Assessing the resilience of cities and human settlements relies on understanding and quantifying the processes that governs the functioning of cities and settlements, understanding and quantifying the potential hazards that may impact societies; and estimate their projected population and built up growth. Satellite imagery have proven valuable in generating up to date urban measures including built-up, and are used to generate services for scientists, practitioners and decision makers. Part of the information on global built-up will be made available through the evolving Copernicus Emergency Mapping Services.
Human settlements are evolving fast. Population and economic growth as well as internal and international migration have accelerated the process of urbanization making it one of the megatrends of the 21st century. Urban growth and societal well-being requires improved management and measurement tools that take into account the complexity and multi-scale processes that shape human settlements. Today, for many metropolitan areas of the world, built-up extends beyond the territorial jurisdiction of a given entity, generating new urban management challenges. Traditional urban issues including access to clean water, clean air and energy; the demand for new infrastructure including adequate sanitation and waste disposal systems need to be analysed over larger territories. Also, hazards seldom strike within cities and more often originate and unfold over areas that cannot be mitigated by authorities from a single jurisdiction. International decision makers and donors are also aware that urbanization statistics generated with current national defined methodologies are not comparable across countries of the World and Earth Observation is being used to address the information gap. In fact, the long term and synoptic view of Earth observation image archives provide the historical record needed to address urbanization and its challenges. The continuity of Landsat image acquisition dating back to the mid 1970’s, is now complemented by Sentinel imagery that provides continuity over time as well as finer scale data.
The assumptions and the definitions of urban and rural areas need to be reconciled with the new urban challenges. A number of research institutions have interacted with decision makers to generate the information layers required. For example, the Global Human Settlement Layer (GHSL) suite of products including built-up and population spatial grids have been combined to generate a methodology that outlines cities and settlement for use in measuring urbanization in a homogenized way. GHS layers are used to understand built up growth, encroachment on other land uses, land degradation, loss of agricultural land to urbanization, estimate of CO2 emission globally. Scientists and practitioners combine datasets to assess past and current emissions, to estimate energy demands and the potential of renewable energy production for climate mitigation.
The GHS layers are not only used to measure patterns and processes but also to report for four international frameworks. GHS layers generate urbanisation statistics used in the New Urban Agenda and for the Sustainable Development Goals, act as exposure layers for assessing disaster risk as requested by the Sendai Framework for Disaster Risk Reduction and have been considered for contributing to assess the Global Stock Take called by The Paris Agreement.
The presentation will provide an insight in current uses of GHS layers by scientists and decision makers. The presentation will also deliver examples of the upcoming novel release of GHSL Datasets and provide key technical specification for the datasets that will be funded by the Copernicus Emergency Mapping Service.

This presentation discusses a modern evolution in research and development from specific/separate urban climate, weather, air quality models to multi-hazard and integrated urban hydrometeorology, climate and environment systems and services. It also provides an overview of joint results from the following large international teams: WMO GURME, IUS, and EU FP FUMAPEX, MEGAPOLI, EuMetChem, MACC and MarcoPolo projects.

Over many decades, developments of urban weather, air quality, hydrology and climate prediction models were progressing separately with very little close collaboration between the research communities. Over the last decade a number of international studies have explored these issues in a complex manner. In particular, relevant experience from the European projects FUMAPEX, MEGAPOLI, MarcoPolo will be demonstrated. MEGAPOLI studies aimed to assess the impacts of megacities and large air-pollution hotspots on local, regional and global air quality; to quantify feedback mechanisms linking megacity air quality, local and regional climates, and global climate change; and to develop improved tools for predicting air pollution levels in megacities (Baklanov et al., 2010). FUMAPEX developed for the first time an integrated system encompassing emissions, urban meteorology and population exposure for urban air pollution episode forecasting, for assessment of urban air quality and health effects, and for emergency preparedness issues in urban areas (UAQIFS: Urban Air Quality Forecasting and Information System; Baklanov, 2006; Baklanov et al., 2002, 2007).

While important advances have been made, new interdisciplinary research studies are needed to increase our understanding of the interactions between emissions, air quality, and regional and global climates. Studies need to address both basic and applied research and bridge the spatial and temporal scales connecting local emissions, air quality and weather with climate and global atmospheric chemistry. WMO has established the Global Atmosphere Watch (GAW) Urban Research Meteorology and Environment (GURME) project which provides an important research contribution to the integrated urban services.

It is also important to remember that most (about 90%) of the disasters affecting urban areas are of a hydro-meteorological nature and these have increased due to climate change (Habitat-III, 2016). Cities are also responsible not only for air pollution emissions, but also for generating up to 70% of the Greenhouse Gas emissions that drive large scale climate change. Thus, there is a strong feedback between contributions of cities to environmental health, climate change and the impacts of climate change on cities and consequently, these phases of the problem should not be considered separately. Further, a single extreme event can lead to a cascading effect that generates new hazards and to a broad breakdown of a city’s infrastructure. There is a critical need to consider the problem in a complex manner with interactions of climate change and disaster risk reduction for urban areas (Grimmond et al., 2014, 2015, 2020; Baklanov et al., 2016, 2018, 2020).

WMO is promoting safe, healthy and resilient cities through the development of Integrated Urban Weather, Environment and Climate Services. The aim is to build urban services that meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts, multi-hazard early warning systems, disaster management plans and climate services. This approach gives cities the tools they need to reduce emissions, build thriving and resilient communities and implement the UN Sustainable Development Goals.

The Guidance on Integrated Urban Hydro-Meteorological, Climate and Environmental Services (IUS), developed by a WMO inter-programme working group and the Commission for Atmospheric Sciences and Commission for Basic Systems, documents and shares good practices that will allow countries and cities to improve the resilience of urban areas to a great variety of natural and other hazards (WMO, 2019, 2021).

References

Baklanov A., A. Rasmussen, B. Fay, E. Berge, S. Finardi, 2002: Potential and Shortcomings of NWP Models in Providing Meteorological Data for Urban Air Pollution Forecasting. Water, Air and Soil Poll.: Focus, 2(5-6), 43-60.
Baklanov, A., 2006: Overview of the European project FUMAPEX. ACP, 6, 2005-2015, doi.org/10.5194/acp-6-2005-2006
Baklanov, A., Hänninen, O., Slørdal, L. H., et al., 2007: Integrated systems for forecasting urban meteorology, air pollution and population exposure, ACP, 7, 855-874, https://doi.org/10.5194/acp-7-855-2007
Baklanov, A., Lawrence, M., Pandis, S., et al., 2010: MEGAPOLI: concept of multi-scale modelling of megacity impact on air quality and climate, Adv. Sci. Res., 4, 115-120., https://doi.org/10.5194/asr-4-115-2010
Baklanov, A., L.T. Molina, M. Gauss, 2016: Megacities, air quality and climate. Atmospheric Environment, 126: 235–249. doi:10.1016/j.atmosenv.2015.11.059
Baklanov A., Grimmond, C.S.B., Carlson, D., et al., 2018: From Urban Meteorology, Climate and Environment Research to Integrated City Services. Urban Climate, 23: 330-341, https://doi.org/10.1016/j.uclim.2017.05.004
Baklanov, A., B Cárdenas, T-C Lee, et al., 2020: Integrated urban services: experience from four cities on different continents, Urban Climate, 32, https://doi.org/10.1016/j.uclim.2020.100610
Grimmond, C.S.B., Tang, X., Baklanov, A., 2014. Towards integrated urban weather, environment and climate services. WMO Bull., 63(1): 10-14.
Grimmond, C.S.B., Carmichael, G., Lean, H., et al., 2015: Urban-scale environmental prediction systems. Chapter 18 in the WWOSC Book: Seamless Prediction of the Earth System: from Minutes to Months, WMO-No. 1156, Geneva, pp. 347-370.
Grimmond, S., V. Bouchet, L.T. Molina, A., et al., 2020: Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages, Urban Climate, https://doi.org/10.1016/j.uclim.2020.100623
HABITAT-III, 2016. The new UN Urban Agenda, The document adopted at the Habitat III Conference in Quito, Ecuador.
WMO, 2019: Guidance on Integrated Urban Hydrometeorological, Climate and Environmental Services. Volume 1: Concept and Methodology, Grimmond, S., Bouchet, S., Molina, L. et al., WMO-No. 1234.
WMO, 2021: Guidance on IUS. Volume 2: Demonstration Cities. Editors Grimmond, S. and Sokhi, R., WMO-No. 1234.

Evapotranspiration (ET) in urban areas mitigates the urban heat island and is a central component of the urban water cycle. Despite its importance to urban ecosystem services, few studies so far have focused on mapping urban ET for an entire city in high spatio-temporal resolution. In this study, we have developed a method for mapping urban ET using open geodata, machine learning (ML), and flux footprint modeling. Two eddy flux towers with contrasting surrounding land cover in Berlin, Germany provided the training and testing data. Footprint modelling of urban flux towers allowed us to incorporate the impact of various land covers (e.g. vegetation cover, impervious pavements, etc.) to train the ML models and subsequently to estimate the urban ET. Open remote sensing and geodata used to model urban ET includes Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. In addition, hourly potential ET (ETo) was selected as a predictor indicating the meteorological conditions. ETo was calculated on a half-hourly basis from ten nearby weather stations and interpolated using ordinary kriging. Modeling was carried out using the random forest (RF) algorithm. Hourly ET maps at a 10-m resolution were generated for the entire city for the year of 2019. Validation was conducted by extracting the predicted ET from the maps using flux footprints and comparing it to the ET measured at the two towers. Lastly, summary statistics between Land Use and Land Cover (LULC) classes and ET were calculated to further check the mapping plausibility and to inform sustainable urban planning. As this approach relies on open source datasets to map ET, it is transferable to other temperate cities with flux towers and potentially generalizable in similar climatic conditions without retraining ML models. Urban ET maps can be used to support cooling and greening initiatives and to optimize the water supply of vegetation in many cities around the world.

Water areas are important and much-used living spaces in urban areas and in the urban hinterland. They are of great economic interest (e.g. ports), are heavily used for leisure, sports and tourism, and serve to improve the urban climate and attractivity of cities.
The City Water Watch project (CIWAWA) has integrated remote sensing methods for the monitoring and management of water bodies in cities and urban environments. In CIWAWA we combined EO and in-situ techniques to show municipalities the advantages of using both techniques for monitoring purposes.
Interest in remote sensing data and derived products for inland water monitoring has increased significantly in recent years. With the availability of various sensors in the medium and high resolution range, good temporal and spatial coverage can meanwhile be provided. In this context, CIWAWA addresses particularly the influence of urban areas on remote sensing of water bodies and the developed algorithmic improvements and new developments. This includes the special influences of the adjacency effect, shadows and neighborhood influences by high buildings or the detection of objects on the water surfaces. Requirements from administrations are discussed and products from remote sensing and in situ measurements are developed accordingly. These must be easily accessible for supporting users in their daily work. Beyond the common measurements such as chlorophyll-a concentration or turbidity in the water bodies, CIWAWA is also dedicated to the questions of algae group differentiation, detection of temperature inputs or riparian vegetation. Existing measurement networks and targeted measurement cruises have been used to collect validation and training data sets. Measurement instruments have been further developed to work as permanent installation in water for fluorometric and spectral reflectance measurements to foster the combination of measurement techniques for an optimized monitoring concept.
Based on the users’ requirements and pre-knowledge, we developed several interfaces for users. Learning that users are more and more interested in including EO methods and in applying EO processes on their own, we are covering a large range of interfaces from pre-defined maps and factsheets to be directly integrated into reports to a tailored ‘processing as a service’ for users to perform their own production and analyses. Datacubes are offered as an easy to use data format for visualization and analysis via scripting.
CIWAWA was focusing on Hamburg but also transferred the developments to other cities. Hamburg characterized by a variety of different water bodies. The Elbe as a tide-influenced water body, including the harbor, the Alster as a dammed inland lake in the urban area, but also the many smaller lakes in and around the city are used in a variety of ways. The presentation will cover different show cases for Hamburg and other cities as well as a demonstration of the interfaces developed for users for getting easy access to data and/or information.

Climate change in NW Europe is causing an increase in both average annual precipitation and in extreme rainfall events. Both changes increase the chance of flooding. Simultaneously, periods of continuous drought occur more often. Long dry periods accelerate soil decomposition, particularly in areas of peat soil, causing subsidence. This subsidence mostly impacts the public space and (private) houses built on shallow foundations. Increased flooding chances combined with increased subsidence of houses means damage related to flooding is in a worsening recurring cycle.

While municipalities can mitigate effects of subsidence in public spaces by heightening the roads and a variety of measures for improved runoff and storage, this is not as simple for private properties like houses. The fact that mitigation in the public space might negatively affect flooding risk for private properties, makes this a wicked problem. SkyGeo is working together with several Dutch municipalities to provide input on their climate strategy, helping them understand which houses are at risk and how this risk will change looking into the future.

High resolution TerraSAR-X was processed on SkyGeo’s proprietary InSAR software. The resulting data was combined with a high resolution LIDAR digital elevation model (DEM), building contours and (in some cases) recent door sill height measurements. Using the InSAR and DEM inputs, we isolated InSAR points on the ground versus on buildings and calculated the average settlement rate per property over the past 10 years. Using this settlement rate, we made a prediction as to the number of years before the building reaches a critical level below NAP (Normal Amsterdam Level). Together with the municipalities, we defined risk thresholds based on expected damages, frequency of damages and projected door sill heights based on the settlement rates.

Using these thresholds, we created a series of risk maps which the municipality uses to visualize and communicate the flooding problem to homeowners. SkyGeo insights and extrapolations help these municipalities plan for the long term effects of climate change.

Urbanization and sustainable settlement growth are key global challenges. They are closely linked to the socio-economic development and affect the surrounding environment, as well as health of a majority of the global population. Greenhouse gas emissions of settlement growth related construction activities, as well as of energy and consumable consumption are major contributors to climate change. The changing (mostly warming) climate leads to more extreme and more frequent natural hazards, which put infrastructures, settlements and their inhabitants at risk. Collaborative efforts, tools and information are needed to mitigate negative impacts of settlement growth, to identify locally adapted compromises between the environment and human well-being, as well as to understand how to take advantage of opportunities that arise out site specific settings. Earth observation (EO) information products and other spatial datasets have been successfully used and exploited in this context by many research projects, as well as planning and decision-making processes for making cities more resilient. While most of these activities were based on solitary and effortful processing and visualization solutions, platform-based initiatives have proven to be game changing technologies, which are capable of revolutionizing service provision, workflows and information products. The Urban Thematic Exploitation Platform (UrbanTEP; urban-tep.eu) is a collaborative system, which focuses on the processing of EO and other spatial information products for delivering multi-source information on trans-sectoral urban challenges on various scales.

UrbanTEP is developed to provide end-to-end and ready-to-use solutions for a wide spectrum of users (i.a. service providers, expert and non-expert users and researchers) for extracting unique information and products required in the urban context (Esch et al. 2018). The key system components are an open, web-based portal connected to distributed and scalable high-level computing infrastructures and providing key functionalities for:

i) high-performance data access and processing (IaaS – Infrastructure as a Service),
ii) modular and generic state-of-the art pre-processing, analysis, and visualization tools and algorithms (SaaS – Software as a Service),
iii) customized development and sharing of algorithms, products and services (PaaS – Platform as a Service), and
iv) networking and communication.

The processing, analysis and visualization of EO data in the frame of the New Urban Agenda (NUA) and the Sustainable Development Goals (SDGs) monitoring, specifically the SDG 11 “Sustainable Cities and Communities”, are central aspects of UrbanTEP. Via the concept of the “City Data Cubes” for urban use cases and hosted processing on the DIAS platforms, UrbanTEP offers capabilities to exploit large EO archives (e.g. Copernicus Sentinel missions). EO-based information products support these analyses. The data analytics and visualization capabilities of UrbanTEP provide functionalities for a user-driven derivation of key urban indicators based on the above-mentioned multi-source data collections. These indicators include built-up extent, built-up density, form and centrality, population distribution and population at risk (e.g., to natural hazards), which allows users and service providers to develop globally applicable services for the on-demand calculation and analysis for the monitoring of SDG 11 indicator 11.3.1. One of the products that contributes to the global mapping and monitoring of human settlements is the World Settlement Footprint (WSF), which outlines built-up areas globally, as well as the WSF Evolution product, which provides annual settlement extents and allows analyzing settlement dynamics (Marconcini et al. 2020; Marconcini et al. 2021). In a scalable use case approach in close exchange with UN-Habitat, SDG 11.3.1 products and urban indicators were developed, processed and visualized for Eastern Africa and made available for the interested user community.

- Esch, T., Asamer, H., Bachofer, F., Balhar, J., Boettcher, M., Boissier, E., D' Angelo, P., Gevaert, C. M., Hirner, A., Jupova, K., Kurz, F., Kwarteng, A. Y., Mathot, E., Marconcini, M., Marin, A., Metz-Marconcini, A., Pacini, F., Paganini, M., Permana, H., Soukup, T., Uereyen, S., Small, C., Svaton, V. & Zeidler, J. N. (2018): Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge. International Journal of Digital Earth, S. 1-22.

- Marconcini, M., Metz-Marconcini, A., Ureyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., Zeidler, J., Esch, T., Gorelick, N., Kakarla, A., Paganini, M. & Strano, E. (2020): Outlining where humans live, the World Settlement Footprint 2015. Sci Data, 7, S. 242.

- Marconcini, M., Metz- Marconcini, A., Esch, T., & Gorelick, N. (2021): Understanding Current Trends in Global Urbanisation - The World Settlement Footprint Suite. GI_Forum, 1, 33–38.