadow compared
to the shadows of the neighboring structures. Lastly, note
the smaller white features in the image. While they may
resemble tents, they are cars, which is clear because cars
are smaller and more elongated than structures or tents.
Gulu, Uganda is captured in both figure 2, 4, and 5.
In figures 2 and 4 the structures align the streets in
a rather orderly manner, which is different from
the scene of Gulu in figure 5. In this frame
structures are scattered, standalone, and settled
amidst vegetation. This demonstrates how
heterogeneous the landscape of a location can be.
The heterogeneous appearance of the AOIs are
attributed to weather and climate, environmental
surroundings, seasonality, the availability of
(natural) building materials, and are influenced by
the standard materials distributed by aid agencies.
For example, differences in soil composition and
soil moisture of each location would impact the
Figure 4. Long shadow cast by structure. The shadow indicates it is taller than the surrounding structures
Gulu, Uganda
11 Nov 2017
Figure 5. Examples of standalone structures in Gulu, Uganda
Gulu, Uganda
11 Nov 2017
14
appearance of the landscape: dry soils appear bright, while water content in saturated soils give
a dark appearance. Alternatively, small settlements may be composed of structures made of
natural materials given the availability of nearby natural resources while larger settlements may
have access to a variety of construction materials.
Tukuls
The imagery examples given so far have primarily been populated with structures constructed
with metallic material in urban areas. Tukuls are one of the most common structure types found
in rural parts of Central and Eastern Africa. Tukuls are recognizable by their circular, conical
roofs, and overhang shadow (figure 6). However, analysts should be aware that the physical
properties, such as shape and size, of tukuls vary across regions and cultures, whether they are
drastically different or subtle. These differences can be attributed to construction materials
available, preferable construction method, or cultural expectations, among other reasons.
The shape of the tukul will influence its shadow. The shadows cast by the tukuls in figure 7 are
crescent-shaped because of their circular shape. The appearance of the shadows changes
depending on the shape and construction of the tukul, the weather condition, and the time of day
of image capture.
Gulu, Uganda
12 Aug 2016
Figure 6. Tukuls in Uganda as seen from satellite imagery (left) and near ground-level (right)
15
Height and size also play an important role in the identification of tukuls. From a distance,
circular features such as crops or trees may resemble tukuls (figure 8). However, by
examining the surrounding tukuls through visual analysis and by measuring the diameter
of the features in question, the tukuls are easily distinguished from the crops or trees.
Figure 8. Tukuls and crops/trees often are the same shape, but can be differentiated by taking measurements
Palabek, Uganda
11 Feb 2016
Palabek, Uganda
11 Feb 2016
Figure 7. Shadow cast by circular shaped tukuls. Comparison of shadow appearance in satellite imagery (left) and a ground photo (right)
16
Structure Density
Structure density also plays an important
role in structure identification. The
standalone structures in Gulu, Uganda
(figure 5) are far easier to distinguish than
the conjoined structures in other parts of
Gulu, Uganda or in Dolo, Ethiopia (figure 2).
Conjoined structures can make it incredibly
difficult to determine where one structure
ends and the other begins (figure 9).
Further, roofs may also be constructed with
several different (colored) materials,
making a single roof appear disjointed.
Meaning, a single structure can appear to be
2-3 structures because the roof is made up
of multiple materials. Often the only way to
verify the status of conjoined structures is
through ground verification.
At times, structures are encompassed by a boundary. Fenced-in structures are often delineated
using natural materials such as vegetation or wood (figure 3: Umm Dalil, Sudan). Such locations
can contain large structures as well as smaller structures or tukuls.
Change Detection
The analysis of satellite imagery enables analysts to identify the type and characteristics of
structures that exist in a location at a given time. The analysis becomes far more powerful when
a location is analyzed over time to detect changes in a scene. Change detection is a useful
technique for monitoring the increase and/or decrease in the number of structures, the
replacement and/or degradation of construction materials, and/or for understanding additional
dynamic changes that take place in the studies environment. When conducting change detection
analysis, it is important to consider anniversary dates and seasonality. The selection of
anniversary dates--whereby two different dates at approximately the same time of year-- helps
to reduce phenological effects and sun angles (i.e. August 2013 and August 2018).
Figure 9. Conjoined structures
Dolo, Ethiopia
4 Dec 2013
17
Seasonality
When considering the effects of seasonality, we can compare images of Abyei town, Sudan from
February 28th and September 3rd, 2013 (figure 10). The same AOI appears dramatically different
during the dry and rainy season. In February, barren earth is visible while the river to the west is
the only area that is fully vegetated. By September of the same year, the rains have transformed
this location into a very green landscape. Seasonality is important to consider for several reasons:
1) canopy can obscure landscape features that are otherwise visible during the dry season, 2) the
contrast between structures and the surrounding environment changes, 3) there may be visible
changes in human activity such as agriculture
Seasonality may have an impact on the appearance of the landscape, which is why it is critical for
the analyst to understand when and what kind of seasonal and environmental changes the AOI
undergoes.
Figure 10. Seasonal comparison of Abyei Town, Sudan
28 Feb 2013 3 Sept 2013
18
Displacement Camps
Generally stated, displaced population camps are either
formally established by agencies or informally set up by
populations on the move. The International Organization
for Migration (IOM) defines a planned camp as being
“government or aid organizations planned camp,
including infrastructure, to house displaced populations”
while a self-settled camp is defined as being
“independent from government or international
organization support, [these] camps are formed by the
displaced population”.1
Formally established camps are frequently structured in
a gridded manner, adding to their recognizability in
satellite imagery (figure 11). Often in planned camps,
tents, and structures are typically pitched tarps of a
variety of sizes and shapes depending on agency, camp
location, structure use, etc. UN agency tents, such as those
used by UNHCR or OCHA, are often either blue or white
in color, and can be square, rectangular, or even
hexagonal in shape (figure 12).
Using change detection, an imagery series of Zaatari
(figure 13) reveals how a single site undergoes
transformation in approximately five years. The first
image in the series, taken on January 9th, 2011, shows arid
terrain, which in January 13th, 2013 becomes
unrecognizable when it becomes occupied by tents and
other structures. By February 2016, the structures are
more diversified in color and shape, which suggests that
1 International Organization for Migration. (2012). Transitional Shelter Guidelines. Retrieved from Shelter Center: https://www.iom.int/files/live/sites/iom/files/What-We-Do/docs/Transitional-Shelter-Guidelines.pdf
Figure 11. Comparison of refugee camps through
satellite imagery
Halowen refugee camp, Ethiopia
Kakuma refugee camp, Kenya
Zaatari refugee camp, Jordan
19
the structures’ construction materials may have changed between 2013 and 2016. In the final
image in the sequence, captured in September 2016, most of the structures in the camp appear to
be covered by dust, decreasing the contrast between the structures and the background, which
make the individual structures more difficult to detect. This image set shows how many changes
can occur over a short period of time in a small area. The only feature that remains consistent
over the years is the road that cuts through the camp, otherwise the number of features and their
appearance changes.
Figure 12. Examples of UN distributed tents
Sep 2011 Jan 2013
Feb 2016 Sep 2016
Figure 13. Zaatari refugee camp between 2011 and 2016
20
Like the image series of Zaatari, the changes seen in
Kakuma refugee camp in Kenya are documented over a 10-
year period in figure 14. Like in Zaatari, the only features
that remain consistent in the images is the road that cuts
through the image in the north and the river to the east.
Otherwise, the human settlements drastically change over
the years. Between December 2003 and January 2007, the
number of structures decreases, and the “footprints” of the
structures are left behind. At times, the markings on the
ground are as bright as the surrounding structures which
makes it seem that they are still there. In this image
however, it is evident that there are far fewer structures
than in the previous image. By July 2013 there appear to be
many more structures than in the previous years and many
of the settlements are surrounded by fences.
However, displaced communities do not always establish
themselves in designated camp sites. During a
humanitarian emergency and/or in the absence of an
existing camps, individuals may settle in existing
communities, otherwise known as host communities. Host
communities or families, “shelter the displaced population
within their households or on their properties”.2
communities may also choose to host displaced
populations in existing structures, such as schools, which
are known as collective centers.3
In Dikwa, Nigeria, individuals situated in a camp on the
outskirts of the town were directed to relocate by the
2 International Organization for Migration. (2012). Transitional Shelter Guidelines. Retrieved from Shelter Center: https://www.iom.int/files/live/sites/iom/files/What-We-Do/docs/Transitional-Shelter-Guidelines.pdf 3 Ibid
Dec 2003
Jan 2007
July 2013
Figure 14. Kakuma refugee camp between 2003 and 2013
21
Nigerian army due to extensive flooding in the area. Many individuals fled to resettle in the town
of Dikwa, where tents can be seen amidst longer standing structures (figure 15).
Tents can also be observed in imagery of Monguno, Nigeria. Some are sporadically dispersed
throughout the image, but the majority can be observed at the town’s northern periphery (figure
16). In the case of both Monguno and Dikwa, there are many empty spaces in the before scenes
that are later occupied by tents and other structures in the after image. The movement of IDPs
towards these settlements can be likely attributed to ongoing violent activities by Non-State
Armed Groups (NSAGs), driving people to the protection of nearby towns like Monguno and
Dikwa. By integrating information on sociopolitical climate of Borno State though news and NGO
reports with the identification of tents, we can reasonably conclude that these spontaneous
settlements were established by IDPs.
Figure 16. Monguno, Nigeria
Figure 15. Dikwa, Nigeria. In 2018 tents are scattered throughout the AOI, particularly in the west
10 Nov 2016 7 Feb 2018
Jan 2015 Mar 2018
22
Impact of Conflict
Conflict events can also have lasting impacts on landscapes. Remote sensing tools enables
analysts to identify and monitor any visible structural damage from afar, whether structures are
partially or entirely destroyed. As in any context, it is essential to establish a comprehensive
understanding of the events that have taken place on the ground to get a better understanding of
damage structures sustain. Incendiary, explosive, fire, and combustible materials used during
conflict can leave specific marks on a structure, thus indicating damage related to the conflict
event. An additional visual identification of structure damage in conflict areas is the roofless
structure without any burn signatures. This suggests that the structure has been dismantled and
the roof was removed by the structures’ inhabitants or the perpetrators themselves.
Fire
Fire is a frequently-used means of inflicting intentional damage to individual tukuls. When a
perpetrator directly approaches, and burns the roof of the structure, the highly-combustible
thatched roof falls within the confines of the tukul. This leaves behind a distinctive charred ring
around the perimeter of the tukul from falling ash, to a dark, ashen center with white ash of
burned material within the tukul confines leaving behind an identifiable signature in the imagery
(figures 17 and 18).
26 May 2011
Figure 17. Burned tukuls in Abyei, Sudan
23
While analysis of a single image reveals valuable information
about a conflict event, change detection enables the comparison
of features in a landscape before, during, and after conflict
events. For example, on March 25, 2011, a series of intentional
fires were set to many tukuls in the Abyei region in Sudan. The
image sequences extracted from Google Earth Pro shows the
extent of the damage (figures 17 and 18). At times, following the
onset of violence, villages that were previously inhabited become
abandoned and are gradually overgrown by vegetation (figure
19). Frequently the footprints of these structures remain visible
in the imagery long after their presence.
27 Sep 2009
27 Sep 2009
25 May 2011
25 May 2011
Figure 18. Before and after comparison of burned tukuls south Mading Achueng, Sudan
03 Sep 2013
Figure 19. Overgrown part of Abyei in which tukuls were destroyed as shown in figure 17
24
When conducting conflict analysis, imagery collection date is a critical consideration. Consider
that an attack has occurred: in this instance, it is more valuable to have a before and after image,
but most importantly, an image acquired immediately after the event is critical to ensure that
artifacts of damage or deliberate fire are still visible and/or present in the image.
Finally, when analyzing an AOI in a conflict area, it is important to differentiate the signatures left
behind from an intentionally-burned structure and from an accidental fire. Phenomena such as
individually burned structures with an absence of scorched earth between those structures are
consistent with acts of intentional burning or destruction. The support behind this assertion, is
formed by examining the patterns and marks left by accidental fires in imagery. Areas
experiencing seasonal dry periods produce an abundance of desiccated vegetation and given the
right conditions, will catch fire. As the fire spreads, it consumes any combustible material in its
path, both on the ground and, on the structures themselves. In this scenario, we expect a
continuous burn pattern present in the satellite image: burned tukuls and scorched earth in-
between. Therefore, we can draw a reasonable conclusion that when individual tukuls are burned
and the earth between remains undamaged, it is likely the result of intentional burning. (It should
be noted that widespread fire can also be ignited intentionally as an act of arson.)
Dismantled Structures
Beyond structures being damaged by means of fire
or ordnance, another visual identification of
structure damage in conflict areas is the roofless
structure without burn signatures (figures 20 and
21). One of the major reasons for this is that if the
roofs are constructed from metal materials, such
as zinc, that are valuable materials to both the
inhabitants of the structure, and possibly, the
actors initiating the attack. So, when the
structures’ inhabitants flee their homes, they may
take these materials with them. If they are unable
to do so, the attackers could take the roofing for
their own benefit.
Figure 20. Dismantled structures among intact structures in Abadan, Nigeria. Room partition can be seen in roofless structures
25
Dismantled structures are not unique to conflict. In natural disasters, there are similar signatures
to recognize in the analysis of damaged and dismantled structures as mentioned above, as well
as some variance. One of the most often identified forms of structure damage during a wind
disaster is the roofless structure as described in in Signal’s imagery interpretation guide on
Assessing Wind Damage to Structures.
In either scenario, while the roof may no longer be atop the structure, the walls and room
partitions within the perimeter of the structure typically stay intact and are captured by satellite
sensors (figure 21 and 22).
17 Dec 2011 27 Sep 2018
Figure 21. Dismantled structures in Rann, Nigeria
Figure 22. Ground view of dismantled structures in Rann, Nigeria
26
LIMITATIONS
While satellite imagery generally enables analysts to conduct frequent and rapid analysis
assessments in (non-permissive) environments, there are several factors that may disadvantage
the analyst.
Weather conditions
When exploring satellite imagery, analysts often rely on satisfactory weather conditions to
capture features on the ground. Therefore, clear skies are ideal for analysis. Haze, cloud cover,
and cloud shadow often diminish the visibility of the scene by affecting the image clarity and
quality by partially or completely obscuring parts of the AOI (figures 23 and 24). The presence of
clouds is of course, attributed by geographic location, proximity to water bodies, frequency of
storms, and/or seasonal cycles. While there are mathematical models to minimize the effects of
clouds, they frequently remain a problematic. Depending on the angle of image capture, cloud
shadows may also be visible. Areas that fall within the cloud shadow are obscured, reducing
contrast and affecting appearance of features.
Figure 24. Cloud cover over Gulu, Uganda obscures most of the area (left)
24 Dec 2013 18 Mar 2012
Figure 23. Haze over Rann, Nigeria reduces image contrast diminish feature visibility
17 Dec 2011 18 Mar 2012
27
Feature contrast
While haze reduces the visibility of the entire scene, there are additional factors that reduce the
contrast between the feature of interest and its surroundings possibly affecting the process of
feature identification. If for example, structures are constructed with natural materials obtained
from its immediate surroundings, the structure will likely appear to be the same color and/or
texture as what is around it, therefore blending into the surroundings.
Also, structures that initially stand out, such as white tents, can weather over time, resulting in
the structures being covered with dirt and sand. An extreme case of this is found in Zataari
refugee camp (figure 25). In this image, structures that were originally white are covered by sand
or earth that was moved by heavy winds, leaving them difficult to differentiate from the
surrounding grounds.
In a somewhat different scenario,
structures that have been in a
certain position for an extended
period can leave a marking or
‘footprint’ in the earth when it is
moved (figure 26). The appearance
of these phenomena can closely
resemble tents covered in earth,
leading to further confusion for the
analyst.
Figure 26. Structure 'footprints' can be seen after structure removal
26 Dec 2003 13 Jan 2007
Figure 25. Structures in Zataari refugee camp before and after a dust storm
18 Feb 2017 23 Sep 2017
28
Technical considerations
When conducting change detection, analysts rely on the proper alignment, or co-registration, of
the images. When datasets are co-registered, analysts can toggle back and forth and perform
mathematical operations on the image set knowing that the pixels are aligned and that change is
accurately detected. However, poor image registration affects the alignment of the images and
can lead to misinterpretation. Co-registration issues could be affected by several factors,
including but not limited to, imagery being captured at different angles, different times, or even
by different sensors.
In addition to co-registration, pixel resolution also greatly impacts the ability to perform visual
analysis. The imagery analysis described in this guide relies on sub-meter resolution, which
allows for the identification of individual structures and possibly of animals or large groups of
people. At times however, a single pixel captures multiple features, so it becomes difficult to
distinguish the feature.
29
CONCLUSIONS
Image interpretation of VHR imagery involves the analysis of large and heterogeneous
landscapes, occupied by structures of different shapes, sizes, and materials. While the appearance
of features will vary across locations, this guide introduces analysts to commonly identified
structures in Sub-Saharan Africa. The examples given demonstrate methods to categorize
structures based on their characteristics in various circumstances and illustrate how change
detection can be used to monitor the evolution of a location over time.
Imagery cannot determine the overall analytical findings and it is not recommended to draw
conclusions from analyzing imagery on its own. An essential part of making interpretations from
satellite imagery is background research. Building a contextual understanding of the AOIs
surroundings, the geography, the history, as well as the cultural and socio-political climate
influences what information is derived from the analysis. To this effect, it is important to
remember that the successful interpretation of VHR imagery is made possible through imagery
analysis and contextual research.
In conclusion, imagery interpretation is an ever-evolving skill. Landscapes are dynamic and
change in them may introduce unfamiliar or unusual features. These unfamiliar features should
prompt further investigation about a location and its context. This guide serves as a foundation
to support your analytical exploration and encourages further discovery using existing resources.
With time and practice, your structure recognition portfolio will expand as you become familiar
with a wider array of features in settlements and humanitarian emergencies.
30
References
Abdou, Bannari & Morin, D & Bonn, F & Huete, Alfredo. (1996). A review of vegetation indices.
Remote Sensing Reviews. 13. 95-120. 10.1080/02757259509532298.
Al Achkar, Ziad & Baker, Isaac L., & Raymond, Nathaniel A. (2016). Imagery Interpretation Guide:
Assessing Wind Disaster Damage to Structures. Retrieved from the Harvard Humanitarian
Initiative.
Humanitarian Data Exchange. Retrieved from https://data.humdata.org/.
“Image Enhancement”. Canada Center for Remote Sensing. Natural Resources Canada.
International Organization for Migration. (2012). Transitional Shelter Guidelines. Retrieved
from Shelter Center.
Open Street Map. Retrieved from https://www.openstreetmap.org/#map=4/38.01/-95.84.
WorldPop. Retrieved from http://www.worldpop.org.uk/.
![Satellite Imagery Product Specificationslps16.esa.int/posterfiles/paper1213/[RD16]_RE_Product... · 2016-04-22 · Satellite Imagery Product Specifications 6 2 RAPIDEYE SATELLITE](https://img.pdfslide.net/doc/110x75/5eba16697328255ddd5746a8/satellite-imagery-product-rd16reproduct-2016-04-22-satellite-imagery-product.jpg)
