A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services

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 under-standing 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

A geospatial web-based integrative analytical tool for the water-energy-food nexus: the iWEF 1.0

Introduction The water-energy-food (WEF) nexus has evolved into an important transformative approach for facilitating the timely identification of trade-offs and synergies between interlinked sectors for informed intervention and decision-making. However, there is a growing need for a WEF nexus tool to support decision-making on integrated resources management toward sustainable development. Methods This study developed a geospatial web-based integrative analytical tool for the WEF nexus (the iWEF) to support integrated assessment of WEF resources to support resilience building and adaptation initiatives and strategies. The tool uses the Analytic Hierarchy Process (AHP) to establish numerical correlations among WEF nexus indicators and pillars, mainly availability, productivity, accessibility, and sufficiency. The tool was calibrated and validated with existing tools and data at varying spatio-temporal scales. Results The results indicate the applicability of the tool at any spatial scale, highlighting the moderate sustainability in the management of WEF resources at various scales. The developed iWEF tool has improved the existing integrative WEF nexus analytical tool in terms of processing time and providing geospatial capabilities. Discussion The iWEF tool is a digital platform that automatically guides policy and decision-making in managing risk from trade-offs and enhancing synergies holistically. It is developed to support policy and decision-making on timely interventions in priority areas that could be showing signs of stress

Independent and combined effects of improved water, sanitation, and hygiene, and improved complementary feeding, on child stunting and anaemia in rural Zimbabwe: a cluster-randomised trial.

BACKGROUND: Child stunting reduces survival and impairs neurodevelopment. We tested the independent and combined effects of improved water, sanitation, and hygiene (WASH), and improved infant and young child feeding (IYCF) on stunting and anaemia in in Zimbabwe. METHODS: We did a cluster-randomised, community-based, 2 × 2 factorial trial in two rural districts in Zimbabwe. Clusters were defined as the catchment area of between one and four village health workers employed by the Zimbabwe Ministry of Health and Child Care. Women were eligible for inclusion if they permanently lived in clusters and were confirmed pregnant. Clusters were randomly assigned (1:1:1:1) to standard of care (52 clusters), IYCF (20 g of a small-quantity lipid-based nutrient supplement per day from age 6 to 18 months plus complementary feeding counselling; 53 clusters), WASH (construction of a ventilated improved pit latrine, provision of two handwashing stations, liquid soap, chlorine, and play space plus hygiene counselling; 53 clusters), or IYCF plus WASH (53 clusters). A constrained randomisation technique was used to achieve balance across the groups for 14 variables related to geography, demography, water access, and community-level sanitation coverage. Masking of participants and fieldworkers was not possible. The primary outcomes were infant length-for-age Z score and haemoglobin concentrations at 18 months of age among children born to mothers who were HIV negative during pregnancy. These outcomes were analysed in the intention-to-treat population. We estimated the effects of the interventions by comparing the two IYCF groups with the two non-IYCF groups and the two WASH groups with the two non-WASH groups, except for outcomes that had an important statistical interaction between the interventions. This trial is registered with ClinicalTrials.gov, number NCT01824940. FINDINGS: Between Nov 22, 2012, and March 27, 2015, 5280 pregnant women were enrolled from 211 clusters. 3686 children born to HIV-negative mothers were assessed at age 18 months (884 in the standard of care group from 52 clusters, 893 in the IYCF group from 53 clusters, 918 in the WASH group from 53 clusters, and 991 in the IYCF plus WASH group from 51 clusters). In the IYCF intervention groups, the mean length-for-age Z score was 0·16 (95% CI 0·08-0·23) higher and the mean haemoglobin concentration was 2·03 g/L (1·28-2·79) higher than those in the non-IYCF intervention groups. The IYCF intervention reduced the number of stunted children from 620 (35%) of 1792 to 514 (27%) of 1879, and the number of children with anaemia from 245 (13·9%) of 1759 to 193 (10·5%) of 1845. The WASH intervention had no effect on either primary outcome. Neither intervention reduced the prevalence of diarrhoea at 12 or 18 months. No trial-related serious adverse events, and only three trial-related adverse events, were reported. INTERPRETATION: Household-level elementary WASH interventions implemented in rural areas in low-income countries are unlikely to reduce stunting or anaemia and might not reduce diarrhoea. Implementation of these WASH interventions in combination with IYCF interventions is unlikely to reduce stunting or anaemia more than implementation of IYCF alone. FUNDING: Bill & Melinda Gates Foundation, UK Department for International Development, Wellcome Trust, Swiss Development Cooperation, UNICEF, and US National Institutes of Health.The SHINE trial is funded by the Bill & Melinda Gates Foundation (OPP1021542 and OPP113707); UK Department for International Development; Wellcome Trust, UK (093768/Z/10/Z, 108065/Z/15/Z and 203905/Z/16/Z); Swiss Agency for Development and Cooperation; US National Institutes of Health (2R01HD060338-06); and UNICEF (PCA-2017-0002)

Mapping surface water in complex and heterogeneous environments using remote sensing

Thesis (PhD)--Stellenbosch University, 2019.ENGLISH ABSTRACT: Global climate change characterised by rising temperatures and changes in the magnitude and intensity of precipitation is projected to affect the spatial and temporal distribution of land surface water (LSW) resources. Accurate and reliable information on the dynamics of LSW is valuable in understanding and monitoring the occurrence and impacts of floods and droughts. This knowledge is also critical for appropriate planning and impact assessment. Research has showed that droughts and floods are the two major hydrological disasters in developing countries such as southern Africa. This is mainly due to the lack of accurate and robust methods and reliable data sources necessary for monitoring the spatial and temporal dynamics of LSW resources. Satellite remote sensing (RS) technology is a promising primary data source and provides techniques suitable for repeated mapping water bodies and flood plains. However, many flood plains and water bodies are characterised by the presence of submerged vegetation, dissolved and suspended substances. These characteristics limit the application of RS in monitoring LSW resources. This study evaluated the potential of remotely sensed data with different temporal, spatial and radiometric properties to map LSW in such challenging environments. Three experiments were carried out. The first experiment evaluated a new spectral indices-based unmixing algorithm that uses a minimum number of spectral bands. The algorithm was applied to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) imagery to map open water and partly submerged vegetation. MERIS FR imagery has high (three days) temporal, but low (300 m) spatial resolution. The quality of the flood map derived from MERIS data was compared to high (30 m) spatial, but low (16 day) temporal resolution Landsat Thematic Mapper (TM) images on two different flooding dates (17 April 2008 and 22 May 2009). The findings show that, despite the low resolution of MERIS, both the spatial and frequency distribution of the water fraction extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. This suggests that the proposed technique can be used to produce reliable and frequent flood maps using low spatial resolution imagery. The use of synthetic aperture radar (SAR) has become increasingly relevant for mapping and monitoring flooded vegetation (FV). In a second experiment, a procedure was constructed and validated based on a time series of Sentinel-1 SAR data for mapping floods in a vegetated floodplain. For each newly available image, the probability of temporary flooded conditions is tested against the probability of not-flooded conditions. The changes in land cover characteristics are considered by the technique. The modelling and testing components were applied independently to the vertical transmit and horizontal receive (VH) polarisation, vertical transmit and vertical receive (VV) and VH/VV ratio. The resulting flood maps were compared to those obtained from Landsat-8 Operational Land Imager (OLI) and ground truthing. Overall classification accuracies showed that the maps produced from the fused Sentinel-1 products (VH and VH/VV) were most accurate (84.5%) and significantly better than when only the VH polarisation was used (78.7%). These results demonstrate that the fusion of VH/VV and VV polarisations can improve flood mapping in vegetated floodplains. The third experiment involved using automatic thresholding of near-concurrent normalized difference water index (NDWI) (generated from Sentinel-2) and VH backscatter bands (generated from Sentinel-1) to map waterbodies with diverse spectral and spatial characteristics. The resulting maps were compared to the classification performances of five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM). The results show that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The LSW maps and techniques developed in this study are critical for flood status monitoring, water resources planning and disaster management, and will as such reduce the impact of floods and droughts on vulnerable communities living in southern Africa. Furthermore, the results of this study will hopefully inspire the remote sensing community to make use of the new generation of freely available multispectral and SAR data (such as those provided by the Sentinel constellations) for operational drought and flood monitoring.AFRIKAANSE OPSOMMING: Globale klimaatsverandering gekenmerk deur stygende temperature en veranderinge in die grootte en intensiteit van presipitasie word geprojekteer om die ruimtelike en temporale verspreiding van hulpbronne vir grondoppervlakwater (GOW) te beïnvloed. Akkurate en betroubare inligting oor die dinamika van GOW is nuttig om die voorkoms en impak van vloede en droogtes te verstaan en te monitor. Hierdie kennis is ook van kritieke belang vir toepaslike beplanning en impakbepaling. Navorsing het getoon dat droogtes en vloede die twee grootste hidrologiese rampe in ontwikkelende lande, soos Suider-Afrika, is. Dit is hoofsaaklik te wyte aan die gebrek aan akkurate en robuuste metodes, tesame met ‘n tekort aan betroubare databronne wat vir die monitering van die ruimtelike en temporale dinamika van GOW-hulpbronne benodig word. Satelliet afstandswaarneming (AW)-tegnologie is 'n belowende primêre databron en bied tegnieke wat vir herhaalde kartering van waterliggame en vloedvlaktes geskik is. Baie vloedvlaktes en waterliggame word egter deur die teenwoordigheid van ondergedompelde plantegroei en opgeloste en gesuspendeerde stowwe gekenmerk. Hierdie eienskappe beperk die toepassing van AW in die monitering van GOW-hulpbronne. Hierdie studie het die potensiaal van afstandswaarnemingdata met verskillende tydelike, ruimtelike en radiometriese eienskappe geevalueer om GOW in sodanige uitdagende omgewings te karteer. Drie eksperimente is uitgevoer. Die eerste eksperiment het 'n nuwe spektrum indeks-gebaseerde ontmenging-algoritme geëvalueer wat gebruik maak van 'n minimum aantal spektrale bande. Die algoritme is toegepas op Medium-Resolusie Beeldvormende Spektrometer Volle Resolusie (MERBS VR) beeldmateriaal om oop water en plante wat gedeeltelik gedompel is te karteer. MERBS VR beeldmateriaal het 'n hoë (drie dae) temporale resolusie, maar 'n lae (300 m) ruimtelike resolusie. Die kwaliteit van die vloedkaart wat afgelei is van die MERBS-data is teen hoë (30 m) ruimtelike resolusie, maar lae (16 dae) temporale Landsat Tematiese Karteerder (TK) beelde van twee verskillende datums (17 April 2008 en 22 Mei 2009) waartydens oorstromings plaasgevind het, geëvalueer. Die bevindings toon dat, ten spyte van die lae resolusie van MERBS, beide die ruimtelike en frekwensieverspreiding van die waterfraksie wat vanuit die MERBS-data verkry is goed ooreengestem het met die hoë-resolusie TK-herwinnings. Dit dui daarop dat die voorgestelde tegniek gebruik kan word om betroubare en gereelde vloedkaarte te produseer deur van lae-ruimtelike-resolusie-beelde gebruik te maak. Die gebruik van sintetiese diafragma-radar (SDR) het toenemend relevant vir die kartering en monitering van oorstroomde plantegroei (OP) geword. In 'n tweede eksperiment is ’n prosedure, gebaseer op 'n tydreeks van Sentinel-1 SDR-data, vir die kartering van oorstromings in 'n vloedvlakte met plante ontwikkel en gevalideer. Vir elke nuwe beskikbare beeld word die waarskynlikheid van tydelik-oorstroomde toestande getoets teen die waarskynlikheid van nie-oorstroomde toestande. Veranderinge in grondbedekkingseienskappe word deur die tegniek oorweeg. Die modellering- en toetskomponente is onafhanklik op die vertikale transmissie en horisontale ontvangs (VH), vertikale transmissie en vertikale ontvangs (VV) en VH/VV verhouding polarisasies toegepas. Die resulterende vloedkaarte is met dié van Landsat-8 Operasionele-grondbeelder (OGB) en grondslag-getrouheid vergelyk. Algehele klassifikasie-akkuraatheid het getoon dat die kaarte wat uit die aaneengesmelte Sentinel-1 produkte (VH en VH/VV) vervaardig is, die akkuraatste (84,5%) was en aansienlik beter was as wanneer slegs die VH polarisasie gebruik is (78,7%). Hierdie resultate toon dat die samesmelting van VH/VV en VV-polarisasies die vloedkartering in beplante vloedvlaktes kan verbeter. Die derde eksperiment het die gebruik van outomatiese drempelbepaling van naby-gelyktydig genormaliseerde verskil-natheid-indeks (GVNI) (gegenereer met Sentinel-2 beelde) en VH-terugverspreidingbande (gegenereer met Sentinel-1 data) behels om waterliggame met uiteenlopende spektrale en ruimtelike eienskappe te karteer. Die resulterende kaarte is vergelyk met die klassifikasieprestasies van vyf masjienleer-algoritmes (MLAs), naamlik besluitboom (BB), k-naaste buurman (k-NN), ewekansige woud (EW) en twee implementasies van die ondersteuningsvektormasjien (OVM). Die resultate toon dat die kombinasie van multispektrale indekse met SDR data uiters voordelig vir die klassifikasie van komplekse waterliggame is en dat die voorgestelde drempelbepalingbenadering waterliggame met 'n algehele klassifikasie-akkuraatheid van 89,3% geklassifiseer het. Die wisselende konsentrasies van gesuspendeerde sedimente (turbiditeit), opgeloste deeltjies en waterplante het egter die klassifikasie-akkuraatheid van die voorgestelde metode negatief beïnvloed, terwyl die MLAs (OVM in die besonder) minder sensitief vir sodanige variasies was. Die GOW-kaarte en -tegnieke wat in hierdie studie ontwikkel is, is van kritieke belang vir vloedstatusmonitering, waterhulpbronbeplanning en rampbestuur en sal sodanig die impak van vloede en droogtes op kwesbare gemeenskappe in Suider-Afrika verminder. Daarbenewens sal die resultate van hierdie studie hopelik die afstandswaarneminggemeenskap inspireer om van die nuwe generasie, vrylik-beskikbare multispektrale en SDR-data gebruik te maak om operasionele droogte en vloede te monitor (soos die wat deur die Sentinel-konstellasies verskaf word)

Comparing thresholding with machine learning classifiers for mapping complex water

CITATION: Bangira, T. et al. 2019. Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water. Remote Sensing, 11(11). doi:10.3390/rs11111351.The original publication is available at https://www.mdpi.com/journal/remotesensingSmall reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, respectively, provided better overall accuracies than when thresholding was applied to any of the individual features considered. The accuracies of this dual thresholding technique were comparable to those of machine learning and may thus offer a viable solution for automatic mapping of waterbodies.https://www.mdpi.com/2072-4292/11/11/1351Publisher’s versio

Remote Sensing Grassland Productivity Attributes: A Systematic Review

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 DirectScience 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

Flood Extent Mapping in the Caprivi Floodplain Using Sentinel-1 Time Series

Deployment of Sentinel-1 (S1) satellite constellation carrying a $C$-band synthetic aperture radar (SAR) enables regular and timely monitoring of floods from their onset until returning to nonflooded (NF) conditions. The major constraint on using SAR for near-real-time (NRT) flood mapping has been the inability to rapidly process the obtained imagery into reliable flood maps. This study evaluates the efficacy of S1 time series for quantifying and characterizing inundation extents in vegetated environments. A novel algorithm based on statistical time-series modeling of flooded (F) and NF pixels is proposed for NRT flood monitoring. For each new available S1 image, the probability of temporarily F conditions is tested against that of NF conditions by means of likelihood ratio tests. The likelihoods for the two conditions are derived from early acquisitions in the time series. The algorithm calibration consists of adjusting two likelihood ratio thresholds to match the reference F area extent during a single flood season. The proposed algorithm is applied to the Caprivi region, the resulting maps were compared to cloud-free Landsat-8 (LS8) derived maps captured during two flood events. A good spatial agreement (85–87%) between LS8 and S1 flood maps was observed during the flood peak in both 2017 and 2018 seasons. Significant discrepancies were noted during the flood expansion and recession phases, mainly due to different sensitivities of the data sources to the emerging vegetation. Overall, the analysis shows that S1 can stand as an effective standalone or gap-filling alternative to optical imagery during a flood event

A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services

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