Global Products & Applications

The NASA Land-Cover/Land-Use Change (LCLUC) program is developing interdisciplinary research combining aspects of physical, social and economic sciences, with a high level of societal relevance, using remote sensing tools, methods and data. LCLUC studies use a combination of space observations, in situ measurements, process studies and numerical modeling. To get the most out of current remote sensing capabilities researchers strive to utilize different remote sensing data sources. This presentation will focus on recent, significant results in the LCLUC urban studies. The highlights will include multi-scale/multi-sensor analysis of urban cluster development in China and India, mega-urban changes in the previous decade, mapping of urban expansion using multi-decadal Landsat and nightlights data over North America, and other studies.

Reliably monitoring global urbanization is of key importance to accurately estimate the distribution of the continually expanding population, along with its effects on the use of resources (e.g., soil, energy, water), infrastructure and transport needs, socioeconomic development, human health, food security, etc. In this context, while in the last few years several global layers mapping the actual settlement extent have been presented in the literature, so far only few datasets outline the settlement growth over time, which is fundamental for modelling ongoing trends and implementing dedicated suitable planning strategies. Furthermore, the existing products are mostly available for few time steps in the past and their quality– yet by simple qualitative assessment against e.g. Google Earth historical imagery – appears rather poor.

To overcome this limitation, the German Aerospace Center (DLR) in collaboration with the Google Earth Engine (GEE) team has designed and implemented a novel iterative technique for outlining the past settlement extent from Landsat-4/5/7/8 multitemporal imagery available from late 1984 to present. First, under the assumption that pixels categorized as non-settlement at a later time cannot be marked as settlement at an earlier time, all areas excluded from the World Settlement Footprint (WSF) 2015 (i.e., the currently existing most updated and accurate mask outlining the 2015 global settlement extent) are discarded a priori from the analysis. Next, for each target year in the past all available Landsat scenes acquired with cloud cover lower than 60% over the investigated area of interest are gathered and cloud masking is performed. Key temporal statistics (i.e., temporal mean, minimum, maximum, etc.) are then extracted for different spectral indices including the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI). Going backwards in time, training samples for the given target year are iteratively extracted by applying morphological filtering to the settlement mask derived for the previous time step as well as excluding potentially mislabeled samples by adaptive thresholding on the temporal mean NDBI, MNDWI and NDVI. Finally, random forest classification in performed.

Extensive experimental analyses over several test sites assessed the great effectiveness and robustness of the methodology. Accordingly, it is currently being employed within the GEE environment for generating the WSF Evolution, i.e. a novel dataset aimed at outlining the growth of settlement extent globally at 30m spatial resolution and high temporal resolution (i.e., 5-year or even finer) from 1985 to 2015. The WSF Evolution is envisaged to be completed in the next few months and it is expected to become a revolutionary product in support to a variety of end users in the framework of several thematic applications.

Unlike the Millennium Development Goals,  the 2030 Agenda and its associated Sustainable Development Goals (SDG) contain a core set of indicators that require use of spatial techniques to report on progress. A core set of these indicators sit in Goal 11, mainly under the custodianship of UN-Habitat. The success of applying spatial and earth observation techniques for global monitoring requires working with multiple partners such as private sector, academia, local governments, national governments, space agencies, etc. Building capacities of data producers and users is essential, given that a substantive role of the reporting burdens now falls on national governments. This presentation will share the experiences of developing the methodologies for global monitoring and reporting on a core set of spatially dependent SDG urban indicators, and examine existing challenges and opportunities for applying spatial and earth observations techniques on the global scale. 

Remote sensing of urban areas can provide valuable data about the function of cities. The considerable technological improvements of the last decade provide new opportunities to use such data and open the way for the development of new applications and services. This presentation discusses the use of satellite thermal data in heat-health applications for disaster risk reduction (DRR) and in particular, how EXTREMA (EXTReme tEMperature Alerts for Europe; URL: extrema.space), a 2018 co-funded European civil protection program, converts geostationary thermal image data into actionable information. This information has the form of personalized heatwave risk estimations that can be readily used by the general public and the city authorities. To achieve this, the EXTREMA project uses a novel retrieval method that combines thermal image data from MSG-SEVIRI with predictions from the Global Forecasting System (GFS). This method can in principle be applied anywhere in Europe and produces datasets of urban air temperatures with a spatial resolution of 1 km and a temporal of 5 min. These data can then (i) be converted to heatwave hazard maps, so as to be used for the real-time monitoring of heatwaves in cities; and (ii) be combined with epidemiological data that describe the effect of heat on the urban population, so as to assess the heatwave risk. The latter is the key input to the EXTREMA mobile application (already adopted by Athens, Palma de Mallorca, Paris and Rotterdam), which is available to everyone in Europe and aims to inform him/her in real-time, about his/hers personalized extreme temperature risk.

A number of different global-scale data products focused on urban settlement and population mapping have been developed based on remote sensing data sources such as medium- and high-resolution imagery, radar, and night-time lights. Each product has advantages and disadvantages with respect to spatial and temporal resolution, coverage, accuracy and precision (which may vary by location, time, and environmental conditions), change detection, and other characteristics. Some users are interested in current best estimates of the number and attributes of buildings and other structures, and associated population, in a specific area; others may be more concerned with annual or decadal changes in land use and settlement patterns across multiple urban or peri-urban areas. The proliferation of products with different characteristics is certainly beneficial for both scientific and applied users, but especially for applied users involved in sustainable development applications, making sense of this increasing variety is a challenge. Moreover, relatively little attention has been given to date to developing systematic tools and metrics for comparing different data products and investing in reference data products and formal intercomparison experiments that can provide more rigorous and quantitative assessments of data quality, uncertainty, and fitness for different applications.

With this in mind, most of the groups that have been developing data related to human settlements, population, and infrastructure have begun meeting and coordinating under the auspices of the Human Planet Initiative of the Group on Earth Observations (GEO) and the POPGRID Data Collective. POPGRID is currently an informal network supported by the Bill and Melinda Gates Foundation, which is being transitioned into a formal project of the Global Partnership for Sustainable Development Data (GPSDD).  Key objectives of POPGRID include improving the accessibility and usability of different data products, catalyzing validation and intercomparison efforts, supporting interactions with stakeholders on needs and resources, and facilitating the uptake of data products and services in sustainable development monitoring and decision making.