The software system to process integrated slant path lidar data has been debugged, modified, documented, and improved in reliability and user-friendliness. The substantial data set acquired since 1979 has been processed and a large body of results has been generated. A database has been implemented to store, organize, and access the results. The lidar data set results--the S ratios, the optical depths, and the mixing layer heights are presented. The seasonal dependence of the lidar solution parameters has been explored. The assumptions made in the lidar solution procedure are investigated. The sensitivity of the S ratio and the particulate extinction coefficient to the system calibration constant is examined. The reliability of the calibration constant is demonstrated by examining the particulate to Rayleigh extinction ratio values above the mixing layer.

M.Sc. (Geography) / The aim of this research is to determine if, by means of Landsat MSS digital data, urban land use classes can be identified and separated, and if changes in land use (urban sprawl) can be detected, over a period of time. Regional authorities function at inter-municipal scale. In order for these instittitions to perform these functions, they need to have access to standardized data (standardized in scale, time and interpretation) in order to obtain a global view of the total area under their authority. Remotely sensed digital data have the potential to fulfil these needs. A secondary objective will then also be to make an evaluation of the various applications of the results to the relevant authorities.

Zhang, Hankui. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 99-118). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Includes bibliographical references. / by Martin Glen Bello. / Ph.D.

M.Sc. / Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data densities, in a very short time-span. However, the high volumes of data associated with LiDAR often require some form of data reduction to increase the data handling efficiency of these datasets, of which the latter could affect the feasibility of Digital Elevation Models (DEMs). Critically, when DEM processing times are reduced, the resultant DEM should still represent the terrain adequately. This study investigated three different data reduction techniques, (1) random point reduction, (2) grid resolution reduction, and (3) combined data reduction, in order to assess their effects on the accuracy, as well as the data handling efficiency of derived DEMs. A series of point densities of 1 %, 10 %, 25 %, 50 % and 75 % were interpolated along a range of horizontal grid resolutions (1-, 2-, 3-, 4-, 5-, 10- and 30- m). Results show that, irrespective of terrain complexity, data points can be randomly reduced up to 25 % of the data points in the original dataset, with minimal effects on the remaining dataset. However, when these datasets are interpolated, data points can only be reduced to 50 % of the original data points, before showing large deviations from the original DEM. A reduction of the grid resolution of DEMs showed that the grid resolution could be lowered to 4 metres before showing significant deviations. When combining point density reduction with grid resolution reduction, results indicate that DEMs can be derived from 75 % of the data points, at a grid resolution of 3 metres, without sacrificing more than 15 percent of the accuracy of the original DEM. Ultimately, data reduction should result in accurate DEMs that reduce the processing time. When considering the effect on the accuracy, as well as the processing times of the data reduction techniques, results indicate that resolution reduction is the most effective data reduction technique. When reducing the grid resolution to 4 metres, data handling efficiencies improved by 94 %, while only sacrificing 10 % of the data accuracy. Furthermore, this study investigated data reduction on a variety of terrain complexities and found that the reduction thresholds established by this study were applicable to both complex and non-complex terrain.

Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested. The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Research Report submitted in partial fulfilment for the degree of Master of Science (Geographical Information Systems and Remote Sensing) School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg. August 2016. / The thematic classification of land use and land cover (LULC) from remotely sensed imagery data is one of the most common research branches of applied remote sensing sciences. The performances of the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) Support Vector Machine (SVM) learning algorithms were subjected to comparative assessment using WorldView-2 and SPOT-6 multispectral images of the Mokhotlong District in Lesotho covering approximately an area of 100 km2. For this purpose, four LULC classification models were developed using the combination of SVM –based image analysis approach (i.e. OBIA and/or PBIA) on high resolution images (WorldView-2 and/or SPOT-6) and the results were subjected to comparisons with one another. Of the four LULC models, the OBIA and WorldView-2 model (overall accuracy 93.2%) was found to be more appropriate and reliable for remote sensing application purposes in this environment. The OBIA-WorldView-2 LULC model was subjected to spatial overlay analysis with DEM derived topographic variables in order to evaluate the relationship between the spatial distribution of LULC types and topography, particularly for topographically-controlled patterns. It was discovered that although that there are traces of the relationship between the LULC types distributions and topography, it was significantly convoluted due to both natural and anthropogenic forces such that the topographic-induced patterns for most of the LULC types had been substantial disrupted. / LG2017

Orientadores: Alvaro Penteado Crósta, Archimedes Perez Filho / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-20T12:01:29Z (GMT). No. of bitstreams: 1 GelerRoffe_Tatiana_D.pdf: 15272397 bytes, checksum: bf09c202cab7336cc64657ab797e6966 (MD5) Previous issue date: 2012 / Resumo: Modelos matemáticos de predição de erosão e produção de sedimentos tem um grande potencial para uso em estudos da degradação e manejo dos recursos naturais, pois permitem estimar os impactos causados pela atividade agrícola. Este trabalho trata da aplicação do modelo hidrológico SWAT (Soil Water Assessment Tool), com a finalidade de previsão dos riscos de erosão devido às mudanças no uso da terra, na bacia do rio Chambas na província Ciego de Ávila, em Cuba. Este modelo tem sido utilizado de forma eficaz em estudos da dinâmica ambiental na representação da variabilidade espacial e temporal de processos hidrosedimentológicos em bacias hidrográficas. O método desenvolvido para modelagem descreve a dinâmica da perda de solo devido à enxurrada produzida pela água e às mudanças no uso da terra. Este permite a avaliação dos riscos de erosão do solo, utilizando um modelo implementado em um Sistema de Informação Geográfica (SIG) em modo raster. O mapeamento de cobertura e uso da terra foi realizado com base na utilização de técnicas de sensoriamento remoto e processamento digital das imagens. O estudo baseou-se no desenvolvimento de diferentes cenários de previsão de comportamento das taxas de erosão do solo, segundo mudanças da cobertura e uso da terra. Tendo em conta este procedimento, o mapeamento da erosão atual e/ou a sua predição no tempo e no espaço, desempenha um papel importante na determinação da variabilidade espacial dos fenômenos, tornando-se uma ferramenta essencial para o planejamento na conservação dos solos. Os resultados obtidos permitem avaliar a aplicabilidade do modelo na modelagem do escoamento superficial e produção de sedimentos em bacias hidrograficas em escala média com predominio de uso agrícola. Esta foi avaliada através dos resultados da simulação em uma escala de tempo mensal e anual sem calibração durante um período de 20 anos de simulação para três cenários de estudo. Foram utilizadas diferentes condições de uso e ocupação do território. A análise dos resultados das simulações no âmbito destes cenários permitiu identificar as mudanças e contrastes que ocorreram e potenciais dos processos hidrosedimentológicos na bacia, o cálculo dos volúmes do fluxo de escoamento e as taxas na produção de sedimentos anual e mensal e a sazonalidade destes processos. Neste sentido se desenvolveu uma metodologia para o cálculo e a avaliação da erosão dos solos por meio da utilização das ferramentas de geoprocessamento. Posteriormente, pretende-se aplicar esta metodologia para outras regiões do país, constituindo-se numa poderosa ferramenta para o prognóstico dos riscos à erosão nas regiões agrícolas de manejo inadequado e também, como instrumento para a tomada de decisões relativas aos impactos da erosão, acelerada pela atividade humana / Abstract: Mathematical models for predicting erosion and sediment production has great potential for use in studies of the degradation and planning of natural resources, that allow estimating impacts caused by agricultural activity. This work deals with the application of the hydrological model SWAT (Soil Water Assessment Tool), in order to forecast the risks of soil erosion due to land use changes, in the Chambas river basin, Ciego de Avila province, Cuba. This model has been successfully used in studies of environmental dynamics in the representation of spatial and temporal variability of hydro-sedimentological processes in watersheds. The method developed for modeling, describes the dynamic in the loss of soil due to runoff produced by water and changes in land use. This allows us to assess the risks in soil erosion, using a model implemented in a Geographic Information System (GIS) raster mode. In determining the mapping of land cover/land use will be use remote sensing techniques and digital image processing. The study is based on the development of different scenarios to predict behavior in the rates of soil erosion when the Land cover /land use change. Taking into account this procedure, the mapping of the existing erosion and/or its prediction in time and space, plays an important role in determining the spatial variability of the phenomena, becoming an essential tool for soils conservation planning. The results obtained allow evaluating the applicability of the model in modeling runoff and sediment yield in medium scale watersheds with predominance of agricultural use. This was evaluated through simulation results on a monthly and annual time scale without calibration over a period of 20 years of simulation to three study scenarios. We used different stages of use and occupation of the territory. The analysis of simulation results under these scenarios, allowed identifying changes and contrasts, occurring and potential in hydro-sedimentological processes in the watershed, the calculation of flow rates of runoff and sediment yield annual and monthly and the seasonality of these processes. In this sense, was developing a methodology that enables using geoprocessing tools for the calculation and assessment of soil erosion and its subsequent generalization to other regions. This provides a powerful tool for forecasting the risk of erosion in agricultural regions, which facilitates decision making about the negative effects of accelerated erosion by human activity / Doutorado / Análise Ambiental e Dinâmica Territorial / Doutor em Geografia

M.Phil. / This work proposes a fast local image feature detector and descriptor that is im- plementable on a GPU. The BFROST feature detector is the first published GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of the orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, the BFROST feature descriptor is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory usage. It is demonstrated that BFROST is usable in real-time applications such as vision-based localization and mapping of images captured from micro aerial platforms.

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013 / Automatic Guided Vehicles (AGVs) are being used more frequently in a manufacturing environment. These AGVs are navigated in many different ways, utilising multiple types of sensors for detecting the environment like distance, obstacles, and a set route. Different algorithms or methods are then used to utilise this environmental information for navigation purposes applied onto the AGV for control purposes. Developing a platform that could be easily reconfigured in alternative route applications utilising vision was one of the aims of the research. In this research such sensors detecting the environment was replaced and/or minimised by the use of a single, omnidirectional Webcam picture stream utilising an own developed mirror and Perspex tube setup. The area of interest in each frame was extracted saving on computational recourses and time. By utilising image processing, the vehicle was navigated on a predetermined route. Different edge detection methods and segmentation methods were investigated on this vision signal for route and sign navigation. Prewitt edge detection was eventually implemented, Hough transfers used for border detection and Kalman filtering for minimising border detected noise for staying on the navigated route. Reconfigurability was added to the route layout by coloured signs incorporated in the navigation process. The result was the manipulation of a number of AGV’s, each on its own designated coloured signed route. This route could be reconfigured by the operator with no programming alteration or intervention. The YCbCr colour space signal was implemented in detecting specific control signs for alternative colour route navigation. The result was used generating commands to control the AGV through serial commands sent on a laptop’s Universal Serial Bus (USB) port with a PIC microcontroller interface board controlling the motors by means of pulse width modulation (PWM). A total MATLAB® software development platform was utilised by implementing written M-files, Simulink® models, masked function blocks and .mat files for sourcing the workspace variables and generating executable files. This continuous development system lends itself to speedy evaluation and implementation of image processing options on the AGV. All the work done in the thesis was validated by simulations using actual data and by physical experimentation.