Earth observing satellite programs such as Landsat, co-managed by NASA and the U.S. Geological Survey, and the Sentinel missions, developed by the European Space Agency (ESA), provide free-of-charge data at spatial scales capable of resolving urbanization from local to global scale. NASA and Conservation International (CI) have united to leverage EO data and science within CI’s Trends.Earth - an innovative, open source tool - to allow users to calculate the Sustainable Development Goal (SDG) indicator 11.3.1, Ratio of land consumption rate to population growth rate, across the globe at five year intervals from 2000-2015.

Achieving the SDGs requires capacity building initiatives including the co-designing of methods and accessible tools and the development of use cases that enable awareness, access, and integration of a multitude of data sources, including Earth observations and geospatial information, census data, administrative and household survey data, among others. The objective of this effort is to support countries in applying Earth observation data and science to assess how land consumption by cities contributes to sustainable urbanization (Target 11.3), and facilitate the tracking, monitoring, and reporting on progress against indicator 11.3.1. To meet this objective, the NASA/ CI team, with guidance from UN-Habitat, developed Trends.Earth, an open source tool that is global in nature, follows the globally adopted methodology for the indicator computation, and is implemented locally based on end-user needs and data availability. Having a flexible tool is important so that individual countries can customize outputs based on their local data sets for improved accuracy, while ensuring that this is done within a broader framework that is consistent and comparable at global scale. Trends.Earth is based on Google Earth Engine (GEE) and its Earth observation (EO) data catalog, QGIS (an open source Geographic Information System), and leverages global, 30m Landsat-based urban extent and imperviousness data and gridded population data from NASA’s Socioeconomic Data and Applications Center.

NASA and CI are leading the tool development in coordination with UN-Habitat. The accompanying methods are informed by previous efforts and implementation of SDG indicators by Colombia’s National Office of Statistics (DANE), previous NASA/CI efforts for monitoring and reporting on SDG indicator 15.3.1, and the global computation methodology developed by UN-Habitat. Collaboration with UN-Habitat and country partners (e.g., Mexico, Morocco) to conduct trainings on the tool’s use at local level are ongoing. Efforts have also begun for tool verification and to refine and standardize the method to conform to end-users’ environments and standard interfaces.

The project has leveraged an ongoing collaboration with Colombia’s DANE, NASA, and the Group on Earth Observations (GEO) to use Earth observations for sustainable development applications. In addition, the team has leveraged DANE’s successful approach in calculating indicator 11.3.1. While examining DANE’s methodology to help scale this up to other countries, the team recognized that some of its elements might not be easily transferrable to countries not possessing data processing and analysis capabilities similar to Colombia’s. An approach that uses the existing Landsat Global Man-made Impervious Surface (GMIS) data set and Google Earth Engine (GEE) was therefore developed to provide more flexibility and global applicability.

Using the 2010 GMIS data for training and GEE satellite archives and machine learning algorithms, the tool is applied to estimate urban extent forward and backward in time for calculation of urban consumption rates from 2000 to 2015, at five year intervals. This work is also informed by parallel science efforts to calculate the indicator over the continental U.S. (Bounoua et al. 2018) and connect it to its physical interpretation on the ground using a novel metric that measures change in land use per capita. Guiding documents and collaboration with UN-Habitat has ensured that the tool follows the global indicator computation methodology, and that the adopted metrics and thresholds are comparable across countries. The project is in its evaluation phase through UN-Habitat and its network of country partners, as well as the UN Inter-Agency Expert Group on the SDGs (IAEG-SDGs) Working Group on Geospatial Information (WGGI) and its UN Member Countries.

A beta version of the tool has been implemented in CI's Trends.Earth and is also incorporated in UN Habitat’s training module for indicator 11.3.1. The platform has been presented at two recent trainings during the 2018 Regional Centre for Mapping Resource for Development (RCMRD) International Conference and the International Seminar on United Nations Global Geospatial Information Management "Geospatial Information for Sustainable Development." The urban extent maps for 2010 produced by the tool compare well spatially with the existing GMIS data. Limited analyses of the performance of the urban extent change analysis is being completed through visual interpretation of Google maps and reference to data sets from DANE, Mexico, and Morocco. Ongoing work on the U.S., using impervious surface per capita, is also being leveraged for comparisons and to complement the interpretation of SGD 11.3.1. We expect results from this ongoing work to be implemented in CI’s Trends.Earth.

CI’s existing collaboration with the UN Convention to Combat Desertification (UNCCD) to assist countries and regions with building national capacities for monitoring and reporting on land degradation, and the development of Trends.Earth that was produced as part of a project funded by the Global Environment Facility, titled “Enabling the use of global data sources to assess and monitor land degradation at multiple scales”, were foundational to this effort. In addition, the availability of global Landsat imagery and data sets such as GMIS have also been essential for this work. Efforts by Colombia's DANE have helped guide the overall approach and methodology. Existing collaboration among NASA, GEO and UN Habitat have also served as a key enabling factor. Future trainings, including webinars and in-person sessions, will facilitate further evaluation across the world and provide valuable feedback for algorithm tuning and tool refinement.

Some noteworthy constraints include: the availability of gridded population growth data sets and their application for SDG calculations, as well as the broad interpretation of SDG calculations as currently implemented. The increasing availability of consistent, high resolution, global urbanization time series data sets, global land cover and land use data, as well as very high spatial resolution reference data for training and verification purposes will provide improved capabilities to quantify and assess urban land consumption, and improve the accuracy and fidelity of the SDG calculations in the future. Variable levels of resources by in-country partners as well as availability of local urban extent or population data within each country may make the approach more or less accessible to certain countries, and thus cause difficulties in comparisons of urban sustainability across countries.

The continued availability of free-of-charge, Landsat-like satellite data in the future is essential to creating an enabling environment that facilitates the monitoring and tracking of sustainable urban growth and planning. These data are also essential as the base data for tools using platforms, such as GEE, to support country level analysis and the SDG reporting process. Thorough evaluation of the data and results through UN-Habitat and country partners, including on-going usability and user experience assessments, is key to help build confidence in the approach both within, and among, countries. Valuable feedback from this evaluation will lead to improvements or more flexibility needed by users. The goal is to achieve global applicability and replicability. The web-based and open-source nature of the data sources, methodology, and tool is important to achieve this goal and to ensure widespread sustainability.

When fully evaluated and implemented, this Earth observation integrated approach will provide users with a flexible and effective method and tool to analyze changes in built up area using a variety of parameters (e.g., impervious surface index, night time lights index, and water frequency), while also enabling the monitoring and reporting on indicator 11.3.1. Further work still remains to assess the accuracy of the urban extent data as well as the urban extent change. Ongoing work on gridded population data sets will also provide insights on best practices related to these data for a more effective calculation of gridded population growth. For many countries that do not have baseline data on SDG 11 and may have limited or no technical capacity to implement the Earth observation - reliant workflows in the short term, this tool offers a unique opportunity to not only collect data, but also establish a baseline upon which other indicators can be built upon. The tool may also, in the long term, allow countries and cities to integrate new secondary indicators, which will help measure related land use trends of local importance, significantly enhancing informed decision-making.

1. UN Habitat Land Consumption Training Module. Retrieved from: https://unhabitat.org/tools-and-guides/
2. Bounoua, L.; Nigro, J.; Thome, K.; Zhang, P.; Fathi, N.; Lachir, A. A Method for Mapping Future Urbanization in the United States. Urban Sci. 2018, 2, 40.
3. Global Man-made Impervious Surface (GMIS) Dataset From Landsat (Version 1). Retrieved from: http://sedac.ciesin.columbia.edu
4. Trends.Earth Tool. Retrieved from: http://trends.earth
5. Earth Observations for Sustainable Development Goals (EO4SDG): http://eo4sdg.org