Using Sentinel-1 GRD SAR data for volcanic eruptions monitoring: the case-study of Fogo Volcano (Cabo Verde) in 2014/2015

3rd Intercontinental Geoinformation Days (IGD), Mersin, Turkey (17-18 november 2021).The last eruption in the Fogo Volcano, which began in November 2014, was the first eruptive event captured by the Sentinel-1 (S1) mission. The present work sought to complement previous research and explore the potential of utilizing data from the Synthetic Aperture Radar (SAR) S1 mission to better monitor active volcanic areas. S1 Ground Range Detected (GRD) data was used to analyze the changes that occurred in the area before, during, and after the eruptive event and was able to identify the progress of the lava flow and measure the affected area (3.89 km2 in total). Using the GRD data on Google Earth Engine (GEE) platform demonstrated high potential in terms of response time to monitor and assess eruptive scenarios in near-real-time, which is fundamental to mitigate risks and to better support crisis management.info:eu-repo/semantics/publishedVersio

Development of cloud-native and scalable algorithms to estimate seagrass composition and related carbon stocks in support of the Nationally Determined Contributions of the Paris Agreement

Seagrasses are one of the world’s most productive ecosystems, playing an important role in climate change mitigation and adaptation. They are vast natural carbon sinks which have important, yet largely overlooked and underestimated implications into national climate agendas like the Nationally Determined Contributions of the Paris Agreement. Precise knowledge of spatially-explicit seagrass distribution and country-specific in-situ blue carbon data is crucial for the ten countries which currently recognise this ecosystem within their Nationally Determined Contributions. This thesis combines open Sentinel-2 multi-temporal data with the open cloud computing platform Google Earth Engine to quantify country-scale seagrass extents and associated carbon stocks. The limited availability of reference data restricted the implementation of the created cloud-native mapping approach to only one country - The Bahamas. The mapped Bahamian seagrass covers an area between 11,779.44 and 27,629.32 km2, which can store 181,610,083.57 to 455,509,862.63 Mg carbon, and sequesters between 31.02 and 72.75 Mt CO2 per year. This equals 17 to 40 times the amount of CO2 emitted by The Bahamas in 2018, causing a carbon-neutral state and underlining the importance of the seagrass ecosystem for the Bahamian Nationally Determined Contributions. The generated data inventories could support interdisciplinary scientific research and management efforts within a regional and global climate action context

An assessment of the potential for cloud computing and satellite thermal infrared sensing to produce meaningful river temperature insights for hydropower operations

Hydropower interacts heavily with river temperature to; meet regulations, maximise profits, and maintain dam safety. Often the operational decisions that dictate this interaction are made without monitoring of river temperature, and so it is proposed that satellite remote sensing may provide a quasi-regular cost-effective method to improve this. This dissertation assesses the viability of using Google Earth Engine cloud computing and Landsat 8 Thermal Infrared satellite measurements to provide actionable insights for hydropower managers. The method was tested in three large rivers (the Saint John River in Canada, the Colorado River in the USA, and the Ganges in India) to assess transferability. No previous study has attempted to extract river temperature from multiple sites in a single study. Three different methods were tested to find the most accurate atmospheric correction algorithm for the task of river temperature measurement. The Statistical Mono-Window algorithm was found to produce the most accurate comparison to kinetic temperature loggers on the Saint John River (±2oc) with a R2 value of 0.96 (n=40, p<0.001). However, this method was not transferable to the Colorado River indicating application in rivers without validation data should be carried out with caution. A Python Package named SatTemp (Valman, 2021b) was developed to assist hydropower operators in implementing the method along with a dashboard app to disseminate results (Valman, 2021a). Concerns were raised with the “black box” nature of Google Earth Engine and this App, meaning that errors and nuances in the method may be missed. These would need to be addressed before this method can be provided to hydropower operators

An assessment of the potential for cloud computing and satellite thermal infrared sensing to produce meaningful river temperature insights for hydropower operations

Hydropower interacts heavily with river temperature to; meet regulations, maximise profits, and maintain dam safety. Often the operational decisions that dictate this interaction are made without monitoring of river temperature, and so it is proposed that satellite remote sensing may provide a quasi-regular cost-effective method to improve this. This dissertation assesses the viability of using Google Earth Engine cloud computing and Landsat 8 Thermal Infrared satellite measurements to provide actionable insights for hydropower managers. The method was tested in three large rivers (the Saint John River in Canada, the Colorado River in the USA, and the Ganges in India) to assess transferability. No previous study has attempted to extract river temperature from multiple sites in a single study. Three different methods were tested to find the most accurate atmospheric correction algorithm for the task of river temperature measurement. The Statistical Mono-Window algorithm was found to produce the most accurate comparison to kinetic temperature loggers on the Saint John River (±2oc) with a R2 value of 0.96 (n=40, p<0.001). However, this method was not transferable to the Colorado River indicating application in rivers without validation data should be carried out with caution. A Python Package named SatTemp (Valman, 2021b) was developed to assist hydropower operators in implementing the method along with a dashboard app to disseminate results (Valman, 2021a). Concerns were raised with the “black box” nature of Google Earth Engine and this App, meaning that errors and nuances in the method may be missed. These would need to be addressed before this method can be provided to hydropower operators

An integrated approach to grassland productivity modelling using spectral mixture analysis, primary production and Google Earth Engine

Thesis (MA)--Stellenbosch University, 2020.ENGLISH ABSTRACT: Grassland degradation can have a severe impact on condition, productivity and consequently grazing potential. Current conventional methods for monitoring and managing grasslands are time-consuming, destructive and not applicable at large-scale. These constraints could be addressed using a remote sensing (RS)-based approach, however, current RS-based approaches also have technological and scientific limitations in the context of grassland management. The inability of RS-based primary production models to discriminate between herbaceous and woody production at sub-pixel level poses constraints for use in grazing capacity (GC) calculation. The integration of fractional vegetation cover (FVC) is posed as a promising solution, specifically estimation using spectral mixture analysis (SMA). Current grassland monitoring approaches are limited by the technological constraints of traditional, desktop-based RS approaches, but the implementation of analysis in a Google Earth Engine (GEE) web application can address these limitations by providing dynamic, continuous productivity estimates. Field data collection and analysis of biophysical parameters were performed to establish crucial relationships between vegetation productivity and RS signals. Biophysical parameters obtained include FVC, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and grass dry matter (DM) production. An important outcome was the improvement of the normalised difference vegetation index (NDVI) and fAPAR regression relationship, achieved by scaling fAPAR using the proportion of green, living biomass. The relationship proved useful in subsequent vegetation productivity modelling. The potential of SMA for FVC estimation using medium resolution imagery (Landsat 8 and Sentinel-2) and relatively few field points, was explored. A linear spectral mixture model (LSMM) was calibrated, implemented and evaluated on accuracy and transferability. A number of bands and spectral indices were identified as core features, specifically the dry bare-soil index (DBSI). DBSI improved discrimination between bare ground and dry vegetation, a common challenge in semi-arid conditions. The calibrated LSMM performed well, with Sentinel-2 providing the most accurate results. The research proved the transferability of the LSMM approach, as accurate FVC estimates were obtained for both arid, dry season conditions and green, growing season conditions. The LSMM-estimated FVC was combined with primary production to improve GC calculation for grassland and rangelands. Annual grassland production was calculated using the Regional Biosphere Model (RBM). Although a water stress factor is a well-known source of uncertainty, the research found its inclusion crucial to the transferability of the model between different climatic conditions. FVC was used to determine the grazable primary production from RBM estimates, thus mitigating the effects of woody components on GC calculations. A comparison of model-estimated GC to the most recent national GC map showed good agreement. Slight discrepancies were likely due to the inability of the model to include species composition and palatability in GC calculations. The final FVC-integrated productivity model was implemented in a GEE web app to demonstrate the practical contribution of the research for continuous, dynamic, multi-scale and sustainable grassland management. Overall, the findings of the research provide valuable insights into improving RS-based modelling of grassland condition and productivity. Operationalisation of this research can aid in identifying potential degradation, highlighting regions vulnerable to food shortages and establishing sustainable productivity levels. Recommendations include investigating alternative methods for estimating water stress and exploring the incorporation of species composition in GC calculation using RS.AFRIKAANSE OPSOMMING: Agteruitgang van grasvelde kan 'n ernstige invloed op kondisie, produktiwiteit en gevolglik weidingspotensiaal hê. Huidige konvensionele metodes vir die monitering en bestuur van grasvelde is tydrowend, vernietigend en nie op groot skaal toepasbaar nie. Hierdie beperkinge kan met behulp van 'n afstandwaarnemings (AW)-gebaseerde benadering aangespreek word, maar huidige AW-metodes het egter ook tegnologiese en wetenskaplike beperkings, veral in die konteks van veldbestuur. Die onvermoë van AW-gebaseerde primêre produksiemodelle om tussen kruidagtige en houtagtige produksie op sub-pixelvlak te onderskei, hou beperkings in vir die berekening van drakapasiteit (DK). Die integrasie van fraksionele plantegroeibedekking (FPB) word aangebied as 'n belowende oplossing. Beraming van FPB deur gebruik te maak van spektrale mengselanalise (SMA) het veral potensiaal. Huidige benaderings vir die monitering van grasvelde word beperk deur die tegnologiese beperkings van tradisionele, rekenaargebaseerde AW-metodes, maar die implementering van analise in 'n Google Earth Engine (GEE) webtoepassing kan hierdie beperkings aanspreek deur dinamiese, deurlopende produktiwiteitsramings te verskaf. Velddata is ingesamel en analise van biofisiese parameters is uitgevoer om belangrike verwantskappe tussen plantproduktiwiteit en AW-seine te bepaal. Die biofisiese parameters sluit in FPB, blaaroppervlakte-indeks (BOI), fraksie van geabsorbeerde fotosinteties aktiewe bestraling (fAFAB) en droë materiaal (DM) produksie. Die verbetering van die genormaliseerde verskilplantegroei-indeks (NVPI) en fAFAB -regressie-verhouding, wat verkry is deur fAFAB te skaleer met behulp van die hoeveelheid groen, lewende biomassa was ‘n belangrike uitkoms. Die verwantskap was nuttig in die daaropvolgende modellering van plantegroei. Die potensiaal van SMA vir die bepaling van FPB deur middel van medium resolusiebeelde (Landsat 8 en Sentinel-2) met relatief min veldpunte is ondersoek. 'n Lineêre spektrale mengelmodel (LSMM) is gekalibreer, geïmplementeer en vir akkuraatheid en oordraagbaarheid geëvalueer. 'n Aantal bande en spektrale indekse is as kernkenmerke geïdentifiseer, spesifiek die droë kaal-grondindeks (DKGI). DKGI het die onderskeid tussen kaal grond en droë plantegroei, 'n algemene uitdaging in semi-droë landskappe, verbeter. Die gekalibreerde LSMM het goed gevaar, met Sentinel-2 wat die akkuraatste resultate gelewer het. Die navorsing het bewys dat die LSMM-benadering oorgedra kan word, aangesien akkurate FPB-ramings vir beide droë seisoen en groen, groeiseisoen toestande verkry is. Die LSMM-beraamde FPB is met primêre produksie ramings gekombineer om die DK-berekening vir grasveld te verbeter. Die jaarlikse grasveldproduksie is met behulp van die Streeks Biosfeer Model (SBM) bereken. Alhoewel 'n waterstresfaktor 'n bron van onsekerheid is, het die navorsing bevind dat dit die gebruik daarvan vir die oordraagbaarheid van die model tussen verskillende klimaatstoestande belangrik is. FPB is gebruik om die weibare primêre produksie volgens SBMramings te bepaal, en het die effekte van houtagtige komponente op DK-berekeninge verminder. 'n Vergelyking van die gemodelleerde DK met die nuutste nasionale DK-kaart het 'n goeie ooreenkoms getoon. Klein afwykings was waarskynlik te wyte aan die onvermoë van die model om spesiesamestelling en eetbaarheid by DK-berekeninge in te sluit. Die finale FPB-geïntegreerde produktiwiteitsmodel is in 'n GEE webtoep geïmplementeer om die praktiese bydrae van die navorsing vir deurlopende, dinamiese, meervoudige en volhoubare grasveldbestuur te demonstreer. In die geheel bied die bevindinge van die navorsing waardevolle insigte in die verbetering van die AW-gebaseerde modellering van veldtoestand en produktiwiteit. Operasionalisering van hierdie navorsing kan tot die identifisering van potensiële agteruitgang, die uitlig van streke wat kwesbaar is vir voedseltekorte en die bepaling van volhoubare produktiwiteitsvlakke bydra. Aanbevelings sluit in die ondersoek van alternatiewe metodes vir die beraming van waterstres en die gebruik van spesiesamestelling in DK-berekening met behulp van AW.Master