Using space-based technologies to predict mosquito-borne disease outbreaks

Mosquitos are often cited as one of the deadliest animals in the world, causing up to one million deaths per year (WHO, 2020; CDC, 2021). They can carry and transmit a variety of diseases, including malaria, West Nile virus, dengue fever, and Zika virus; transmitting illness across the globe (Figure 1). To help decrease the burden of disease resulting from mosquitos, researchers are utilising satellite data and remote sensing models to better predict where mosquito breeding grounds may occur in the future.

Ethiopia, like many developing countries, faces significant threat from droughts triggered by climate change. The country's heavy reliance on agriculture for production, export revenues, and employment makes it highly susceptible to climate change-induced challenges, such as frequent floods, droughts and rising temperatures. Therefore, this research aims to assess drought-prone areas in Meyo district, Borena Zone, thereby contributing to the attainment of SDG 13.1 and the creation of a more resilient and sustainable future in the face of climate change. To achieve the objective, the study employs the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as indicators and the drought risk map was developed using weighted overlay analysis. Landsat images and rainfall datasets from December in the years 2002, 2012, and 2022 were analyzed to track changes. The result reveals a clear inverse relationship between NDVI and LST, where higher temperatures coincide with decreased NDVI values, signifying vegetation stress caused by reduced water availability. The study also highlights the deficient rainfall and high drought vulnerability in the norther and eastern parts of the study area. The provided drought risk map classifies areas into Low, Moderate, and High risk, illustrating the evolving drought scenario and it signifies increasing severity of drought risk in recent years, particularly from 2012 to 2022. The finding holds vital information for decision-makers, policymakers, and stakeholders in devising effective strategies to mitigate the adverse effect of drought and build resilience in the of climate change.

Sarhan Zerouali became fascinated with water at a young age through learning about water scarcity around the world and about traditional methods for locating groundwater. In a space applications course Sahran then learnt about space-based technologies. He is currently working on a research project on how remote sensing and other technologies can help alleviate global challenges arising from land degradation. As an aerospace engineer, Sahran has worked with various modern technologies in his work including nanosatellites, artificial intelligence, and feature extraction algorithms.

What began as the development of a cubesat (BIRD-5) at the Kyushu Institute of Technology in Japan took off on a spacecraft to the International Space Station from the Mid-Atlantic Regional Spaceport at the National Aeronautics and Space Administration's (NASA's) Wallops Flight Facility in Virginia, US on 6 November 2022 (watch the video of the launch of the CRS2 NG-18 (Cygnus) Mission (Antares), in the video below the article).

Digital Earth Africa learning platform

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.

Image showing hands reaching into the night sky. Source: ESA

Satellite-based ecohydrological analysis for spring revival in Nepal's Middle Hills - in development and need for input

Published on 12 Jun 2025

Satellite-based ecohydrological analysis for spring revival in Nepal's Middle Hills - in development and need for input

Published on 12 Jun 2025 , by

Tejendra Kandel

Ph.D. Researcher (Ph.D. Candidate) at University of Virginia

Yolanda Lopez-Maldonado

Indigenous Scholar

Hannah Nevill

- at Omanos Analytics

Shipra Singh

IIASA-Postdoctoral fellow at International Institute for Applied Systems Analysis

Omanos Analytics

International Institute for Applied Systems Analysis

Addressed challenge(s)

Map of Sharadha Khola watershed in Nepal

The ecohydrological trade-off in Nepal’s Middle Hills: mapping spring decline and groundwater loss in community forests through space-based solutions

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)

Omanos Analytics

International Institute for Applied Systems Analysis

Tejendra Kandel

Ph.D. Researcher (Ph.D. Candidate) at University of Virginia

Yolanda Lopez-Maldonado

Indigenous Scholar

Hannah Nevill

- at Omanos Analytics

Shipra Singh

IIASA-Postdoctoral fellow at International Institute for Applied Systems Analysis

Suggested solution

This solution combines multi-source satellite remote sensing, ecohydrological modeling, and community science to address spring decline and water insecurity caused by afforestation and land-use changes in Nepal's Middle Hills. The integrated approach offers a pathway to scientifically informed, community-driven forest and water management.

Satellite Data Fusion: The first core strategy involves fusing multi-temporal and multi-sensor satellite data to assess vegetation trends, hydrological changes, and potential spring recharge zones.

Vegetation Monitoring: Time-series NDVI, EVI, and LAI are derived using Sentinel-2, Landsat, NOAA and MODIS. These indices help detect vegetation growth trends and assess forest types based on phenological signatures.
Cloud Mitigation: Nepal’s rugged terrain and monsoon conditions create persistent cloud cover challenges. While no perfect cloud-removal technique exists, we aim to apply machine learning and established algorithms like CLAY3 or Fmask to improve data quality for vegetation and land cover analysis.
Hydrological Metrics: ET using MODIS, Soil moisture is mapped using SMAP data, downscaled using terrain parameters such as slope and elevation. GRACE data informs groundwater trends. Satellite-based precipitation datasets are validated against DHM station data to compensate for missing or sparse in-situ observations.

RHESSys Ecohydrological Simulation: The RHESSys model simulates the complex interactions between vegetation, soil moisture, surface and subsurface water flow, and groundwater storage. The model is run in growth mode to evaluate how forest type changes influence spring discharge.

MODIS ET and Sentinel-1 soil moisture serve as validation inputs.
The model provides spatial outputs including groundwater depth, lateral flow, and baseflow dynamics—critical for delineating micro-watersheds and assessing recharge efficiency.

Recharge Zone and Spring Hotspot Mapping: Topographic indices such as TWI (Topographic Wetness Index) and HAND (Height Above Nearest Drainage), derived from ASTER DEMs, are used to identify spring recharge zones.

These zones are further validated using RHESSys outputs, satellite data layers, and available field measurements.
Machine learning (e.g., Random Forest) and participatory mapping help cross-check locations of active and declining springs.
The resulting maps guide protection measures and afforestation policies targeting hydrologically sensitive areas.

Field Validation and Community Co-Design: Local participation through spring monitoring and mapping ensures the integration of indigenous knowledge with scientific analysis. Field measurements validate model predictions and support community-driven management strategies.

Requirements

Data

Remote sensing: NDVI, EVI, LAI, Evapotranspiration (MODIS), Soil Moisture (SMAP), satellite-based precipitation, terrestrial Groundwater storage (GRACE), Land Use Land Cover, DEM (ASTER).
In-situ: Precipitation and temperature data from DHM Nepal

Software

Google Earth Engine (cloud computing, satellite data analysis)
GRASS GIS and QGIS (terrain analysis, TWI, HAND)
RHESSys (eco-hydrological modeling)
R/Python (statistical modeling, ML integration)
LEAF Toolbox for LAI/phenology

Physical Requirement

Cross-validation of spring locations and groundwater depth measurements through field visits.

Priority Support Areas: To realize objectives, we seek support in the following areas:

High-Resolution and Cloud-Free Satellite Data: Technical assistance in accessing and processing Sentinel-1 SAR, Sentinel-2, and Landsat data, and applying cloud-removal algorithms.
Downscaling Remote Sensing Products: Assistance in refining MODIS-based NDVI, EVI, LAI, and phenological indicators using auxiliary terrain and land cover datasets.
Integration of Hydrological and Remote Sensing Data: Guidance on synchronizing outputs from RHESSys with MODIS, SMAP, and GRACE datasets for robust cross-validation.
Mapping Recharge and Spring Zones: Technical support in combining terrain indices with RHESSys-derived metrics to map spring recharge zones and inform land-use planning.

Outline steps for a solution

Phase 1: Satellite Analysis and Vegetation Mapping (completed)

Use GEE to analyze NDVI, EVI, LAI, ET from NOAA, MODIS, Sentinel, and Landsat.
Define AOI, set date ranges, apply quality filters, visualize maps, and export CSV time series.
LULC maps are from Global Land Cover (Chinese Academy of Sciences).
Enhanced vegetation index (EVI)
Leaf area index (LAI)
Normalized Difference Vegetation Index (NDVI)
Evapotranspiration (ET)
Pending: Improve cloud filtering using CLAY3, Fmask; downscale to higher resolution.

Phase 2: GIS and Terrain Modeling (In Progress)

Use DEM, LULC, TWI, HAND for recharge/discharge mapping.
Analyze terrain factors (slope, curvature, aspect, valley) for moisture prediction.

Phase 3: Hydrological Simulation and Analysis (To Do)

Run RHESSys in growth mode to simulate hydrological-vegetation dynamics.
Validate against spring discharge logs.
Output: groundwater depth, saturation deficit, soil moisture, flow trends, seasonal water availability.

Phase 4: Community Co-Design and Policy Translation (To Do)

Share vulnerability maps with CFUGs and municipal planners.
Recommend native species afforestation rather than heavily water dependent species.
Promote thinning, selective logging, litter removal, etc. (forest management plant).
Water availability zone mapping for settlement

RHESSys model

Note: "The full code and comprehensive instructions for running the model are provided in this repository."

Results

Preliminary analysis shows increasing trends in both evapotranspiration and LAI, indicating higher water consumption by vegetation and less water available for downstream use.
Mapped recharge zones and high-risk spring areas.
Groundwater storage trends over time and identification of water-available zones suitable for settlement planning.
Spatial maps and hydrologic models to support forest and water governance.
Policy briefs on forest-water tradeoffs and spring recovery.
Community awareness on how certain forest types and species are accelerating water loss and increasing water stress for downstream communities, prompting migration.
Technical findings paired with management strategies offer actionable insights for land-use planning and ecosystem resilience.

Leaf Area Index of Sharada Khola Watershed

Evapotranspiration for Sharadha Khola Watershed

Relevant publications

The degradation of spring water resources in Nepal: some policy gaps

Drying Water Springs

Comparative Hydrological Dynamics and Water Security in Sundarijal Watershed: A RHESSys Modeling Approach for Broadleaf and Conifer Forests

Related space-based solutions

Keywords (for the solution)

Remote Sensing Groundwater recharge Spring Normalized Difference Vegetation Index (NDVI)Evapotranspiration Ecohydrology

Climate Zone (addressed by the solution)

Dry winter subtropical highland

Habitat (addressed by the solution)

Mountain habitats

Region/Country (the solution was designed for, if any)

Nepal

Relevant SDGs

Vegetation classification for land of Maori communtiy - in development

Published on 20 Apr 2023

Land use Land Cover Data Normalized Difference Vegetation Index (NDVI)Google Earth Engine Hydrography Flood New Zealand

Vegetation classification for land of Maori communtiy - in development

Published on 20 Apr 2023 , by

Padmi Ranasinghe

PhD Student at University of Texas at Arlington

Cadence Kaumoana

Chair, Te Huia & Rangiwherowhero Trust

Alexandre De Amorim Teixeira

GIS Specialist at National Water and Sanitation Agency of Brazil

Flemish Institute for Technological Research-Remote Sensing

National Water and Sanitation Agency of Brazil

United Nations Office for Outer Space Affairs

Addressed challenge(s)

Long view of Ngakoahia Stream and surrounding vegetation

Lacking historic knowledge on vegetation cover and surface water extent / river course

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)

Flemish Institute for Technological Research-Remote Sensing

National Water and Sanitation Agency of Brazil

United Nations Office for Outer Space Affairs

Padmi Ranasinghe

PhD Student at University of Texas at Arlington

Cadence Kaumoana

Chair, Te Huia & Rangiwherowhero Trust

Alexandre De Amorim Teixeira

GIS Specialist at National Water and Sanitation Agency of Brazil

Suggested solution

Note: this description is a work in progress developed by the collaborating entities in a workshop. If you would like to contribute reach out to office@space4water.org, or your trusted Space4Water point of contact.

Required Software

Google Earth Engine
Google Earth Engine Apps - Global Forest Change

1. Data collection

To collect historic and high-resolution up-to-date imagery over the area, UNOOSA contacted the Land and Information New Zealand Data Service, which provided both historical aerial imagery and LIDAR data sources.
Historic data for the relevant land patch can be accessed via the Retrolens New Zealand Service.
Up-to-date aerial photos of the area can be accessed here at the New Zealand Data Service. Tile 503 and 603 are the ones of interest.
Relevant Landsat data are available from 1989. For the study area, Landsat 7 data is available from 2 July 1999, and Landsat 4 from 2 February 1989.
Global Forest Change data can be retrieved from Google Earth Engine Apps
HydroSHEDS: The core data products of HydroSHEDS are a series of gridded datasets designed for use in hydro-environmental model development and custom GIS applications. Data layers include the original digital elevation model (DEM) that underpins HydroSHEDS, a hydrologically conditioned version of the DEM, the derived flow direction and flow accumulation grids, as well as land mask and sink grids. These data products form the digital foundation of the derived secondary data products. HydroSHEDS core data products are currently available for HydroSHEDS v1 only, which is mostly based on SRTM elevation data. HydroSHEDS v2, which is derived from TanDEM-X elevation data, is currently under development and is scheduled for release in 2022.
A digital elevation model (DEM) is available at 30m resolution by Copernicus is available at the Terrascope website.

Screenshot of the New Zealand Data Serivce, Waikato Rural areal Photos — Figure 1: Screenshot of the New Zealand Data Service, Waikato Rural areal Photos

Figure 3: Google Earth Engine Apps - Global Forest Change with an overlay of the hydrograph developed in the solution linked below, as well as the boundary of the Maori communtiy in the Ngutunui region, New Zealand

Changes in tree cover - Google Earth Engine Apps - Global Forest Change — Figure 4: Changes in tree cover derived from Google Earth Engine Apps - Global Forest Change

NDVI Analysis on the lands of the Maori community and the surrounding area — Figure 5: NDVI Analysis of the area

2. Mapping the historical land use and land cover surrounding the river (in progress)

Using Google Earth Engine Apps - Global Forest Change data it is possible to identify recent deforestation (2016) upstream and near the Ngutunui region;
No other change in the land use is observed upstream the Ngutunui region between 1985-1999.
According to the vegetation cover analysis, there have not been many changes over the past 20+ years. It has been observed that the Manori community and the surrounding area have maintained almost the same vegetation cover, however some patches adjacent to the community boundary downstream have caused some distractions.
Limitation- In the case of a small land mass and narrow river, limits many satellite-based analyses.

Other methods - Conducting a community survey

To obtain historical knowledge on the identification of vegetation, tree species, a community survey appears to be the only option available, since the challenge requires data extending back 50 years. While space-based data (aerial photos) are available, the possibility of identifying each species of tree is very limited, because of the canopy layer, understory plant species cannot be seen.
This approach will enable to gather data on dominant plant species, their abundance, tree diameters, and the boundaries between different vegetation.
Data obtained from the community survey provide a valuable historical record of vegetation patterns over the decades and help identify any changes or disruptions.
"i-nature" app- tree species can be identified by taking a simple picture of a leaf. The app then provides a detailed description of the identified tree species, including information about its characteristics and habitat.

Further information on vegetation identification

Using NDVI allows for identifying the type of vegetation but not the specific species. One can see whether the type of vegetation has changed from trees to grassland, but specific plants cannot be seen.

Retrolense provides aerial photographs taken from an aeroplane at which the relevant bands for NDVI calculation (infrared and red) are missing.

We can examine vegetation cover over the last 30+ years using NDVI with Landsat data.

A study called Aerial photography for assessing vegetation change: A Review of applications and the relevance of findings for Australian vegetation history by Fensham and Fairfax published in 2022 in the Australian Journal of Botany and on the CSIRO page is accessible here.

Relevant publications

Related space-based solutions

Keywords (for the solution)

Land use Land Cover Data Normalized Difference Vegetation Index (NDVI)Google Earth Engine Hydrography Flood

Climate Zone (addressed by the solution)

Oceanic

Habitat (addressed by the solution)

Region/Country (the solution was designed for, if any)

New Zealand

Relevant SDGs

Map with y axis being latitude and x axis being longitude indicatingdecadal precipitation in Kenya

Rainwater harvesting in Samburu County – in development

Published on 03 Nov 2023

Rainwater harvesting Standardized Precipitation Index (SPI)CHIRPS Normalized Difference Vegetation Index (NDVI)Precipitation Precipitation monitoring Kenya

Rainwater harvesting in Samburu County – in development

Published on 03 Nov 2023 , by

Lilian Nguracha Balanga

Founder of Women .conserve at Samburu women

Hannah Ritchie

PhD Researcher at Cranfield University

María José Molina-Montero

Geographer at FAB-LAB at Inter-American Institute for Cooperation on Agriculture

Isabel Zetina

Intern United Nations Office for Outer Space Affairs

Kenya Space Agency

Inter-American Institute for Cooperation on Agriculture

Addressed challenge(s)

Samburu woman drinking (brownish) well water filtered through the fabric of her dress

Samburu tribe lacks access to safe drinking water - Dry spells due to water scarcity

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)

Kenya Space Agency

Inter-American Institute for Cooperation on Agriculture

Lilian Nguracha Balanga

Founder of Women .conserve at Samburu women

Hannah Ritchie

PhD Researcher at Cranfield University

María José Molina-Montero

Geographer at FAB-LAB at Inter-American Institute for Cooperation on Agriculture

Isabel Zetina

Intern United Nations Office for Outer Space Affairs

Suggested solution

Rainwater harvesting is a crucial solution for water scarcity in semi-arid countries like Kenya. Kenya’s arid and semi-arid lands (ASALs) cover 80% of its territory, making rainwater harvesting essential. There are various reasons why this approach can be beneficial in Samburu County.

Water Scarcity Mitigation: Semi-arid regions face unpredictable rainfall and frequent droughts, exacerbated by climate change. Rainwater harvesting captures the little rainfall received, providing a reliable water source.
Sustainable Water Supply: Rainwater harvesting techniques include small planting basins, trenches, stone bunds, and grass strips. These structures redirect runoff toward crops and pastures. By capturing rainwater, communities can sustain livestock, crop production, and domestic needs.
Environmental Resilience: Droughts in Kenya are becoming more frequent due to environmental degradation and climate change. Rainwater harvesting helps mitigate
the impact of these droughts.
Cost-Effective and Low-Tech: Rainwater harvesting doesn’t require complex infrastructure. It utilizes existing resources effectively.

Outline of the solution

Steps to be taken:

Rainy season identification: the rainy season in the selected area needs to be identified. Further, the precipitation data from the past three to ten years needs to be studied.
A geological study needs to be developed, this includes the study of the geology of the region, the development of geological maps, digital elevation model (DEM) maps, and normalized difference vegetation index (NDVI) map.
Precipitation maps of the location need to be developed.
Site Selection: Identify suitable locations for rainwater harvesting. Factors such as rainfall patterns, topography, and proximity to communities need to be considered. Choose areas with consistent rainfall during specific seasons.
Catchment area: Determine the catchment area where rainwater will be collected. Common catchment surfaces include rooftops, roads, or open fields. Ensure that the catchment area is clean and free from contaminants.
Conveyance system: Design an efficient system to channel rainwater from the catchment area to storage facilities. Components include gutters, downspouts, pipes, and first-flush diverters. Proper sizing and maintenance are crucial.
Storage tanks or reservoirs: Select appropriate storage options based on community needs. Common choices include:
Roof catchment tanks: Placed near buildings to store rainwater from rooftops.
Ground-level tanks: Buried or partially buried to store larger volumes.
Rock catchments: Natural depressions or excavated pits lined with impermeable materials.
Consider tank capacity, material durability, and accessibility for maintenance.
Water quality and treatment: Rainwater may contain impurities. Implement filtration systems to improve water quality. Use first-flush diverters to discard initial runoff (which may contain debris).
Climate resilience: Adapt the project to changing climate conditions. Monitor rainfall patterns and adjust storage capacity accordingly.

Accomplished progress

Steps 1 - 3 have been successfully accomplished. Step 2 was developed in another space-based solution.

Rainy season identification

Figure 1: Decadal Precipitation in Kenya. Precipitation information during 21-31 December 2023. (Source: Dekadal Rainfall (meteo.go.ke))

Precipitation data from at least the last three years: CHRIPS

Decadal rainfall data — Figure 2: Decadal rainfall in mm from July 2020 to July 2023. (Source: Dekadal Rainfall (meteo.go.ke))

Digital elevation Model (DEM)

Samburu DEM map — Figure 3: Precipitation DEM map made with QGIS. Version 3.32.3 / Version 3.28.11 LTR.

Precipitation maps

samburu precipitation map — Figure 4: Precipitation map from 2023 made with QGIS. Version 3.32.3 / Version 3.28.11 LTR. The yellow areas indicate heavy rainfall, the green areas indicate moderate rainfall.

Development of precipitation maps

To create a precipitation map in QGIS, raster data representing precipitation values is needed. Therefore, the data is added to a raster layer in the QGIS project.

Steps to create a raster layer to map precipitation data:

Obtain precipitation data: First, obtain precipitation data from a reliable source in a compatible format. Common formats for precipitation data include GeoTIFF (.tif), NetCDF (.nc), or ASCII grid (.asc) files.
Open QGIS: Launch QGIS on your computer. QGIS is open for download: Download QGIS
Add Raster Layer:

Go to the "Layer" menu and select "Add Layer"> "Add Raster Layer".
Or click the "Add Raster Layer" button in the toolbar.
Alternatively, use the shortcut Ctrl+Shift+R.

Browse for Precipitation Data: In the "Data Source Manager" dialog that appears, navigate to the directory where your precipitation data is stored.

Select precipitation data file: Select the precipitation data file for the map. Make sure to choose the correct file format that matches the data (e.g., GeoTIFF, NetCDF, ASCII grid).

6. Add Layer to the map: Once the precipitation data file is selected, click "Open" or "Add" to add the raster layer to your QGIS project.

7. Display the precipitation map: Now that you've added the precipitation data as a raster layer, you can visualize it on the map canvas in QGIS. Depending on the spatial resolution and coverage of your precipitation data, you may need to zoom or pan the map to view the data effectively.

Once the precipitation data is added as a raster layer, the map layout can be customized to include legend, latitude and longitude factors, title, and other properties using the Print Layout functionality.

Open Print Layout: Go to the "Project" menu and select "New Print Layout" to create a new print layout. Give your layout a name and click "OK".
Add Map to Layout: In the print layout view, click on the "Add Map" button in the toolbar, then click and drag to create a rectangle where the map is to appear on the layout.

3. Add Legend: Click on the "Add Legend" button in the toolbar, then click and drag to create a rectangle where the legend is to appear on the layout.

4. Add Title: Go to the "Layout" menu and select "Add Item" > "Label". Click and drag to create a rectangle where the title is to appear on the layout. Double-click on the label element to edit the text and customize the font, size, and style.

5. Add Other Elements: You can add additional elements such as scale bars, north arrows, text boxes, images, and annotations using the "Add Item" menu in the toolbar.

6. Add Latitude and Longitude Grid: Go to the "Layout" menu and select "Add Item" > "Map Grid". Click and drag to create a rectangle where the grid is to appear on the layout.

Configure Grid Properties: Double-click on the grid element to open the "Item Properties" panel. Here, you can configure various properties of the grid, including:
Grid Type: Choose between "Frame and Annotations", "Grid Lines", "Annotation Only", or "Frame Only" depending on the desired appearance.
Interval Units: Choose the units for the grid intervals (e.g., degrees for latitude and longitude).
Interval X and Y: Set the interval for latitude and longitude gridlines.
Annotation X and Y: Choose whether to annotate the gridlines with latitude and longitude values.
Customize Appearance (Optional): You can further customize the appearance of the gridlines, such as line style, color, and labeling options, using the options available in the "Item Properties" panel.

Export and save: the layout can be exported to various formats such as PDF, image files, or print directly from QGIS.

Future steps

Steps 5-13 will be developed once step 3 (site selection) has been developed:

Determine if enough water can be stored during the rainy season to last the dry season.
Determine seasonal river location and river width: determines the optical data that can be used (due to spatial resolution)
Sediment load of the river
outcrops in bed and bank: Ideally don’t want to have to excavate >5m deep
Existing scoop holes on the river existing far into the dry season (indicates good water storage already)
Vegetation on the banks (indicates localized water source)
Slope of riverbanks (shouldn’t be too shallow)
Slope of riverbed (ideally 1 - 5 %)
Stream length
Catchment area (from DEM)
Location of faults and fractures

Finally, for the implementation of the rainwater harvesting plan, sediment samples from the selected seasonal river need to be studied.

Relevant publications

Related space-based solutions

Change detection analysis for prioritized basins in Venezuela using space-based technologies - in development

Groundwater resource management using artificial intelligence and remote sensing technologies - in development

Satellite-based ecohydrological analysis for spring revival in Nepal's Middle Hills - in development and need for input

Spatiotemporal analysis of hydrometeorological disasters in the Indian Himalayas: integrating space-based techniques for enhanced disaster resilience

Data-driven irrigation demand forecasting for rotational water management under the Warabandi system - in development

Intensity Image and Treshold 3 Binary image

SAR based monitoring of hydrocarbon contamination of water bodies in Eleme, Nigeria - in development

Screenshot for GEE App for Monitoring Lake and Habitat

Earth observation for ecosystem resilience: the Lake Ol' Bolossat monitoring initiative - in development and need for input

Wetland extent mapping - North Central Nigeria, Ibaji - Kogi State - in development

Map of potential shallow groundwater locations or recharge areas in Bahrain

Identification of potential locations/recharge for shallow groundwater in geographically small countries - mapping completed - need in situ data

Flood modeling for melting glacier - discontinued

Potential contimation source newar the river - a farm

Identify upstream potential pollution sources - in development

HAND of the investigated region showing flooded araeas and drainage lines

Surface water extent river course - in development

A sand dam in Kenya. Credit: Hannah Ritchie

Construction of sand dams for Samburu County - in development

Rainwater harvesting in Samburu County – in development

NASA Earth Observatory images by Joshua Stevens, using data courtesy of Vishal Arya and the NASA DEVELOP Program. The maps above show some of the satellite data used to determine suitable locations. The first image shows precipitation from 2006 to 2016, which has remained fairly constant. The precipitation data came from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission and were validated with NOAA’s Global Surface Summary of the Day.

Determining optimum sites for rainwater harvesting - in development

Vegetation classification for land of Maori communtiy - in development

Water suitability map (Samburu County, Kenya) - in development

Keywords (for the solution)

Rainwater harvesting Standardized Precipitation Index (SPI)CHIRPS Normalized Difference Vegetation Index (NDVI)Precipitation Precipitation monitoring

Climate Zone (addressed by the solution)

Dry

Habitat (addressed by the solution)

Region/Country (the solution was designed for, if any)

Kenya

Relevant SDGs

Surface water extent river course - in development

Published on 30 Nov 2023

Land use Land Cover Data Normalized Difference Vegetation Index (NDVI)Digital Elevation Model (DEM)Geographic Information System (GIS)Hydrography New Zealand

Surface water extent river course - in development

Published on 30 Nov 2023 , by

Cadence Kaumoana

Chair, Te Huia & Rangiwherowhero Trust

Alexandre De Amorim Teixeira

GIS Specialist at National Water and Sanitation Agency of Brazil

Padmi Ranasinghe

PhD Student at University of Texas at Arlington

National Water and Sanitation Agency of Brazil

University of Natural Resources and Life Sciences Vienna

Addressed challenge(s)

Lacking historic knowledge on vegetation cover and surface water extent / river course

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)

National Water and Sanitation Agency of Brazil

University of Natural Resources and Life Sciences Vienna

Cadence Kaumoana

Chair, Te Huia & Rangiwherowhero Trust

Alexandre De Amorim Teixeira

GIS Specialist at National Water and Sanitation Agency of Brazil

Padmi Ranasinghe

PhD Student at University of Texas at Arlington

Suggested solution

The solution approach begins with identifying the region's main rivers and understanding their hydrology using mapping and geoprocessing tools. After understanding the hydrography of the area and mapping the surface water extent river course through the building a hydrographic dataset, multiple image sources are used to map the historical land use and land cover surrounding the river.

1. Resources needed

Software

QGIS https://www.qgis.org/en/site
TerraHidro 5 - Console applications https://www.dpi.inpe.br/terrahidro/doku.php
PostgreSQL https://www.postgresql.org
PostGIS Spatial Database System https://postgis.net/
PgHydro extension for PostgreSQL/PostGIS http://pghydro.org/
PgHydro Plugin for QGIS https://plugins.qgis.org/plugins/PghydroTools/

Data

Forest And Buildings removed Copernicus DEM

Publications

see reference in the bibliography below.

2. Steps to the solution & status

Overivew

Plot the Region of Interest (completed)
Identify the region's main rivers and understand their hydrology (completed);
Identify the region's potential flood areas using H.A.N.D.;
Build a hydrography dataset (completed);
Identify multiple image sources for land cover analysis (completed);
Map the historical land use and land cover surrounding the river (in progress);

Step-by-step

1. Plot the Region of Interest (completed)

Download and install QGIS to plot the KML files of the region of interest

Figure 1: Example KML plot of the strip of land of the Maori communtiy who submitted the challenge

2. Identify the region's main rivers and understand their hydrology (completed)

Download the FABDEM data for the Region of Interest.
FABDEM (Forest And Buildings removed Copernicus DEM) is a global elevation map that removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM) (https://data.bris.ac.uk/data/dataset/25wfy0f9ukoge2gs7a5mqpq2j7).

Figure 2: A FEABDEM Digital Elevation Model of the Ngutunui region, New Zealand.
Download and Install TerraHidro 5 - Console applications (https://www.dpi.inpe.br/terrahidro/doku.php) to extract the hydrograph products derived from the FABDEM to understand the hydrography setup of the area (Flow direction, flow accumulation and drainage lines and areas, H.A.N.D.).

Figure 3: Flow direction in the Ngutunui region, New Zealand
Figure 4: Flow accumultation in the Ngutunui region, New Zealand

Figure 5: Sintetic draingage lines and areas Ngutunui region, New Zealand

3. Identify the region's potential flood areas using H.A.N.D.

Building on Nobre et. al (2011) in which the HAND terrain model that "normalizes topography according to the local relative heights found along the drainage network, and in this way, presents the topology of the relative soil gravitational potentials, or local draining potentials" is introduced by the authors.

Height Above the Neaerest Drainage (HAND)in the Ngutunui reiong, — Figure 6: Height Above the Neaerest Drainage (HAND) in the Ngutunui region showng the areas for potential flooding in darker blue. In the current map this is in the bottom right quarter of the image.

4. Build a hydrography dataset (completed)

Download and instal PostgreSQL/PostGIS Spatial Database System (https://www.postgresql.org/) (https://postgis.net/), PgHydro extension for PostgreSQL/PostGIS (http://pghydro.org/) and PgHydro Plugin for QGIS;(https://plugins.qgis.org/plugins/PghydroTools/).
Build the Hydrograph Dataset;(https://www.youtube.com/channel/UCgkCUQ-i72bBY41a1bhVWyw) using the Drainage Lines and Drainage Areas extracted from FABDEM;
Information like drainage area, upstream area, drainage line length and distance to sea information are now available.

Figure 7: Hydrography dataset of the Ngutunui region in New Zealand

5. Identify multiple image sources for landing cover analysis (completed);

To collect historic and high-resolution up-to-date imagery over the area, UNOOSA contacted the Land and Information New Zealand Data Service, which provided both historical aerial imagery and LIDAR data sources.
Historic data for the relevant land patch can be accessed via the Retrolens New Zealand Service (https://retrolens.co.nz/Map/#/1784971.9859981549/5783474.532151884/1786387.2653498782/5784857.564632303/2193/12).
Up-to-date aerial photos of the area can be accessed here at the New Zealand Data Service. Tile 503 and 603 are the ones of interest (https://data.linz.govt.nz/layer/112048-waikato-03m-rural-aerial-photos-index-tiles-2021-2023/history/).
Relevant Landsat data are available from 1989. For the study area, Landsat 7 data is available from 2 July 1999, and Landsat 4 from 2 February 1989;
Google Earth Engine Apps - Global Forest Change (https://google.earthengine.app/view/forest-change)

6. Map the historical land use and land cover surrounding the river (in progress);

Relevant publications

Related space-based solutions

Keywords (for the solution)

Land use Land Cover Data Normalized Difference Vegetation Index (NDVI)Digital Elevation Model (DEM)Geographic Information System (GIS)Hydrography

Climate Zone (addressed by the solution)

Humid subtropical

Habitat (addressed by the solution)

Region/Country (the solution was designed for, if any)

New Zealand

Relevant SDGs

Water suitability map (Samburu County, Kenya) - in development

Published on 11 Apr 2023

Topographic Satellite Mapping Normalized Difference Vegetation Index (NDVI)Bored well Groundwater Groundwater abstraction Well Spring Kenya

Water suitability map (Samburu County, Kenya) - in development

Published on 11 Apr 2023 , by

Lilian Nguracha Balanga

Founder of Women .conserve at Samburu women

Claire Muhungi

Ag Deputy Director at Kenya Space Agency

Isabel Zetina

Intern United Nations Office for Outer Space Affairs

Groundwater Relief

Kenya Space Agency

Addressed challenge(s)

Samburu tribe lacks access to safe drinking water - Dry spells due to water scarcity

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)

Groundwater Relief

Kenya Space Agency

Lilian Nguracha Balanga

Founder of Women .conserve at Samburu women

Claire Muhungi

Ag Deputy Director at Kenya Space Agency

Isabel Zetina

Intern United Nations Office for Outer Space Affairs

Suggested solution

Background information on the geology of Kenya

The geology of Kenya was studied to develop several maps with ArcGIS to understand the geological and topographical setting of the locations of the communities. The maps were obtained from the European Commission, Joint Research Centre (see sources).
The geology of Kenya shows Metamorphic rocks in the western part due to high metamorphic processes.
Tertiary and Quaternary Volcanic rock are located in the East due to the split of the East African Rift System causing volcanic activity during these periods.

Geology map of Kenya — Figure 1: National Atlas of Kenya - Geological Map. (n.d.). European Commission, Joint Research Centre.

Location of Samburu in the geological map of Kenya

The geology of Samburu County is mostly magmatic with Quaternary elements (for details see Figure 3).

Geology map of Samburu County Krhoda et al. (2015). — Figure 3: Detailed geological map of Samburu County (Krhoda et al. 2015).

Location of the communities SW of Samburu County

Map of important community locations — Figure 4: Map of locations of the community, from where water access would be needed in the vicinity.

Legend of the map of community locations

Samburu County's topographic map

A topographic map from Samburu County was developed to understand where the major recharge zones are located. These zones in arid areas can also be indicated by the Normalised Differentiated Vegetation Index (NDVI).

Figure 5: Topographic map of Samburu province in Kenya. Credit: Topographic Map (2023)
Location of water sources

The type and location of water sources in the Samburu District were studied with a map provided by the KSA. With the study of the geological maps and the water sources map, a geological and hydrological comparison was made, to understand where groundwater could be located.
Fig.6. Samburu District - Type and Location of Water Sources, Key Landforms, and Soils (Symbols - See Map 11). (n.d.). European Commission, Joint Research Centre. https://esdac.jrc.ec.europa.eu/content/samburu-district-type-and-locati… (visited: 19.10.2023)

Figure 6: Water sources in the Samburu county. Map provided by Kenya Space Agency. Source: G. de Sourza, Dept. Geography, University of Nairobi, J. Keza, Ministry of Water Development.

Development of the maps

With the support from datasets obtained from the Kenya Space Agency a differentiated vegetation map (NDVI), an elevation map (DEM), and a water points map were developed using ArcGIS Pro 3.

DEM map in ArcGIS Pro3

Add your Samburu county map dataset to the project. You can do this by going to the "Map" tab and using the "Add Data" button to import your county shapefile or feature class.
Add Samburu Elevation Data: To create an elevation map, you need elevation data. Download the Digital Elevation Model (DEM) data. Once downloaded, add the DEM to your map.
Symbolize Elevation Data: Symbolize the elevation data to represent different elevation ranges. You can do this by right-clicking on the DEM layer, selecting "Symbology," and choosing a suitable color ramp and classification method.
Add Legend: Insert a legend to the map by going to the "Insert" tab and selecting "Legend." Configure the legend properties to display the layers and symbology correctly.
Add North Arrow and Scale Bar: Insert a North arrow and scale bar by going to the "Insert" tab and selecting "North Arrow" and "Scale Bar." Adjust the properties to suit your map layout.
Adjust Map Layout: Go to the "Layout" tab to set up the map layout. Adjust the size of the map, add a title, and organize the legend, scale bar, and north arrow as desired.
Save and Export: Save your project, go to the "Share" tab, and export the map as an image, PDF, or any other desired format.

Samburu county elevation map — Figure 7: Elevation Map made with QGIS. Version 3.32.3 / Version 3.28.11 LTR.

NDVI map in ArcGIS Pro3

ArcGIS Pro involves using the raster calculator to perform the necessary mathematical operations on the input bands. NDVI is typically calculated using the near-infrared (NIR) and red bands from a multispectral image.

Add county dataset: Add your Samburu county dataset to the map. You can do this by going to the "Map" tab and using the "Add Data" button to import your county shapefile or feature class.
Add satellite imagery: import Samburu County Landsat or Sentinel imagery containing the necessary bands (Red and Near Infrared) for NDVI calculation. You can add the imagery by going to the "Map" tab and selecting "Add Data" or using the "Add Raster Data" option.
Calculate NDVI: Open the "Image Analysis" window by going to the "Analysis" tab and selecting "Tools." Use the "NDVI" tool to calculate NDVI from the available bands.
The equation of NDVI is as follows: NDVI = ((IR - R)/(IR + R)); IR = pixel values from the infrared band and R = pixel values from the red band
Symbolize NDVI: Symbolize the NDVI layer to visually represent vegetation health. Typically, healthy vegetation appears in shades of green, while less healthy or bare areas might be represented in browns or grays.
Add legend: Insert a legend to the map by going to the "Insert" tab and selecting "Legend." Configure the legend properties to display the NDVI layer and symbology correctly.
Add North Arrow and Scale Bar: Insert a North arrow and scale bar by going to the "Insert" tab and selecting "North Arrow" and "Scale Bar." Adjust the properties to suit your map layout.
Adjust map layout: Go to the "Layout" tab to set up the map layout. Adjust the size of the map, add a title, and organize the legend, scale bar, and north arrow as desired.
Save and export: Save your project, go to the "Share" tab, and export the map as an image, PDF, or any other desired format.

Samburu NDVI map — Figure 8: NDVI made with QGIS. Version 3.32.3 / Version 3.28.11 LTR.

Water points map in ArcGIS 3

Add county dataset: Add the Samburu county dataset to the map. You can do this by going to the "Map" tab and using the "Add Data" button to import your county shapefile or feature class.
Add water points dataset: Import your water points dataset into the map. Use the "Add Data" button to add the water points layer. Make sure the dataset contains information about the location of water points.
Symbolize water points: Symbolize the water points on the map. Right-click on the water points layer, go to "Symbology," and choose an appropriate symbol to represent water points. You may want to use a distinctive symbol like a blue dot.
Add legend: Insert a legend to the map by going to the "Insert" tab and selecting "Legend." Configure the legend properties to display the water points layer and its symbol correctly.
Add North arrow and scale bar: Insert a North arrow and scale bar by going to the "Insert" tab and selecting "North Arrow" and "Scale Bar." Adjust the properties to suit your map layout.
Adjust map layout: Go to the "Layout" tab to set up the map layout. Adjust the size of the map, add a title, and organize the legend, scale bar, and north arrow as desired.
Save and export: Save your project, go to the "Share" tab, and export the map as an image, PDF, or any other desired format.

Map showing the Samburu county water points — Figure 9: Elevation Map made with QGIS. Version 3.32.3 / Version 3.28.11 LTR.

Suggested aquifer location map

Add the geological map of Kenya dataset: You can do this by going to the "Map" tab and using the "Add Data" button to import your county shapefile or feature class.
Add Water points dataset: Import your water points dataset into the map. Use the "Add Data" button to add the water points layer. Make sure the dataset contains information about the location of water points. Delete the ones that are not relevant to the communities
Add legend: Insert a legend to the map by going to the "Insert" tab and selecting "Legend." Configure the legend properties to display the water points layer and its symbol correctly.
Add North arrow and scale bar: Insert a North arrow and scale bar by going to the "Insert" tab and selecting "North Arrow" and "Scale Bar." Adjust the properties to suit your map layout.
Adjust map layout: Go to the "Layout" tab to set up the map layout. Adjust the size of the map, add a title, and organize the legend, scale bar, and north arrow as desired.
Save and export: Save your project, go to the "Share" tab, and export the map as an image, PDF, or any other desired format.

Map showing possible groundwater resources in the Samburu County based on the geology — Figure 10. Suggested aquifer locations in Samburu County based on the geology.

Interpretation

An aquifer could be located in a magmatic/metamorphic basement, which suggests they moderate productivity and low groundwater potential. This is due to the fact that magmatic rocks have low permeability and therefore the groundwater recharge is low.

Ideal outcome: A possible groundwater source exists near the communities homes and a borehole could be developed.

Conclusion

The communities are located in the SW of the Samburu District. The geology of the area is mostly magmatic and metamorphic. This suggests that the ground has very low permeability. However, near the location of the communities, there are two springs. These springs point to an aquifer in the area, where a well for the communities in the area could be developed. Ideally, various groundwater sources could be located with the maps and the support from space technologies. The next steps to be taken are with external actors, e.g. drilling and pumping tests approved by the local authorities

Future steps

Work with hydrogeologists to prepare a borehole siting report as well as an Environmental Impact Assessment. Groundwater relief has some trusted hydrogeologists in their network in Kenya who could implement that and submit the information to the Kenya Water Resources Agency.
Kenya Water Resources Agency needs to grant permission for drilling.
Drilling and pumping test: A contractor performs drilling and a pumping test. The latter is to identify the appropriate pump to be used.
Study the groundwater level, type of aquifer, groundwater recharge, groundwater vulnerability.

Relevant publications

Related space-based solutions

Sources

Barasa, M., Crane, E., Upton, K., Ó Dochartaigh, B.É. & Bellwood-Howard, I. (2018): Africa Groundwater Atlas: Hydrogeology of Kenya. British Geological Survey. Accessed [22.09.2023]. http://earthwise.bgs.ac.uk/index.php/Hydrogeology_of_Kenya

Kuria, Z. (2013): Groundwater Distribution and Aquifer Characteristics in Kenya. Developments in Earth Surface Processes, Elsevier. 16, 8, p. 83-107.

Krhoda, G., Nyandega, I. & Amimo, M. (2015): Geophysical investigations of Suyien Earthdam in Maralal, Samburu County, Kenya. International Journal of Physical Sciences. 2, p.33-49.

Makinouchi, T., Koyaguchi, T., Matsuda, T., Mitsushio, H. & Ishida, S. (1984): GEOLOGY OF THE NACHOLA AREA AND THE SAMBURU HILLS, WEST OF BARAGOI, NORTHERN KENYA. African Study Monographs, Supplementary Issue 2, p. 15-44.

Touber, L. (1986): Landforms and solid of samburu District, Kenya. A site evaluation for rangeland use. The Winand Staring Centre for Integrated Land, Soil and Water Research. Report 6.

Data sources (maps)

National Atlas of Kenya - Geological Map. (n.d.). European Commission, Joint Research Centre. https://esdac.jrc.ec.europa.eu/content/national-atlas-kenya-geological-map (visited: 19.10.2023).

Samburu District - Type and Location of Water Sources, Key Landforms, and Soils (Symbols - See Map 11). (n.d.). European Commission, Joint Research Centre. https://esdac.jrc.ec.europa.eu/content/samburu-district-type-and-location-water-sources-key-landforms-and-soils-symbols-see-map-11 (visited: 19.10.2023)

Software
QGIS: A Free and Open Source Geographic Information System. Version 3.32.3 / Version 3.28.11 LTR.
ArcGIS Pro. Version ArcGIS Pro 3.

Keywords (for the solution)

Topographic Satellite Mapping Normalized Difference Vegetation Index (NDVI)Bored well Groundwater Groundwater abstraction Well Spring

Climate Zone (addressed by the solution)

Dry

Habitat (addressed by the solution)

Region/Country (the solution was designed for, if any)

Kenya

Relevant SDGs

Normalized Difference Vegetation Index (NDVI)

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Data

Software

Physical Requirement

Outline steps for a solution

Results

Vegetation classification for land of Maori communtiy - in development

Vegetation classification for land of Maori communtiy - in development

Padmi Ranasinghe

Cadence Kaumoana

Alexandre De Amorim Teixeira

Lacking historic knowledge on vegetation cover and surface water extent / river course

Padmi Ranasinghe

Cadence Kaumoana

Alexandre De Amorim Teixeira

Required Software

1. Data collection

2. Mapping the historical land use and land cover surrounding the river (in progress)

Other methods - Conducting a community survey

Further information on vegetation identification

Rainwater harvesting in Samburu County – in development

Rainwater harvesting in Samburu County – in development

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Hannah Ritchie

María José Molina-Montero

Isabel Zetina

Samburu tribe lacks access to safe drinking water - Dry spells due to water scarcity

Lilian Nguracha Balanga

Hannah Ritchie

María José Molina-Montero

Isabel Zetina

Suggested solution

Outline of the solution

Steps to be taken:

Accomplished progress

Development of precipitation maps

Future steps

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Spatiotemporal analysis of hydrometeorological disasters in the Indian Himalayas: integrating space-based techniques for enhanced disaster resilience

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SAR based monitoring of hydrocarbon contamination of water bodies in Eleme, Nigeria - in development

Earth observation for ecosystem resilience: the Lake Ol' Bolossat monitoring initiative - in development and need for input

Wetland extent mapping - North Central Nigeria, Ibaji - Kogi State - in development

Identification of potential locations/recharge for shallow groundwater in geographically small countries - mapping completed - need in situ data

Flood modeling for melting glacier - discontinued

Identify upstream potential pollution sources - in development

Surface water extent river course - in development

Construction of sand dams for Samburu County - in development

Rainwater harvesting in Samburu County – in development

Determining optimum sites for rainwater harvesting - in development

Vegetation classification for land of Maori communtiy - in development

Water suitability map (Samburu County, Kenya) - in development

Surface water extent river course - in development

Surface water extent river course - in development

Cadence Kaumoana