Your search found 8 records
1 Radingoana, M. P.; Dube, T.; Mazvimavi, D. 2020. Progress in greywater reuse for home gardening: opportunities, perceptions and challenges. Physics and Chemistry of the Earth, 102853 (Online first). [doi: https://doi.org/10.1016/j.pce.2020.102853]
Water reuse ; Wastewater irrigation ; Crop production ; Domestic gardens ; Innovation adoption ; Technology ; Water scarcity ; Freshwater ; Food security ; Climate change ; Environmental effects ; Developing countries ; Communities ; Households / South Africa
(Location: IWMI HQ Call no: e-copy only Record No: H049574)
(0.73 MB)
Water is one of the most essential natural resource that sustains livelihoods. Freshwater consumption and demand have, spiralled over the years, due to population growth, agricultural and industrial intensification. Innovative water conservation techniques (greywater reuse, rainwater harvesting, seawater desalination and ground water extraction, etc.), especially in the face of climate change and climate variability are central in minimizing water shortages, hunger and poverty alleviation, as well as health challenges. Most of water conservation methods remain ineffective and have less adoption, due to associated costs, inaccessibility and technical expertise in addressing water challenges, particularly in developing countries. Greywater reuse, which approximately represents 43–70% of the total domestic wastewater volume remains as the alternative and effective source of water that can help reduce pressure on freshwater for food production and poverty alleviation in third-world countries. Great research strides have been demonstrated on greywater reuse for agricultural use, but much remains unknown with regard to adoption rates, especially in developing countries. This work provides a detailed review on greywater reuse in crop production with particular emphasis on community perceptions, challenges and opportunities, lessons from other countries and possible implications on food security. The study has demonstrated that greywater reuse is a common practice in both developed and developing nations as a coping strategy. However, it was observed that some communities remain cautious and sceptic on its use for home gardening purpose. This resource is regarded as unclean and unfit for food crop irrigation. Limited adoption rates seem to be due to limited information or awareness programs and platforms on the potential of greywater reuse as supplement for freshwater, especially in developing countries like South Africa. However, strategies i.e. installation of greywater systems, incentivising greywater use have seen a rise in the adoption greywater in developed world. There is a need to find possible ways on how strategies from developed countries can be adopted in developing countries to promote greywater reuse for home gardening purposes.
2 Moyo, M.; Van Rooyen, A.; Bjornlund, H.; Parry, K.; Stirzaker, R.; Dube, T.; Maya, M. 2020. The dynamics between irrigation frequency and soil nutrient management: transitioning smallholder irrigation towards more profitable and sustainable systems in Zimbabwe. International Journal of Water Resources Development, 26p. (Online first) [doi: https://doi.org/10.1080/07900627.2020.1739513]
Irrigation schemes ; Smallholders ; Farmers ; Soil fertility ; Soil moisture ; Nutrient management ; Irrigated farming ; Irrigation water ; Water productivity ; Agricultural productivity ; Maize ; Water use ; Rain ; Fertilizers ; Sustainability ; Decision making ; Monitoring techniques ; Household surveys / Zimbabwe / Mkoba Irrigation Scheme / Silalatshani Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H049729)
(3.28 MB)
Successful irrigated agriculture is underpinned by answering two critical questions: when and how much to irrigate. This article quantifies the role of the Chameleon and the Wetting Front Detector, monitoring tools facilitating decision-making and learning about soil-water-nutrient dynamics. Farmers retained nutrients in the root zone by reducing irrigation frequency, number of siphons, and event duration. Water productivity increased by more than 100% for farmers both with and without monitoring tools. Transitioning smallholder irrigation systems into profitable and sustainable schemes requires investment in technology, farmers and institutions. Importantly, technologies need embedding in a learning environment that fosters critical feedback mechanisms, such as market constraints.
3 Kiala, Z.; Jewitt, G.; Senzanje, A.; Mutanga, O.; Dube, T.; Mabhaudhi, Tafadzwanashe. 2022. EO-WEF: a earth observations for water, energy, and food nexus geotool for spatial data visualization and generation. In Mabhaudhi, Tafadzwanashe; Senzanje, A.; Modi, A.; Jewitt, G.; Massawe, F. (Eds.). Water - energy - food nexus narratives and resource securities: a global south perspective. Amsterdam, Netherlands: Elsevier. pp.33-48. [doi: https://doi.org/10.1016/B978-0-323-91223-5.00011-3]
(Location: IWMI HQ Call no: e-copy only Record No: H051170)
(0.35 MB)
WEF (water–energy–food) nexus analyses have become a rapidly growing field since the Conference on Water, Energy and Food Security Nexus–Solutions for the Green Economy in Bonn in 2011. They have the potential to help stakeholders and policymakers to better understand the interlinkages between the different components of a nexus system and lead to solutions that are socially and environmentally beneficial. However, assembling wide-scope nexus has been challenged by issues such as proprietary considerations and data evolution over time, among others. Earth observations (EOs) have a huge offering of data sets that can provide data for most of the components of a nexus at a relatively low cost and various temporal and spatial resolutions. Furthermore, the advent of cloud computing has made possible the processing of massive information. This chapter introduces the Earth Observation for WEF nexus (EO-WEF), a multisectorial information system to visualize customizable data and generate time series data at any location. Google Earth Engine, a cloud computing platform that includes data archives of regularly updated EO and scientific data sets for a period of more than 40 years, powers this application. The capability of EO-WEF in generating spatial data was tested in the Songwe River Basin case study. Overall, the EO-WEF application provides data for key variables of a nexus that can be supplemented by other kinds of data that cannot be captured by EOs.
4 Masenyama, A.; Mutanga, O.; Dube, T.; Bangira, T.; Sibanda, M.; Mabhaudhi, T. 2022. A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services. GIScience and Remote Sensing, 59(1):1000-1025. [doi: https://doi.org/10.1080/15481603.2022.2088652]
Grasslands ; Ecosystem services ; Remote sensing ; Technology ; Earth observation satellites ; Hydrological modelling ; Systematic reviews ; Biomass ; Leaf area index ; Canopy ; Vegetation index ; Sensors ; Water management ; Monitoring ; Machine learning ; Forecasting
(Location: IWMI HQ Call no: e-copy only Record No: H051246)
https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2088652
https://vlibrary.iwmi.org/pdf/H051246.pdf
https://vlibrary.iwmi.org/pdf/H051246.pdf
(3.82 MB) (3.82 MB)
The last decade has seen considerable progress in scientific research on vegetation ecosystem services. While much research has focused on forests and wetlands, grasslands also provide a variety of different provisioning, supporting, cultural, and regulating services. With recent advances in remote sensing technology, there is a possibility that Earth observation data could contribute extensively to research on grassland ecosystem services. This study conducted a systematic review on progress, emerging gaps, and opportunities on the application of remote sensing technologies in quantifying all grassland ecosystem services including those that are related to water. The contribution of biomass, Leaf Area Index (LAI), and Canopy Storage Capacity (CSC) as water-related ecosystem services derived from grasslands was explored. Two hundred and twenty-two peer-reviewed articles from Web of Science, Scopus, and Institute of Electrical and Electronics Engineers were analyzed. About 39% of the studies were conducted in Asia with most of the contributions coming from China while a few studies were from the global south regions such as Southern Africa. Overall, forage provision, climate regulation, and primary production were the most researched grassland ecosystem services in the context of Earth observation data applications. About 39 Earth observation sensors were used in the literature to map grassland ecosystem services and MODIS had the highest utilization frequency. The most widely used vegetation indices for mapping general grassland ecosystem services in literature included the red and near-infrared sections of the electromagnetic spectrum. Remote sensing algorithms used within the retrieved literature include process-based models, machine learning algorithms, and multivariate techniques. For water-related grassland ecosystem services, biomass, CSC, and LAI were the most prominent proxies characterized by remotely sensed data for understanding evapotranspiration, infiltration, run-off, soil water availability, groundwater restoration and surface water balance. An understanding of such hydrological processes is crucial in providing insights on water redistribution and balance within grassland ecosystems which is important for water management.
5 Masenyama, A.; Mutanga, O.; Dube, T.; Sibanda, M.; Odebiri, O.; Mabhaudhi, T. 2023. Inter-seasonal estimation of grass water content indicators using multisource remotely sensed data metrics and the cloud-computing Google Earth Engine platform. Applied Sciences, 13(5):3117. (Special issue: Remote Sensing Applications in Agricultural, Earth and Environmental Sciences) [doi: https://doi.org/10.3390/app13053117]
Grasslands ; Plant water relations ; Estimation ; Remote sensing ; Datasets ; Leaf area index ; Vegetation index ; Climatic factors ; Indicators ; Satellite observation ; Forecasting ; Spatial distribution ; Models / South Africa / KwaZulu-Natal / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H051820)
https://www.mdpi.com/2076-3417/13/5/3117/pdf?version=1677581546
https://vlibrary.iwmi.org/pdf/H051820.pdf
https://vlibrary.iwmi.org/pdf/H051820.pdf
(4.12 MB) (4.12 MB)
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m-2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m-2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R 2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m-2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m-2 and R 2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m-2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m-2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.
6 Bangira, T.; Mutanga, O.; Sibanda, M.; Dube, T.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing grassland productivity attributes: a systematic review. Remote Sensing, 15(8):2043. [doi: https://doi.org/10.3390/rs15082043]
Grasslands ; Productivity ; Prediction ; Remote sensing ; Estimation ; Monitoring ; Techniques ; Ecosystem services ; Leaf area index ; Above ground biomass ; Canopy ; Chlorophylls ; Nitrogen content ; Vegetation index
(Location: IWMI HQ Call no: e-copy only Record No: H051841)
https://www.mdpi.com/2072-4292/15/8/2043/pdf?version=1681347101
https://vlibrary.iwmi.org/pdf/H051841.pdf
https://vlibrary.iwmi.org/pdf/H051841.pdf
(3.26 MB) (3.26 MB)
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.
7 Dube, T.; Seaton, D.; Shoko, C.; Mbow, C. 2023. Advancements in earth observation for water resources monitoring and management in Africa: a comprehensive review. Journal of Hydrology, 623:129738. [doi: https://doi.org/10.1016/j.jhydrol.2023.129738]
Earth observation satellites ; Water resources ; Monitoring ; Hydrology ; Climate change ; Sustainable development ; Precipitation ; Soil moisture ; Surface water ; Runoff ; Water quality ; Water security ; Land use ; Land cover ; Remote sensing ; Moderate resolution imaging spectroradiometer ; Machine learning ; Evapotranspiration ; Ecosystems ; Vegetation ; Surface temperature ; Landsat / Africa
(Location: IWMI HQ Call no: e-copy only Record No: H052061)
https://www.sciencedirect.com/science/article/pii/S0022169423006807/pdfft?md5=3de3b4a2f3076b4ebe97c67e18a7be87&pid=1-s2.0-S0022169423006807-main.pdf
https://vlibrary.iwmi.org/pdf/H052061.pdf
https://vlibrary.iwmi.org/pdf/H052061.pdf
(8.62 MB) (8.62 MB)
This paper provides an overview of the progress made in remote sensing of water resources in Africa, focusing on various applications such as precipitation estimation, land surface temperature analysis, soil moisture assessment, surface water extent measurement, surface runoff and streamflow analysis, water quality evaluation, land cover/land use mapping, and groundwater analysis. Specifically, the study sheds light on the remarkable progress made in remote sensing applications, showcasing scientific advancements and highlighting the challenges encountered. Moreover, it identifies crucial knowledge gaps that need to be addressed in order to further advance this field. The review's key findings indicate that the availability of sensors and observations, along with analytical models, has contributed to monitoring Africa's water resources at various scales. The availability and accessibility of hydrological data for monitoring and assessing water resources in Africa have been partially improved through the adoption of satellite data and processing technologies. Additionally, the emergence of various international remote sensing initiatives, North-South research collaborations, and projects has contributed to the research progress. Prominent satellite data series such as Landsat, MODIS, and GRACE have played significant roles in African hydrological research. However, the limited and malfunctioning in-situ hydrological monitoring networks in Africa have affected the accurate calibration and validation of remotely sensed hydrological models. Insufficient long-term rainfall and climate data pose challenges to long-term earth observation research on African water systems. The lack of high-resolution spatial and temporal data hampered accurate monitoring of hydrological processes at smaller scales. Despite the widespread use of rainfall satellite products, validation attempts over Africa, particularly in western and southern regions, have been limited. Furthermore, the reliability of hydrological satellite datasets is affected by factors such as the number and coverage of surface stations, retrieval algorithms, data integration techniques, and cloud cover. Overall, this work demonstrates the importance of earth observation in understanding Africa's hydrology, previously hindered by the lack of in-situ data. Nevertheless, efforts are therefore needed to enhance the adoption and application of remote sensing, particularly in groundwater and surface water estimation at smaller scales. Future research should focus on multi-source data integration, assimilation, and big data analytics using cloud computing and machine learning to address complex hydrological research questions at various scales.
8 Abrahams, M.; Sibanda, M.; Dube, T.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. A systematic review of UAV applications for mapping neglected and underutilised crop species’ spatial distribution and health. Remote Sensing, 15(19):4672. (Special issue: Crops and Vegetation Monitoring with Remote/Proximal Sensing II) [doi: https://doi.org/10.3390/rs15194672]
Underutilized species ; Mapping ; Unmanned aerial vehicles ; Plant health ; Crop production ; Remote sensing ; Machine learning ; Food security ; Precision agriculture ; Spatial distribution ; Stomatal conductance ; Smallholders ; Farmland ; Vegetation index ; Systematic reviews
(Location: IWMI HQ Call no: e-copy only Record No: H052234)
https://www.mdpi.com/2072-4292/15/19/4672/pdf?version=1695462413
https://vlibrary.iwmi.org/pdf/H052234.pdf
https://vlibrary.iwmi.org/pdf/H052234.pdf
(11.70 MB) (11.7 MB)
Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.
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