Your search found 10 records
1 Sambo, C.; Senzanje, A.; Mutanga, O.. 2021. Assessing inequalities in sustainable access to improved water services using service level indicators in a rural municipality of South Africa. Journal of Water, Sanitation and Hygiene for Development, 11(6):887-901. [doi: https://doi.org/10.2166/washdev.2021.234]
Water supply ; Water access ; Indicators ; Rural areas ; Sustainable Development Goals ; Water quality ; Water policies ; Communities ; Households ; Human rights / South Africa / Limpopo / Makhudutamaga Local Municipality
(Location: IWMI HQ Call no: e-copy only Record No: H050813)
https://iwaponline.com/washdev/article-pdf/11/6/887/966795/washdev0110887.pdf
https://vlibrary.iwmi.org/pdf/H050813.pdf
https://vlibrary.iwmi.org/pdf/H050813.pdf
(1.26 MB) (1.26 MB)
Sustainable access to improved water services is a human right recognized by the Sustainable Development Goals (SDG) agenda and the constitution of South Africa. In recognition of this, South Africa implemented the Free Basic Water (FBW) policy outlining six recommended service level standards (e.g. distance, reliability and cost) to guide improved water services provision, especially in rural municipalities. However, despite implementing the rights-based approach policy, a significant proportion of the rural population is reported to have limited/poor access to improved water services. For this reason, the study adopted the FBW standards as indicators to assess inequalities in sustainable access to improved water services in Makhudutamaga Local Municipality (MLM) in South Africa. The findings indicate inequalities in access to improved water services based on FBW standards. Overall, the improved water services complied with the FBW standard for distance but not with the other standards. The non-compliance with the other standards indicated limited/poor access to improved water services and improper implementation of the FBW policy. This work provides water managers with an understanding of levels of water services provided based on FBW standards for planning and management to improve access to improved water services and enforce proper implementation of the FBW policy.
2 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.
3 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.
4 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Odindi, J.; Mutanga, O.; Naiken, V.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2022. Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform. Drones, 6(7):169. [doi: https://doi.org/10.3390/drones6070169]
Crop growth stage ; Maize ; Temperature measurement ; Stomatal conductance ; Estimation ; Water stress ; Thermal infrared imagery ; Unmanned aerial vehicles ; Machine learning ; Forecasting ; Models ; Precision agriculture ; Smallholders ; Small-scale farming ; Crop water use ; Indicators / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H051298)
https://www.mdpi.com/2504-446X/6/7/169/pdf?version=1657704795
https://vlibrary.iwmi.org/pdf/H051298.pdf
https://vlibrary.iwmi.org/pdf/H051298.pdf
(7.44 MB) (7.44 MB)
Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.
5 Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing, 15(6):1597. (Special issue: Retrieving Leaf Area Index Using Remote Sensing) [doi: https://doi.org/10.3390/rs15061597]
Maize ; Leaf area index ; Vegetation index ; Remote sensing ; Unmanned aerial vehicles ; Multispectral imagery ; Small-scale farming ; Smallholders ; Growth stages ; Monitoring ; Forecasting ; Models ; Machine learning ; Agricultural productivity ; Crop yield / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H051818)
https://www.mdpi.com/2072-4292/15/6/1597/pdf?version=1678869485
https://vlibrary.iwmi.org/pdf/H051818.pdf
https://vlibrary.iwmi.org/pdf/H051818.pdf
(3.96 MB) (3.96 MB)
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity.
6 Cho, M. A.; Mutanga, O.; Mabhaudhi, Tafadzwanashe. 2023. Understanding local actors’ perspective of threats to the sustainable management of communal rangeland and the role of Participatory GIS (PGIS): the case of Vulindlela, South Africa. South African Geographical Journal, 105(4):516-533. [doi: https://doi.org/10.1080/03736245.2023.2190153]
Sustainable land management ; Rangelands ; Common lands ; Local knowledge ; Participatory rural appraisal ; Geographical information systems ; Land governance ; Mapping ; Techniques ; Grazing lands ; Land productivity ; Ecological factors ; Socioeconomic aspects ; Pastoralists ; Communities ; Livelihoods ; Inclusion ; Assessment / South Africa / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H051819)
(2.79 MB)
Rangelands in arid and semi-arid regions serve as grazing land for domesticated animals and therefore offer livelihood opportunities for most pastoral communities. Thus, the exposure of most rangelands in arid and semi-arid regions to threats that are associated with natural, social, economic, and political processes affects their capacity to provide socioeconomic and environmental support to the immediate and global communities. In spite of the effects of rangeland transformations on both the natural and human environment, the assessment of threats affecting rangeland productivity has often been approached from a conventional scientific perspective. Most existing literature is focused on the assessment of threats to the biophysical environment. As such the social dimension of rangeland threats is not well understood. This research employed participatory rural appraisal (PRA) and PGIS techniques to assess rangeland threats and management actions from a local perspective. The result revealed that local actors prioritize threats to their social and economic needs over threats to the biophysical environment and their preference is informed by the frequency and magnitude of the threats. The outcome of the research demonstrates the need to promote rangeland governance through interdisciplinary and inclusive participation in research and development.
7 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.
8 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.
9 Sibanda, M.; Ndlovu, H. S.; Brewer, K.; Buthelezi, S.; Matongera, T. N.; Mutanga, O.; Odidndi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system. Smart Agricultural Technology, 6:100325. [doi: https://doi.org/10.1016/j.atech.2023.100325]
Crop damage ; Hail damage ; Maize ; Remote sensing ; Smallholders ; Farmers ; Farmland ; Small-scale farming ; Unmanned aerial vehicles ; Plant health ; Leaf area index ; Vegetation index ; Agricultural productivity ; Climate change / South Africa / KwaZulu Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052320)
https://www.sciencedirect.com/science/article/pii/S2772375523001545/pdfft?md5=79fa6611a7221a58090e695d915834b8&pid=1-s2.0-S2772375523001545-main.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
(17.40 MB) (17.4 MB)
Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm- 2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m- 2 and 76.2 µmol m- 2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2 /m2 and 0.19 m2 /m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change.
10 Sibanda, M.; Buthelezi, S.; Mutanga, O.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Gokool, S.; Naiken, V.; Magidi, J.; Mabhaudhi, Tafadzwanashe. 2023. Exploring the prospects of UAV-remotely sensed data in estimating productivity of maize crops in typical smallholder farms of Southern Africa. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023:1143-1150. (ISPRS Geospatial Week 2023, Cairo, Egypt, 2-7 September 2023) [doi: https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1143-2023]
Agricultural productivity ; Small farms ; Smallholders ; Maize ; Yield forecasting ; Models ; Remote sensing ; Unmanned aerial vehicles ; Vegetation index / Southern Africa / South Africa / KwaZulu-Natal
(Location: IWMI HQ Call no: e-copy only Record No: H052490)
https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1143/2023/isprs-annals-X-1-W1-2023-1143-2023.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
(1.59 MB) (1.59 MB)
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 = 0.85, RMSE = 0.1, RRMSE = 5.08%) and proportional yield (R2 = 0.92, RMSE = 0.06, RRMSE = 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms – a previously challenging task with coarse spatial resolution satellite sensors.
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