Tsunamis are among the most threatening natural hazards that can affect coastal communities and infrastructures. In order to provide useful information for coastal protection, one of my aims in this dissertation is to identify the physical metrics that better represent the damage cause by tsunamis. I approach this problem by carrying out three-dimensional-SPH numerical simulations of solitary waves which allow to track spatial-temporal evolution of physical variables during their breaking. By comparing these evolutions it is possible to visualize the complex hydrodynamic process that occurs during breaking. Results show that the highest danger lies in the environment of the shoreline. However the highest vulnerability of coastal communities and infrastructures lies onshore where they find themselves more exposed to the destructive capacity of extreme tsunami waves. In this regard, the second main goal in this dissertation is to understand how coastal vegetation reduces and modifies the onshore wave inundation. I address this problem by using shallow water equations and Serre-Green-Naghdi equations employed in a set of two-dimensional depth-integrated simulations. Analysis of results indicate the existence of a transition zone located between where runup is not affected at all and where runup suffers the maximum reduction by the vegetation. This infers the requirement of a minimum length of the vegetated barrier in order to achieve the maximum runup reduction under a specific set properties such as barrier location, barrier width, beach slope and/or wave amplitude. Overall we conclude, after intense validation work, that numerical approaches are very convenient tools to analyze difficult wave processes. However it is necessary to be aware of the limitation of each numerical approach. / Ph. D. / Tsunamis are long waves with large wave height that are mainly generated by ocean-based earthquakes. They can also be a consequence of other natural events such as landslides, intense volcanic activities, large storm floods or even asteroid impacts. Coastal communities tend to not consider these low-frequency threats and occupy large coastal areas and so they become very vulnerable to tsunamis. In this dissertation, two main goals are addressed: The first one is to identify where and when the highest dangerousness of the tsunamis occur so coastal habitants can avoid such areas. The second goal focuses on the flooding areas caused by tsunamis where onshore habitants are more vulnerable. We study how a natural element, such as coastal vegetation, affects, reduces and modifies the flooding due to tsunamis. Some design criteria are presented for the coastal vegetation to reduce the flooding to a maximum.

Evapotranspiration (ET) is a significant hydrological process. It can be studied and estimated using remote sensing based methods at multiple spatial and temporal scales. Most commonly and widely used remote sensing based methods to estimate actual evapotranspiration (AET) are a) methods based on energy balance equations, b) vegetation coefficient based method and c) contextual methods. These three methods require reflectance and land surface temperature (LST) data measured at optical and thermal portion of the electromagnetic spectrum. However, these data are available only for clear sky conditions and fail to be retrieved under overcast conditions creating gaps in the data, which result in discontinuous of AET product. Moreover, energy balance equation based methods and evaporative fraction (EF) based contextual methods are difficult to apply over overcast conditions. In this context, vegetation coefficient based (Tasumi et al., 2005; Allen et al., 2005) and microwave remote sensing based methods can be applied under cloudy sky conditions (Sun et al., 2012), since microwave radiations can penetrate through clouds, but these data are available at coarse resolution. In the vegetation coefficient method temporal upscaling can be avoided. Therefore in this research vegetation coefficient based method is employed over Cauvery basin to estimate daily AET for clear and cloudy sky conditions. Required critical variables for this method such as reference evapotranspiration (ETo) and vegetation coefficients are obtained using LST and optical vegetation indices for all sky conditions. In this study, all sky conditions refer to both clear and cloudy sky conditions. Most important variable for estimation of ETo using radiation and temperature based models is air temperature (Ta). In this study, for better accuracy of Ta, two satellite based approaches namely, Temperature Vegetation Index (TVX) and Advance Statistical Approaches (ASA) were evaluated. In the TVX approach, in addition to traditional Normalized Difference Vegetation Index (NDVI), other vegetation indices such as Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI) were also examined. In case of ASA, bootstrap technique was used to generate calibration and validation samples and Levenberg Marquardt algorithm was used to find the solution of the models. The better of the Ta results obtained out of these two approaches were employed in the ETo models and are referred as Ta based ETo models. Instead of Ta, processed LST data obtained directly from the satellite (Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS)) was applied in the ETo models and these are referred as LST based ETo models. These Ta and LST based Hargreaves-Samani (H-S), Makkink (Makk) and Penman Monteith Temperature (PMT) models were evaluated by comparing with the FAO56 PM model. Additionally, simple LST based equation (SLBE) proposed by Rivas et al. (2004) was also examined. Required solar radiation (Rs) data for ETo estimation was obtained from Kalpana1/VHRR satellite data. Results implied that, Ta based PMT model performed better than the Ta based H-S, Makk and SLBE with less RMSE, MAPE and MBE values for all land cover classes and for various climatic regions for clear sky conditions. LST based H-S, PMT, Makk and Ta based Makkink advection models predominantly overestimated ETo for the study region. In the case of TVX approach, to estimate maximum Ta (Tmax), GVMI performed better than NDVI and EVI. Nevertheless, TVX approach poorly estimated Tmax in comparison with statistical approach. ASA performed better for both Tmax and minimum Ta. This study demonstrates the applicability of satellite based Ta and ETo models by considering very few variables for clear sky conditions. Spatially distributed vegetation coefficients (Kv) data with high temporal resolution is another important variable in vegetation coefficient method for daily AET estimation and also it is in demand for crop condition assessment, irrigation scheduling, etc. But available Kv models application hinders because of two main reasons i.e 1) Spectral reflectance based Kv accounts only for transpiration factor but not evaporation, which fails to account for total AET. 2) Required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the Kv data. Hence there is a necessity of a model which accounts for both transpiration and evaporation factors and also gap filling method, which produces accurate continuous quantification of Kv values. Therefore, different combinations of EVI, GVMI and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain best model. This best Kv model had been compared with Guershman et al. (2009) Kv model. To fill the gaps in the data, initially, temporal fitting of Kv values have been examined using Savitsky-Goley (SG) filter for three years of data (2012 to 2014), but this fails when sufficient high quality Kv values were unavailable. In this regard, three gap filling techniques namely regression, Artificial Neural Networks (ANNs) and interpolation techniques have been analyzed. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate Kv values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations and also yielded better results than Guershman et al. (2009) Kv model. Furthermore, the results indicated that SG filter can be used for temporal fitting and for filling the gaps, regression technique can be used as it performed better than other techniques for Berambadi station. Land Surface Temperature (LST) with high spatiotemporal resolution is required in the estimation of ETo to obtain AET. MODIS is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatiotemporal resolution LST under cloudy conditions during daytime and night time without employing in-situ LST measurements. To achieve this, ANN based models were employed for different land cover classes, utilizing MPDI at finer resolution with ancillary data. MPDI was derived using resampled (from 0.250 to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology was quantitatively evaluated through three performance measures namely correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86) K to 4.34(4.00) K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. For night time, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70) K to 2.43(2.12) K and NSE from 0.43 (0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable limits. Under cloudy conditions, results of microwave derived LST were evaluated with Ta which indicated that the approach performed well with RMSE values lesser than the results obtained under clear sky conditions for land cover classes for both day and nighttimes. These predicted LSTs can be applied for the estimation of soil moisture in hydrological studies, in climate studies, ecology, urban climate and environmental studies, etc. AET was estimated for all sky conditions using vegetation coefficient method. Essential parameter ETo under cloudy conditions was estimated using LST and Ta based PMT and H-S models and required solar radiation (Rs) in these two models estimated using equation proposed by Samani (2000). In this equation it was found that the differences between LSTmax or Tmax and LSTmin or Tmin could able to capture the variations due to cloudy sky conditions and hence can be used for estimating ETo under cloudy sky conditions. Results revealed that the estimated Rs correlated well with observed Rs for Berambadi station under cloudy conditions for the year 2013. PMT based ETo values were corresponded with observed ETo under cloudy sky condition. The difference between LST and Ta was less during cloudy conditions, therefore LST or Ta can be used as the only input in temperature based PMT model to estimate ETo. AET estimated correlated well with the observed AET values for clear and cloudy sky conditions. In addition, AET estimated using vegetation coefficient method was compared with two source energy balance (TSEB) method developed by Nishida et al. (2003) under clear sky conditions. It was found that the improved vegetation coefficient method performed better than the TSEB method for Berambadi station. Other microwave vegetation indices such as Microwave Vegetation Indices (MVIs) and Emissivity Difference Vegetation Index (EDVI) are available in literature. Therefore in this study, MVIs are used to predict LST under cloudy conditions using proposed methodology to check whether the MVIs could yield better LST values. Results showed that MPDI performed better than MVIs to predict LST under cloudy sky conditions. Furthermore, MPDI obtained using dual polarizations of 37 GHz channel Tb has advantage of having fine spatial resolution compared to MVIs, as it requires Tb of 19 GHz in addition to Tb of 37 GHz channel which is of coarse resolution and therefore uncertainties resulting from re-sampling technique can be minimized. x

Changes in large-scale vegetation structure triggered by processes such as deforestation, wildfires, and tree die-off alter surface structure, energy balance, and associated albedo-all critical for land surface models. Characterizing these properties usually requires long-term data, precluding characterization of rapid vegetation changes such as those increasingly occurring in the Anthropocene. Consequently, the characterization of rapid events is limited and only possible in a few specific areas. We use a campaign approach to characterize surface properties associated with vegetation structure. In our approach, a profiling LiDAR and hemispherical image analyses quantify vegetation structure and a portable mast instrumented with a net radiometer, wind-humidity-temperature stations in a vertical profile, and soil temperature-heat flux characterize surface properties. We illustrate the application of our approach in two forest types (boreal and semiarid) with disturbance-induced tree loss. Our prototype characterizes major structural changes associated with tree loss, changes in vertical wind profiles, surface roughness energy balance partitioning, a proxy for NDVI (Normalized Differential Vegetation Index), and albedo. Multi-day albedo estimates, which differed between control and disturbed areas, were similar to tower-based multiyear characterizations, highlighting the utility and potential of the campaign approach. Our prototype provides general characterization of surface and boundary-layer properties relevant for land surface models, strategically enabling preliminary characterization of rapid vegetation disturbance events.

Student Number : 0310612G - MSc research report - School of Geography, Archaeology and Environmental Studies - Faculty of Science / Wildfire prediction and management is an issue of safety and security for many rural communities in South Africa. Wildfire prediction and early warning systems can assist in saving lives, infrastructure and valuable resources in these communities. Timely and accurate data are required for accurate wildfire prediction on both weather conditions and the availability of fuels (vegetation) for wildfires. Wildfires take place in large remote areas in which land use practices and alterations to land cover cannot easily be modelled. Remote sensing offers the opportunity to monitor the extent and changes of land use practices and land cover in these areas. In order for effective fire prediction and management, data on the quantity and state of fuels is required. Traditional methods for detecting vegetation rely on the chlorophyll content and moisture of vegetation for vegetation mapping techniques. Fuels that burn in wildfires are however predominantly dry, and by implication are low in chlorophyll and moisture contents. As a result, these fuels cannot be detected using traditional indices. Other model based methods for determining above ground vegetation biomass using satellite data have been devised. These however require ancillary data, which are unavailable in many rural areas in South Africa. A method is therefore required for the detection and quantification of dry fuels that pose a fire risk. ASTER and MAS (MODIS Airborne Simulator) imagery were obtained for a study area within the Lowveld region of the Limpopo Province, South Africa. Two of the ASTER and two of the MAS images were dated towards the end of the dry season (winter) when the quantity of fuel (dry vegetation) is at its highest. The remaining ASTER image was obtained during the middle of the wet season (summer), against which the results could be tested. In situ measurements of above ground biomass were obtained from a large number of collection points within the image footprints. Normalised Difference Vegetation Index and Transformed Vegetation Index vegetation indices were calculated and tested against the above ground biomass for the dry and wet season images. Spectral response signatures of dry vegetation were evaluated to select wavelengths, which may be effective at detecting dry vegetation as opposed to green vegetation. Ratios were calculated using the respective bandwidths of the ASTER and MAS sensors and tested against above ground biomass to detect dry vegetation. The findings of this study are that it is not feasible, using ASTER and MAS remote sensing data, to estimate brown and green vegetation biomass for wildfire prediction purposes using the datasets and research methodology applied in this study. Correlations between traditional vegetation indices and above ground biomass were weak. Visual trends were noted, however no conclusive evidence could be established from this relationship. The dry vegetation ratios indicated a weak correlation between the values. The removal of background noise, in particular soil reflectance, may result in more effective detection of dry vegetation. Time series analysis of the green vegetation indices might prove a more effective predictor of biomass fuel loads. The issues preventing the frequent and quick transmission of the large data sets required are being solved with the improvements in internet connectivity to many remote areas and will probably be a more viable path to solving this problem in the near future.

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016. / Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-6

Wave attenuation by vegetation is a highly dynamic process and its quantification is important for accurately understanding and predicting coastal hydrodynamics. However, the influence of vegetation on wave dissipation is not yet fully established nor implemented in current hydrodynamic models. A series of laboratory experiments were conducted at the Haynes Coastal Engineering Laboratory and in a two-dimensional flume at Texas A and M University to investigate the influence of relative vegetation height, stem density, and stem spacing uniformity on wave attenuation. Vegetation fields were represented as random cylinder arrays where the stem density and spatial variation were based on collected field specimens. Experimental results indicate wave attenuation is dependent on relative vegetation height, stem density, and stem spacing standard deviation. As stems occupy more of the water column, an increase in attenuation occurred given that the highest wave particle velocities are being impeded. Sparse vegetation fields dissipated less wave energy than the intermediate density; however, the extremely dense fields dissipated very little, if any, wave energy and sometimes wave growth was observed. This is possibly due to the highest density exceeding some threshold where maximum wave attenuation capabilities are exceeded and lowering of damping ensues. Additionally, wave attenuation increased with higher stem spatial variation due to less wake sheltering. A one-dimensional model with an analytical vegetation dissipation term was developed and calibrated to these experimental results to capture the wave transformation over the vegetation beds and to investigate the behavior of the vegetation field bulk drag coefficient. The best fit between predicted and measured wave heights was obtained using the least squares method considering the bulk drag coefficient as the single calibration parameter. The model was able to realistically capture the wave transformations over vegetation. Upon inspection, the bulk drag coefficient shared many of the dependencies of the total wave dissipation. The bulk drag coefficient increased with larger relative vegetation heights as well as with higher stem spacing standard deviation. Higher densities resulted in a lowering of the bulk drag coefficient but generally an increase in wave attenuation. These parameters and their influences help in identifying the important parameters for numerical studies to further our understanding of wave attenuation by wetlands.

This study investigates whether the flood regime in a catchment is sensitive to the condition of riparian vegetation along the river network. The research is based on a comprehensive assessment and synthesis of field and laboratory measurements of vegetation flow resistance. A new numerical model is developed to estimate the roughness characteristics of multi-species riparian assemblages at a cross-section. Reach-scale and catchment-scale flood routing models are then applied to estimate the impact of vegetation on flood characteristics at successively larger scales. The investigation reveals that when riparian vegetation is removed at catchment-scale, peak stage declines as channel capacity increases but is also increased as the upstream catchment responds more rapidly to rain. In fact, the two competing impacts tend to cancel out leaving flood peak stage relatively insensitive to riparian condition. However, the overbank duration of a flood and flow speeds (including wave celerity) were both found to be sensitive to vegetation condition; respectively increasing and decreasing with density of vegetation. The first stage of this research examines the magnitude of the vegetation contribution to overall channel roughness, and established a means to predict it. The features of the flow resistance generated by six plant types (mature trees; grasses; aquatic plants; flexible saplings; and large woody debris) were distilled from a comprehensive review of over 160 existing publications (Chapter 2).

Thesis (M.S.)--University of Nevada, Reno, 2008. / "August, 2008." Includes bibliographical references (leaves 76-81). Online version available on the World Wide Web.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [248]-277).

Thesis (M.S.)--University of Wyoming, 2009. / Title from PDF title page (viewed on Apr. 12, 2010). Includes bibliographical references (p. 74-77).