Make cities inclusive, safe, resilient and sustainable
Cities are hubs for ideas, commerce, culture, science, productivity, social development and much more. At their best, cities have enabled people to advance socially and economically. With the number of people living within cities projected to rise to 5 billion people by 2030, it’s important that efficient urban planning and management practices are in place to deal with the challenges brought by urbanization.
Many challenges exist to maintaining cities in a way that continues to create jobs and prosperity without straining land and resources. Common urban challenges include congestion, lack of funds to provide basic services, a shortage of adequate housing, declining infrastructure and rising air pollution within cities.
Rapid urbanization challenges, such as the safe removal and management of solid waste within cities, can be overcome in ways that allow them to continue to thrive and grow, while improving resource use and reducing pollution and poverty. One such example is an increase in municipal waste collection. There needs to be a future in which cities provide opportunities for all, with access to basic services, energy, housing, transportation and more.
Facts and Figures
In 2016, over 64.4% of products exported by the least developed countries to world markets faced zero tariffs, an increase of 20% since 2010.
Evidence from developing countries shows that children in the poorest 20 per cent of the populations are still up to three times more likely to die before their fifth birthday than children in the richest quintiles.
Social protection has been significantly extended globally, yet persons with disabilities are up to five times more likely than average to incur catastrophic health expenditures.
Despite overall declines in maternal mortality in most developing countries, women in rural areas are still up to three times more likely to die while giving birth than women living in urban centers.
Up to 30 per cent of income inequality is due to inequality within households, including between women and men. Women are also more likely than men to live below 50 per cent of the median income
Space-based Technologies for SDG 11
Preventing and managing disasters and enhancing inclusive and sustainable urbanisation are fundamental challenges to our communities. Space technologies are essential for reducing disaster risks and managing disasters and for planning sustainable human settlements. UN-SPIDER facilitates cooperation between providers and users of satellite data and helps developing countries use space-based information. Read more here.
Sawaid Abbas, Assistant Professor at the Centre for Geographical Information System, University of the Punjab, Lahore, Pakistan discussed his extensive work in addressing water-related challenges through the nexus between smart sensing and space technologies. His thematic focus spans water scarcity, food security, climate risks, and environmental monitoring with an emphasis on the Asia-Pacific region, including Pakistan and China. Key Sustainable Development Goals (SDGs) guiding his work include SDG2 (Zero Hunger), SDG13 (Climate Action), SDG15 (Life on Land), and SDG11 (Sustainable Cities and Communities).
Abbas's passion for water emerged during his early career at the World Wide Fund for Nature (WWF), where he was involved in Pakistan’s Wetland Program and witnessed the impact of water on associated ecosystems. This sparked his interest in understanding and managing water, forestry, and wildlife resources. He recently studied coastal ecosystems and their responses to climate and anthropogenic stressors in the Asia-Pacific region. The Living Indus – Investing in Ecological Restoration has become a new focus of interest for him, addressing sustainability challenges related to food security, river basin management, and efficient water use in alignment with the UN Decade of Ocean objectives.
Abbas shared his fascination with water, recognizing its complex and essential nature. He is captivated by its beauty in all forms and acknowledges its fundamental importance for life on Earth. This water connection further motivates his commitment to addressing global water challenges and promoting sustainable water use through innovative solutions.
Sawaid Abbas's work, stimulated by both professional commitment and personal fascination, stresses the critical role of space technologies, particularly earth observation, smart sensing nexus, and artificial intelligence in addressing water-related challenges. His research contributes to the development of innovative solutions for sustainable water use, environmental protection, and disaster response, aligning with global goals for a more resilient and water-secure future.
The following interview with Dr. Sherine Ahmed El Baradei is focusing on water quality and its relation to space technology. Water is the essence of life. Thus preservation of water quality is of a big concern to human health and to fauna and flora in water bodies. The interview explains what is water quality and what are water quality parameters of water bodies. Furthermore, the importance of using space technologies and applications in contributing to water quality monitoring and determination of hydraulic and hydrologic conditions is thoroughly discussed. For example, temporal resolution of satellites and their role in obtaining accurate imaging and data is clarified and the satellites concerned with water quality monitoring are pointed out. Considering the important role of groundwater in arid regions, the use of GRACE Mission data in Egypt is mentioned. Moreover, key influences on water quality in Egypt are discussed and the relation of water quality to water scarcity in the country and ways to preserve water quality is being discussed. Furthermore, the potential of space-based monitoring used to address water issues from hydrological to water resources issues in the country or region is pointed out. The challenges of the use of space technology for hydrology and water-related topics in the MENA region is also discussed. Light is shed on the project done by NASA to recycle astronauts’ waste into energy and power. Sustainability is of a great importance to or communities, and thus it is discussed how sustainable it is to build cities in the desert, or to divert water to where people are instead of moving people to existing water sources. Finally, a discussion about ways we can employ to improve awareness and capacity building on the use of space technology for water and challenges in this field are discussed.
Please describe how your professional (and/or personal) experience relates to space technologies and their applications to water resources management.
I am an expert in hydroinformatics, mainly involved in research projects and research supervision of MSc and PhD students. My research focusses on physically based models for inland waters (rivers and lakes). One of the major fields where modelling is used in water resources is flooding. In order to have adequate representation of floods, most models require large amounts of data, both for model building and model usage.
How do you personally and professionally relate to water?
Growing up in Israel, water scarcity was a constant backdrop to my childhood. The arid climate and frequent droughts shaped my relationship with water from an early age. One vivid memory that remains stamped in my mind is the series of TV campaigns highlighting the importance of water conservation. I recall sitting in front of the television, concerned by the urgency conveyed in those campaigns. The images of dry landscapes and the emphasis on every drop of water as precious left a lasting impression.
Advancements in multi-hazard modelling are rapidly reshaping how we anticipate and respond to complex disaster scenarios. Dr. van den Bout underscores that while cutting-edge innovations have expanded our understanding of cascading impacts—from earthquakes triggering tsunamis to storms igniting landslides—persistent model uncertainties and patchy data threaten the reliability of truly integrated systems. He argues that only through close collaboration—melding the strengths of researchers, forecasters, and local experts—can we build the operational, resilient tools communities need. Capturing data during rare, destructive events remains a formidable hurdle, but embracing both foundational research and unconventional, “out-of-the-box” approaches will be vital to surmount these obstacles.
Bringing multi-hazard disaster management from theory to practice hinges on precise model calibration, something that often demands boots-on-the-ground expertise and tailored field studies. Space-borne technologies—satellite imagery for landscape mapping and retrospective event analysis—play a growing role in refining water-related hazard forecasts, yet they must be complemented by detailed regional insights and rich observational datasets. For those eager to dive into flood modelling, online courses and math communicators offer accessible entry points. Beyond his technical pursuits, Dr. van den Bout credits a lifelong love of programming and video games for inspiring creative experimentation, reminding us that true innovation flourishes when we carve out time for curiosity—whether swimming in his favorite liquid state of water with family or scouring the internet for fresh data.
Джакарта, «тонущий город», является нынешней столицей Индонезии. Расположенный на берегу Яванского моря, этот прибрежный город является домом для почти 30 миллионов человек в районе Большой Джакарты. Джакарта десятилетиями боролась с проблемами управления водными ресурсами, что привело к некоторым нынешним кризисам, связанным с водой. Доступ к надежному питьевому водоснабжению крайне ограничен, поскольку существует значительная разница между теми, кто имеет доступ к водопроводной воде, и теми, кто его не имеет. Граждане, не имеющие доступа к водопроводной воде, в значительной степени зависят от грунтовых вод и в результате вырыли тысячи нерегулируемых колодцев. Это привело ко второму водному кризису: постоянному чрезмерному извлечению водоносных горизонтов Джакарты. Проседание грунта вызывает наибольшую обеспокоенность, поскольку этот тонущий город подвергается высокому риску наводнений со стороны окружающего океана. В результате примерно 40 процентов территории Джакарты в настоящее время находится ниже уровня моря, и прогностические модели предполагают, что к 2050 году весь город окажется под водой (Gilmartin, 2019). Эти проблемы усугубляются тем, что климатический кризис привел к значительному повышению уровня моря, поскольку ледники и ледяные шапки продолжают таять (Intergovernmental Panel on Climate Change, 2019; Lindsey, 2022). В связи с тем, что город Джакарта продолжает опускаться, а уровень моря повышается, миллионы жителей Джакарты подвергаются чрезвычайно высокому риску наводнений, особенно в сезон муссонов (рис. 1). Тысячи жителей уже были вынуждены покинуть свои дома в поисках улучшенных условий и возвышенностей (Garschagen et al., 2018).
L’affaissement de terrain est un phénomène mondial et se définit comme :
"Un tassement progressif ou un affaissement soudain de la surface de la Terre dû à l'enlèvement ou au déplacement de matériaux terrestres souterrains" - National Oceanic and Atmospheric Administration (2021)
Recently, in July 2021, destructive and deadly floods occurred in Western Europe. The estimated insured losses only in Germany could approach 5 billion Euros (AIR Worldwide, 2021). However, the total amount of the damage is currently not foreseeable due to the variety and complexity of the damage patterns and the unbelievable extent of the disaster. It seems the socio-economic losses will dramatically increase and break a new record in the insurance industry after evaluating the complete record of damages’ reports (see Figure 1).
Short summary:
Digital twin (DT) technology for water systems is currently blooming. How are DT applied in water systems and why did they become so popular? In this article, the framework of DT and crucial technologies to build them such as space-based satellites, modern communication technologies, artificial intelligence, etc. are revealed to present how DT functionality is implemented. Application scenarios of DT from global to regional are shown with typical examples for modeling the global water cycle, regional floods, and urban water supply systems. Though DT offers a valuable solution in the context of water systems, attention needs to be given to accuracy, interoperability and data security of DT. DT can be smart systems, helping in comprehensive analysis to support decision making.
When you think about agriculture, you probably imagine a few basic things in your mind. Huge stretches of flat land, massive harvesting machines, the heat on your skin from sunlight and, perhaps most importantly, soil. This image in your mind is a common one. Humans have been tilling, seeding, and farming land since the dawn of civilization, and modern industrial farm techniques tend to dominate our conception of agriculture.
As population becomes larger the demand for water soars, including water needed for domestic, industrial and municipal uses (Mogelgaard 2011). One example of that, is India, where on 20 June 2019 the city of Chennai almost run out of water. Satellite images show the extent of the water shortage in the city (figure 1). While people are queuing up to get water from water trucks that transfer water to the city, the greatest struggle is taking place in the city’s municipal buildings and businesses. Hospitals are facing the threat of not having enough water to treat patients and to clean equipment, and businesses are forced to shut down and wait until the crisis is over.
What does your morning routine look like? For most readers I’d assume you use the toilet, wash your hands, and maybe take a shower. However, do you ever stop to consider the water you use to shower, or the soap you use to wash your hands? Often, especially in developed countries, these things are taken for granted, rightly considering access to adequate water, sanitation, and hygiene (WASH) as basic Human Rights (Figure 1).
Digital twin technology is increasingly being used to simulate the effects of sea level rise, providing valuable tools for decision-makers in areas such as urban planning, coastal management, and disaster preparedness. These virtual models integrate real-time data from various sources, including geospatial imagery, AI, and environmental monitoring systems, to create detailed simulations of how rising sea levels could impact specific regions.
Merci à Martin Sarret d'avoir traduit cet article volontairement.
Les caractéristiques élémentaires de l´agriculture nous viennent tous assez facilement à l´esprit. De larges étendues de terrain, d'imposantes machines de récolte, la chaleur du soleil sur la peau et, peut-être le plus important, la terre. Cette image mentale est finalement assez logique. L´humanité laboure, ensemence et cultive la terre depuis la nuit des temps, et les techniques agricoles industrielles modernes ont tendance à s'accaparer notre imaginaire sur l'agriculture.
As population becomes larger the demand for water soars, including water needed for domestic, industrial and municipal uses (Mogelgaard 2011). One example of that, is India, where on 20 June 2019 the city of Chennai almost run out of water. Satellite images show the extent of the water shortage in the city (figure 1). While people are queuing up to get water from water trucks that transfer water to the city, the greatest struggle is taking place in the city’s municipal buildings and businesses. Hospitals are facing the threat of not having enough water to treat patients and to clean equipment, and businesses are forced to shut down and wait until the crisis is over.
À quoi ressemble votre routine matinale ? Pour la plupart des lecteurs, je suppose que vous utilisez les toilettes, vous vous lavez les mains et peut-être que vous prenez une douche. Cependant, vous arrive-t-il de vous arrêter pour réfléchir à l'eau que vous utilisez sous la douche ou au savon que vous utilisez pour vous laver les mains ?
Flooding poses significant environmental, social and economic challenges globally. With ever-increasing, weather extremes induced by climate change, flooding becomes frequent and severe, particularly in coastal regions like Matuga state in Kenya. Therefore, this study assesses flood risk and its spatial distribution focusing on the interplay between land use land cover, elevation, slope, soil type and rainfall. Using remote sensing data and GIS techniques, a flood risk map for Matuga was generated to identify vulnerable zones. The result signifies that poorly vegetated areas combined with steep topography and high rainfall intensity are key contributors to flooding. Conversely, areas dominated by Ferralic Arenosols and Dystric Arenosols coupled with low slope and extensive shrub cover exhibit lower flood risks. The findings of this study provide critical insights for policymakers, urban planners and environmental managers in designing sustainable flood mitigation strategies. This study underscores the importance of integrating sustainable land management and land use planning in flood risk management for climate-resilient development in Matuga, Kenya.
Plus la population augmente, plus la demande en eau augmente, notamment l'eau nécessaire aux usages domestiques, industriels et municipaux (Mogelgaard 2011). L'Inde en est un bon exemple : le 20 juin 2019, la ville de Chennai a failli manquer d'eau. Des images satellites ont montré l'ampleur de la pénurie d'eau dans la ville (schéma 1). Alors que les habitants faisaient la queue pour de l'eau stockée dans des camions-citernes qui la rendaient disponible dans la ville, le véritable défi de gestion concernait les bâtiments municipaux et les entreprises de la ville. La pénurie d´eau a gravement affecté la capacité des hôpitaux à soigner les patients et à nettoyer les équipements, et a contraint les entreprises à fermer leurs portes jusqu'à la fin de la crise.
The impacts of climate change are ever more apparent. The frequency and scale of devastation and destruction of weather hazards are on an increasing trend. According to the latest Intergovernmental Panel on Climate Change Report (IPCC, 2021) climate change is intensifying the water cycle. This will cause more intense droughts in many regions. Moreover, water-related extremes impact the quality of life disproportionately strong. Drought accounts for 25% of all losses from weather-related disasters in the United States of America (Hayes et al., 2012).
Jakarta, “the sinking city”, is the current capital city of Indonesia. Located on the Java Sea, this coastal city is home to nearly 30 million people within the greater-Jakarta area. Jakarta has grappled with water management issues for decades, leading to several current day water-related crises. Access to a reliable, potable water supply is extremely limited as there is a significant disparity between those with piped water access and those without. Citizens without piped water access have consequently relied heavily on groundwater and have dug thousands of unregulated wells as a result. This has led to a second water crisis – the chronic overextraction of Jakarta’s underground aquifers. Land subsidence is of the utmost concern as this sinking city is placed at high flood risk from the surrounding ocean. Approximately 40% of Jakarta now lies below sea level as a result and predictive models suggest that the entire city will be underwater by 2050 (Gilmartin, 2019). Compounding these problems, the climate crisis has led to significant sea level rise as glaciers and ice caps continue to melt (Intergovernmental Panel on Climate Change, 2019; Lindsey, 2022). As the city of Jakarta continues to sink and sea levels rise, millions of citizens within Jakarta are at extremely high risk of flooding, particularly during monsoon season. Thousands of residents have already been forced to abandon their homes in search of improved conditions and higher ground (Garschagen et al., 2018).
Land subsidence is a global phenomenon and is defined as:
“a gradual settling or sudden sinking of the Earth's surface due to removal or displacement of subsurface earth materials” - National Oceanic and Atmospheric Administration (2021)
Joshua is a Master’s student in Tropical Hydrogeology and Environmental Engineering at Technische Universität of Darmstadt. His interest is focused on hydrogeological processes, groundwater modelling, application of remote sensing and GIS in environmental studies, water management and climate change. He also works as a graduate Intern at AgriWatch BV, a company that applies geospatial solutions for precision Agriculture. As a graduate intern, he applies his interdisciplinary knowledge in developing smart-farming solutions using space-based technologies to farmers in the Twente region of the Netherlands. He deploys satellite imagery, field studies and machine learning algorithms to predict the effect of climate change on arable crops. He also utilizes precipitation data to predict rainfall events to aid farmers in determining planting and harvesting periods.
Joshua earned a bachelor’s degree in Geological Sciences, his bachelor’s thesis research aimed at carrying out paleoenvironmental reconstruction using paleocurrent indicators of water flow and direction, and application of ArcGIS to produce maps. Currently, he is working on his master’s thesis with emphasis on the impact of the ancient climate on the paleoenvironment particularly on vegetation, where he tries to research plants response to long-term greenhouse periods and short-term warming events on various timescales throughout Earth's history.
His research interests revolve around the application of space technologies in providing solutions and tackling climate change.
The following interview with Dr. Sherine Ahmed El Baradei is focusing on water quality and its relation to space technology. Water is the essence of life. Thus preservation of water quality is of a big concern to human health and to fauna and flora in water bodies. The interview explains what is water quality and what are water quality parameters of water bodies. Furthermore, the importance of using space technologies and applications in contributing to water quality monitoring and determination of hydraulic and hydrologic conditions is thoroughly discussed. For example, temporal resolution of satellites and their role in obtaining accurate imaging and data is clarified and the satellites concerned with water quality monitoring are pointed out. Considering the important role of groundwater in arid regions, the use of GRACE Mission data in Egypt is mentioned. Moreover, key influences on water quality in Egypt are discussed and the relation of water quality to water scarcity in the country and ways to preserve water quality is being discussed. Furthermore, the potential of space-based monitoring used to address water issues from hydrological to water resources issues in the country or region is pointed out. The challenges of the use of space technology for hydrology and water-related topics in the MENA region is also discussed. Light is shed on the project done by NASA to recycle astronauts’ waste into energy and power. Sustainability is of a great importance to or communities, and thus it is discussed how sustainable it is to build cities in the desert, or to divert water to where people are instead of moving people to existing water sources. Finally, a discussion about ways we can employ to improve awareness and capacity building on the use of space technology for water and challenges in this field are discussed.
Short description of community and hydrogeology of the area
Yucatan is located in the southeast portion of Mexico. The total area of Yucatan is 124, 409 km2 and the population (by 2018) was ca. 2.1 million inhabitants. The landscape of the area is defined by a highly permeable karstic soil, a notable absence of rivers or permanent freshwater resources in the surface, and a high number of natural wells or sinkholes (locally called cenotes, from the Maya word t´sonot).
Please describe how your professional (and/or personal) experience relates to space technologies and their applications to water resources management.
I am an expert in hydroinformatics, mainly involved in research projects and research supervision of MSc and PhD students. My research focusses on physically based models for inland waters (rivers and lakes). One of the major fields where modelling is used in water resources is flooding. In order to have adequate representation of floods, most models require large amounts of data, both for model building and model usage.
How do you personally and professionally relate to water and/or space technologies?
Water and space technologies are deeply intertwined with my research focus and professional journey. My work primarily revolves around studying the impacts of climate change and human activities on ecosystems, particularly in mountainous regions like the Alps. Water is a crucial component in this context, as it plays a significant role in both vegetation dynamics and ecosystem health.
How do you personally and professionally relate to water?
Growing up in Israel, water scarcity was a constant backdrop to my childhood. The arid climate and frequent droughts shaped my relationship with water from an early age. One vivid memory that remains stamped in my mind is the series of TV campaigns highlighting the importance of water conservation. I recall sitting in front of the television, concerned by the urgency conveyed in those campaigns. The images of dry landscapes and the emphasis on every drop of water as precious left a lasting impression.
Advancements in multi-hazard modelling are rapidly reshaping how we anticipate and respond to complex disaster scenarios. Dr. van den Bout underscores that while cutting-edge innovations have expanded our understanding of cascading impacts—from earthquakes triggering tsunamis to storms igniting landslides—persistent model uncertainties and patchy data threaten the reliability of truly integrated systems. He argues that only through close collaboration—melding the strengths of researchers, forecasters, and local experts—can we build the operational, resilient tools communities need. Capturing data during rare, destructive events remains a formidable hurdle, but embracing both foundational research and unconventional, “out-of-the-box” approaches will be vital to surmount these obstacles.
Bringing multi-hazard disaster management from theory to practice hinges on precise model calibration, something that often demands boots-on-the-ground expertise and tailored field studies. Space-borne technologies—satellite imagery for landscape mapping and retrospective event analysis—play a growing role in refining water-related hazard forecasts, yet they must be complemented by detailed regional insights and rich observational datasets. For those eager to dive into flood modelling, online courses and math communicators offer accessible entry points. Beyond his technical pursuits, Dr. van den Bout credits a lifelong love of programming and video games for inspiring creative experimentation, reminding us that true innovation flourishes when we carve out time for curiosity—whether swimming in his favorite liquid state of water with family or scouring the internet for fresh data.
Padmi is currently reading for her Ph.D. focusing on Nature-based Solutions (NbS) for climate change risk reduction and resilience cities. She believes NbS can reduce hydro-meteorological hazards such as floods, droughts, and landslides in the long run. It is a strategy to minimize the gaps in decarbonizing and reducing greenhouse gases and a path to Net-zero cities. NbS, are actions to protect, sustainably manage, and restore natural and modified ecosystems that address societal challenges effectively and adaptively, benefiting people and nature (IUCN & World Bank, 2022). Ecosystem-based adaptation (EbA), ecosystem-based disaster risk reduction (Eco-DRR), ecosystem-based mitigation (EbM), and green infrastructure are some branches under the umbrella of NbS. NbS include conserving forests, mangroves, and wetland ecosystems, halting deforestation, increasing reforestation, climate-smart agriculture, and opening green spaces. According to her, space technology is integral to planning, monitoring, and analysis. Space technology today is so advanced that it can capture and predict changes in the water cycle, climate change variables and so forth. Remote sensing data and satellite-derived information are essential in obtaining accurate data on a specific site anywhere on the Earth's surface. Most recently, she has been involved in projects utilizing urban NbS such as the conservation of Ramsar-Colombo to mitigate urban floods and adapt to climate change. To conduct wetland inventories, space-based data and GIS techniques can be utilized to detect the presence of wetlands and/or water in wetlands. Though there can be some challenges encountered such as limited coverage of specific areas within the wetland, clouds often hiding images, and the low resolution of data making it difficult to differentiate floral species. Unmanned Aerial Vehicles (drones) can provide enhanced accuracy and consistency in measuring wetlands, as well as the presence of water in wetlands, using space technologies. Data and technologies from space contribute to watershed management, sediment measurements and many other environmental aspects.
I am currently a PhD candidate at the University of Stirling in Scotland, funded by the Natural Environmental Research Council through the IAPETUS DTP. My research focuses on using SAR Polarimetry to map and monitor floods in Scotland and Guyana. Additionally, I use ground radar to understand signal interactions under simulated flooding conditions, aiming to improve flood detection. My goal is to enhance the management and protection of floodplains and wetlands through advanced radar satellite technology and field-tested methodologies.
Before my PhD, I worked as an assistant hydrologist at the SERVIR Eastern and Southern Africa project at the Regional Centre for Mapping of Resources for Development in Nairobi, Kenya, from 2019 to 2022. In this position, I led the development of an operational hydrological model that improved access to hydrological data for ungauged rivers in East Africa. I was also the lead hydrologist in the implementation of a flood early warning system in Malawi, integrating ground measurements and satellite-derived water level data to issue flood forecasts.
Ruvimbo Samanga, despite her age, has vast experience in the law, space, and water sectors. She is presently involved in a regional study on the integration of GIS and statistical information in Zimbabwe, working towards the promulgation of GIS standards and legislation to support a National Spatial Data Infrastructure (NSDI). Ruvimbo is excited by the merging of sustainable development for water management with space technologies because it is scalable, environmentally friendly, and cost-effective over the long run. Ruvimbo feels strongly that space technologies have a role to play in policy and legal affairs, and also sees potential especially in the use of emerging technologies such as block chain, artificial intelligence (AI) and quantum computing.
Webster is a PhD student at the University of Twente’s Faculty of Geoinformation Science and Earth Observation. His PhD thesis is entitled: Observing Zambezi Basin from Space: Satellite based bias correction for hydrological modelling: Webster is also lecturer and researcher at the University of Zimbabwe’s Construction and Civil Engineering Department. He is the coordinator of the regional master’s degree programme in Integrated Water Resources Management, a capacity building programme for the water sector in Southern and Eastern Africa. His research interests are in the areas of GIS and Earth Observation applications in water resources management, sanitation, water quality and disaster management. He is also a consultant who has been seconded as a GIS mentor to many government institutions and developmental partners in Southern Africa. Webster has over 60 publications, numerous regional and international conference papers in areas of spatial and quantitative hydrology, water resources management, quantification of water cycle components and feedbacks between climate, land-uses, water cycles and other societal influences. Webster is the Chief Editor of the Journal of Environmental Management in Zimbabwe (JEMZ).
Dr. Aziza Baubekova's research tackles critical environmental and water-related challenges in water-scarce regions using innovative approaches like remote sensing and machine learning. Her work not only advances scientific knowledge but also offers practical and policy solutions for developing countries. By applying quantifiable methods, her research provides actionable tools for integrated water resources and ecosystem management, addressing issues related to hydrologic conditions and human impact.
Despite earning all her degrees in Europe, Dr. Baubekova maintains a deep connection to Central Asia, focusing her research on the region's unique environmental challenges. As a Postdoctoral Researcher in the Water, Energy, and Environmental Engineering Research Unit at the University of Oulu, she contributes significantly to projects like TU-NEXUS, which aims to develop decision-making tools for transboundary river management in Central Asia. Her PhD, completed with distinction in 2023, covers topics such as hydrologic changes, climate change impacts, and coastal ecosystem threats.
Beyond her academic work, Dr. Baubekova actively fosters partnerships between Finland and Central Asian institutions, supporting knowledge transfer and technology exchange. As Vice Chair of Young Water Professionals Finland, she promotes professional development, knowledge sharing, and networking opportunities for young water experts.
Could you describe how your professional and/or personal experience relate to water? Where does your interest in space technology for water come from?
I have a solid understanding of the fundamentals of hydrologic and hydraulic engineering, which is relevant to water. I studied many courses in my undergraduate and postgraduate degrees where I learned how runoff in a watershed is generated from meteorological parameters including rainfall, evapotranspiration and infiltration. I also applied my theoretical knowledge to various projects.
Sawaid Abbas, Assistant Professor at the Centre for Geographical Information System, University of the Punjab, Lahore, Pakistan discussed his extensive work in addressing water-related challenges through the nexus between smart sensing and space technologies. His thematic focus spans water scarcity, food security, climate risks, and environmental monitoring with an emphasis on the Asia-Pacific region, including Pakistan and China. Key Sustainable Development Goals (SDGs) guiding his work include SDG2 (Zero Hunger), SDG13 (Climate Action), SDG15 (Life on Land), and SDG11 (Sustainable Cities and Communities).
Abbas's passion for water emerged during his early career at the World Wide Fund for Nature (WWF), where he was involved in Pakistan’s Wetland Program and witnessed the impact of water on associated ecosystems. This sparked his interest in understanding and managing water, forestry, and wildlife resources. He recently studied coastal ecosystems and their responses to climate and anthropogenic stressors in the Asia-Pacific region. The Living Indus – Investing in Ecological Restoration has become a new focus of interest for him, addressing sustainability challenges related to food security, river basin management, and efficient water use in alignment with the UN Decade of Ocean objectives.
Abbas shared his fascination with water, recognizing its complex and essential nature. He is captivated by its beauty in all forms and acknowledges its fundamental importance for life on Earth. This water connection further motivates his commitment to addressing global water challenges and promoting sustainable water use through innovative solutions.
Sawaid Abbas's work, stimulated by both professional commitment and personal fascination, stresses the critical role of space technologies, particularly earth observation, smart sensing nexus, and artificial intelligence in addressing water-related challenges. His research contributes to the development of innovative solutions for sustainable water use, environmental protection, and disaster response, aligning with global goals for a more resilient and water-secure future.
Lukas Graf used to take clean drinking water for granted. As he grew up, and conversations around climate change and environmental destruction became increasingly intense, he started to become more aware of the importance and scarcity of water resources. Around a similar time, he became increasingly enthusiastic about space, realising that space technologies could be used to explore many of the pressing topics that he was interested in. He has participated in research projects that used remote sensing methods to study the effects of global change on ecosystems and especially on water availability. Lukas is interested in a range of topics from virtual water and water quality to irrigation and agriculture. He believes that interdisciplinary approaches and mutual dialog with societies and stakeholders need to be deepened for sustained resource management.
Mina Konaka works at the Japan Aerospace Exploration Agency (JAXA) as a satellite engineer and is currently working on the satellite ALOS-4, which can detect changes in groundwater on land. She attended the International Space University, participating in the project AWARE (Adapting to Water and Air Realities on Earth), in which participants aimed to provide solutions for flood and air quality risks due to climate change, using earth observation data and ground-based sensors. Mina feels strongly about the need to talk more globally about water management solutions, rather than on an individual country basis. Mina also hopes that in the future there will be more female engineers who pursue dreams of space, and that gender balance is no longer an issue.
This interview was conducted as part of the young professional program of the space4water program. The interview begins by asking about my professional and personal journey as a researcher specializing in water and space technologies, particularly in the context of environmental challenges. Growing up in Bangladesh, how my exposure to multiple water related challenges influenced my deep interest in remote sensing and Earth observation technologies. Then the question focuses on how I am addressing water related challenges using satellite imagery and geospatial data. The conversation also explores the role of space-based technologies, such as satellite Earth observations, in monitoring coastal erosion and riverbank changes. As part of response, I explain how the combination of high-resolution imagery with machine learning can predict environmental shifts and help mitigate the impacts on vulnerable populations. Finally, I shared my advice for aspiring professionals in water management, emphasizing the importance of interdisciplinary skills, including geospatial analysis, data science, and policy understanding. I also talked about the value of curiosity, collaboration, and access to advanced technologies for driving innovation in water related challenges worldwide.
Margherita is an interdisciplinary Earth scientist and drone pilot with a background in geologic and environmental sciences. She has international experience working in fields such as Earth Observation (EO), remote sensing, drones & geospatial data analysis applied to the environmental and humanitarian sectors, sustainability and climate change. Margherita is passionate about natural and climate-related technologies that can be used to develop sustainable and long-lasting solutions. She is working for a more inclusive world (Women in Geospatial+), without any sort of geographical or social barriers.
Keywords: Science communication, Climate Change, STEM, inclusivity, sustainability, nature, hydrosphere, hydrology, water risks, Earth Observation (EO), satellite data, flood modeling, vulnerability, resilience, lifelong learning
Region/Country mentioned: Temperate climates, Arid climates, Luxembourg, Niger
Relevant SDG targets: 1, 4, 6, 9, 11, 13, 17
Padmi is currently reading for her Ph.D. focusing on Nature-based Solutions (NbS) for climate change risk reduction and resilience cities. She believes NbS can reduce hydro-meteorological hazards such as floods, droughts, and landslides in the long run. It is a strategy to minimize the gaps in decarbonizing and reducing greenhouse gases and a path to Net-zero cities. NbS, are actions to protect, sustainably manage, and restore natural and modified ecosystems that address societal challenges effectively and adaptively, benefiting people and nature (IUCN & World Bank, 2022). Ecosystem-based adaptation (EbA), ecosystem-based disaster risk reduction (Eco-DRR), ecosystem-based mitigation (EbM), and green infrastructure are some branches under the umbrella of NbS. NbS include conserving forests, mangroves, and wetland ecosystems, halting deforestation, increasing reforestation, climate-smart agriculture, and opening green spaces. According to her, space technology is integral to planning, monitoring, and analysis. Space technology today is so advanced that it can capture and predict changes in the water cycle, climate change variables and so forth. Remote sensing data and satellite-derived information are essential in obtaining accurate data on a specific site anywhere on the Earth's surface. Most recently, she has been involved in projects utilizing urban NbS such as the conservation of Ramsar-Colombo to mitigate urban floods and adapt to climate change. To conduct wetland inventories, space-based data and GIS techniques can be utilized to detect the presence of wetlands and/or water in wetlands. Though there can be some challenges encountered such as limited coverage of specific areas within the wetland, clouds often hiding images, and the low resolution of data making it difficult to differentiate floral species. Unmanned Aerial Vehicles (drones) can provide enhanced accuracy and consistency in measuring wetlands, as well as the presence of water in wetlands, using space technologies. Data and technologies from space contribute to watershed management, sediment measurements and many other environmental aspects.
I am currently a PhD candidate at the University of Stirling in Scotland, funded by the Natural Environmental Research Council through the IAPETUS DTP. My research focuses on using SAR Polarimetry to map and monitor floods in Scotland and Guyana. Additionally, I use ground radar to understand signal interactions under simulated flooding conditions, aiming to improve flood detection. My goal is to enhance the management and protection of floodplains and wetlands through advanced radar satellite technology and field-tested methodologies.
Before my PhD, I worked as an assistant hydrologist at the SERVIR Eastern and Southern Africa project at the Regional Centre for Mapping of Resources for Development in Nairobi, Kenya, from 2019 to 2022. In this position, I led the development of an operational hydrological model that improved access to hydrological data for ungauged rivers in East Africa. I was also the lead hydrologist in the implementation of a flood early warning system in Malawi, integrating ground measurements and satellite-derived water level data to issue flood forecasts.
Ruvimbo Samanga, despite her age, has vast experience in the law, space, and water sectors. She is presently involved in a regional study on the integration of GIS and statistical information in Zimbabwe, working towards the promulgation of GIS standards and legislation to support a National Spatial Data Infrastructure (NSDI). Ruvimbo is excited by the merging of sustainable development for water management with space technologies because it is scalable, environmentally friendly, and cost-effective over the long run. Ruvimbo feels strongly that space technologies have a role to play in policy and legal affairs, and also sees potential especially in the use of emerging technologies such as block chain, artificial intelligence (AI) and quantum computing.
Webster is a PhD student at the University of Twente’s Faculty of Geoinformation Science and Earth Observation. His PhD thesis is entitled: Observing Zambezi Basin from Space: Satellite based bias correction for hydrological modelling: Webster is also lecturer and researcher at the University of Zimbabwe’s Construction and Civil Engineering Department. He is the coordinator of the regional master’s degree programme in Integrated Water Resources Management, a capacity building programme for the water sector in Southern and Eastern Africa. His research interests are in the areas of GIS and Earth Observation applications in water resources management, sanitation, water quality and disaster management. He is also a consultant who has been seconded as a GIS mentor to many government institutions and developmental partners in Southern Africa. Webster has over 60 publications, numerous regional and international conference papers in areas of spatial and quantitative hydrology, water resources management, quantification of water cycle components and feedbacks between climate, land-uses, water cycles and other societal influences. Webster is the Chief Editor of the Journal of Environmental Management in Zimbabwe (JEMZ).
Dr. Aziza Baubekova's research tackles critical environmental and water-related challenges in water-scarce regions using innovative approaches like remote sensing and machine learning. Her work not only advances scientific knowledge but also offers practical and policy solutions for developing countries. By applying quantifiable methods, her research provides actionable tools for integrated water resources and ecosystem management, addressing issues related to hydrologic conditions and human impact.
Despite earning all her degrees in Europe, Dr. Baubekova maintains a deep connection to Central Asia, focusing her research on the region's unique environmental challenges. As a Postdoctoral Researcher in the Water, Energy, and Environmental Engineering Research Unit at the University of Oulu, she contributes significantly to projects like TU-NEXUS, which aims to develop decision-making tools for transboundary river management in Central Asia. Her PhD, completed with distinction in 2023, covers topics such as hydrologic changes, climate change impacts, and coastal ecosystem threats.
Beyond her academic work, Dr. Baubekova actively fosters partnerships between Finland and Central Asian institutions, supporting knowledge transfer and technology exchange. As Vice Chair of Young Water Professionals Finland, she promotes professional development, knowledge sharing, and networking opportunities for young water experts.
Could you describe how your professional and/or personal experience relate to water? Where does your interest in space technology for water come from?
I have a solid understanding of the fundamentals of hydrologic and hydraulic engineering, which is relevant to water. I studied many courses in my undergraduate and postgraduate degrees where I learned how runoff in a watershed is generated from meteorological parameters including rainfall, evapotranspiration and infiltration. I also applied my theoretical knowledge to various projects.
Lukas Graf used to take clean drinking water for granted. As he grew up, and conversations around climate change and environmental destruction became increasingly intense, he started to become more aware of the importance and scarcity of water resources. Around a similar time, he became increasingly enthusiastic about space, realising that space technologies could be used to explore many of the pressing topics that he was interested in. He has participated in research projects that used remote sensing methods to study the effects of global change on ecosystems and especially on water availability. Lukas is interested in a range of topics from virtual water and water quality to irrigation and agriculture. He believes that interdisciplinary approaches and mutual dialog with societies and stakeholders need to be deepened for sustained resource management.
Mina Konaka works at the Japan Aerospace Exploration Agency (JAXA) as a satellite engineer and is currently working on the satellite ALOS-4, which can detect changes in groundwater on land. She attended the International Space University, participating in the project AWARE (Adapting to Water and Air Realities on Earth), in which participants aimed to provide solutions for flood and air quality risks due to climate change, using earth observation data and ground-based sensors. Mina feels strongly about the need to talk more globally about water management solutions, rather than on an individual country basis. Mina also hopes that in the future there will be more female engineers who pursue dreams of space, and that gender balance is no longer an issue.
This interview was conducted as part of the young professional program of the space4water program. The interview begins by asking about my professional and personal journey as a researcher specializing in water and space technologies, particularly in the context of environmental challenges. Growing up in Bangladesh, how my exposure to multiple water related challenges influenced my deep interest in remote sensing and Earth observation technologies. Then the question focuses on how I am addressing water related challenges using satellite imagery and geospatial data. The conversation also explores the role of space-based technologies, such as satellite Earth observations, in monitoring coastal erosion and riverbank changes. As part of response, I explain how the combination of high-resolution imagery with machine learning can predict environmental shifts and help mitigate the impacts on vulnerable populations. Finally, I shared my advice for aspiring professionals in water management, emphasizing the importance of interdisciplinary skills, including geospatial analysis, data science, and policy understanding. I also talked about the value of curiosity, collaboration, and access to advanced technologies for driving innovation in water related challenges worldwide.
Margherita is an interdisciplinary Earth scientist and drone pilot with a background in geologic and environmental sciences. She has international experience working in fields such as Earth Observation (EO), remote sensing, drones & geospatial data analysis applied to the environmental and humanitarian sectors, sustainability and climate change. Margherita is passionate about natural and climate-related technologies that can be used to develop sustainable and long-lasting solutions. She is working for a more inclusive world (Women in Geospatial+), without any sort of geographical or social barriers.
Keywords: Science communication, Climate Change, STEM, inclusivity, sustainability, nature, hydrosphere, hydrology, water risks, Earth Observation (EO), satellite data, flood modeling, vulnerability, resilience, lifelong learning
Region/Country mentioned: Temperate climates, Arid climates, Luxembourg, Niger
Relevant SDG targets: 1, 4, 6, 9, 11, 13, 17
Joshua is a Master’s student in Tropical Hydrogeology and Environmental Engineering at Technische Universität of Darmstadt. His interest is focused on hydrogeological processes, groundwater modelling, application of remote sensing and GIS in environmental studies, water management and climate change. He also works as a graduate Intern at AgriWatch BV, a company that applies geospatial solutions for precision Agriculture. As a graduate intern, he applies his interdisciplinary knowledge in developing smart-farming solutions using space-based technologies to farmers in the Twente region of the Netherlands. He deploys satellite imagery, field studies and machine learning algorithms to predict the effect of climate change on arable crops. He also utilizes precipitation data to predict rainfall events to aid farmers in determining planting and harvesting periods.
Joshua earned a bachelor’s degree in Geological Sciences, his bachelor’s thesis research aimed at carrying out paleoenvironmental reconstruction using paleocurrent indicators of water flow and direction, and application of ArcGIS to produce maps. Currently, he is working on his master’s thesis with emphasis on the impact of the ancient climate on the paleoenvironment particularly on vegetation, where he tries to research plants response to long-term greenhouse periods and short-term warming events on various timescales throughout Earth's history.
His research interests revolve around the application of space technologies in providing solutions and tackling climate change.
How do you personally and professionally relate to water and/or space technologies?
Water and space technologies are deeply intertwined with my research focus and professional journey. My work primarily revolves around studying the impacts of climate change and human activities on ecosystems, particularly in mountainous regions like the Alps. Water is a crucial component in this context, as it plays a significant role in both vegetation dynamics and ecosystem health.
Short description of community and hydrogeology of the area
Yucatan is located in the southeast portion of Mexico. The total area of Yucatan is 124, 409 km2 and the population (by 2018) was ca. 2.1 million inhabitants. The landscape of the area is defined by a highly permeable karstic soil, a notable absence of rivers or permanent freshwater resources in the surface, and a high number of natural wells or sinkholes (locally called cenotes, from the Maya word t´sonot).
This learning platform helps users understand the significance of Earth observations, explore Digital Earth Africa datasets through an interactive map, and get started on the basics of python coding for spatial analysis.
Digital Earth Africa makes Earth observation (EO) data readily available, delivering decision-ready products to the African continent. Data generated by Digital Earth Africa will provide valuable insights for better decision-making across many areas, including resource management, food security and urbanisation.
Decision-makers are faced with the constant challenge of maintaining access to and understanding new technologies and data, as information and communication technologies (ICTs) are constantly evolving and as more and more data is becoming available. Despite continually improving technologies, informed decision-making is being hindered by inadequate attention to enabling conditions, e.g. a lack of in-service education and professional training for decision-makers.
River and floodplain landscapes are constantly undergoing change due to natural and manmade processes putting pressure on fluvial systems, such as reservoirs, intensive agriculture, high-impact repetitive droughts and floods and the overall effects of climate change. All these bring about considerable changes, some of which irreversibly degrade ecosystem services, local economies and impact lives, particularly in sensitive transitional zones such as the Sahel region in Africa and its Niger River Basin (NRB).
e-shape is a unique initiative that brings together decades of public investment in Earth Observation and in cloud capabilities into services for the decision-makers, the citizens, the industry and the researchers. It allows Europe to position itself as global force in Earth observation through leveraging Copernicus, making use of existing European capacities and improving user uptake of the data from GEO assets. EuroGEO, as Europe's contribution to the Global Earth Observation System of Systems (GEOSS), aims at bringing together Earth Observation resources in Europe.
The Geohazards Risk Mapping Initiative is an initiative that deploys volunteer youths, who are skilled at using Geographic Information Systems and satellite imagery analysis to create flood susceptibility and post-disaster maps in Nigeria.
The United Nations University Institute on Comparative Regional Integration Studies (UNU-CRIS) is a research and training institute of the United Nations University. UNU is a global network of institutes and programs engaged in research and capacity development to support the universal goals of the UN. It brings together leading scholars from around the world with a view to generate strong and innovative knowledge on how to tackle pressing global problems. UNU-CRIS focuses on the study of processes of global cooperation and regional integration and their implications.
mWater is an operating system for digital governance used by governments, civil society organizations, and water and sanitation service providers in over 190 countries. The platform's free features allow users to collect data using smartphones, bring in data from Earth observations and other sources, and create effective analytics and visualizations to help prioritize interventions. mWater is designed to facilitate collaboration and longitudinal monitoring of individual pieces of infrastructure as well as entire water systems.
A need to monitor precipitation extremes from space is widely recognized, especially for regions where ground-based observations are limited or unavailable. The Japan Aerospace Exploration Agency (JAXA) has developed the Global Satellite Mapping of Precipitation (GSMaP) in the Global Precipitation Measurement (GPM) mission. The JAXA participated in the Space-based Weather and Climate Extremes Monitoring (SWCEM) of the World Meteorological Organization (WMO) by providing the GSMaP Near-real-time Rainfall Product.
ISME-HYDRO is a platform that helps monitor water resources of dams, thus enabling water resources managers to better execute their duties. It employs linked data infrastructure integrating in-situ measurements, satellite data, GIS data, domain knowledge, deep learning, and provides capabilities of forecasting of water volumes, of alerting for hazardous situations, of interaction with the data through four kinds of search and GIS interactivity. The platform is easily extendable and customizable.
mWater is an operating system for digital governance used by governments, civil society organizations, and water and sanitation service providers in over 190 countries. The platform's free features allow users to collect data using smartphones, bring in data from Earth observations and other sources, and create effective analytics and visualizations to help prioritize interventions. mWater is designed to facilitate collaboration and longitudinal monitoring of individual pieces of infrastructure as well as entire water systems.
mWater is an operating system for digital governance used by governments, civil society organizations, and water and sanitation service providers in over 190 countries. The platform's free features allow users to collect data using smartphones, bring in data from Earth observations and other sources, and create effective analytics and visualizations to help prioritize interventions. mWater is designed to facilitate collaboration and longitudinal monitoring of individual pieces of infrastructure as well as entire water systems.
The historical disasters of the study region, the Garhwal Himalaya, were collected, and the types of hydrometeorological disasters (HMD) were tabulated with location, attribute, morbidity, and extent from 1803 to 2025. The Garhwal region has been divided into 58 tehsils (sub-administrative regions). For analysing past HMDs and to map Multi-Hazard Susceptibility Zonation on the tehsil level, QGIS, Google Earth Engine, satellite data, k-means clustering, and AHP techniques were used.
Requirements
Data
Survey of India map of the study area
Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM)
Tropical Rainfall Measuring Mission (TRMM) rainfall data
Sentinel-2 Land Use / Land Cover (LULC) data
Global Land Ice Measurements from Space (GLIMS) Glacier data
National Bureau of Soil Survey & Land Use Planning (NBSS&LUP) Soil data
Disaster data from Emergency Events Database (EM-DAT)
National Disaster Management Authority (NDMA)
Various research publications
Along with regional newspapers
Software
QGIS
Google Earth Engine (GEE)
Steps to a solution
Study Area
The Garhwal region is spread over approximately 32,366 square kilometres in northwestern Uttarakhand and comprises 58 sub-administrative divisions (tehsils). This region is a major part of the Indian Himalayan Region (IHR) and has steep slopes, rugged terrain, and a geologically fragile structure, and hence is highly vulnerable to natural hazards. Though associated with steep topography, its intense monsoonal rainfall, changing land use patterns, and glacial influence all make the region highly vulnerable to hydrometeorological disasters (HMDs) such as floods, flash floods, landslides, GLOFs, cloud bursts, and avalanches.
Figure 1. Google Earth satellite image of the Garhwal region
Figure 2. DEM of Garhwal region
Figure 3. Spatial distribution of HMDs and fatalities in the Garhwal Himalayas (1803–2025)
Collecting and processing
Historical HMD data for the Garhwal region (1803–2025) have been collected from a variety of sources, including EM-DAT, scientific publications, NDMA, SDMA, and regional media reports.
Table 1. Geospatial Datasets Used in the Study
S.No.
Dataset / Layer
Source / Method
Resolution / Format
Year / Period
1
Study Area Shapefile
Survey of India / Custom Digitisation
Vector (Shapefile)
Latest Available
2
Digital Elevation Model (DEM)
NASADEM
30 m (Raster)
2020
3
Slope and Elevation
Derived from NASADEM using QGIS
30 m (Raster)
2020 (Processed)
4
Monsoon Rainfall
TRMM via GEE
Monthly, ~25 km (Raster)
1998-2015
5
Land Use / Land Cover (LULC)
ESA WorldCover (Sentinel-2)
10 m (Raster)
2021
6
Glacier Cover
GLIMS / ESA
~30 m (Raster)
Latest Available
7
Proximity to Rivers
HydroSHEDS
Variable (Rasterised)
Processed Layer
8
Soil Erosion Class
NBSS&LUP Database, India
Vector -> Raster Conversion
Latest Available
How Thematic Layer Preparation Works
Seven thematic layers were created for the Garhwal region using satellite remote sensing data in QGIS and the GEE environment:
Slope
Elevation
Rainfall
Land Use/Land Cover (LULC)
Soil Erosion
The region’s closeness to rivers
Glacier Proximity
The thematic layers were created using the data sourced from Table 1. Thematic layers were brought to the same scale (1–5) and brought together using AHP to develop a single risk zonation map.
The k-means clustering is done on the QGIS 3.42.3 platform using K-Means Clustering ABC (Attribute-based clustering) tab in the processing toolbox. The attributes were selected like location, elevation, and impact severity.
Application of AHP
To evaluate HMD susceptibility using AHP, the main influencing factors were selected: slope, elevation, rainfall, LULC, soil erosion, rivers and glacier proximity. To create these layers, data from DEM for slope and elevation, image data from satellites for LULC, and hydrological data are used. Based on AHP, a table is filled, with one factor compared to another according to Saaty’s 1–9 scale to decide their relative weight. Weights are calculated with an eigenvector analysis, and a small consistency ratio (less than 0.1) indicates sensible conclusions. Finally, using an AHP weighted overlay in GIS, all the relevant layers are combined, and the outcome is a map showing where HMD susceptibility is highest.
Figure 4. Methodology Flowchart
Results
Most (77.6 per cent) HMDs happened during the Monsoon season, followed by pre-monsoon (14.3 per cent), Winter season (6.1 per cent), and the post-monsoon season (2 per cent).
The K-means clustering of disaster events in the Garhwal Himalayas yielded the clusters-based partitioning them based on shared characteristics (e.g., elevation, impact severity, location).
The multi-hazard zonation using the AHP system shows that the north eastern or north tehsils like Joshimath and Chamoli have very high levels of risk, while places like Haridwar and Roorkee in the south have much lower risks.
Figure 5. AHP-Based Multi-Hazard Risk Zonation in the Garhwal Himalayas
Future work
Access and incorporation of socio-economic and infrastructure vulnerability cum exposure data for risk zonation
Combining GIS outputs with participatory approaches to validate and refine vulnerability maps on the ground
Make the methodology suitable and easily workable for the entire Himalayan Region to strengthen resilience against disasters
Future climatic scenario along with ML to recognize and forecast disaster patterns
Using space-based techniques for ecosystem-based disaster reduction
Department of Soil & Water Conservation Engineering, GBPUAT Pantnagar
Indian Institute of Technology Delhi (IIT Delhi)
Dr. Rajendra Prasad Central Agricultural University, Pusa
Central University of Jharkhand, India
Assam University, Silchar
Remote sensing can significantly aid in groundwater resource management. Further with the integration of Internet of Things (IoT), the information of groundwater storage and change in groundwater level can be shared through mobile technology to end users, policy makers and also to the government.
Here are a few key steps showing how it can be useful:
Mapping and monitoring land use/land cover: Remote sensing helps identify areas of vegetation, agriculture, urbanisation and water bodies, which influence groundwater recharge and extraction.
Identifying potential groundwater zones: Satellite imagery, combined with GIS, can analyse geological, hydrological and geomorphological features to locate promising groundwater zones.
Monitoring groundwater levels and storage: Missions like Gravity Recovery and Climate Experiment (GRACE) measure changes in Earth's gravity field, enabling estimation of groundwater storage changes over time.
Assessing drought and recharge conditions: Remote sensing provides data on precipitation, soil moisture and evapotranspiration, essential for evaluating recharge potential and drought impacts.
Supporting sustainable management: Continuous remote sensing data supports long-term planning, policy-making and sustainable groundwater resource development.
Integration of remote sensing with IoT: IoT modules can be developed for groundwater level; total groundwater storage; drought level etc and can be sent to end users using mobile technology.
Requirements
Data
GRACE & GRACE FO satellite data set
Central Groundwater Board, India
Water Resources Information System (WRIS) India
USGS data sets of remote sensing imagery
Software
QGIS
Visual MODFLOW flex
Hydrologic Engineering Center's River Analysis System (HEC-RAS)
MATLAB
R software
ERDAS IMAGINE
Physical
Workstation as servers lab for developing IoT for groundwater management
Outline steps to a solution
Worked on GRACE satellite data and used it in field condition to study groundwater anomalies of few cities of India (completed).
Developed spatio-temporal maps of Standardized Groundwater Index (SGI) (completed).
Water quality monitoring of water bodies using remote sensing (in progress).
Water spread mapping and its monitoring, of various water bodies using remote sensing and artificial intelligence (research is in progress).
Internet of Things (IoT) models which can link groundwater depletion/anomalies information with the end-users (in progress).
Steps to a solution
Study area and data acquisition
Study area has to be selected for groundwater monitoring and management.
The shapefile of the study area has to be downloaded from government websites or can be ordered on request basis. Gridded GRACE products (Level-3) can be used from the Jet propulsion Laboratory (JPL), the National Aeronautics and Space Administration (NASA) to get the monthly water equivalent thickness data.
Development of Standardized Groundwater Index (SGI) for understanding the severity of groundwater anomalies or draught
Standardized Groundwater Index (SGI) is a drought indicator which was developed by Bloomfield and Marchant (2013) to quantify groundwater drought. It is used for estimating groundwater level deficit at any time scale which reflects the extreme drought condition of any location. It is similar to the traditional drought index, Standardized Precipitation Index (SPI) and can be calculated on the same basis like SPI. In SGI, groundwater level data is used for measuring drought condition, instead of precipitation data which is used in SPI. Groundwater time series data obtained from ground observation can be appropriately normalized to evaluating the groundwater drought. SGI values can also be analysed by calculating groundwater deviation from the mean groundwater value (Halder et al., 2020). SGI can be given by following formula
where, K is groundwater level of the respective year; M is long term mean groundwater level of 18 years, σ is standard deviation
Gravity Recovery and Climate Experiment satellite for groundwater anomalies study
GRACE was launched by NASA on March 17, 2002. It was a joint mission of NASA and German Aerospace Centre (DLR). The two twin satellites of GRACE are monitored to observe the changes in the Earth's gravity field. GRACE satellite, a first remote sensing satellite which provides an efficient and cost-effective way to map Earth’s gravity field and measure the total groundwater storage changes (TWS) with unprecedented accuracy (Yirdaw et al, 2008). GRACE studies the variation in the gravity which are caused due to effects that include: changes due to deep currents in the ocean; runoff and ground water storage on land mass; exchanges between ice sheets or glaciers and the oceans, and variations of mass within the solid Earth. The distance between the twin satellite as they orbit the Earth help in measuring changes in the Earth's gravity field for each month. From these monthly gravity field, time series of regional mass anomalies can be derived using specially designed averaging function. GRACE mission provides an opportunity to directly measure the total groundwater storage changes and with the help of gravity field data of GRACE drought conditions can also be monitored over a region. GRACE satellite has coarse resolution of 300-400 km and provides data in an interval of 30 days. The distance between the two satellite is about 200 km at a starting altitude of about 500 km. The GRACE gridded TWS products (1˚×1˚) from spherical harmonics are provided by the Centre of Research (CSR) at the University of Texas, the Jet Propulsion Laboratory (JPL) and German Research Centre for Geoscience (GFZ). The gridded products estimate the changes in mass in unit of water equivalent thickness (WET).
Machine learning algorithms to model data from GRACE with observed data
Using machine learning (ML) for modelling GRACE satellite data alongside observation datasets (e.g., in-situ hydrological measurements, meteorological data) is a powerful approach to extract spatiotemporal patterns, downscale or predict terrestrial water storage (TWS) anomalies. Machine learning algorithms like artificial neural network, random forest, support vector machines etc can be used to model satellite data with observed data.
IoT for groundwater monitoring and sending information to end users
Using IoT for groundwater level monitoring is an effective way to automate the collection, transmission and dissemination of real-time groundwater data to decision-makers, farmers or the public.
Results
Today, no remote sensors can directly monitor groundwater, a combination of surface features anomalies and gravity data obtained by various satellites, allows for optimal groundwater management. Example satellites for monitoring include: GRACE and its Follow-On mission (GRACE-FO) to study groundwater fluctuations, Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS) etc, groundwater management can be done using space technology.
I developed a Standardized Groundwater Index (SGI) for Bihar state of India which proved to be very important to understand the severity of groundwater problems in that region. The spatio-temporal variation of SGI using geographical information systems (see figure 1) was published in the peer reviewed Journal of the Geological Society of India (Kumar and Kumari, 2024).
To address the challenge of water security in Bahrain, this solution integrates space-based technologies and geospatial analysis to identify and monitor potential water resources, particularly shallow groundwater. The methodology involves the use of satellite-derived datasets and terrain modelling tools to analyse hydrological behaviour, soil moisture, and elevation-based drainage characteristics.
Three main data sources were incorporated into the solution:
GRACE (Gravity Recovery and Climate Experiment) data is used to assess changes in terrestrial water storage at the regional scale by detecting gravity anomalies related to mass variations in groundwater. GRACE data is retrieved and visualised through platforms such as Google Earth Engine and ArcGIS Pro, enabling temporal monitoring of water resources.
HAND (Height Above Nearest Drainage) modelling was employed to identify topographic wetness and assess the hydrological potential of the landscape. HAND normalises elevation relative to the nearest drainage, highlighting areas where water is more likely to accumulate or infiltrate. This method supports the identification of suitable zones for groundwater recharge, such as infiltration basins or artificial wetlands, especially in an arid environment like Bahrain. The HAND model was derived using the GLO-30 Copernicus DEM (2023_1 DGED version), processed through the TerraHidro platform, and included the generation of essential layers such as flow direction (D8), contributing area (D8CA), slope, and drainage networks with thresholds of 10, 100, and 300 pixels.
Soil moisture analysis was conducted using two approaches:
SAR (Synthetic Aperture Radar) data from the Sentinel-1 constellation, which provides all-weather, day-and-night measurements of surface moisture conditions.
Optical-based soil moisture estimation, calculated from Landsat-8 imagery using vegetation and thermal indices (e.g., Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST)). This dual approach allows for consistent monitoring of surface moisture, which is crucial for assessing recharge potential and supporting irrigation planning.
Together, these tools provide a multi-faceted view of Bahrain's hydrological landscape, enabling decision-makers to strategically identify areas with groundwater potential and implement more sustainable water resource management practices.
Solution requirements
Gravity Recovery and Climate Experiment (GRACE)
GRACE is a joint mission by the National Aeronautics and Space Administration (NASA) and the German Aerospace Center (DLR) to measure Earth's gravity field anomalies from its launch in March 2002 to the end of its mission in October 2017. The GRACE Follow-On (GRACE-FO) is a continuation of the mission launched in May 2018. GRACE provides information on how mass is distributed and is varied over time through its detection of gravity anomalies. Because of this, a significant application of GRACE is groundwater anomalies detection. Hence, GRACE data has been explored as a solution for this challenge.
Two software platforms have been utilised to download and visualise GRACE data for Bahrain:
Google Earth Engine (GEE): A cloud-based platform that facilitates remote sensing analysis with a large catalogue of satellite imagery and geospatial datasets. The platform is free for academic and research purposes.
QGIS: A desktop application that allows the exploration, analysis and visualisation of geospatial data. This application is open source.
Height Above Nearest Drainage (HAND)
The Height Above Nearest Drainage (HAND) is a terrain model that normalises elevation data relative to the local drainage network, offering a hydrologically meaningful representation of the landscape. By calculating the vertical distance between each point on the terrain and the nearest drainage channel, HAND allows for the identification of topographic wetness zones and the classification of soil water environments. It has shown strong correlation with water table depth and has been effectively validated in various catchments, particularly in the Amazon region. The HAND model supports physically based hydrological modelling and has broad applicability in areas such as flood risk assessment, soil moisture mapping, and groundwater dynamics, using only remote sensing-derived topographic data as input.
Soil moisture using Synthetic Aperture Radar (SAR) imagery
SAR data from Sentinel-1 constellation was used to generate relative soil moisture values. Seninel-1 is a radar-based satellite which acquires data with 6 days repeat cycle, and is neither affected by clouds, weather nor time of the day. Being a dual-polarimetric platform, it acquires data in VV (Vertical-transmit and Vertical received) polarization and VH (Vertical-transmit and Horizontal received) polarization. The data was analysed in GEE.
Soil moisture using multispectral and thermal imagery (Optical)
The data utilised to detect soil moisture are satellite imagery from Landsat-8 downloaded through GEE. Landsat-8 provides multispectral and thermal satellite imagery with 16 days repeat cycle. The specific bands required to calculate soil moisture index are the red, near-infrared bands and thermal infrared bands.
Solution outline and steps
GRACE
Figure 1 illustrates the steps taken to extract the recent GRACE Monthly Mass Grids Version 04 - Global Mascon (CRI Filtered) Dataset from GEE.
Figure 1. Download steps for GRACE Data
HAND
The elevation data downloaded and processed for the region of interest were derived from the GLO-30 dataset. The Copernicus DEM, a Digital Surface Model (DSM), represents the Earth's surface, including features such as buildings, vegetation, and infrastructure. This DSM is based on the WorldDEM product, which has undergone extensive editing to ensure the flattening of water bodies, consistent river flow representation, and correction of terrain anomalies, including shorelines, coastlines, and features like airports. The WorldDEM itself was generated using radar satellite data from the TanDEM-X mission, a Public Private Partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space. The GLO-30 data used in this work corresponds to the 2023_1 version of the Defence Gridded Elevation Data (DGED), provided via ESA’s https PRISM service and made accessible through OpenTopography.
The following products were processed using the TerraHidro software from the GLO-30 dataset: removepits.tif, d8.tif, d8ca.tif, slope.tif, drainage_10.tif, drainage_100.tif, and drainage_300.tif, as well as the HAND-derived products hand_10.tif, hand_100.tif, and hand_300.tif. Each product has a specific role in hydrological modeling:
removepits: This process modifies the original Digital Elevation Model (DEM) to eliminate depressions or pits that are not hydrologically realistic, ensuring that every cell has a defined downstream flow direction.
d8: The D8 (Deterministic 8) flow direction model calculates the steepest descent path from each pixel to one of its eight neighbors, indicating the primary direction of surface water flow.
d8ca: The D8 Contributing Area represents the number of upstream cells that contribute flow to each cell, allowing the identification of areas of potential accumulation and drainage.
slope: This product calculates the slope of the terrain in degrees, essential for understanding runoff velocity and erosion potential.
drainage_10, drainage_100, and drainage_300: These are drainage networks derived from the D8 contributing area, using threshold values of 10, 100, and 300 pixels, 0.9ha, 9ha and 27ha, respectively. They represent streams formed when the contributing area exceeds the specified number of pixels, with higher thresholds resulting in more generalised drainage networks.
From these products, the following HAND (Height Above Nearest Drainage) models were generated:
hand_10, hand_100, and hand_300: These datasets represent the vertical distance (in meters) from each pixel to the nearest drainage cell identified in the corresponding drainage network (with thresholds of 10, 100, and 300 pixels, respectively). These HAND maps are used to characterise terrain wetness, identify flood-prone areas, and support soil moisture and hydrological modeling.
Several steps were executed to derive the mean soil moisture conditions over the study area between 2017 and 2024. A step-by-step guide is shown in Figure 2. The values of soil moisture estimated is relative to the maximum soil moisture recorded in the region such that the wettest will be the maximum and the driest will be the minimum. These are used to normalise the final output into values between 0 and 1 where 0 is the driest and 1 is the wettest.
Figure 2. Processing steps for SAR soil moisture
Soil moisture (Optical)
Similar to the soil moisture calculation with SAR, an average of the soil moisture from 2017 to 2024 has been derived. The interrelations between the derived vegetation through the Normalized Difference Vegetation Index (NDVI) as well as Land Surface Temperature (LST) have been the basis for generating the soil moisture map. Figure 3 demonstrates the steps followed to generate optical soil moisture.
Figure 3. Processing steps for optical soil moisture
Shallow groundwater locations/recharge areas
To estimate potential suitable locations for shallow groundwater or groundwater rechange, the results from the HAND, SAR and optical soil moisture have been aggregated to formulate a final classification map. To perform this, the following has been done:
Classification of HAND, SAR and optical soil moisture results to ranges from 1-5, with 5 being the most suitable region based on the related values.
Spatial modelling of these three classifications to formulate a final suitability value from 1-5 with 5 being the most suitable region overall. HAND has been given a weightage of 50 per cent while SAR and optical soil moisture have been given a weightage of 25 per cent each to represent 50 per cent overall for soil moisture.
Map generation
Different maps have been generated for each component of this solution (HAND, SAR soil moisture, optical soil moisture, shallow groundwater locations/recharge areas). The subsequent steps illustrate the steps needed to develop the maps for this solution:
A basemap is added to the map for visualisation purposes. This is done through using the QGIS plugin called QuickMapServices. To install plugins, go to the Plugins tab and select Manage and Install Plugins.
Figure 4. Map generation - Step 1
In the search box of the Plugins window, search for QuickMapServices and install the plugin.
Figure 5. Map generation - Step 2
The plugin logo should appear in the QGIS panel. Click on the logo for Search QMS Panel. This label would appear if you hovered over the logo.
Figure 6. Map generation - Step 3
In the Search QMS Panel on the right, search for Google Satellite and add the basemap. It should appear in the list of layers.
Figure 7. Map generation - Step 4
Now we have a base layer that we can place our analysis on top of. Add the layer to the QGIS project if it is not already added. This can be done through drag and drop.
Figure 8. Map generation - Step 5
Right click on the layer and select Properties to adjust visualisation parameters.
Figure 9. Map generation - Step 6
In the Layer Properties window, click on Symbology and discover the most appropriate visualisation method for the data layer. This is an example for the set classifications for the HAND.
Figure 10. Map generation - Step 7
Once the layer visualisation has been set, the map layout can be generated. Go to Project > New Print Layout and name the layout.
Figure 11. Map generation - Step 8
Figure 12. Map generation - Step 8
In the Layout window, items such as the layers map, legend, scales can be added. This is accessed through the Add Item tab.
Figure 13. Map generation - Step 9
The items added to the map can then be moved and arranged by selecting the Edit tab then either Select/Move Content to move the locations of the specific content or Move Content to move the position/scale of the map.
Figure 14. Map generation - Step 10
Each item’s properties such as size, colour and fonts can also be edited in the Item Properties panel in the right.
Figure 15. Map generation - Step 11
The final generated layout is then exported in the desired format: png, pdf or svg. This is achieved through clicking on the Layout tab.
Figure 16. Map generation - Step 12
Results and maps
GRACE
The GRACE data has been downloaded and analysed through GEE. The main limitation of this dataset is its course resolution of 55.6 km2 as downloaded from the platform. This is due to the small geographical area of Bahrain at around 800 km2, causing water storage monitoring in specific locations to be a difficult task. Figure 17 demonstrates the span of GRACE data relative to the area of Bahrain.
Figure 17.GRACE Mascon- 2002 to 2024 Bahrain
HAND
The HAND model shown in the figure 18 provides valuable insights for addressing water scarcity in Bahrain. The low-lying areas highlighted in blue indicate regions where water tends to accumulate or water table is relatively shallow, suggesting potential zones for managed aquifer recharge (MAR) or stormwater harvesting. These areas could be prioritised for infiltration basins, recharging wells, or constructed wetlands to enhance groundwater storage. Conversely, the higher elevation zones in grey are less likely to retain surface water but could be strategically used for runoff collection and diversion to recharge areas. Given Bahrain’s arid climate and dependence on non-conventional water sources, integrating HAND-based terrain analysis into water resource planning can support more resilient, localised, and efficient water management strategies, particularly in optimising land use for recharge, storage, and flood mitigation purposes.
Figure 18. HAND results map
Soil moisture (SAR)
Figure 19 shows the mean soil moisture values of different regions of Bahrain. The southern regions seem to be drier while most central regions are wet. The analysis excluded urban regions.
Figure 19. SAR soil moisture results map
Soil moisture (Optical)
Figure 20 illustrates the soil moisture map with optical imagery for Bahrain. The results here highlight the northern west regions with high soil moisture values and the central, southern regions as dry with some specific location in the central and southern regions as wet.
Figure 20. Optical soil moisture results map
Shallow groundwater locations/recharge areas
Through Figure 5, the combinations of HAND, SAR and optical soil moisture has yielded to the potential locations for shallow groundwater locations/recharge areas. The areas highlighted in red represent the locations with highest potential.
Figure 21. Shallow groundwater locations/recharge areas results map
Solution impact
With the establishment of a methodology that identifies locations of shallow groundwater or recharge, significant information is being derived about the hydrological state of the country. This importance is placed due to the lack of remote sensing data that enables direct measurement of groundwater in the area. Hence the information extracted from this methodology can be initially integrated with sample in-situ data to calibrate the model; and then, be relied on solely for future measurements. Additionally, with the country’s rigorous focus on addressing groundwater scarcity, this type of information can greatly support decision-making when it comes to the formulation and execution of different projects and policies related to this matter.
Future work
To enhance the accuracy, applicability, and long-term impact of this solution in addressing water scarcity in Bahrain, several future developments are proposed:
Integration of additional remote sensing products: Incorporate higher-resolution satellite data to improve spatial resolution in soil moisture and elevation analyses, enabling finer-scale hydrological modeling and more localised identification of recharge zones. Moreover, the inclusion of land cover and geological characteristics can enhance the spatial modelling conducted.
Validation with in-situ data: Collaborate with local water authorities to collect and integrate ground-truth data such as groundwater levels, soil profiles, and well yields to validate and calibrate the HAND model and soil moisture outputs. This is also vital to assess the suitable weightage and classification for spatial modelling to be done to combine all three products generated.
Development of a Decision Support System (DSS): Create an interactive platform or dashboard that integrates HAND, GRACE, and soil moisture maps to assist policymakers in identifying priority areas for groundwater recharge, stormwater harvesting, and drought preparedness.
Temporal analysis and trend monitoring: Implement time-series analyses of GRACE and soil moisture data to detect trends, seasonal variations, and anomalies in water availability, supporting early warning systems and long-term planning.
Hydrological modelling coupling: Link HAND-derived terrain data with physically based hydrological models (e.g., SWAT, DHSVM) to simulate runoff, infiltration, and recharge scenarios under different land use and climate conditions.
Community engagement and capacity building: Conduct training workshops and knowledge-sharing activities with national institutions and stakeholders to build local capacity in geospatial water resource monitoring using open-source and space-based tools.
By pursuing these developments, the solution can evolve into a comprehensive and replicable model for sustainable groundwater resource management in water-scarce regions worldwide.
Relevant publications
Related space-based solutions
Sources
Nobre, A. D., Cuartas, L. A., Hodnett, M., Rennó, C. D., Rodrigues, G., Silveira, A., Waterloo, M., & Saleska, S. “Height Above the Nearest Drainage – a hydrologically relevant new terrain model.” Journal of Hydrology 404, no. 1–2 (2011): 13–29. https://doi.org/10.1016/j.jhydrol.2011.03.051.
The system involves a direct collection of rainwater into storage tanks. Implementing such a system has several challenges that a space-based GIS system can address comprehensively. The developed Rain4Drinking portal will be an opensource tool.
The model will be implemented in Google Earth Engine (GEE) and made available to communities through GEE App where they can just provide the shapefile of their rooftop or draw it on the interactive interface to get:
Size of tank they need
Type of tank they need
Best time of the year to store water for drinking
Filtration requirements
Indigenised filter available
Feasibility of the rooftop rainwater collection system
Cost benefit analysis of the rooftop rainwater collection system
Local availability of materials for potential required filtration
Software
Open-source cloud computing platform, Google Earth Engine (GEE) will be used for the development of this solution and the code will be public in form of GEE app.
Physical
Storage tank for collecting and storing water is a variable that needs to be constructed physically based on the output from space-based solutions. The important parameters for the tanks are their size and type.
Size of the tank:
The size of storage tank depends on the amount of rainwater received (varies locally) and size of collection area (rooftop). This requires a temporary track of precipitation in a region.
The rainfall data is also important for quantifying the purity of rainwater, as the month with a little rainfall gives more polluted water than the month with extensive rainfall.
Type of the tank:
Type of the tank usually comes with economic suitability. Plastic tanks are a ready-made solution but transportation to remote areas is very expensive, so their suitability zone is urban areas and its outskirts. The other option includes ferrocement tanks that can be built on the spot (with a low cost of material transportation) with huge sizes to store even thousands of gallons of water. The two famous types are Kalabashi Tank and Pumpkin Tank
Steps to a solution
The solution will be implemented in following three stages:
Data access: There are global repositories of air quality and precipitation, developed using both space and ground-based measurements.
Global rainfall repositories provide rainfall patterns, average precipitation, months of maximum rainfall in a specific region that can help communities to estimate the suitable tank size and time of the year with the best quality of rainwater as well as to meet the water demand during both wet and dry seasons.
Global air quality repositories provide details of particulate matter, volatile organic compounds and other contaminants in the atmosphere.
Processing / modelling
All the online available global repositories can be combined through multi-criteria analysis, helping communities determine their requirements, to estimate cost benefit analysis, and to conduct sustainability as well as risk assessment of their investment in harvesting rain for drinking purposes.
Implementation of Rain4Drinking: This requires the development of portal allowing governments, organizations and communities to determine their feasibility plans and help them understand the benefits they can have from such investments.
Three (3) different space-based solutions have been attempted to solve the problem of hydrocarbon contamination. The first method involves the use of an oilspill detection tab in SNAP. This method has limitations if the user cannot primarily identify the area where the spill occurred. The second method is the use of a script where all the steps which could be done manually are incorporated within the script. This solution also works well with a limitation of understanding and editing scripts. The last solution involves identifying thresholds of pixel values to detect the spill regions. This solution has produced the most desirable results, however it involves lengthy processes.
Requirements
Data
Pre-spill and During spill Sentinel 1 SAR data from Alaska Satellite Facility ASF Home | Alaska Satellite Facility .You might need to create an account to enable download
Click on the “vertex” icon on the left side of the “services” icon. A new page opens
On the map interface, click drawing tool (with the letter “A” written in it) under the icon “Area of Interest”. Select “Draw a polygon” and draw a polygon around your area of interest.
Click on “Filters” You can import your shapefile if you wish. Move down and enter your search dates (start and end dates)
You can add additional filters like the file type. Under the file type, it is advisable to choose the ground products (GRD) which are georeferenced. The Single Look Complex (SLC) products are preferred for Interferometric (InSAR) analyses. You can change the beam mode to IW (Interferometric Wide Swath)
You can leave the other parameters under “additional filters” as default to avoid limiting your search results.
Then download product
Software
The Sentinel Application Platform (SNAP) 11.0 software was used for solutions 1 and 3. SNAP is an open-source software developed by the European Space Agency (ESA) to support the processing and analysing of earth observation data particularly from the Sentinel repository. Link to the Sentinel platform SNAP Download – STEP
Scroll down and click on either Windows, Mac or Linux depending on your computer’s operating system requirement.
Google Earth Engine (GEE) was used for solution 2. GEE is an open source, cloud-based platform for processing geospatial data. It works with JavaScript and uses codes to pull out the needed satellite data from different repositories
Click on “platform” and then “code editor”. This takes you to the code editor interface where you can write and save your scripts or run them.
Physical
Information from the National Oilspill Detection and Response Agency (NOSDRA)was used to validate the occurrence of the spill
Outline steps for a solution
1 Manually Download Image- The first step is to manually download two (2) Sentinel 1 images from the Alaska Satellite Facility as described above. One image (archived image) before the spill and the other during the spill taking into consideration the temporal resolution (revisit period) of the sensor. The images will be zipped. There is no need to unzip the images
2 Load Image- Load the images onto the SNAP software. Simply drag and drop the zipped images onto the product explorer interface. Click on the icon on the left of the image file and the metadata and band icons will be visible. Click on the “bands” icon to reveal Amplitude VV, Amplitude VH, and Intensities. Load the Amplitude VV bands for both pre and post spill images and compare
3 Subset Image-Create a subset of your Area of Interest by using the subset tool under the icon “Raster”. This helps to reduce processing time as you will be focusing on a smaller area (area of interest),rather than the whole satellite image.
4 Image Pre-processing- Preprocessing the image by Radiometric (speckle filtering and calibrating) and Geometric Corrections in SNAP
4.1 Speckle filtering- This is the removal of background noise, which literally appears as speckles from an image. A speckle filtered image always appears less “grainy” than a non-speckle filtered image. In the Radar toolbar, you will find speckle filtering as the third option, select the single product speckle filter sub-option and use the subset file as input. Under processing parameters, choose amplitude VV, which is the best channel for detecting oil spills. Choose the Lee filter (3*3) and run. Finally, go to file in the menu bar and select tile horizontally, to have pre-speckle filtered image and post speckle filtered image side by side for comparison. After speckle filtering, the result produced on the product explorer space usually has the extension (.spk)
Image showing the “speckle filtering” location in SNAP
4.2 Calibration- This is done to improve radiance and reflectance to ensure that digital numbers (DN) accurately represent the reflectance of the physical characteristics of the SAR image. Speckle filtering and calibration are types of radiometric corrections. The calibration tool can be found under the “radar” icon in the SNAP software. Convert the result (sigma vv) from linear to db (decibels). This results in a virtual band. Right click (sigma-vv-db) and convert to band to have a real band. After speckle filtering and calibrating, the result produced on the product explorer interface usually has the extension (.spk cal)
Image showing the “calibration” location in SNAP
4.3 Terrain Correction- IftheArea of Interest was only the ocean, we might not need to do a terrain correction. However, it will be needed in this case as we are more concerned with the terrestrial environment. Go to “Radar” then “Geometric”, then “Terrain correction”. Choose the Range doppler terrain correction option. Set the options for your Digital Elevation Model (DEM)and run
Image showing “terrain correction” location in SNAP
After terrain correction, the result on the product explorer interface will have the extension (Spk.Cal.TC-1)
The three pre-processing steps documented above are required for solutions 1 and 3
Next step for Solution 1
Oilspill detection Tool: Within the Radar toolbar in SNAP, there is a “SAR Applications” window which further opens into “Ocean Applications” and then the oil spill detection tool can be found. After entering the parameters and running the tool, it creates a mask of the spill area and the geometry to aid spill area calculation.
Solution 2
GEE Platform
The steps listed below are the steps coded in the script to delineate areas with oil spills
1 Define Area of interest (AOI): Theregion of interest must be defined either by using a shapefile or geotag.
2 Define Temporal Scale: The revisit time (temporal resolution) of the Sentinel 1 SAR sensor is twelve (12) days. Dates must be entered to accommodate this temporal resolution
3 Load Sentinel-1 Ground Data:TheSentinel-1 data with the ‘vertical-vertical’ polarization (VV) which is best for oil spill detection is loaded,
4 Speckle filtering to reduce noise: Most Sentinel images come with speckles which may obscure the viewing of important information. Speckle filtering helps to provide a clearer image and better feature extraction.
5 Oil spill Change Mask and Thresholding is applied: This is done using the differences in the band sensitivity between the oil and water areas to delineate the oil spill areas.
6 Validation using the Nigerian Oilspill Detection and Response Agency (NOSDRA) database:
Code will be submitted in a separate document.
Solution 3 (Thresholding)
After the preprocessing steps explained above, the next steps are as follows
1 Layer Stacking-This is done to perform advanced analysis on the bands. Go to Radar, under “Radar”, you find coregistration then “stack tools” then “create stack”. Choose the bands you want to layerstack, this should be the (sigma0-vv-db bands) of the pre spill image and the post spill image.
2 Open RGB image- Now that the bands are stacked, you can do a band combination to have an idea of the areas where the spill occurred. Right click on the stack and “open RGB image”, put the post spill data in red and green and leave the pre spill data in blue, then run. This returns an image with the areas where the spill likely occurred in the image.ee RGB image window below.
Image showing “RGB” window in SNAP
The green areas are the likely spill areas. The shape file covers the region of interest but we are interested in the surrounding areas as well.
Measurement of the spill area to ascertain correctness of analysis (2.75+/-9.2*10-3)
Histogram Generation to obtain threshold values- Thehistogram shows the region of the oilspill.Load the histogram of the post spill image by highlighting the image in the product explorer window (sigma00-vv-db), click “Analysis”, scroll down and click histogram, then generate histogram. This gives the range of pixel values where the spill can be detected. See histogram below
Histogram of the post-spill image
Here, we see that the histogram range is from 0 to -25. However, from -15 to -25 looks like a false peak. So the threshold value chosen is -15 to -17
Also, from the image, if you hover around the green areas and check the pixel info on the “pixel info” tab next to “product explorer”, it ranges from -15 to -25, it is explained above why -15 was chosen.
The last step is to use this chosen value or range of values as the case may be to create an expression using the band maths tool to obtain the final spill area.
Band Maths Expression -Access the band maths tool which is the first option under “raster”. Create an expression with your chosen threshold values as seen in the figure below.
In the band maths expression above, the pre-spill data (24th May 2023) is subtracted from the post-spill data (17th June 2023) to obtain the spill area.
Results
There is an image with likely spill areas
Describe the impact this solution has on the ground
