Research outputs of the EO AFRICA R&D projects are listed below.
A workflow for forecasting primary productivity and its determining climatic factors using remote sensing in the eastern Sahel region
Understanding the dynamics of primary productivity and determining climatic factors is essential to determine a given area’s socio-economic and ecological vulnerability. The eastern Sahel region has been described as a hotspot of land degradation, resulting in significant natural and human crises. While the region’s population is expected to grow faster than the rest of the world, the climate of the eastern Sahel is predicted to become more arid. As such, increased demand for food will likely coincide with a decline in agricultural productivity in the region. An innovative approach is required for positive transformation and effective natural resources management. In this regard, the use of earth observation (EO) data and inexpensive cloud computing technologies in Africa has been under-represented and, therefore, needs a dramatic boost to inform the sustainable management of primary productivity. This project aims to forecast primary productivity and climatic factors (soil moisture, precipitation etc.) using remote sensing techniques. The project seeks to develop an open-source interactive workflow to predict for 12 months ahead, considering the prevalence of subsistence livelihoods that largely rely on short-to-medium term preparations in the region. The project also offers hands-on training for African stakeholders to handle African challenges by leveraging cutting-edge open-source EO algorithms.
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Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria
The proposal focuses on assessing groundwater level within the Sokoto Rima Basin in the north-western Nigeria. The basin occupies approximately 64,000 km2, with an average elevation of 325 m OD, and underlain by multi-layered aquifer systems. The area is densely populated, with an irregular distribution of rainfall in space and time and a prolonged dry season. Hence, there is a massive dependence on local groundwater resources to meet the agricultural, domestic and industrial needs for the area. The system to be developed consists of five independent modular-structured processes, supported by data obtainable from both European Space Agency and third-party mission platforms. The system will allow the estimation of the recharge flux using soil moisture balance approach; quantitative groundwater resource assessment using calibrated and validated flow model; quantitative estimation of water requirements for agriculture, domestic and industrial purposes; training programs related to the developed system and effective water usage, and resource allocation under prioritized conditions. The workflow will be integrated within a Python-based open-source interactive notebook. The system will be activated in a typical serial processing mode, and the developed workflow will be published in an open access, peer-reviewed journal.
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Crop Stress Monitoring in the semi-arid context of Doukkala, Morocco
Responding to call objective Identifying and mapping crop stress/drought/failure, the project aims at developing a procedure for crop yield estimates and extreme events crops shocks monitoring or pest and diseases by integrating multiple satellite data and water-energy-crop modelling, able to support farmers precision agriculture. EO data from different sensors at high spatial resolution will be used to retrieve vegetation (e.g., leaf area index (LAI) from Sentinel 2, land surface temperature (LST) from the Third-party LANDSAT, and soil moisture (SM) from Sentinel 1). The innovative EO modelling chain will be implemented into open-source Jupyter Notebooks. The FEST-EWB energy water balance model (Corbari et al., 2011), coupled with the SAFY crop growth model (Corbari et al., 2021), will allow to compute continuously in time and distributed in space both soil moisture (SM) and evapotranspiration (ET) fluxes, along with crop yield. The model will be implemented in the African case study in Morocco, in the Doukkala irrigated area. Data assimilation procedures of different EO data will be routinely implemented along the crop season with the objective of detecting and monitoring crop exposure to shocks which are not reproducible by the model alone, which alter the canopy morphology and physiology.
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DroughT impACt on the vegeTation of South African semIarid mosaiC landscapes:
Implications on grass-crop-lands primary production
Semiarid rangelands (grasslands with scattered trees and shrubs) are one of Africaâs most complex and variable biomes. They are a mosaic of land uses, where extensive livestock is the main economic activity, and agriculture, soil for livelihood, or conservational uses are also crucial. They are highly controlled by the availability of water, e.g., pasture and rainfed crop production. Although the vegetation is adapted to variable climatic conditions and dry periods, the increase in drought intensity, duration, and frequency, changes in agricultural practices and other socioeconomic and environmental factors precipitate their degradation. Through the integration of EO data into models, we can evaluate, on the one hand, the water consumed by semiarid ecosystems and their vegetation water stress and, on the other, its primary production. Thus, allowing us to assess the interaction of both processes, improving our knowledge about the vegetation’ behaviour in the face of drought. TACTIC will map water consumption and primary production of semiarid mosaic crop-rangelands at the optimal spatiotemporal scales, setting up an open-source cloud framework to monitor these processes’ interaction in the long term and analyse system tipping points. This information can help reduce the uncertainty associated with the administration and farmersâ decision-making processes.
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Improvement of Agricultural Statistics in the cotton zone of Mali thanks to the synergy of the Sentinel-1 and 2 time series
The European Copernicus program produces massive amounts of valuable data for crop recognition and monitoring provided it can be processed efficiently using cloud-based computing infrastructure to provide crop mapping and early estimates of agricultural land within a sufficiently short period of time. The project will allow a Malian team to demonstrate this processing capacity for an area of approximately 150,000 kmÂČ (estimated population of 8 million habitants in 2017) while testing the contribution of cloud-insensitive Sentinel-1 data. This ambition is possible thanks to open-source tools such as Sen2Agri and Sen4Stat and experience in field data collection acquired within the framework of the Sen2Agri project. The objective is to improve the agricultural statistics available in Mali for the main crops in the cotton zone using a transparent, precise and reproducible method. The sustainability of the project’s achievements is ensured by the financing of a doctoral scholarship for a member of the Malian team with the European partner.
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Integration of open-source solutions with deep learning for estimating crop production in data-scarce smallholder farming areas
In smallholder farming areas where crop production is the mainstay of livelihood, early identification and monitoring of crop production status will be very useful for assessing the forthcoming food availability, food security and food market stability of the region at a preharvest season. In the presence of climate related shocks, this information would be even necessary for estimation of damage and further insurance pay-outs. Collection of this information through manual approaches is time consuming, influenced by human and technical bias and mostly impractical because of inaccessible terrain, resources and time. Presence of wide array of earth observing satellite has provided possibility of monitoring and mapping of objects and phenomena everywhere in the world. Though this is the general trend, mapping of crops and crop production using conventional approaches is challenging which is constrained by inherent characteristics of smallholder farming areas like seasonality of crops, fragmented small fields and dominance in complex topography. Therefore, in our proposed project we have planned to integrate optical and radar satellite imagery with artificial intelligence (deep learning models) with powerful statistical tools to map crop types and crop production in smallholder farming areas, in selected Ethiopian landscape using open-source innovative solutions.
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Monitoring by optical and radar satellite imagery of the level and volume of water in the lakes Buyo and Kossou dams in CĂŽte d’Ivoire
Cote dâIvoire like many countries in west Africa is experiencing climate change impact such drought which affect water resources. Indeed, the decrease in rainfall and runoff has significantly impacted the filling of dams (figure 1), the production of hydroelectric power, the supply of water to urban areas, and agropastoral activities (Fratmat, 2021). The Kossou and Buyo lakes and reservoirs, two large water reservoirs that are strategic for the Ivorian economy in terms of both agricultural and energy production, have experienced variations in water levels in recent years (figure 2). These variations are at the origin of load shedding, water shortages in CĂŽte d’Ivoire and decreases in economic and fishing activities (Goli Bi et al. 2019). Indeed, in several regions of the country, access to drinking water and electricity are dependent on the level and volume of water present in these reservoirs (Jeune Afrique, 2018; Afrobarometer 2018 and 2020).
In response to these problems, Ivorian authorities have undertaken different strategies to ensure a sustainable management of water resources. These measures consist of monitoring water resources both quantitatively and in terms of its spatial distribution in order to have continuous long-term and readily available data on lakes of CĂŽte d’Ivoire.
The water resources of lakes can be monitored in 3 ways: using in situ measurements, modeling process or based on remote sensing data. In recent years, water resources monitoring of Buyo and Kossou lakes has become increasingly difficult due to the decreasing number of measuring stations, the high cost of maintenance, the tedious collection and analysis of data, and the difficulty in modeling water resources.
The use of satellite data remains the only way to go. Indeed, recent developments in remote sensing technology has spawned more and more access to high resolution images that allow regular monitoring of lake extent in time and space. In addition, the use of satellite radar altimetry can contribute to monitor changes in lake water level. This study aims to monitor water volume fluctuations by combining in situ data, satellite images and several satellite altimetry measurements.
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Quantifying Soil Moisture from Space-based Synthetic Aperture Radar (SAR) and Ground-based Geophysical and Hydrological Measurements
Soil moisture (SM), which is the water content in the soil, plays an essential role in agriculture activities. With climate change and global warming, water scarcity is exacerbating in many parts of the world. Africa is facing the most significant challenges of water stress because food production and security depend on those water resources. Thus, monitoring SM at high resolution is of vital importance for irrigation activities, estimation of crop yields, and food security. Our proposal focuses on a 100-meter resolution soil moisture product, whose resolution is suitable for agriculture studies, as this is a characteristic size of an agricultural parcel. We investigate the feasibility of using use Sentinel-1 C-band Synthetic Aperture Radar (SAR) data recorded in the VV (vertical-transmit, vertical-receive) polarisation to retrieve 100m SM every 6 days and combine it with ground high resolution (cm) measurements collected twice a day to both validate and improve the estimate. The innovation of our approach is the integration of large-scale satellite data with field-scale ground geophysical measurements, such as electrical resistivity tomography (ERT) and active multichannel analysis of surface waves (MASW), and point observations such as time domain reflectometry (TDR) to downscale the SM results both in space and in time.
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Rising with temperature! Reconstructing the hydroclimatic record of Lake Naivasha with Earth Observation
Recent observations have shown that the Naivasha Lake’s water level has been increasing at unprecedented rates, damaging the Lake’s ecosystem services and social-economic goods. Climate change is the leading hypothesis explaining the increase in water level. Precipitation analysis shows a drastic (>50%) increase in rainfall intensity over the Lake since 2018. This surplus of rains has caused an upsurge in river discharge from the Gligil and Maliwa rivers feeding the Lake. In addition, agriculture practices and land use increase sediment load in the rivers, which cause siltation. As the outflow rate of the Lake’s underground outlet is lower than the inflow, the water level rises, and fine sediment accumulates. Except for analysing the rainfall and river discharge data, there is no scientific evidence verifying and expanding on the climate change hypothesis. This project will research the climate change hypotheses and unravel the main drivers of rising water levels. We will use Earth Observation (EO) data and models to map the distribution of precipitation, evaporation, water extent and level, and the sedimentation rate. ITC and RCMRD will develop a framework integrating these EO-products to reconstruct the Lake’s hydroclimatic record and attribute the Lake’s water level fluctuations to the different hydro- meteorological drivers.
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Sentinel-1 and -2 data fusion for mapping smallholder cropping areas in southern Africa to support crop monitoring and yield forecasting
Millions of people in sub-Saharan Africa depend merely on small-scale and rain-fed crop farming for their livelihood, but the changing global climate is threatening crop production in this region, with crop failure occurring frequently. Governments and donor agencies oftentimes intervene by providing food supplies to the affected communities to safeguard food security, but lack of timely information on affected areas undermines such efforts. Earth observation (EO) can provide timely information on crop development to identify areas at high risk of crop failure, but the generation of such information is hindered by lack of accurate basemaps for cropping areas to enable farm-level crop monitoring and yield estimation using satellite data. This project aims to fill this gap by (i) creating consistent reference dataset for cropfield boundaries to train and validate EO algorithm for mapping cropping areas, (ii) developing an innovative E0 algorithm and workflow for mapping cropping areas across a diversity of landscapes in southern Africa using imagery from European Copernicus Sentinel-1 and -2 satellites. The factors that affect accurate mapping of cropping areas across various landscapes in southern Africa will be identified.
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SENTINELs for Cape Verde Water & Food Security Monitoring
Small Island Development States face particular challenges with respect to management of natural resources. As most Western part of the Sahel region, Cape Verde is particularly vulnerable to extreme climate i.e., rainfall variations, presenting an enormous challenge for water and food security. Recurring droughts are an entire part of its climate and socio-economic history. During the Atlantic cyclone season (Aug-Oct) however, severe weather, next to supplying rainfall and water resources, creates high hazards to natural resources, infrastructure and populations. Notwithstanding these environmental challenges, Cape Verde is successfully working towards sustainable use of its natural resource base, through development of tourism, marine resources, renewable energy and trade. However, rural populations remain dependent on rainfall, rainfed-irrigated agriculture for water and food security and income generation. Seasonal weather forecasts such as the WMO Regional Climate Outlooks give a generic 3-month forecast, but do not provide practical information for Cape Verde water resources & agricultural management. This is due to the small extend and steep topography of the islands, and cyclonic nature of most rainfall. Near real-time satellite-based spatial – temporal rainfall observations, coupled to vegetation-index based agricultural monitoring can provide more adequate information for water resources and agricultural prediction, incl. extreme weather event forecasting. The EO-Africa SENCAPE project will design, implement and validate Sentinel and EU-Copernicus data derived cloud-computing workflows for rainfall, vegetation & agricultural forecasting, optimized for Cape Verde conditions, as an EO use case for small island development states.
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Towards daily maps of water hyacinth cover: exploiting synergies between Sentinel-2 and 3
Water HYacinth (WHY) is one of the worldâs most disturbing invasive plant species. The weed is a nuisance to boat traffic and fishery, clogs water treatment plants and hydroelectric dams, and renders lakes and reservoirs unattractive to tourists. Removal of WHY is an expensive undertaking due to the sheer scale of infestation. Numerous studies have shown that dense, floating mats of WHY can be detected using satellite instruments such as MSI2 on Sentinel-2. The OLCI3 instrument on Sentinel-3 has a coarser spatial resolution and is less suitable for WHY detection. However, it achieves global coverage in two days, compared to ten days for MSI, providing an opportunity to monitor WHY at near-daily resolution. This is crucial, considering that WHY cover patterns are highly variable due the plantsâ rapid reproduction and the influences of wind and currents. We propose the development of an algorithm that exploits the complementarity of MSIâs high spatial resolution and OLCIâs high temporal resolution to create daily maps of WHY cover. These would improve the understanding of the driving forces of WHY spread and aid policy makers in combating WHY or curbing its effects on traffic and fishing.
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West Africa Lake Monitoring System
In most West African countries, water authorities face real challenges in monitoring and managing lakes. These challenges include the limitation of resources for continuous field-based data collection for the assessment of both the ecological status and the implementation of regulations measures on these water bodies. In such a context, remote sensing-based monitoring could provide a sustainable solution. The current study proposes to use EO data to develop an open-source online monitoring system (dashboard) on water quality and a traditional fish method (called acadja) for West African lakes to improve water management for food security. The issues that will be addressed are related to the spread of water hyacinth, proliferation of acadja and frequent algae blooms. Lake Ahémé and Lake Nokoué in Benin will be used as study sites to develop the dashboard. On these ecosystems field data will be used for accuracy assessment. A mid-term stakeholder meeting will be organised to demonstrate already implemented features and get their feedback towards an operational monitoring system. After the project the tool can be upscaled to other lakes in West Africa.
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BIG data archetypes for crops from EO
As population increases, so does the demand for food and food security. Field crops supply a large proportion of this globally, but pressures on crop area and productivity continue to grow. Earth Observation (EO) already provides much information, and new datasets offer more quality and detail for crop development and yield prediction at the field-scale and beyond. We use algorithms to interpret the data, but they are a complex response to a large number of soil, crop, and atmospheric conditions that must be disentangled to get at the crop information. We will test a new technique to map crop condition from EO for winter wheat in South Africa. We use physics models of the EO to describe their response to biophysical parameters, then machine learning to calculate the parameters from the measurements. But many parameters are uncertain when estimated like this. We use big data analysis globally to build typical models of the parameters that we call archetypes. We use these to provide robust estimates of all parameters for the entire growth cycle. But this science needs testing and application to areas of need, which is what we will do here in a collaboration between UK and South African scientists.
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Advances in deep learning model for irrigated agricultural area mapping: the case of Upper Awash Basin, Ethiopia
Well-managed irrigation farming has taken as the key strategic direction for achieving the food security scheme of Ethiopia. The irrigated farming area has increased in Ethiopia; however, its extent is rarely available and varies from source to source in Ethiopia. Although accurate irrigated farm area information is an indispensable input for decision-makers, it can be argued that the irrigated farming areas are not accurately known and precisely mapped. Therefore, the objective of this project is to develop an enhanced deep learning model using optical/SAR EO data and ResUNet model; for wheat crop irrigated farming area mapping. We will use S1/2, ICEYE and Planet time series complemented with in-situ and ancillary datasets for this analysis. Consequently, we will evaluate the developed data fusion algorithm and deep learning model as well as the resulting wheat irrigation maps using a highly accurate spatially explicit ground truth dataset using AOI-scale surveys of irrigation extent. The project output will contribute a lot for realising the application of EO data and deep learning model for the next 10 years Ethiopia’s agricultural perspective development plan (Goal 1), as well as âAgenda 2063 the Africa We Wantâ (Goals 1, 2 & 5) and the UNâs SDGs 1, 2 & 17).
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Fodder quality assessment in Senegalese rangelands based on Sentinel-2 images
Rangelands are the main source of fodder for animal feed in Sahelian pastoral systems. To ensure the sustainability of fodder resources and the resilience of pastoral populations to the impacts of climate change, timely assessment of fodder availability is essential. Operational tools for the evaluation, in particular by satellite remote sensing, of the fodder quantity have been developed for the natural rangelands of Senegal, but the question of the evaluation of the nutritional value of the pastures remains unanswered. Thus, the objective of the FATIMA project is to develop models for evaluating forage quality during the dry season by combining Sentinel-2 (S2) type images with field data analyzed by Near Infra-Red Spectroscopy. (SPIR) via statistical modeling techniques. The innovation concerns the integration of S2 images with NIRS data for the large-scale assessment of forage quality in the Sahelian zone. The models developed will complete the operational system of the Ecological Monitoring Center (CSE) for monitoring Senegalese routes. This system will thus contribute more effectively to the national framework for the management of natural rangelands for productive and sustainable pastoral farming, in the current context of global change.
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Leveraging inland radar altimetry over rivers with low cost GNSS reflectometry
Rivers fulfil a vital role in many countries by providing as a water resource, fishery ground, and as means of transport. At the same time, rivers can also pose hazards due to flooding and contamination. Managing a riverâs resource poses significant challenges among stakeholders, especially for river systems such as the Nile, which crosses national boundaries. Modern earth observation missions such as Sentinel-3 and Sentinel-6 could potentially gauge many remote river locations and provide important observations to hydrological forecasts systems, but this currently still requires expert knowledge on processing algorithms and ground truth data. The overall goal of this project is to lower the barrier for (African) stakeholders to make use of satellite products for river altimetry and enable a low-cost and scalable solution for cross-validating observed river stages. To achieve this goal, our approach encompasses two components. The first one is the development of an open-source python module capable of ingesting and selecting satellite radar altimetry data. The second component involves the building and deployment of several low-cost GNSS-reflectometers at strategic river locations along the Nile and its tributaries, which serve as demonstrators for a flexible way to validate altimetry river stage observations.
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Riverine flood and crop monitoring and assessment using cloud computing and earth observation: case of Comoe Catchment, CĂŽte dâIvoire
West African countries are subject to recurrent extreme natural disasters including droughts, landslides and flooding of land (especially agricultural land). Crop flooding reduces the countries’ agricultural production and negatively impacts the economic power of the populations. Floods are amongst the most devastating natural hazards in the world. In 2020, according to OCHA, floods affected nearly 1.8 million people in West Africa. People living mainly on riverbeds and in urban areas will be increasingly exposed to this phenomenon. Research has shown that flood risks will not decrease in the future and may occur more frequently with the onset of climate change, and high rainfall intensities. This research proposal seeks to exploit and integrate advanced EO tools and mapping with hydrological numerical modelling, to vastly improve riverine flood and crop monitoring and services for Comoe River in CĂŽte dâIvoire. The aim is to enhance the efficiency of national flood and agriculture area monitoring services and develop flood early warning systems to support sustainable development and reduce the vulnerability of local populations and practice to flood damage. This research will provide a replicable approach and algorithms to assess and model flood extents, monitor crop land and provide locally relevant products and services including flood and agriculture areas flooded maps of the study area.
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Using high spatial and temporal resolution data for monitoring wheat and rice crop agronomic variables for smallholder agriculture in the Nile Delta, Egypt
Agriculture is one of many sectors which is influenced significantly by climate change challenges especially in Africa as the increasing temperatures reduced the agricultural production which leads to a great risk on food security. The need for increasing food production despite the shortage of resources stimulate the need for accurate and timely information on crop agriculture variables (crop yield forecasts or phenology development stages) to help decision makers and to satisfy producers demands for increased profits. Remote sensing can be used to quickly obtain spatially continuous crop growth conditions and crop yield forecasts. Crop yield prediction is an essential task for the decision-makers at national and regional levels (e.g., the African Union, or European Union levels) for rapid decision making. An accurate crop yield prediction model at parcel level can help farmers to decide on what to grow, when to grow and the need for agronomic interventions. Egyptian farmers can cultivate different varieties of crops including grains, fruits and vegetables due to warm climate, still abundant water along the Nile and excellent fertile soil specially in the Nile delta. However, the most important cereal crops are wheat (given its large area planted) and rice (second most exported crop after cotton). Therefore, this research project will focus on monitoring wheat and rice crops by leveraging the Sen2Like dataset, which provides harmonized Landsat and Sentinel-2 data at high spatial (10m) and temporal resolution (average 2.3 days revisit) and will also explore the integration of Sentinel-1 data and its potential added value to the optical dataset. Particularly, we will develop crop type maps of these crops, track their phenological stages, and develop field level crop yield forecasting models that will be aggregated at regional level over the Gharbiya governorate from 2017 to 2022. Additionally, given the experience of the research team in developing crop yield models of wheat and rice in Spain, we will also explore the applicability of these field level yield models to the Egypt region, looking for developing robust EO-based models that can be transferable across different regions.
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Remote sensing and modelling to assess crop-specific response to climate stressors
Understanding the interaction between climate and cropping systems is essential for sustainable management of the resources and mitigation of the impacts of climate shocks. Events such as droughts, and heat stress can significantly impact crop production. Although several studies have been focusing on this, often they are using a single source of data, aggregated over large regions. Here we propose a novel approach that integrates the data regarding cropping systems, and drought information with climate forecasting to generate key information for decision-makers. The project will leverage on and harmonise the existing crop type information for Busia county in Kenya as well as optical and radar time series, in order to derive early-season crop maps. These crop maps, together with the meteorological forecast, existing drought risk, and yield information will produce essential agro-climatic indicators for this agricultural area, scalable to other regions. This will allow further implementation of new adaptation technologies that can further increase agricultural productivity.
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Monitoring and assessment of dry biomass in the Sahel rangelands using satellite images
In the Sahel region the pastoral resources are strongly linked to the fluctuation of the biomass production in the rangelands. Annual herbaceous plants grow only during the short period of the rainy season (about 4 months) and then consumed by animals on the move during the dry season. Additionally, the availability of fodder biomass for the pastoral campaign depends on the amount of rainfall recorded; therefore, is highly sensitive to climatic variations. The proposed research will develop a tool, based on EO data, for an efficient monitoring and assessment of the dynamics of dry biomass in the natural rangelands of the Sahel region, mainly during the long dry season. The approach will be based on the acquisition and processing of Sentinel 1 and 2 images to quantify the dry biomass for decision making in the field. In situ measurement of dry biomass, in a pilot site location in Niger, will be used to assess the performance and validate the model that will be derived from the satellite images. The results of the research will help improve livestock productivity, conserve and protect rangeland resources and reduce conflicts between herders and sedentary farmer.
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Cloud computing for seasonal cropland monitoring in degraded Shilansha catchment, Ethiopia Rift Valley Basin
Earth observation (EO) based cropland monitoring requires accurate land cover (LC) information. But EO data is affected by uncertainty. Seasonal representation of crop growth in space and time domains is not guaranteed when LC is observed at a single time instant. Thus cropping, and its cropping stages, must be monitored by sound and quick classification methodologies. Apart from single-sensor, multi-sensor EO is valuable to improve LC information. This study aims to generate seasonal LC and cropping information of rainfed croplands by fusing vegetation index and biophysical parameters with optical and SAR imagery. Cloud computing and cloud computing algorithms in Jupyter notebook will be used. As cloud computing results deteriorate by lack of local data, this study incorporates detailed local datasets for respective seasons. This study aims to improve LC classification accuracy for seasonal cropping by optimally selection of seasonal feature sets to parameterize the fusing algorithm. The workflow and all EO data + fusing algorithms will be made available as open source for (African) researchers to so open up opportunities for cropland monitoring studies anywhere in African.
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Forest disturbance detection by means of radar and optical remote sensing data fusion and artificial intelligence
Tropical forests play a significant role in earth ecosystems by providing a more stable water supply than other ecosystems, thus reducing local flooding, soil erosion, etc. They help promote life, mitigate climate change, and sustainable food production. This allows plants and animals to co-exist in perfect harmony and serenity. Due to various natural (e.g. uncontrolled fires) and human events (charcoal and wood production, livestock grazing, or cropland expansion), forest disturbances (FD) are expanding. Scientists and policymakers are aware of the FD consequences, thus many commitments (e.g. COP26) were contained to preserve this important ecosystem. Passive remote sensing (RS) proved to be an effective tool for monitoring various ecosystems. In tropical regions, the number of cloud-free observations is limited. Therefore, the application of freely available RS cloud-free images from synthetic aperture radar data from the Sentinel -1 mission offers an alternative for FD monitoring. Nonetheless, continuous processing of RS data is quite expensive. Thus, the goal of this project is to implement a free Python-based machine learning algorithm for the near-real-time (NRT) FD alert with RS data fusion in the area of southern Nigeria, which suffers exceptionally due to FD in recent years and is involved in various forest-preserving commitments.
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Mapping and monitoring spatiotemporal desertification patterns in the steppic belt of Algeria
Desertification is at the forefront of the environmental issues that face many parts of the world. Climate change and anthropic practices, such as unsustainable agriculture, overgrazing, and lack of regulation policies have been identified as its key drivers. Algeria is one of the most affected countries in Africa, in particular in the steppic belt that stretches over more than 1200 km from the west to east of the country. The project aims at developing an analytical workflow for mapping out the spatiotemporal evolution of desertification in this region and understanding its driving factors. It will benefit from the availability of earth observation data and recent advances in machine learning (ML) to detect land cover change and assess land degradation over time. First, temporal maps based on several desertification indices will be generated using classification ML algorithms. Then, advanced clustering schemes with regionalization constraints will be employed to bring out the main desertification patterns. The foreseen methodology is intended to be both reproducible and applicable to other exposed geographical areas. The implemented workflow will provide decision support for policy makers to have a better view and control of desertification progress and areas where strong mitigation measures should be implemented.
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A composite drought indicator for the Borana rangelands in Ethiopia combining multi-source Earth Observation data and the three-source balance model: a support to the index-based insurance for pastoralists in Ethiopia
A large section of the world’s population is vulnerable to drought, a dreadful natural disaster, especially in semi-arid and dry areas. The drought events that have recently affected Ethiopia, Kenya, and Somalia have killed millions of livestock and forced people to flee their homes, leaving them food insecure. Thus, up-to- date knowledge of the current drought situation and extent is essential because droughts grow slowly over months or years, severely impacting the food and water supply. Traditional methods of assessing and monitoring drought rely on rainfall data, which is difficult to obtain in most low-income countries, frequently inaccurate, and scarce in some areas. Satellite data, on the other hand, is constantly accessible and can be used to determine the start of a drought as well as its length and severity. In this research, a composite drought severity index will be developed by combining a number of drought indicators, each representing a different component of drought. To successfully execute the research, we will use state-of- the-art physical models and machine learning algorithms, implemented within ESAâs Innovation Lab cloud computing infrastructure. Finally, historical droughts and their extent of damage in the study area will be analyzed, mapped and projected.
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Application of remote sensing data to support pests monitoring and agriculture statistics in Rwanda
Agriculture is the main stain of the Rwandan economy, covering more than 62% of the countryâs employment. However, pest infections pose a serious threat to Rwandan agriculture as well as food security with severe yet unaccounted loss of agriculture productivity. This is because traditional methods for pests monitoring are often expensive, time-consuming and do not match the current pace of pests infestations in the wake of climate change. Consequently, local organizations lack data to support intervention initiatives, agriculture statistics, and policy formulation. Remote sensing data can help to monitor and prevent pests infestation at the early stage of crop development, support statistical reporting and ensure sustainable agriculture and food security. Here we propose a two-stage analytical workflow to monitor plant stress due to pests infestation and develop a risk assessment for pests outbreak at the national scale. First, we will combine remote sensing data and machine learning algorithms to monitor and map the extent of pest infestation with a focus on fall armyworm and mango mealybug. Second, we will combine maps of crop stress and biophysical data to conduct a risk assessment for potential pest outbreaks at the national scale. The resulting data can serve as baseline to support agriculture statistics, design intervention measures, and understand the drivers of pest diseases in the Rwandan context.
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Save the Faleme River: use of EO data for monitoring water pollution and scarcity due to anthropic activities
The FalĂ©mĂ© River is the natural border between Senegal and Mali. FalĂ©mĂ© plays an important role in the food value chain such as fishing, livestock and agriculture for local populations. Today 25% of this river risks disappearing from the hydrological maps of these two countries due to the effects of climate change and anthropogenic activities such as artisanal gold mining, agriculture, dam construction projects hydroelectric, etc… This project aims to characterize, monitor over time and understand the impacts of human activities on the river using earth observation data and field measurements. This will specifically involve developing an algorithm that will (i) quantify the variability of the water surface and the evolution of the water level of the FalĂ©mĂ© and (ii) monitor the quality of the water in the river. using data and time series of multi-spectral imagery (water color) from Sentinel 1, 2 and 3 missions. The purpose is to produce a platform for monitoring the quality and level of water in the river that will serve as a decision-making tool for States in order to avoid an ecological catastrophe.
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Olive trees health and yield prediction through EO data and machine learning
The olive tree (Olea Europaea) is native across the Mediterranean region. It is among the oldest fruit trees cultivated in north African countries. In Morocco alone, it occupies 65% of the national arboricultural area with a production exceeding 1.4 Tons between 2016 and 2019, creating more than 50 million workdays. However, this cultivation faces hardship ahead, mainly because of climate change and water deficiency, hence the urgent need to take rapid action to enable high-yield, high-quality, sustainable, and resilient production. This study aims to assess the olive treesâ health and predict their yield using EO data of different sensors including Sentinel (1 and 2), Landsat, Mohamed VI satellite imagery and Unmanned Aerial Vehicle. The EO data will be combined with climatic data and Machine Learning models. The main objective is to develop an open-source EO workflow that will be applied to other regions of Africa and be helpful in monitoring tree health and early forecasting of olive production. The developed workflow could be used by different types of end-users such as governmental institutions, researcher institutions as well as farmers for deriving the information at national, regional or local scale respectively.
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Exploring aquatic weed coexistence using Sentinel-2 satellite data for informed aquatic weeds management for inland waterbodies
Aquatic weeds continue to threaten the quality of surface water resources and, broadly, the provision of services for economic development and human livelihoods. A variety of aquatic weeds are invading surface water resources such as rivers, dams, and lakes. The spatial distribution of these weeds varies strongly over time. Within the WHYmapping project, started during the first EO AFRICA phase, we develop an algorithm to derive daily maps of water hyacinth from Sentinel-2 and -3. Now we propose to take this a step further: first, by applying the WHYmapping algorithm to archived data, creating long time series. These will be used to systematically investigate the effects of meteorology and herbicide spraying on the prolification of aquatic weeds. Second, by expanding the Sentinel-2 algorithm to discriminate between different types of aquatic vegetation. The satellite data will be validated using field observations from dedicated campaigns. At Hartbeespoortdam reservoir, stakeholders recently started annually spraying the dam with a selective herbicide, which leads to removal of water hyacinth, but subsequent proliferation of Salvinia molesta. Salvinia gradually disappears as water hyacinth re-emerges. This research also seeks to examine effects of meteorology and herbicide spraying on the co-existence balance between algae and macrophytes in Hartbeespoort dam.
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Space-based and geospatial technology for disaster risk reduction: flood monitoring and prediction in Amibara, Afar, Ethiopia
Flood is the major worldwide natural hazards causing fatalities and economic damage of more than USD 30 billion annually (UNISDR, 2015). This disaster has been increasing due to climate variability, population growth, and urbanization mainly in low-income countries like Ethiopia where adequate flood mitigation measures are often lacking, and floodplains are heavily populated that increases the risk. According to national disaster risk management commission (NDRMC) report, Ethiopia is highly vulnerable to a wide range of disasters among which flooding is the most. According to NDRMC (2018), flood and drought are the most meteorological natural hazards in the country causing socio-economic and environmental sectors. As such, floods have real consequences for poverty and national food security in Ethiopia. To fill this gap, an open source operational/semi- operational flood warning tools are key to saving lives and reducing risks and damages. Near real-time satellite driven products can be used to drive hydrologic forecasts (i.e., streamflow forecast model and inundation model) in downstream areas of poorly gauged basin (Lower Awash River basin). Machine learning models such as long short-term memory (LSTM), Support vector machine and random forest will be compared and for inundation model (Thresholding model) will be applied.
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