Strengthening Food Security Planning and Climate–Health in Timor-Leste

Timor-Leste is highly exposed to climate variability and long-term climate change, with direct consequences for agricultural productivity, food security, and public health. Shifts in rainfall patterns affect crop growth and water availability, while environmental conditions influence the spread of vector-borne diseases such as dengue. These risks are compounded by high rural dispersion, reliance on rain-fed agriculture, exposure to floods, droughts and landslides, and limited health system capacity.

To address critical data gaps and better understand the impacts of climate change on health, agriculture, and population vulnerability, ESA’s Global Development Assistance (GDA) Public Health activity, in collaboration with the World Bank and implemented by Diginove and BSC, developed a suite of Earth Observation (EO) services covering 2016–2024. These services combine satellite observations, climate reanalysis, and demographic modelling to generate spatially explicit indicators of land use change, population distribution, crop dynamics, and climate-sensitive disease drivers.

The services provide decision-ready insights: they identify underserved rural populations, detect recurrent dry-season crop stress, quantify the rainfall–dengue relationship, and reveal how urban and agricultural expansion are reshaping exposure to climate risks. Built entirely on open global datasets, the approach is scalable and replicable in other data-scarce regions.

The EO products were co-developed with the World Bank’s Health, Nutrition, and Population (HNP) team, ensuring methodological robustness, iterative validation, and direct alignment with operational needs. The resulting analytics support flagship outputs, including the Timor-Leste Climate and Health Vulnerability Assessment (CHVA) and the Country Climate and Development Report (CCDR), and are already informing policy dialogue with national authorities.

Timor-Leste faces a combination of structural and environmental constraints that limit effective planning and risk management. Approximately 70% of the population depends on subsistence agriculture, making livelihoods highly sensitive to climate variability. Concurrently, the spatial distribution of the population and infrastructure remains poorly characterised, particularly in rural areas.

The absence of consistent, high-resolution data on land use, population, and environmental conditions constrains the ability of national institutions and development partners to assess vulnerability, monitor trends, and design targeted interventions. In addition, understanding the drivers of climate-sensitive diseases requires integrated datasets that combine environmental, climatic, and epidemiological information at appropriate spatial and temporal scales.

The World Bank identified these gaps within the framework of the Timor-Leste Accelerating Human Capital PASA (P58828) and required robust geospatial analytics to support the Climate and Health Vulnerability Assessment. The EO services were designed to address these needs by providing harmonised, multi-sectoral datasets enabling spatially explicit analysis of risks, exposure, and vulnerability under changing climatic conditions.

The EO services provide a coherent set of products addressing key dimensions of climate-related vulnerability in Timor-Leste. Land use maps derived from Sentinel-2 imagery show how the landscape evolved between 2016 and 2024, with urban and cropland expansion occurring alongside grassland decline. These changes highlight increasing pressure on natural ecosystems and help identify areas where settlement growth overlaps with flood- and erosion-prone zones, where hazards are expected to intensify under climate change.

A second product line focuses on population distribution. By combining satellite-detected settlement footprints with demographic modelling and census data, the project generated high-resolution maps of population density and settlement patterns. These outputs are especially valuable in rural Timor- Leste, where information on who lives where remains limited but is essential for planning services, infrastructure, and exposure assessments.

A third product line supports crop monitoring through vegetation and moisture indices, including Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalised Difference Moisture Index (NDMI), and Moisture Stress Index (MSI). These indicators reveal strong seasonal variability linked to wet and dry cycles and identify regions experiencing recurrent dry-season stress, pointing to areas where irrigation support, crop diversification, or food-security measures may be needed.

Finally, climate and epidemiological data were combined to better understand disease dynamics. Dengue incidence was found to be strongly influenced by rainfall seasonality, with measurable time lags between rainfall peaks and case increases, providing a quantitative basis for early warning and targeted intervention. Malaria analysis indicated a low reintroduction risk, consistent with the declining disease burden in neighbouring Indonesia.

Together, these products provide a multi-sectoral, climate-informed evidence base for assessing risk and guiding interventions.

The EO services rely on a combination of globally available datasets and national information sources, making the methodology reproducible and scalable.

Copernicus Sentinel-2 multispectral imagery was used for land use mapping and vegetation monitoring. Sentinel-1 radar and Sentinel-2 optical data were combined to detect settlements and support population modelling. ERA5 climate reanalysis provided variables such as precipitation, temperature, humidity, and soil temperature, enabling the analysis of climatic drivers of crop stress and disease dynamics. OpenStreetMap contributed information on settlement structure and infrastructure, while census and health records provided the demographic and epidemiological reference data needed for calibration and validation.

The integration of EO, climate, and socio-economic information made it possible to derive indicators that are otherwise difficult to measure consistently, such as settlement-level population distribution, drought stress patterns, and rainfall-driven disease dynamics.

The EO services have significantly strengthened the analytical and decision-support capacity of both the World Bank and stakeholders in Timor-Leste. The World Bank HNP team reported a high level of satisfaction with the accuracy, reliability, and usability of the EO-derived products, highlighting the effectiveness of the co-development approach, iterative validation, and clear visualisation outputs. The iterative process ensured that the outputs met operational needs and could be directly applied in flagship reports.

The analytics are now integrated into the CHVA and CCDR, where they support policy dialogue not only with the Ministry of Health but also with wider government stakeholders, including actors responsible for finance, infrastructure, and planning. This creates a strong pathway from EO analysis to public policy and investment decisions.

Operationally, the provided population distribution layer has emerged among the most immediately actionable outputs. It is being used to assess potential demand for services, define catchment areas for existing and planned health facilities, and support decisions on rehabilitation or expansion of infrastructure. In a context where such information is often lacking, this provides a much stronger evidence base for service delivery planning.

In the health sector, the identification of a clear rainfall-to-dengue transmission relationship provides a scientific basis for early warning systems and anticipatory vector control. In the agricultural sector, vegetation and moisture indicators help identify drought-prone regions and recurrent dry-season stress, supporting more targeted irrigation planning and food-security interventions.

The services are also recognised as highly scalable. The World Bank views the Timor-Leste case as a blueprint that can inform similar work in other countries, particularly across East Asia and the Pacific and in data-scarce environments globally. Interest has also emerged in applying related EO-based population analytics in Sub-Saharan Africa, especially for improving health facility geodata and accessibility analysis.

Overall, the project demonstrates how EO data, when co-developed with users and integrated into operational workflows, can directly support more evidence-based policy-making, better resource allocation, and stronger resilience to climate change.