STEP M: Space to Effectively Prepare for Migration

EarthzineOriginal, Technology

Countries can follow STEP M in parallel, which will provide advice on how to obtain, integrate and operate space data within their adaptation plan for climate change and possible migration.


Climate change will increasingly impact the Earth’s natural environment in many ways, such as more frequent natural disasters, heat waves, melting polar ice, and rising sea levels. Many of the anticipated effects of climate change also will become driving factors of human migration over short and long timescales. Climate change driven migration (CCDM) results from complex interactions between several possible push and pull factors, but with climate change acting as a primary motivator for migration. It has been predicted that climate change will force 200 million people to migrate by 2050 (IOM, 2012).

Although climate change and migration have been thoroughly investigated individually, little academic consideration has been given to the intersection of these two phenomena. Unfortunately, the current understanding of the relationship between climate change and migration is far from complete and modeling a link between climate change and migration has proven to be difficult. Even less research has been conducted to investigate the effective use of space technologies, such as remote sensing and satellite communications, to address issues arising from CCDM. Although the utility of remote sensing for predicting and monitoring climate change is well known, no comprehensive study exists on its utility as a tool for planning adaptation measures to migration caused by climate change.

The United Nations Framework Convention on Climate Change (UNFCCC) recognized that strategic adaptation to climate change may be a necessary risk-management strategy, and has created a process for the UN‰ÛÔdesignated Least Developed Countries (LDCs) to submit National Adaptation Plans (NAPs) (UNFCCC, 2011). These plans must address national vulnerabilities to climate change through the development of adaptation strategies. NAPs, to date, have not fully considered migration as a major consequence of climate change, or as a strategy to adapt to it.

What is STEP M?

åÊEighteen Master of Science students at the International Space University (ISU) in France undertook a team project to investigate the utility of applying space technologies to the complex phenomenon of human migration for their team project during 2013-14. After a comprehensive literature review, it was determined that CCDM is likely to have the most impact on lesser-developed countries. These countries will be confronted by the effects of climate change and the drivers of migration over large areas. Current terrestrial and aerial techniques of acquiring climate change and migration data have significant limitations. Unlike these methods, space assets can provide a majority of the data necessary for countries to make informed decisions regarding CCDM.

Unfortunately, the nations that will suffer the most from the effects of climate change are also the least likely to be able to acquire the necessary data to predict, mitigate and manage them. STEP M was designed to inform the LDCs of the value of this data, provide a path to acquisition and use of the data, and advise the LDCs of available support in these efforts. The STEP M process can be followed by any LDC that intends to implement a UNFCCC NAP for CCDM. It is an interdisciplinary approach for integrating space-based data into each step of the UNFCCC NAP process (Figure 1) when it is applied to CCDM.

Figure 1. The UNFCCC NAP Process

Figure 1. The UNFCCC NAP Process


Large numbers of people are displaced each year by extreme weather events and such trends are predicted to become more common as the global climate continues to shift. To address these challenges, the use of technology becomes increasingly important. The global scale of CCDM requires information on an equally large scale. The only tools that can gather data on such a scale are satellites, which will be crucial to providing the information necessary to efficiently address CCDM. By implementing STEP M, LDCs can gain access to the necessary data and the ability to refine that data into the information necessary to make key adaptation decisions.

Satellites can provide key information that is crucial to understanding both climate change and migration, such as comprehensive mapping of available natural resources. Furthermore, space data can drive analysis methods, such as predictive climate models, that can better inform policymakers and local populations.

Research on how climate change model outputs can be used as inputs for migration models is still at an early stage. Economic, political and social influences, together with climate change and migration, have shaped local and international communities for centuries. Predicting migration by using models based on a wide variety of sociological, economic, environmental and political assumptions can be complicated, as the list of input variables grows with the number of migration push/pull factors. TableåÊ1 lists some issues that drive migration, and associates them with related climate variables; these key climate variables can all be measured with space assets, indicating the utility of this data in migration modeling.

 Table 1. Linking Migration Issues to Climate Variables (adapted from Ansdell, 2009)

The use of these variables provides some indication of the climactic drivers of migration, but a comprehensive understanding of migration over the long-term also requires defining relationships between climate variables and independent factors such as income (Perch and Nielsen, 2004). In general, understanding CCDM requires understanding the socioeconomic and cultural factors that also play a large role. The process of how space assets can be utilized to address CCDM is illustrated in Figure 2. While GNSS and communication satellites are important tools, their utility is general and there is no utility in defining their role here.

Figure 2. Proposed Data Analysis Process

Figure 2. Proposed Data Analysis Process

Through proper associations and analysis, remote sensing data can be refined into the information necessary for policymakers to craft effective adaptation responses. These responses could predict the need for migration and provide a longer lead time; as a result, populations could strategically retreat from climate change effects instead of being routed by them. Extra time could also allow governments to implement mitigation strategies that would reduce or eliminate the necessity for migration. But in the worst case, space assets could be used to effectively manage migration as it occurs.

LDCs can follow STEP M to achieve the potential benefits of using satellite data without developing their own space assets. STEP M discusses how this can be achieved, along with potential difficulties and risks that may be encountered, and mitigated thereafter. These countries may also not be familiar with the best methods to manage space programs or to navigate the international legal environment. STEP M illustrates these issues along with potential solutions. Furthermore, STEP M allows LDCs to take advantage of UNFCCC aid mechanisms by working in parallel with the NAP process.

How does STEP M fit in with UNFCCC NAPs?

The UNFCCC has provided detailed guidance on how an LDC can develop a NAP, and a simplified version of this process is represented graphically in Figure 1. This process is generalized to help LDCs generate adaptation plans to climate change. When an LDC is creating a NAP that addresses migration due to climate change, they can follow STEP M in parallel, which will provide advice on how to obtain, integrate and operate space data within their adaptation plan (Figure 2). Because most LDCs don’t currently own the requisite satellites, STEP M also details how the LDCs can use the UNFCCC aid mechanisms of capacity building, technology transfer and funding to acquire data, processing capacity and the development of technical expertise for analysis of the acquired data. A high-level representation of STEP M is shown in Figure 3. A case study was performed as a preliminary validation case for STEP M to illustrate how it operates in parallel with each step of the NAP development process.


The Case Study of Bangladesh


Figure 3. STEP M’s role in UNFCCC NAP process

Bangladesh was chosen as the subject of a validation case study for the STEP M process, based on the following criteria: the probability of CCDM, population density, vulnerability to climate change, and GDP per capita. Bangladesh has been severely ravaged by frequent natural disasters, including floods, droughts, soil erosion, sea level rise and cyclones. Being a low-lying country, with an average height above sea level of 12 meters, it is particularly vulnerable to hydraulic threats. Natural disasters have increasingly threatened population centers and sources economic activity in Bangladesh. This trend is projected to continue and increase in severity. Current estimates suggest that there are currently more than 7 million Bangladeshis living abroad, with 3.3 million migrants living in India alone (Marshall and Rehman, 2013).

Figure 4. Urban population per size class of Bangladesh in 2011 (UN Population Division/DESA, 2012)

Figure 4. Urban population per size class of Bangladesh in 2011 (UN Population Division/DESA, 2012)

Bangladesh experiences the effects of climate change in literally every portion of the country. The validation case was applied to the country as a whole. Of the many regions, the coastal region is one of the most affected. The Bangladesh coastline is home to the Sundarbans, the largest mangrove forest in the world. As a protective barrier of the shoreline, mangroves serve as key bio-shields of coastal communities. There is now concern that these bio-shields will degrade due to frequent and repeated exposure to extreme climate events (Giri et al., 2008). The people living along the coastline are also affected by sea level rise, which is expected to reduce the costal land mass by 15 percent (Figure 5). Increased soil salinity (FigureåÊ6) is also a significant issue, by reducing reduce the amount of available arable land. . Sea level rise and increased salinity are affecting the groundwater and limiting its consumption for irrigation and potability. A primary concern for coastal communities is access to freshwater. Increased waterlogging and salinity could reduce arable land by an estimated 40 percent by the end of the 21st century (CIA, 2013).

Figure 5. Potential impact of a 1.5m sea-level rise  (UNEP/GRID, 1989)

Figure 5. Potential impact of a 1.5m sea-level rise
(UNEP/GRID, 1989)

Figure 6. Estimated impact of different levels of salinity ingress (Siddique et al., 2011)

Figure 6. Estimated impact of different levels of salinity ingress (Siddique et al., 2011)







Optical and radar satellite imagery from Landsat,åÊSPOT, and Indian Remote Sensing satellites (IRS) have been used to map and characterize existing mangrove forests. A method for studying mangrove density involves analysis of erosion and sea level rise data over an extended period of time. This can be integrated with weather models that predict storm surges so that change can be modeled and predicted. Furthermore, changes in salinity due to increased water temperature and growth of aquatic species must be monitored since this will greatly impact mangrove survival (Barbier, 2006; Wieland, 2005). Satellite imagery is also useful to assess areas for potential mangrove planting. Parameters such as humidity, nutrient levels, soil moisture, and soil type need to be monitored as optimal conditions for root development, anchorage, and stabilization (Karkhanis, n.d). Digital elevation models indicating areas most vulnerable to sea-level rise (for topographic reasons, improper drainage, etc.) would direct the planting efforts of governmental programs and local communities.

Figure 7 shows the radar analysis of different stages of mangrove growth. The Bangladesh space agency, SPARRSO, currently lacks data on the extent to how much land is affected by salinity (BINA, 2013). Multi-spectral optical and microwave remote sensing techniques can be used to quantify the area affected by salt, which has a different surface reflectance than non-affected areas (Metternicht and Zinck, 2003).

Figure 7. (a) Radar analysis of different stages of mangrove growth (Kuenzer et al., 2011)

Figure 7. (a) Radar analysis of different stages of mangrove growth (Kuenzer et al., 2011)

Figure 7(b) Surface reflectance of soil crusts affected by salt (Metternicht et al., 2003)

Figure 7(b) Surface reflectance of soil crusts affected by salt (Metternicht et al., 2003)

Remote sensing data also can be used to address the issues of freshwater availability. Both SAR and optical remote sensing can easily determine locations of bodies of water. Effective ground-water management can arise from using IRS Linear Imaging Self Scanner and Landsat Thematic Mapper, in combination with radar interferometry and LIDAR altimetry for topographic modeling. On an experimental level, NASA’s Gravity Recovery and Climate Experiment mission was even able to monitor the movement and storage of ground water (NASA, 2014; Kresic and Smith, 2013). The deep aquifers that exist in the Bangladesh basin can potentially serve as a freshwater source for several decades to come, if the government begins infrastructure development to do so (Ravenscroft et al., 2013).

Another significant driver of migration is the increased occurrence of flooding in low-lying areas of Bangladesh. Climate change also has increased the flow rates of Bangladesh’s rivers through upstream melting and runoff from the Himalayas (higher overall temperature) as well as increased rainfall during the year. This has led to riverbank erosion and a constantly changing flow pattern (Dhar, n.d.). Figure 8 shows flood-prone areas in Bangladesh.

Figure 8. Map showing Flooding Areas in Bangladesh (left; Mahmood, 2012) and NOAA AVHRR and RADARSAT images of flooding in Bangladesh (right; Dhar, n.d)

Figure 8. Map showing Flooding Areas in Bangladesh (left; Mahmood, 2012) and NOAA AVHRR and RADARSAT images of flooding in Bangladesh (right; Dhar, n.d)

Currently, space systems are employed to help mitigate the effects of river erosion in Bangladesh, including the use of GNSS systems to provide bank line measurements as well as remote sensing data provided by imagery (Landsat, SPOT, GeoEye-1, and other satellites) (Azim Uddin and Basak, n.d.). Such data has been made available to Bangladesh for some time now, the earliest images being made available in 1980. The GNSS systems, such as the GPS, are used to tag specific riverbanks and coastlines with positioning tags. This provides very accurate physical positioning of the terrain and rivers, which can be useful in urban planning and infrastructure maintenance. Areas of high river erosion likelihood can be identified for reinforcement or evacuation of the surrounding peoples (Azim Uddin and Basak, n.d.).

Optical or synthetic aperture radar (SAR) satellite imagery can be used to augment or replace costly ground sensors required to collect GNSS data, although these provide less precision. Data from the Landsat-8 satellite is currently used by Bangladesh to detect riverbank erosion. This sun-synchronous satellite can image in visible, Near IR, and SWIR. These spectral bands are suitable for images of urban planning, agriculture, and forestry applications, among others (USGS, 2013). Satellites are able to distinguish the differences between land and water, as well as buildings near riverbanks. An example of a satellite with SAR payload that Bangladesh and SPARRSO can utilize is RADARSAT-2. Data from optical and SAR satellites can be used to predict areas most affected by riverbank erosion.

The STEP M process includes merging remote-sensing information with population data to allow the government to determine the links between climate change and migration, to predict CCDM, and strategically plan for it. Figure 9 shows climate-change related events overlaid the migration pattern of Bangladesh for 2010; space assets relevant to measuring causes of CCDM also are mentioned.

Figure 9. Examples of CCDM Issues in Bangladesh and the 2010 Migration Pattern

Figure 9. Examples of CCDM Issues in Bangladesh and the 2010 Migration Pattern


The role of space technology in the areas of natural resource management and environmental monitoring has become increasingly important. Providing food security and fresh water are the two important challenges that states face in the event of migration. The stress of climate change on human population movement has further heightened these problems. Using satellites to aid decision-makers in addressing these topics has been recognized and increasingly encouraged by international organizations. Resource management through space includes managing fresh water resources, crop mapping, energy resources management, etc. The UN Program on Space Applications is implementing a ‰ÛÏNatural Resources Management and Environmental Monitoring Program to support developing countries in incorporating space-based solutions for solving environmental monitoring and natural resources management issues‰Û (UNOOSA, 2011). Most space-faring nations presently haveåÊnational agricultural monitoring systems, which traditionally utilizes rainfall data, sample field measurements, agricultural statistics, and agro-meteorological modeling.

Bangladesh, being a member of the Group on Earth Observations (GEO), has access to EO data through GEO, which can be useful for ensuring the health and safety of the migrant populations in the event of natural disasters, as well as for CCDM adaptation on a long-term basis. Table 2 summarizes useful satellites for addressing Bangladesh’s climate change challenges.

Table 2. Examples of Relevant Instruments and Satellites, by Agency (adapted from CEOS, 2014)

Table 2. Examples of Relevant Instruments and Satellites, by Agency (adapted from CEOS, 2014)


One of the predicted impacts of climate change is the degradation of inhabited areas to the point that they become unsustainable for human life. The issue of CCDM has been acknowledged by the international community, but has yet to be critically addressed, assessed and planned for or considered as an adaptive strategy. STEP M is intended for use by any UN-designated LDC that sees potential benefits in using space systems within NAPs that address CCDM. When developing STEP M, attention was given to provide enabling factors such as sources of funding, examples of governance of space-related resources, and resources for LDCs to acquire space-based data for little or no cost. The assistance these organizations provide does not end with data provision; there are examples such as SERVIR that provides capacity building and technology transfer to enable LDCs to acquire, process, and interpret satellite data indigenously.


While the STEP M process is focused on enabling any LDC to use space-resources within a NAP that addresses migration, this may seem intangible without a concrete application; therefore, STEP M is applied to Bangladesh as a validation case study. For Bangladesh, STEP M identified climate change variables with strong linkages to migration and identified relevant measurements for these variables that can be made using satellite remote sensing systems. The information acquired by these satellite systems can have several applications, such as climate change models, informing national or regional decision makers, and aiding the management of CCDM. Space-based capabilities are highly versatile and any LDC should not feel constricted to use space assets only for migration, but also should consider space as a tool to provide insights into other national issues. In this way, development of expertise with space capabilities can apply to several aspects of an LDC’s development and potentially have positive social and economic impacts.

A final report on STEP M is available here. An executive summary also is online.


In addition to the three listed authors, this article reflects the work of the 15 other members of STEP M: Alix Dudley, Andrew Alexander, Patricia Randazzo, Michio Hirai, Pulkit Kanwar, Raul Hernandez, Isaac Llorens, Cristel Devrieze, Wang Xia, Shihai Li, Zhao Xiaofeng, Paul Kelly, Lawal Danzangi, Vatsala Khetawat and Jan Stastna. Furthermore, the authors are grateful for the assistance of faculty advisers: Mr. J. Nakahara and Professor Emeritus John Farrow, as well as other associated members of the ISU faculty. We also would like to extend our gratitude to Dr. B. Ryan (GEO), Dr. V. Singhroy (NRCAN), Dr. J. Arnould (CNES), and Mr. A. Ismail (IPX) for their suggestions and help with this project.


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