The lack of proper wastewater treatment inhibits the social and economic development in many communities. The South African town Wakkerstroom is an example where wastewater is first treated before it is released. Due to the lack of technical expertise and funding to manage the sewage disposal system, a large part of the wastewater goes directly, without any treatment, into a stream feeding the Wakkerstroom wetland. The wetland purifies the wastewater and provides clean water downstream, thus is indispensable for its detoxification capacity. One relatively cheap method to determine the absorption capacity of a wetland with respect to nitrogen loading is to investigate the nitrogen uptake by the wetland vegetation. In this study, the nitrogen uptake of the vegetation in the Wakkerstroom wetland during the growing seasons between the years 2000-2018 was investigated by using harvested biomass and its nitrogen content as a proxy. The interannual variability of Net Primary Production (NPP) was calculated using a Light Use Efficiency (LUE) model for the period 2000-2018. The NPP derived with LUE-modelling was compared to NPP based on an end-of season harvest of biomass in March 2019. The nitrogen content and carbon and nitrogen (C:N) ratio were determined in the harvested biomass by carbon and nitrogen content analysis. The annual nitrogen uptake of the growing seasons between the years 2000-2018 was subsequently determined by multiplying the calculated NPP by the fraction of nitrogen found in the harvested material. The NPPtot based on harvested biomass (NPPharvest) towards the end of the growing season 2018/2019 was estimated to be 2.01 kg‧m-2‧season-1. The NPPtot calculated from LUE modelling (NPPLUE) varied between 0.49-1.64 kg‧m-2 for the growing seasons between 2000-2018. NPPharvest was between 1.2-4 times higher compared to NPPLUE, probably due to overestimation of NPPharvest because of biomass sampling of more than one-year production, or underestimation of NPPLUE due to a low maximum radiation conversion efficiency factor, εmax. The community mean nitrogen (N) content found in the biomass harvested aboveground was 1.29 % for the Phragmites community and 1.00 % for the Typha community. The nitrogen uptake of the vegetation was estimated to vary between 6.10-20.5 g N∙m-2 per growing season between the years 2000-2018. / Bristen på adekvata reningstekniker för att behandla avloppsvatten hämmar den sociala och ekonomiska utvecklingen i många samhällen. Den sydafrikanska staden Wakkerstroom är ett exempel där avloppsvatten först renas innan det släpps ut. På grund av brisen på teknisk kompetens och finansiering att hantera reningsverket som avlägsnar avloppsvatten så läcker en stor del av det orenade avloppsvattnet ut i en våtmark i Wakkerstroom via en närliggande å. Våtmarken är av regional betydelse för sin reningskapacitet då den renar avloppsvattnet och förser användare nedströms med rent vatten. En viktig aspekt för att bestämma en våtmarks reningskapacitet med avseende på kväve (N) är att undersöka växternas kväveupptag i våtmarken. Kväveupptaget hos växterna i våtmarken i Wakkerstroom under växtsäsongerna mellan år 2000–2018 undersöktes genom att använda skördad biomassa och dess kväveinnehåll som proxy. Den årliga variabiliteten hos nettoprimärproduktionen (NPP) beräknades genom att använda en LUE (Light Use Efficiency)-modell för perioden 2000-2018. NPP framtaget med LUE-modellering jämfördes med NPP baserat på biomassa skördad i slutet av växtsäsongen i mars 2019. Kväveinnehållet och kol-kväve (C:N) kvoten bestämdes hos den skördade biomassan genom en kol- och kväveanalys. Det årliga kväveupptaget under växtsäsongerna mellan 2000–2018 togs därefter fram genom att multiplicera beräknad NPP med kvävefraktionen erhållen från den skördade biomassan. NPPtot framtaget med biomassa skördad i slutet av växtsäsongen 2018/2019 (NPPbiomassa) uppskattades vara 2,01 kg‧m-2‧säsong-1. NPPtot beräknat med LUE-modellering (NPPLUE) varierade mellan 0,49–1,64 kg‧m-2 under växtsäsongerna mellan år 2000–2018. NPPbiomassa var 1,2–4 gånger högre i jämförelse med NPPLUE, vilket troligtvis berodde på att NPPbiomassa överskattades på grund av att mer än en årsproduktion av biomassa skördades, eller för att NPPLUE underskattades på grund av ett för lågt värde på den maximala effektivitetsfaktorn εmax valdes. Medelvärdet för kväveinnehållet erhållen i biomassan skördad ovanför vattennivån var 1,29 % för Phragmites-samhället och 1,00 % för Typha-samhället. Kväveupptaget hos växterna varierade mellan 6,10–20,5 g N∙m-2 per växtsäsong mellan år 2000–2018.

<p>This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change Modeller and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant waterdependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices.</p>

<p>This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change Modeller and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant waterdependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices.</p>

Magister Scientiae (Biodiversity and Conservation Biology) - MSc (Biodiv and Cons Biol) / This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change Modeller and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant waterdependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices. / South Africa

Aerosol properties are highly variable in space and time which makes the aerosol study more complex. The sources and production mechanism of aerosols decide the properties of the aerosols. Aerosol radiative forcing is defined as the perturbation to the radiative fluxes of the earth atmosphere system caused by the aerosols. High uncertainty in the aerosol radiative forcing values exists today due to the lack of the exact chemical composition data of the aerosols everywhere. There are previous studies which have introduced methods to estimate ‘optical equivalent’ composition of aerosols using spectral aerosol optical depth measurements at the surface. The impact of aerosols on the solar radiative flux depends on its size distribution and composition. Hence, measurements of downward solar spectral fluxes at the surface can be used to infer ‘optically equivalent’ composition of aerosols. Measurements of downward solar spectral flux at Bangalore were made on clear days using a spectroradiometer. This data has been used to infer the aerosol composition following an iterative method with the help of the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). Aerosols have been classified as water soluble, black carbon and three types of dust. Influence of the different aerosol types on spectral down welling irradiance at the surface have been simulated using Optical Properties of Aerosols and Clouds (OPAC) and SBDART models. The strong spectral dependence influence of water soluble aerosols and the dust aerosols on the spectral irradiance is shown. Aerosol composition was inferred following least square error minimization principle. This method can be used to estimate near-surface aerosol concentration if the vertical profile of aerosols is known a priori. This method also enables derivation of spectral single scattering albedo. The aerosol spectral radiative forcing has been estimated using downward spectral flux at the surface and compared with modeled fluxes. The contribution to the total forcing by the wavelength band 360 – 528 nm is around 60% of the total forcing. The wavelength band of 453-518 nm contributes maximum to the total forcing and it is seen that the shape of the spectral forcing is a major function of shape of the incoming solar spectrum. Aerosol spectral radiative forcing from observations of radiative fluxes agreed with modeled values when derived aerosol chemical composition was used as input. This study demonstrates that spectral flux measurements at the surface are useful to infer aerosol composition (which is optically equivalent) when and where the conventional chemical analysis is unavailable.

This thesis deals with luminance parameters measurement and ways to accomplish this using LabView software. The first part focuses on luminance parameters measurable by spectroradiometer and their meaning. Following part introduces reader to hardware equipment that is used in the measurement. Third part contains description of LabView software and explains what is required to make a communication between equipment and PC work. Next part explains how the communication with peripherals was achieved. Following up is the part where it is described how programs controlling AC and DC power supplies work. In this part the user interface is described.

Satellites provide humanity with data to infer properties of the earth that were impossible a century ago. Humanity can now easily monitor the amount of ice found on the polar caps, the size of forests and deserts, the earth’s atmosphere, the seasonal variation on land and in the oceans and the surface temperature of the earth. In this thesis, new hypertemporal techniques are proposed for the settlement detection problem in South Africa. The hypertemporal techniques are applied to study areas in the Gauteng and Limpopo provinces of South Africa. To be more specific, new sequential (windowless) and non-sequential hypertemporal techniques are implemented. The time-series employed by the new hypertemporal techniques are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is on board the earth observations satellites Aqua and Terra. One MODIS dataset is constructed for each province. A Support Vector Machine (SVM) [1] that uses a novel noise-harmonic feature set is implemented to detect existing human settlements. The noise-harmonic feature set is a non-sequential hypertemporal feature set and is constructed by using the Coloured Simple Harmonic Oscillator (CSHO) [2]. The CSHO consists of a Simple Harmonic Oscillator (SHO) [3], which is superimposed on the Ornstein-Uhlenbeck process [4]. The noise-harmonic feature set is an extension of the classic harmonic feature set [5]. The classic harmonic feature set consists of a mean and a seasonal component. For the case studies in this thesis, it is observed that the noise-harmonic feature set not only extends the harmonic feature set, but also improves on its classification capability. The Cumulative Sum (CUSUM) algorithm was developed by Page in 1954 [6]. In its original form it is a sequential (windowless) hypertemporal change detection technique. Windowed versions of the algorithm have been applied in a remote sensing context. In this thesis CUSUM is used in its original form to detect settlement expansion in South Africa and is benchmarked against the classic band differencing change detection approach of Lunetta et al., which was developed in 2006 [7]. In the case of the Gauteng study area, the CUSUM algorithm outperformed the band differencing technique. The exact opposite behaviour was seen in the case of the Limpopo dataset. Sequential hypertemporal techniques are data-intensive and an inductive MODIS simulator was therefore also developed (to augment datasets). The proposed simulator is also based on the CSHO. Two case studies showed that the proposed inductive simulator accurately replicates the temporal dynamics and spectral dependencies found in MODIS data. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

>Magister Scientiae - MSc / The Cape Floristic Region (CFR) in South Africa, is one of the world's five Mediterranean hotspots, and is also one of the 34 global biodiversity hotspots. It has rich biological diversity, high level of species endemism in flora and fauna and an unusual high level of human induced threats. The Sandveld forms part of the CFR and is also highly threatened by intensive agriculture (potato, rooibos and wheat farming), proliferation of tourism facilities, coastal development, and alien invasions. These biodiversity threats have led to habitat loss and are threatening the long-term security of surface and ground water resources. In order to understand trends in such biodiversity loss and improve in the management of these ecosystems, earth-orbiting observation satellite data were used. This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change ModelIer and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant water dependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices.

Indiana University-Purdue University Indianapolis (IUPUI)

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