While global poverty rates have been cut by more than half since 2000, one in ten people in developing regions are still living with their families on less than the international poverty line of US$1.90 a day, and there are millions more who make little more than this daily amount. Significant progress has been made in many countries within Eastern and Southeastern Asia, but up to 42% of the population in Sub-Saharan Africa continues to live below the poverty line.
Poverty is more than the lack of income and resources to ensure a sustainable livelihood. Its manifestations include hunger and malnutrition, limited access to education and other basic services, social discrimination and exclusion as well as the lack of participation in decision-making.
Economic growth must be inclusive to provide sustainable jobs and promote equality. Social protection systems need to be implemented to help alleviate the suffering of disaster-prone countries and provide support in the face of great economic risks. These systems will help strengthen responses by afflicted populations to unexpected economic losses during disasters and will eventually help to end extreme poverty in the most impoverished areas.
Facts and Figures
783 million people live below the international poverty line of US$1.90 a day
In 2016, almost 10 per cent of the world’s workers live with their families on less than US$1.90 per person per day
Globally, there are 122 women aged 25 to 34 living in extreme poverty for every 100 men of the same age group.
Most people living below the poverty line belong to two regions: Southern Asia and sub-Saharan Africa
High poverty rates are often found in small, fragile and conflict-affected countries
One in four children under age five in the world has inadequate height for his or her age
As of 2016, only 45% of the world’s population were effectively covered by at least one social protection cash benefit.
In 2017, economic losses due to disasters, including three major hurricanes in the USA and the Caribbean, were estimated at over $300 billion.
Space-based Technologies for SDG 1
Climate-related extreme events and other disasters can plunge communities into poverty. Space-based technologies can contribute to all phases of the disaster management cycle, including prevention, preparedness, early warning, response and reconstruction. Space-based technologies can contribute to all phases of the disaster management cycle, including prevention, preparedness, early warning, response and reconstruction. UNOOSA helps vulnerable communities access space technology to build their resilience and reduce their exposure and vulnerability to environmental shocks and disasters. Read more here.
In 2019, floods caused 43.5% of all deaths due to natural disasters and thereby represent the deadliest type of disaster with an increasing number of events compared to previous years (CRED, 2019). Floods furthermore lead to the highest number of people affected compared to other disasters as they affect human activities and the economy (CRED, 2019; Elagib et al. 2019).
What does your morning routine look like? For most readers I’d assume you use the toilet, wash your hands, and maybe take a shower. However, do you ever stop to consider the water you use to shower, or the soap you use to wash your hands? Often, especially in developed countries, these things are taken for granted, rightly considering access to adequate water, sanitation, and hygiene (WASH) as basic Human Rights (Figure 1).
À quoi ressemble votre routine matinale ? Pour la plupart des lecteurs, je suppose que vous utilisez les toilettes, vous vous lavez les mains et peut-être que vous prenez une douche. Cependant, vous arrive-t-il de vous arrêter pour réfléchir à l'eau que vous utilisez sous la douche ou au savon que vous utilisez pour vous laver les mains ?
Margherita is an interdisciplinary Earth scientist and drone pilot with a background in geologic and environmental sciences. She has international experience working in fields such as Earth Observation (EO), remote sensing, drones & geospatial data analysis applied to the environmental and humanitarian sectors, sustainability and climate change. Margherita is passionate about natural and climate-related technologies that can be used to develop sustainable and long-lasting solutions. She is working for a more inclusive world (Women in Geospatial+), without any sort of geographical or social barriers.
Keywords: Science communication, Climate Change, STEM, inclusivity, sustainability, nature, hydrosphere, hydrology, water risks, Earth Observation (EO), satellite data, flood modeling, vulnerability, resilience, lifelong learning
Region/Country mentioned: Temperate climates, Arid climates, Luxembourg, Niger
Relevant SDG targets: 1, 4, 6, 9, 11, 13, 17
Margherita is an interdisciplinary Earth scientist and drone pilot with a background in geologic and environmental sciences. She has international experience working in fields such as Earth Observation (EO), remote sensing, drones & geospatial data analysis applied to the environmental and humanitarian sectors, sustainability and climate change. Margherita is passionate about natural and climate-related technologies that can be used to develop sustainable and long-lasting solutions. She is working for a more inclusive world (Women in Geospatial+), without any sort of geographical or social barriers.
Keywords: Science communication, Climate Change, STEM, inclusivity, sustainability, nature, hydrosphere, hydrology, water risks, Earth Observation (EO), satellite data, flood modeling, vulnerability, resilience, lifelong learning
Region/Country mentioned: Temperate climates, Arid climates, Luxembourg, Niger
Relevant SDG targets: 1, 4, 6, 9, 11, 13, 17
River and floodplain landscapes are constantly undergoing change due to natural and manmade processes putting pressure on fluvial systems, such as reservoirs, intensive agriculture, high-impact repetitive droughts and floods and the overall effects of climate change. All these bring about considerable changes, some of which irreversibly degrade ecosystem services, local economies and impact lives, particularly in sensitive transitional zones such as the Sahel region in Africa and its Niger River Basin (NRB).
Decision-makers are faced with the constant challenge of maintaining access to and understanding new technologies and data, as information and communication technologies (ICTs) are constantly evolving and as more and more data is becoming available. Despite continually improving technologies, informed decision-making is being hindered by inadequate attention to enabling conditions, e.g. a lack of in-service education and professional training for decision-makers.
The emerging demand of GIS and Space Applications for Climate Change studies for the socio-economic development of Pakistan along with Government of Pakistan Vision 2025, Space Vision 2047 of National Space Agency of Pakistan, and achievement of UN Sustainable Development Goals (SDGs) impelled the Higher Education Commission of Pakistan (HEC) to establish Remote Sensing, GIS and Climatic Research Lab (RSGCRL) at University of the Punjab, Lahore, Pakistan.
The United Nations University Institute on Comparative Regional Integration Studies (UNU-CRIS) is a research and training institute of the United Nations University. UNU is a global network of institutes and programs engaged in research and capacity development to support the universal goals of the UN. It brings together leading scholars from around the world with a view to generate strong and innovative knowledge on how to tackle pressing global problems. UNU-CRIS focuses on the study of processes of global cooperation and regional integration and their implications.
mWater is an operating system for digital governance used by governments, civil society organizations, and water and sanitation service providers in over 190 countries. The platform's free features allow users to collect data using smartphones, bring in data from Earth observations and other sources, and create effective analytics and visualizations to help prioritize interventions. mWater is designed to facilitate collaboration and longitudinal monitoring of individual pieces of infrastructure as well as entire water systems.
mWater is an operating system for digital governance used by governments, civil society organizations, and water and sanitation service providers in over 190 countries. The platform's free features allow users to collect data using smartphones, bring in data from Earth observations and other sources, and create effective analytics and visualizations to help prioritize interventions. mWater is designed to facilitate collaboration and longitudinal monitoring of individual pieces of infrastructure as well as entire water systems.
mWater is an operating system for digital governance used by governments, civil society organizations, and water and sanitation service providers in over 190 countries. The platform's free features allow users to collect data using smartphones, bring in data from Earth observations and other sources, and create effective analytics and visualizations to help prioritize interventions. mWater is designed to facilitate collaboration and longitudinal monitoring of individual pieces of infrastructure as well as entire water systems.
To establish an integrated monitoring and decision-support system that uses Earth Observation data and machine learning to track the status of Lake Ol' Bolossat, enabling evidence-based conservation and sustainable development actions.
Requirements
Data
Below is a table showing the data requirements and sources.
Data source
Use case
Period
JRC GSW
Historical water extents
1984 - 2023
Sentinel-1 SAR
Water extent during cloud-cover seasons
2014 - present
Sentinel-2 2 MSI
Habitat classification, NDVI, MNDWI, NDBI
2015 - present
MODIS
NDVI/ET anomalies and drought indicators
2000 - present
Rainfall and climate (CHIRPS/ERA5)
Climate trend correlation with hydrological changes
1984 - present
Population/Human settlement (WorldPop, GHSL)
Land use pressure mapping
2000 - present
Field surveys and local NGO data
Validation and community-level observations
As available
Software
The analysis is being done using open-source platforms and software: Google Earth Engine and QGIS.
To access Google Earth Engine, one needs a Google account that will be linked to the platform link. If you are new to the platform, create an account, and you can start using it. If you already have an account, just sign in and be directed to the code editor. If you are new to the software, you can access the training manual here.
To access QGIS, you need to download it as it is a software, link. If you are new to the software, you can access the training manual here.
Physical
Establishment of Ground Monitoring Stations
Purpose: To validate satellite data and collect real-time, on-the-ground water level, rainfall, and biodiversity observations.
Components: Water gauges, weather sensors, camera traps for biodiversity, and simple soil moisture probes.
Community Information Boards or Digital Kiosks
Purpose: To display maps, water level trends, and habitat updates to residents in a simplified, accessible format.
Location: Strategic points around the lake (e.g., near schools, water collection points, community centers).
Buffer Zone Demarcation and Fencing
Purpose: To physically protect critical wetland habitats and prevent encroachment or grazing in sensitive areas.
Details: Fencing or natural barriers like vegetation planting along designated riparian zones.
Construction of a Local Conservation and Data Hub
Purpose: To provide a space for community meetings, training sessions, citizen science coordination, and storing field equipment.
Location: Ideally within a local government or NGO compound near the lake.
Rehabilitation of Degraded Wetlands
Purpose: Restore areas where the lakebed or surrounding wetlands have been severely altered.
Methods: Planting of indigenous wetland vegetation, removal of invasive species, and controlled re-wetting.
Water Resource Management Infrastructure
Purpose: To improve the regulation and sustainable use of the lake's water.
Examples: Controlled inflow/outflow channels, community-led irrigation management systems, water pans for livestock to reduce direct lake access.
Signage and Protected Area Boundary Markers
Purpose: To raise awareness of Lake Ol’ Bolossat’s legal protection status and to visually communicate boundaries to land users.
Materials: Durable signs, educational posters, and protected area plaques.
Solar-Powered Connectivity Units (Optional but strategic)
Purpose: For uplinking field sensor data or enabling access to the online dashboard in remote locations.
Components: Solar panels, GSM routers, rugged tablets or data loggers.
Outline steps for a solution
Phase 1: Planning and Stakeholder Engagement – To do
The first phase involves defining the objectives of the monitoring system and identifying measurable success indicators aligned with conservation priorities and local needs. This is followed by engaging key stakeholders such as the National Environment Management Authority (NEMA), Kenya Wildlife Service (KWS), Water Resources Authority (WRA), Nyandarua County Government, and local community-based organizations. Stakeholder consultations are critical for gathering input on data needs, identifying decision-making gaps, and ensuring buy-in from both policy actors and community leaders. A situational analysis should be conducted to map existing infrastructure, technical capacity, internet access, and human resources available on the ground, helping to identify opportunities and constraints for implementation.
Phase 2: Data Collection and System Design – In progress
In this phase, a comprehensive monitoring framework is developed, specifying the key indicators to be tracked, such as seasonal water extent, land cover transitions, and flood-prone zones. Relevant Earth observation datasets are selected, including Sentinel-1 SAR for water extent, Sentinel-2 for habitat classification, JRC Global Surface Water for historical trends, and CHIRPS for rainfall data. A prototype dashboard is developed using Google Earth Engine, visualizing these datasets through maps, time series graphs, and interactive overlays. Simultaneously, field validation activities are conducted to ground-truth satellite-derived maps. This includes collecting GPS points, photos, and observations on vegetation, land use, and visible signs of degradation, ensuring the remote sensing outputs are accurate and contextually relevant.
Phase 3: System Testing and Expansion – To do
Once the prototype is ready, it is tested with stakeholders through pilot sessions and community workshops. These engagements are used to collect feedback on the dashboard’s usability, relevance, and user experience, particularly for non-technical audiences. Revisions are made to improve clarity, layer toggling, labelling, and interpretability. In parallel, basic physical interventions begin, such as the installation of simple water gauges, informational signboards, and boundary markers for conservation zones. These elements help translate digital insights into tangible tools for the community. Plans for expanding field infrastructure, such as creating buffer zones or establishing a local conservation hub, are also explored during this phase.
Phase 4: Deployment and Knowledge Sharing – In progress
Following successful pilot testing and system refinement, the full monitoring platform is deployed on a publicly accessible hosting environment, such as Firebase, Earth Engine Apps, or a custom-built website. The platform is shared with agencies and conservation partners, accompanied by a rollout plan that includes formal training sessions. These capacity-building workshops are designed to empower users, ranging from government officers to youth groups, with the skills to interpret dashboard outputs and use the data in planning and response. User guides, translated materials, and offline summaries are provided to support long-term usability and local ownership.
Phase 5: Monitoring, Maintenance, and Scaling – To do
The final phase focuses on monitoring the performance and real-world impact of the system. Regular evaluations are conducted to assess usage, data accuracy, stakeholder engagement, and improvements in environmental decision-making. Lessons learned are used to refine system features, add new datasets, and introduce functionalities such as alert notifications or mobile-friendly access. The success of the Lake Ol’ Bolossat solution creates a foundation for scaling to other endangered wetlands across Kenya, such as Lakes Baringo, Naivasha, or Kanyaboli. Finally, the project contributes to the broader Space4Water and open science communities by publishing methods, code, and findings on platforms like GitHub and Earth Engine’s asset repository, ensuring transparency, replicability, and collaboration.
Results
The Lake Ol’ Bolossat monitoring system, currently at prototype stage, holds significant potential to transform how freshwater ecosystems are managed at local and national levels. By integrating satellite-derived water and habitat data into an accessible dashboard, the system aims to bridge the gap between Earth observation science and on-the-ground conservation action. Once implemented with key stakeholders and end users, the following impacts are anticipated:
Support for Environmental Agencies and County Governments: The system could enhance the capacity of institutions such as the National Environment Management Authority (NEMA), Kenya Wildlife Service (KWS), Water Resources Authority (WRA), and the Nyandarua County Government by providing timely, location-specific data for decision-making on lake and wetland management.
Early Warning for Hydrological and Ecological Risks: The dashboard could enable stakeholders to detect abnormal patterns in water extent, such as persistent shrinkage or sudden expansion, triggering early intervention to prevent ecological degradation or disaster impacts on nearby communities.
Community Awareness and Engagement: By visualizing seasonal and long-term changes, the system can be used to build awareness among residents, farmers, and water users around Lake Ol’ Bolossat, empowering them to engage in sustainable practices and to advocate for the protection of the lake.
Policy-Relevant Monitoring Tool: The platform can serve as a long-term environmental monitoring tool to support the implementation of wetland protection policies, local water catchment strategies, and integrated land use planning frameworks.
Scalability to Other Freshwater Ecosystems: Once validated, the approach used at Lake Ol’ Bolossat can be adapted to other small inland water bodies across Kenya and East Africa, particularly those facing similar risks of drying, encroachment, or biodiversity loss.
Alignment with Global and National Development Goals: The system supports Kenya’s contributions to Sustainable Development Goals (SDGs), particularly:
SDG 6: Ensure availability and sustainable management of water and sanitation
SDG 13: Take urgent action to combat climate change and its impacts
SDG 15: Protect, restore and promote sustainable use of terrestrial ecosystems and halt biodiversity loss
Wetlands are some of the most important habitats in the ecosystem, with the most diverse group of organisms. Wetlands are transition points between land and water. Due to pollution and climate change, our wetlands are diminishing at a geometric rate. There is an urgent need for restoration of these wetlands to encourage plant and animal diversity and ultimately ensure sustainability. Its noticed that most of the swampy lands (wetlands) in the eastern part of Kogi state of Nigeria are diminishing very quickly. Wetlands are important to the environment because they help keep nature balanced and clean. Furthermore, they serve as a buffer zone for flood hazards in a catchment. The wetlands in the eastern part of Kogi state are mostly used for agriculture, as some crops are well suited to that type of water-logged soil. Recently (In the last 20 years, the wetlands in the region have started to disappear at a geometric rate. This disappearance of wetlands is probably caused mainly by climate change or pollution and will prevent the attainment of SDGs 1,2,6,11,12,13,14,15.
Problem Statement
Among the most productive ecosystems in the world, wetlands offer vital functions such as flood control, carbon sequestration, water filtering, and a biodiverse habitat. Wetlands in states like Kogi, Benue, Plateau, and Niger in North Central Nigeria are essential to local livelihoods, agriculture, and fisheries. However, a combination of increasing environmental degradation, the effects of climate change, and human activities like inadequate water resource management and agricultural development is causing these habitats to disappear quickly. The degradation of wetlands in this region is driven by several interconnected factors. Wetland regions are disappearing as a result of rising pollution, deforestation, and land conversion for industrial and urban purposes. Wetland hydrology is changing due to rising temperatures, unpredictable rainfall patterns, and protracted droughts, which reduces the capacity of these ecosystems to support biodiversity and offer ecosystem services. Wetland degradation and loss have been exacerbated by the drainage and conversion of wetlands for agricultural purposes, driven by the growing demand for arable land. The natural flow of water into wetlands has been disturbed by poor water resource management, including the construction of dams and extraction of large amounts of water. The result of the shrinking of wetlands, which are home to rare plants and animals, is the loss of biodiversity. Insufficient comprehension of the intricate connections between human endeavors and wetland ecosystems has led to unsustainable behaviors that cause harm. Despite their ecological and socio-economic importance, there is limited research on the extent, drivers, and impacts of wetland loss in North Central Nigeria. Existing studies have largely focused on other regions, such as the Niger Delta, leaving a significant gap in understanding the dynamics of wetland degradation in this area. The lack of detailed geospatial data and comprehensive analysis hinders the development of effective conservation strategies and policy interventions. The disappearance of wetlands in North Central Nigeria has far-reaching consequences, including reduced water quality, loss of biodiversity, increased vulnerability to flooding, and diminished livelihoods for local communities. Without urgent action, the continued degradation of these ecosystems will exacerbate environmental and socio-economic challenges, particularly in the face of climate change.
Research Objectives
This research seeks to address this gap by achieving the following main objectives. By achieving these objectives, this research will contribute to a deeper understanding of wetland dynamics in North Central Nigeria and provide actionable recommendations for sustainable management and conservation.
1. Map the spatial extent of wetlands and document wetland transformation.
Apply supervised classification using algorithms like Random Forest (RF) in Google Earth Engine
Use NDWI, MNDWI, and SMI spectral indices to help identify water and moist soil features.
Perform accuracy assessment with confusion matrices and field-collected points
2. Analyze temporal changes in wetland areas: Assess how wetland areas have changed over time, using historical and current data. Identify and record the drivers and patterns of wetland loss and degradation in the region.
Perform post-classification comparison of the classified maps for each time period.
Generate change maps to show wetland loss, gain, or transformation (e.g., to farmland, built-up, etc.).
Calculate area statistics for each class and change category using GEE.
3. Provide Scientific Evidence for Conservation Efforts: Generate data-driven insights to support the development and implementation of effective wetland conservation strategies.
Expected outcomes of the space-based solution developed in the context of Space4Water
Quantify wetland loss over time since 1995
Determine trends in wetland change
Create comprehensive geospatial documentation
Assist with conservation and environmental policy which will establish baseline information for ecological restoration
Literature Review
Ogunlade (2024) conducted a geospatial analysis of wetland distribution in Ilaje Local Government Area, Ondo State, Nigeria, using remote sensing and geospatial techniques such as NDVI, NDWI, NDMI, and NDBI. Although the study focused on southern Nigeria, its methodology and findings are relevant to understanding wetland dynamics in North Central Nigeria. The study revealed significant transformations in wetland areas, with an increase from 253.1 km² in 1986 to 354.8 km² in 2015, followed by a decline to 318.14 km² by 2019. The study projected ongoing changes by 2030, emphasizing the need to control urban encroachment to mitigate environmental hazards like flooding and soil erosion. Tobore et al. (2021) assessed the suitability of wetland soils for rice production in Ajibode, Nigeria, using GIS and remote sensing. The study analyzed Landsat 7 and 8 imagery from 2000 and 2016, alongside soil sampling, to evaluate land use changes and soil properties. Findings indicated that 75% of the soils were marginally suitable for rice production, while 20% were unsuitable. The study highlighted the importance of geospatial techniques in guiding land use decisions to sustain agricultural productivity (Tobore et al., 2021). This study is particularly relevant to North Central Nigeria, where agriculture is a major driver of wetland loss. Abubakar and Abdussalam (2024) analyzed land use changes and wetland dynamics in Kaduna Metropolis using Landsat TM/OLI imagery and SRTM DEM data. The study found a significant increase in built-up areas (194.9 km²) and a corresponding decline in wetlands, with marshlands losing 15 km² and riparian vegetation declining by 28.6 km². Urban expansion and agricultural activities were identified as primary drivers of wetland loss, underscoring the need for sustainable land use planning (Abubakar & Abdussalam, 2024). While Kaduna is in the Northwest, the findings are applicable to North Central Nigeria, where similar trends are observed. Lin and Yu (2018) investigated the loss of natural coastal wetlands in three Chinese coastal city clusters (Bohai Rim, Yangtze River Delta, and Pearl River Delta) from 1990 to 2015. The study attributed wetland loss to land conversion for agriculture and urban development, as well as ecological degradation from water pollution. The Bohai Rim experienced the highest land conversion loss, while the Yangtze River Delta faced severe ecological degradation due to pollution from inland rivers. The study emphasized the role of regional economic development in driving wetland loss and called for integrated conservation strategies (Lin & Yu, 2018). Although focused on China, the study's findings on the drivers of wetland loss are relevant to North Central Nigeria, where similar factors are at play. Akei and Babila (2022) examined wetland dynamics in Bamenda II and III Municipalities, Cameroon, using satellite imagery and field surveys. Wetlands in Bamenda II decreased from 33.91 km² in 1980 to 28.58 km² in 2020, while Bamenda III experienced a more severe decline from 13.58 km² to 9.09 km². Rapid urbanization, pollution, and biodiversity loss were identified as key drivers. The study highlighted the environmental consequences, including habitat loss and increased flooding, and advocated for both engineering and non-engineering adaptation strategies (Akei & Babila, 2022). These findings are relevant to North Central Nigeria, where urbanization and pollution are significant threats to wetlands. Several studies have demonstrated the effectiveness of using Landsat imagery combined with machine learning techniques for wetland mapping and monitoring. Xie et al. (2019) utilized Landsat-8 data with classifiers such as Random Forest and Support Vector Machines to classify wetlands in the Canadian Prairie Pothole Region, achieving high accuracy despite the complexity of wetland spectral signatures. Similarly, Wang et al. (2018) applied Random Forest to Landsat time-series data to monitor wetland changes in China’s Sanjiang Plain, achieving classification accuracy above 85%. These studies underscore the potential of Landsat-based machine learning approaches for accurate, scalable, and cost-effective wetland monitoring and conservation planning. These technologies could be applied in North Central Nigeria to improve wetland monitoring and management.
Research Methodology
Study Area
Kogi State lies between latitudes 6°30′N and 8°40′N and longitudes 5°10′E and 8°10′E, The ground elevations range between 140 and 300 m above sea level (Ifediegwu et al., 2019). The climate of the study area is designated as Sub-Humid (AW by Koppen classification). Kogi state, is found in the Guinea savannah region with the presence of gallery forest along water courses (riparian vegetation). Time period to be studied is 30years (1995 - 2025)
ibaji Area,kogi State
Suggested solution
This ongoing project uses a space-based solution to monitor and analyze the disappearing wetlands in North Central Nigeria, particularly in Ibaji, Kogi State. collaborators have extended the methodology by incorporating wet season composites (e.g., WaterMasks_1995_2024_Fixed_Threshold_v2) to differentiate between temporary and permanent water bodies. Due to limited access to high-resolution training and validation data and time constraints, the team is currently adopting a thresholding approach to identify annual water extent patterns.
The analysis leverages Google Earth Engine and Landsat archives, focusing on changes in surface water extent over a 30-year period. The ultimate goal is to generate consistent, interpretable insights into wetland dynamics—providing a foundation for long-term conservation strategies, policy action, and ecological restoration .
Step-by-Step development of the solution to document wetland transformation
Description of all the steps to get to a solution
Study area definition (completed)
Data Acquisition (completed)
Preprocessing (in progress)
Feature extraction (in progress)
Classification of land cover mapping (in progress)
Accuracy Assessment (in progress)
Map Generation and Export (in progress)
Temporal and spatial analysis (in progress)
Reporting and interpretation (in progress)
1. Study area definition
Identify and define the geographic boundary of the wetland area using shapefiles or administrative boundary data.
For this project, the focus area is Ibaji in Kogi State, North Central Nigeria which can be loaded from the "FAO/GAUL/year/level2" data
Figure 1: study area map
2. Data Acquisition
Access satellite imagery through Google Earth Engine (GEE) for the years 1995, 2005, 2015, 2025
Use Landsat 5, 7, 8, or 9 imagery for historical and current data
Select images for both wet and dry seasons to capture seasonal variations. (If necessary, otherwise use the date for the beginning of the dry season.). In our study area, the wet season is April-October and the dry season is November-March.
Apply filters for cloud cover (e.g., <10%) to ensure image clarity.
Collect in situ data for validation to identify features: wetland, farmlands, urban, tree, water body, grassland.
High-confidence interpreted data from high-resolution photography or ground-truth information gathered straight from the field are referred to as "in situ data." Every point or polygon has the appropriate land cover class (such as "wetland, water, urban, etc.") labeled on it. In situ data can be loaded as an asset, shapefile, or fusion table. When training a supervised classification algorithm such as Random Forest or SVM in GEE, you use in situ data as labeled samples (features with geometry + land cover class). Following classification, the model's accuracy in recognizing each class—for example, wetlands vs. farmlands—is evaluated using a different subset of the in situ data, known as the test set.
3. Prepossessing
Cloud Masking: Use built-in cloud masking functions (e.g., QA_PIXEL) to remove clouds.
The built-in cloud masking functions like QA_PIXEL are part of satellite image datasets in Google Earth Engine (GEE). Example: Cloud mask band: QA_PIXEL Collection: 'LANDSAT/LC08/C02/T1_L2'
Composite Creation: Generate median composites for each year or season to reduce noise.
Image Clipping: Clip all images to the defined study area for consistency.
Preprocessing of in situ data (if they lie outside of the area of interest)?
To compensate for the not enough in-situ validation points within the Area of interest (AOI), we adopted a spectral similarity analysis approach. This involved extracting the multiband spectral signatures (e.g., from Landsat imagery) of known land cover types from reference points situated around and within the AOI. These signatures will then search for spectrally similar pixels within the AOI
By computing spectral distances (e.g., Euclidean distance) between each AOI pixel and the reference signatures, we identified and mapped areas with high spectral similarity. These areas are considered likely to represent the same land cover types as the reference points and serve as proxy validation regions for assessing the classification output.
4. Feature Extraction
Compute relevant spectral indices to enhance wetland detection:
NDWI (Normalized Difference Water Index)—highlights surface water.
MNDWI (Modified NDWI) – better distinguishes water in urban/vegetated areas.
In Google Earth Engine (GEE), supervised classification combined with spectral indices is an effective approach for wetland mapping. These indices help differentiate wetlands from other land cover types based on water content, vegetation, and soil moisture First, Landsat imagery is used to compute key indices such as NDWI (to highlight open water), MNDWI (to separate water from urban/vegetated areas), NDVI (to capture vegetation density), and SMI (to estimate soil moisture). These indices are added as input features. then labeled in situ data representing various land cover types (including wetlands) is used to train a classifier such as Random Forest. The trained model then classifies the image based on the spectral patterns of the indices, allowing for accurate identification and mapping of wetland areas across the landscape.
In summary
· NDWI and MNDWI separate water from land.
· NDVI identifies vegetated wetlands.
· SMI enhances soil water content, especially useful in seasonal wetlands
5. Classification for land-cover mapping
Land cover classification using satellite imagery involves identifying different surface types such as water, wetlands, vegetation, urban areas, and farmlands. In this analysis, the Random Forest algorithm is used because it handles large datasets, performs well with minimal tuning, and is robust to noise and unbalanced class sizes. Training samples are collected for each land cover type using visual interpretation of imagery. The classifier is trained using spectral bands like B3, B4, B5, and B6 (Green, Red, NIR, SWIR) , along with indices such as NDVI and NDWI. Once trained, the model is applied to classify the entire image into the defined land cover classes.
Land cover classification plays a crucial role in wetland mapping because it helps distinguish wetlands from other land cover types such as open water, vegetation, farmlands, and urban areas. Wetlands often share similar spectral characteristics with nearby features, so using classification allows for more accurate identification and delineation.
6. Accuracy Assessment
Collect validation samples or use known land cover points.
Use a confusion matrix to calculate accuracy metrics (e.g., overall accuracy, kappa coefficient).
Adjust classification parameters if accuracy is unsatisfactory.
7. Map Generation and Export
Generate classified wetland maps for each target year (1995, 2005, 2015, 2025) or desired epoch
Use GEE’s Export.image.to Drive to save maps as GeoTIFFs or other formats.
Optionally, create map visualizations with legends and coordinates for reporting.
Analyze Temporal Changes in Wetland Areas
8. Temporal and Spatial Analysis
Conduct change detection to observe wetland expansion or loss over time.
Use zonal statistics or pixel-based comparisons to quantify changes.
Compare seasonal images to distinguish between permanent and seasonal wetlands.
9. Reporting and Interpretation
Visualize results through maps, graphs, and time-series charts.
Integrate findings with contextual layers (e.g., soil, elevation) for deeper insights.
Prepare outputs suitable for scientific publication, policy briefs, or community engagement.
Results
Preliminary results
A first attempt to map wetland time series was performed by identifying surfacing water from the Landsat archive. The implemented method was a simple thresholding based on MNDWI. In the first round, a fixed threshold was used (0), and then dynamic thresholding was used.
One limitation is the low availability of Landsat scenes: the first images available are from 1999. The time series was thinner at the beginning (less than 10 scenes for the season, until 2003) and became thicker to the last years (more than 30 scenes, up to 60 in 2023).
Another challenge is the broader definition of wetlands, which goes beyond surfacing water bodies and includes specific vegetation adapted to live in saturated soil conditions, so the current method addresses only partially the wetland characterization.
To overcome the first limitation, we have no possibility from space because Landsat is the longest space mission available. For the second, we will explore the most suitable features to characterize these complex environments, such as phenological features from optical data and soil moisture from SAR data.
Impact of the solution
Accurate Monitoring of Wetlands
The space-based system will give accurate and up-to-date information on the location and extent of wetlands in North Central Nigeria. Satellite images and GIS tools make it easier to track changes in wetland areas over time. This is especially significant in recognizing regions where wetlands are diminishing or disappearing as a result of climate change or human activities.
Informed Decision-Making
By analyzing the patterns and causes of wetland loss, the solution will assist policymakers and environmental managers in making educated decisions. It will help to design focused conservation policies, such as safeguarding vulnerable areas, regulating land use, and directing ecological restoration activities. The data collected will be used as scientific proof for environmental planning and sustainable development.
Community Awareness and Engagement
In addition, the solution will assist in raising awareness among communities and stakeholders by offering visual maps and simple insights. These tools can help the public understand the importance of wetlands and inspire community involvement in their protection.
Support for Biodiversity and Climate Resilience
Over time, this method will help to conserve biodiversity, minimize flood risk, and improve the region's ability to adapt to climate change. Wetlands serve an important role in preserving ecological balance, and protecting them helps to ensure long-term environmental and socioeconomic stability.
Relevant publications
Abubakar, Muhammad Lawal, and Auwal Farouk Abdussalam. "Geospatial analysis of land use changes and wetland dynamics in Kaduna Metropolis, Kaduna, Nigeria." Science World Journal 19, no. 3 (2024): 687-696.
Lin, Qiaoying, and Shen Yu. "Losses of natural coastal wetlands by land conversion and ecological degradation in the urbanizing Chinese coast." Scientific reports 8, no. 1 (2018): 15046.
Leemhuis, Constanze, Frank Thonfeld, Kristian Näschen, Stefanie Steinbach, Javier Muro, Adrian Strauch, Ander López, Giuseppe Daconto, Ian Games, and Bernd Diekkrüger. 2017. "Sustainability in the Food-Water-Ecosystem Nexus: The Role of Land Use and Land Cover Change for Water Resources and Ecosystems in the Kilombero Wetland, Tanzania" Sustainability 9, no. 9: 1513. https://doi.org/10.3390/su9091513
Meusburger, Katrin. "Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland." (2018).
