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;

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.

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

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 [email protected]

Laure Boudinaud Earth Observation Analyst Analysis and Trends Services World Food Programme [email protected]

Rogerio Bonifacio Head of Geospatial Analysis Unit Analysis and Trends Services World Food Programme [email protected]

Sarah Muir Earth Observation Analyst Analysis and Trends Services World Food Programme [email protected]

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

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

6 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

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). (푁퐼푅 − 푅푒푑) 푁퐷푉퐼 = (푁퐼푅 + 푅푒푑)

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.

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.

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

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

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 11 Nov 2017

Figure 4. Long shadow cast by structure. The shadow Gulu, Uganda is captured in both figure 2, 4, and 5. indicates it is taller than the surrounding structures 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 Gulu, Uganda 11 Nov 2017 the standard materials distributed by aid agencies. For example, differences in soil composition and Figure 5. Examples of standalone structures in Gulu, Uganda soil moisture of each location would impact the

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.

Gulu, Uganda 12 Aug 2016

Figure 6. Tukuls in Uganda as seen from satellite imagery (left) and near ground-level (right)

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.

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)

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.

Palabek, Uganda 11 Feb 2016

Figure 8. Tukuls and crops/trees often are the same shape, but can be differentiated by taking measurements

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, Dolo, Ethiopia making a single roof appear disjointed. 4 Dec 2013 Meaning, a single structure can appear to be 2-3 structures because the roof is made up Figure 9. Conjoined structures 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).

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.

28 Feb 2013 3 Sept 2013

Figure 10. Seasonal comparison of Abyei Town, Sudan

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 Halowen refugee camp, Ethiopia “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 Kakuma refugee camp, Kenya 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

Zaatari refugee camp, Jordan unrecognizable when it becomes occupied by tents and other structures. By February 2016, the structures are Figure 11. Comparison of refugee camps through satellite imagery 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 18

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 19

Like the image series of Zaatari, the changes seen in Dec 2003 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 Jan 2007 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

July 2013 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 Figure 14. Kakuma refugee camp between 2003 and 2013

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

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).

10 Nov 2016 7 Feb 2018

Figure 15. Dikwa, Nigeria. In 2018 tents are scattered throughout the AOI, particularly in the west

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.

Jan 2015 Mar 2018

Figure 16. Monguno, Nigeria 21

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

27 Sep 2009 25 May 2011

Figure 18. Before and after comparison of burned tukuls south Mading Achueng, Sudan

While analysis of a single image reveals valuable information 03 Sep 2013 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 Figure 19. Overgrown part of Abyei in which tukuls were destroyed as shown in figure 17 in the imagery long after their presence. 23

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 Figure 20. Dismantled structures among intact structures in Abadan, Nigeria. Room partition can be seen in roofless their own benefit. structures

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

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.

17 Dec 2011 18 Mar 2012

Figure 23. Haze over Rann, Nigeria reduces image contrast diminish feature visibility

24 Dec 2013 18 Mar 2012

Figure 24. Cloud cover over Gulu, Uganda obscures most of the area (left)

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.

18 Feb 2017 23 Sep 2017

Figure 25. Structures in Zataari refugee camp before and after a dust storm

In a somewhat different scenario, 26 Dec 2003 13 Jan 2007 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, Figure 26. Structure 'footprints' can be seen after structure removal leading to further confusion for the analyst.

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.

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.

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/.