Evaluating large numbers of genotypes and phenotypes in multi-environment trials is key to crop improvement for biomass performance in sorghum. In this dissertation, we developed an approach that integrates crop growth models with remote-sensing data and genetic information for modeling and predicting sorghum biomass yield. The goal of studies described in Chapter 2 was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits for 18 commercial grain and biomass sorghum hybrids. These studies showed that (i) biomass sorghum hybrids tended to have higher maximum plant height, final dry biomass and radiation use efficiency (RUE) than grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass potential in longer growing environments and (iii) the parameterized APSIM models performed well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize models for larger plant breeding populations. Understanding the genetic architecture of biomass productivity and bioenergy-related traits is another key aspect of bioenergy sorghum breeding programs. In Chapter 3, 619 sorghum genotypes from the sorghum diversity panel were individually crossed to ATx623 to create a half-sib population that was planted and evaluated in field trials in three consecutive years. Single-nucleotide polymorphisms (SNPs) were used in a genome-wide association study (GWAS) to identify genetic loci associated with variation in plant architecture and biomass productivity. A few SNPs associated with these traits were located in previously described genes including the sorghum dwarfing genes <i>Dw1</i> and <i>Dw3</i> and stay-green QTLs <i>Stg1</i> and <i>Stg4</i>. Of particular interest were seven genetic loci that were discovered for biomass yield. For three of these loci, the minor or uncommon allele exhibited a favorable effect on productivity suggesting opportunities to further improve the crop for biomass accumulation through plant breeding. Marker-assisted and genomic selection strategies may provide tools to introgress and exploit these genes for bioenergy sorghum development. Since parameterizing biophysical crop models requires extensive time and manual effort, a simple model was developed in Chapter 4 that used time-dependent measurements of RGB canopy cover and daily radiation coupled with end-of-season biomass for estimating seasonal radiation use efficiency (SRUE) in 619 sorghum hybrids. SRUE was shown to be a stable and heritable trait that has a positive relationship with aboveground dry biomass (ADB) over seasons. GWAS identified 11 SNPs associated with SRUE with the favorable effect represented by the minor allele for seven of these SNPs. Increasing the frequency of these favorable alleles may improve the breeding population. These results demonstrated that the simple model for calculating SRUE can be used in genetic studies and for parameterizing biophysical crop models. The studies integrating crop growth models with remote sensing technologies provide an opportunity to evaluate a large number of phenotypes for the target population to understand the underlying genetic variation of bioenergy sorghum.

Harmful Algae Blooms (HABs) in inland waterbodies (e.g., lakes and ponds) pose serious threat to human health and natural ecosystem. Thus, it is imperative to assess HABs and their potential triggering factors over broader spatiotemporal scales. This study utilizes Chlorophyll-a (Chl-a) concentration in water samples collected from lakes in Illinois as an indirect measurement of HABs. The major objectives were to assess the spatiotemporal pattern of HABs over Illinois regions in recent decades, and to examine different machine learning models for predicting the Chl-a concentration based on publicly available water quality datasets. The Chl-a dataset was compiled from two different sources, the regular monitoring program by Illinois Environmental Protection Agency (IEPA) and the Voluntary Lake Monitoring Program (VLMP), the latter of which was primarily collected by citizen participants. Seven environmental and water quality zones were selected for spatial analyses. Additionally, the temporal patterns were assessed using time-series decomposition of monthly Chl-a concentration datasets. The machine learning pipeline includes two tasks: a regression modeling task for predicting Chl-a concentration, and a classification task for estimating lake trophic status. Different meteorological, land use and land cover, and lake morphometry variables were used as independent variables. Four regression models, i.e., Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVR), Artificial Neural Network Regression (ANNR), and Random Forest Regression (RFR) were used for the first task of the modeling pipeline, and four classification models, i.e., Logistic Regression Classification (LRC), Support Vector Machine Classification (SVC), Artificial Neural Network Classification (ANNC), and Random Forest Classification (RFC), were used for the second task. Results indicate that: a) the Collinsville region in southwestern part of Illinois exhibited higher mean concentration of Chl-a in its lakes than any other regions from 1998 to 2018; b) the lakes that showed increasing trends in their monthly mean Chl-a concentrations were also clustered in the southwestern region; c) Random Forest outperformed all other models in both classification (Accuracy=60.06%) and regression (R2=38.88%); and d) the land use and land cover variables were found as the most important set of variables in Random Forest models.

The Red Sea is an arid environment, without riverine inputs, oligotrophic waters and extreme temperature and salinity. Avicennia marina is the dominant vegetation in the shores of the Red Sea. However, little is known about their distribution, dynamics, and services. Therefore, the aim of this Ph.D. was to obtain the basic information needed to evaluate their role in the coastal ecosystems and quantify their services. With that objective we 1) estimated the past and present distribution of mangroves in the Red Sea, 2) investigated the growth, leave production and floration 3) examined the growth limiting factors 4) measured the nutrients and heavy metal dynamics in the leaves and 5) estimated carbon sequestration. We found an increase of about 12% in the last 41 years, which contrasts with global trends of decrease. The extreme conditions in the Red Sea contributed to limit their growth resulting in stunted trees. Hence, we surveyed Central Red Sea mangroves to estimate their node production with an average of 9.59 node y-1 then converted that number into time to have a plastochrone interval of 38 days. As mangroves are taller in the southern Red Sea where both temperature and nutrients are higher than the Central Red Sea, we assessed nutrient status Avicennia marina propagules and naturally growing leaves to find the leaves low in nutrient concentrations (N < 1.5 %, P < 0.09 %, Fe < 0.06) and that nutrients are reabsorbed before shedding the leaves (69%, 72% and 35% for N, P, and Fe respectively). As a result, we conducted a fertilization experiment (N, P, Fe and combinations) to find that iron additions alone led to significant growth responses. Moreover, we estimated their leaf production and used our previous estimates of both the total cover mangrove in the Red Sea along with plastochrone interval to assess their total nutrients flux per year to be 2414 t N, 139 t P and 98 t Fe. We found them to sequester 34 g m-2 y-1, which imply 4590 tons of carbon sequestered per year for the total mangroves covered by the Red Sea.

Orientador: Marcelo Andreotti / Resumo: A irrigação vem sendo desenvolvida há séculos pela humanidade e atualmente mais da metade da produção de alimentos provém de áreas irrigadas. Além da garantia de produtividade das culturas, a irrigação traz desenvolvimento socioeconômico para a região onde é instalada, sendo assim é necessário o aumento das áreas irrigadas aliado à boa gestão dos recursos hídricos para a garantia dos usos múltiplos da água. A estimativa da evapotranspiração de determinada região contribui para um melhor conhecimento do ciclo hidrológico e uma melhor capacidade de quantificar mudanças futuras na gestão dos recursos hídricos. Este trabalho objetivou-se em apresentar a evolução das áreas irrigadas por pivô central na região Noroeste Paulista entre 2000 e 2018 e ainda, estimar a evapotranspiração atual em larga escala na região, comparando os anos 2010, 2017 e 2018, utilizando técnicas de sensoriamento remoto. Observou-se a implantação de 241 equipamentos tipo pivô central irrigando 10.473 hectares do ano 2000 a 2018, acumulando uma área irrigada de 17.135 hectares com 320 sistemas. A evapotranspiração atual se diferiu no espaço e no tempo na região, tendo valores que variaram entre 0,5 e 2,5 mm dia-1, sendo as maiores taxas registradas nas áreas irrigadas e as maiores médias foram obtidas no período de chuvas da região em todos os anos estudados, tendo uma evapotranspiração incremental de 0,2 mm dia-1 entre 2010 e 2018. / Abstract: Irrigation is being developed for centuries by man kind and currently more than half of food production come from irrigated areas. Besides the guaranty of crop productivity, irrigation brings social-economic development for the regions where it is being installed, thus it’s necessary the expansion of irrigated areas associated to a good management of hydric resources for the guaranty of multiple uses of water. The evapotranspiration estimation of a certain region contributes to a better knowledge of the hydrological cycle and a better capacity to quantify future changes in the management of hydric resources. This work aimed in showing the evolution of irrigated areas by central pivot in the Paulista Northwest region between 2000 and 2018 and yet, estimate the current evapotranspiration in large scale in the region, comparing it to the years 2010, 2017 and 2018, using remote censoring techniques. It was observed the implementation of 241 equipments of central pivot kind irrigating 10,473 hectares from 2000 to 2018, accumulating an irrigated area of 17,135 hectares with 320 systems. The current evapotranspiration differed in space and in time in the region, showing values that vary from 0.5 and 2.5 mm day-1 , being the highest rates recorded in the irrigated areas and the higher rates were obtained in the rainy season of the region in all studied years, having an incremental evapotranspiration of 0.2 mm day-1 between 2010 and 2018. / Mestre

Wheat consumption has become more widespread and is increasing in South Africa especially in the urban areas. The wheat industry contributes four billion rands to the gross value of agriculture and is a source of employment to approximately 28 000 people. Wheat yield forecasting is crucial in planning for imports and exports depending on the expected yields and wheat health monitoring is important in minimizing crop losses. However, current crop surveying techniques used in South Africa rely on manual field surveys and aerial surveys, which are costly and not timely (after harvest). This research focuses on wheat health monitoring and wheat yield prediction using remote sensing, which is a cost effective, reliable and time saving alternative to manual surveys. Hence, the research objectives were: (i) to identify remotely sensed spectral indices that comprehensively describe wheat health status. (ii) Develop an Normalized Difference Vegetation Index (NDVI) based wheat yield forecasting model and (iii) to evaluate the impact of selected agrometeorological parameters on the NDVI based forecasting model. Landsat 8 images were used for determining spectral indices suitable for wheat health monitoring by relating the spectral indices to the land surface temperature. Results show that the Normalized Difference Water Index (R2 between 0.65 and 0.89) and NDVI (R2 between 0.36 and 0.62) were the most suitable indices for wheat health status monitoring. Whereas, the Normalized Difference Moisture Index (R2 between 0.53 and 0.79) and the Green Normalized Difference Vegetation Index (R2 between 0.28 and 0.41) were found to be less suitable for wheat health monitoring. Moderate Resolution Spectroradiometer (MODIS) derived NDVI for fourteen years was used to build and test a wheat yield forecasting model. The model was significant with an R2 value of 0.73, a p-value of 0.00161 and an RMSE of 0.41 tons ha-1. The study established that the period 30 days before harvest during the anthesis growth stage, is the best period to use the linear regression model for wheat yield forecasting. Satellite derived agrometeorological parameters such as: soil moisture, evapotranspiration and land surface temperature were added to the NDVI based model to form a multi-linear regression model. The addition of these parameters to the NDVI model improved it from an R2 of 0.73 to an R2 of 0.82. Through the use of a correlation matrix, the NDVI (r=0.88) and evapotranspiration (r=0.58) were highly correlated to wheat yield as compared to soil moisture (r=0.27) and land surface temperature (r=-0.02). This research provided evidence that remote sensing can be used at acceptable levels of accuracy for wheat monitoring and wheat yield predictions compared to manual field surveys which are costly and time consuming. / Dissertation (MSc)--University of Pretoria, 2017. / Agricultural Research Council / National Research Foundation / Spatial Business IQ / GeoTerra Images / University of Pretoria / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

Rangelands are extremely important for livestock grazing purposes in South Africa. Grazing should thus be regulated in order to conserve grass, shrubs and trees thereby ensuring sustainability of rangelands. In South Africa, the existing national grazing capacity estimate was developed in 1993 and updated in 2005 using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite data. Largely due to changing land use practices (as well as changing data availability), there exists a clear need to create a new estimate, making use of current available data. For Limpopo, a province shown to be prone to recent land degradation, droughts and climate change, developing such an updated carrying capacity (CC) product (adjusted monthly according to monitoring data and seasonal forecasts) may help support more sustainable agricultural practices. The main objectives of the study are to update current CC products and to create deviation maps from CC for several years with relevant data. For estimation of the CC product, input data have included Satellite Pour l'Observation de la Terre (SPOT) VEGETATION Dry Matter Productivity (DMP), vegetation map of 2009 and downscaled coupled model data (ECHAM4.5–MOM3-DC2). A tree density product of 2003, observed rainfall and secondary ground truth data are also used. Study results show that Remote Sensing (RS) and Geographic Information System (GIS) technology, Earth Observation System (EOS) data and products, climate data and ground truth data are successfully used in a series of steps, processes with modelling to ultimately estimate grazing capacity. It is clear that rainfall is a primary determinant of DMP. The Coupled General Circulation Model (CGCM) shows that the December-January-February (DJF) rainfall season is important as a predictor season for the November through to April (NDJFMA) DMP growing season for the Limpopo Province. This model can discriminate high and low DMP (and GC) seasons. This study shows that the DMP product can, with certain assumptions, be used as a proxy for grass biomass. There is a strong drive towards the application of seasonal forecasts in agriculture. This project demonstrates the development of a tailored forecast, an avenue that should be explored in enhancing relevance of forecasts to agricultural production. / Dissertation (MSc)--University of Pretoria, 2016. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

PhD (Environmental Sciences) / Department of Hydrology and Water Resources / Luvuvhu River Catchment (LRC) exhibits diverse land use and land cover patterns that are influenced by seasonality and socio-cultural practices of the local communities. From 1950, the catchment has been undergoing land cover changes caused by expanding villages, new urban centres and clearing forest land for agriculture. Conversion of natural landscape for agricultural and urban purposes degraded the catchment by negatively affecting the hydrologic processes. This study was therefore conducted to evaluate the impact of land cover and land use change on the hydrology and water resources of LRC. Geographical Information Systems (GIS) and remote sensing techniques were applied to evaluate the impact of the changes on the catchment. Remotely sensed imagery was used as the primary sources of data for classification and detection of changes. Digital Elevation Models (DEMs) were used for hydrologic and geomorphic modeling in combination with information from remotely sensed imagery. Field data sets for soil and meteorology were obtained from selected sampling segments, based on the area frame sampling. The method of direct expansion was used to quantify land use classes. Flood frequency was analysed using probability distribution methods at recurrence intervals of 2, 5, 10, 20, 25, 50, 100, and 200 years. The FAO CROPWAT software based on Penman-Montheith equation was used to assess the impact of land cover changes on evapotranspiration regimes. To study the hydrological response of land cover change in the catchment, the Soil Conservation Services-Curve Number (SCS-CN) method was first used independently to simulate surface runoff and investigate the impact of land use change on runoff under historical land cover regimes. The Soil and Water Assessment Tool (SWAT) model was then applied in the Tshakhuma-Levubu subcatchment to assess the impact of land management practices on the soil and water bodies in the catchment. The results indicated that changes were having negative impacts on the hydrology of the catchment. The impact of land use and land cover change on hydrology of LRC was manifested in stream flow, surface runoff, suspended sediment and flood frequency and magnitudes. There was significant land cover and land use change from forestland, woodland and open grassland to medium size farms, subsistence agriculture and built-up land. These developments were concentrated on hillsides and hilltops in the catchment and they were of concern as they were ix impacting on the hydrological processes. Throughout the 2000’s, land use change revealed a decrease in natural forest from 32.15% to 20.67%, giving rise to agriculture which rose to 38.57% in 2010. Runoff was observed to be highly variable during the month of February with maximum runoff records of 1.63 m3 and 3.84 m3 upstream and downstream, respectively. Flood frequency results showed that an increase in the peak discharges was to be expected, especially for the discharge range corresponding to smaller and medium flood magnitudes. The use of imagery and DEMs within GIS was found to efficiently represent ground surface and allow automated extraction of features, thus bringing advantages in terms of processing efficiency, cost effectiveness, and accuracy assessments. This technique could therefore be adopted to improve land use planning, water management, and rapid identification of slopes and elevations in consideration for their functional and structural requirements. Analysis showed that the SWAT model was suitable for predicting the location and extent of pollution in the catchment. It assumed sheet and rill erosion as the dominant erosion type contributing to siltation and water pollution in rivers. The study recommends close monitoring and sustained enforcement of the rural land use regulations to prevent the conversion of land to urban land use. / NRF

Between 2015 and 2050, half of the net increase in the world's urban populationis expected to take place in Sub-Saharan Africa (SSA), driving drastic landcover changes and challenging the spatial organization of human societies.Understanding past and present dynamics of this urbanization process is criticalto achieve a sustainable pattern of urban development, yet is limited by thelack of accurate and multi-temporal spatial data on urban expansion. Since the2000s, the rise of satellite-based Earth Observation (EO) enabled the productionof several global urban maps, thereby mitigating the issue of data scarcity. ButSSA is still characterized by lower accuracies in satellite-based maps becauseof various issues, such as: a lower satellite imagery availability, a lack ofreference datasets, and a high heterogeneity across the urban areas of theregion.In this thesis, I propose to leverage open-access satellite catalogs along withvolunteered geographic information to improve large-scaled and automated mappingof the built environment in SSA. The proposed approach makes use ofOpenStreetMap to support model training and calibration, thereby bypassing theneed for reference datasets or manual digitization campaigns. This method wasassessed in 10 urban areas of SSA, reaching classification performances similarto manual approaches.Moreover, the combined use of multispectral and synthetic-aperture radar (SAR)imagery was explored. In 11 out of 12 case studies in SSA, multi-sensorclassification schemes outperformed single-sensor approaches. More specifically,multi-sensor classification dramatically increased built-up detection rates inarid and semi-arid regions---where bare soil and buildings may share a similarspectral signature.These findings were implemented to map the built environment of 46 urban areasat five different dates from 1995 to 2015, with an average F1-score of 0.93. Thestatistical interpretation of the produced dataset revealed the highheterogeneity that characterizes urban areas in SSA, and confirmed that thespatial patterns of urbanization highly depends on demographic and economicfactors. Overall, the present thesis provides promising insights for large-scaled andautomated mapping of the built environment in data-scarce regions. Severalissues are still affecting the mapping accuracies, such as: multi-temporalinconsistencies caused by the use of imagery from 7 different sensors, lowavailability of historical imagery in SSA, or missing data in OpenStreetMap.Still, with the growing availability of open-access EO catalogs and theincreasingly completeness of OpenStreetMap, the proposed approach is expected tobecome even more relevant in the near future. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Rock slope failures leading to rock falls and rock slides are caused by a multitude of factors, including seismic activity, weathering, frost wedging, groundwater and thermal stressing. Although these causes are generally attributed as separate causes, some of them will often act together to cause rock slope failures. In this work, two of the above factors, seepage of water through cracks and crack propagation due to the after effects of blasting are considered. Their combined impact on the development of rock falls and rock slides is modeled on ANSYS workbench using the Bingham Canyon mine slope failure of 2013 as a case study. Crack path modeling and slope stability analysis are used to show how a combination of crack propagation and seepage of water can lead to weakening of rock slopes and ultimate failure. Based on the work presented here, a simple approach for modeling the development of rock falls and rock slides due to crack propagation and seepage forces is proposed. It is shown how the information from remote sensing images can be used to develop crack propagation paths. The complete scope of this method involves demonstrating the combination of basic remote sensing techniques combined with numerical modeling on ANSYS workbench.

Studying recent volcanic and tectonic events in the Red Sea region is important for improving our knowledge of the Red Sea plate boundary and for regional geohazard assessments. However, limited information has been available about the past activity due to insufficient in-situ data and remoteness of some of the activity. In this dissertation, I have used satellite remote sensing to derive new information about several recent volcanic and tectonic events in the Red Sea region. I first report on three volcanic eruptions in the southern Red Sea, the 2007-8 Jebel at Tair eruption and the 2011-12 & 2013 Zubair eruptions, which resulted in formation of two new islands. Series of high- resolution optical images were used to map the extent of lava flows and to observe and analyze the growth and destructive processes of the new islands. I used Interferometric Synthetic Aperture Radar (InSAR) data to study the evolution of lava flows, to estimate their volumes, as well as to generate ground displacements maps, which were used to model the dikes that fed the eruptions. I then report on my work of the 2009 Harrat Lunayyir dike intrusion and the 2004 Tabuk earthquake sequence in western Saudi Arabia. I used InSAR observations and stress calculations to study the intruding dike at Harrat Lunayyir, while I combined InSAR data and Bayesian estimation to study the Tabuk earthquake activity. The key findings of the thesis are: 1) The recent volcanic eruptions in the southern Red Sea indicate that the area is magmatically more active than previously acknowledged and that a rifting episode has been taken place in the southern Red Sea; 2) Stress interactions between an ascending dike intrusion and normal faulting on graben-bounding faults above the dike can inhibit vertical propagation of magma towards the surface; 3) InSAR observations can improve locations of shallow earthquakes and fault model uncertainties are useful to associate earthquake activity with mapped faults; 4). The successful application of satellite remote sensing technologies in studying the recent volcanic and tectonic processes in the Red Sea region implies that remote sensing data are an important resource for the local authorities to monitor geohazards.