Submitted by Rafaela Soares Niemann (rafaelaniemann@gmail.com) on 2018-01-30T19:22:21Z No. of bitstreams: 1 dissertacao_rafaela_soares_niemann.pdf: 20839099 bytes, checksum: 3e520cbdddb994f623e86eb596a2eeae (MD5) / Approved for entry into archive by Ana Paula Santulo Custódio de Medeiros null (asantulo@rc.unesp.br) on 2018-01-31T11:10:28Z (GMT) No. of bitstreams: 1 niemann_rs_me_rcla.pdf: 19917697 bytes, checksum: f24581bad9ffd4cf3c683c92a81563b2 (MD5) / Made available in DSpace on 2018-01-31T11:10:28Z (GMT). No. of bitstreams: 1 niemann_rs_me_rcla.pdf: 19917697 bytes, checksum: f24581bad9ffd4cf3c683c92a81563b2 (MD5) Previous issue date: 2017-12-07 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os modelos digitais de elevação são importantes para a geração de informações sobre variáveis ambientais correlacionadas à topografia, principalmente como subsídio à análises geomorfométricas. O sensoriamento remoto pode contribuir com a geração de modelos digitais de elevação, principalmente através do uso de sensores de alta resolução espacial e tecnologias avançadas. Os VANTs – Veículos Aéreos Não Tripulados – tem sido cada vez mais explorados no âmbito da cartografia e topografia, com atuação cada vez mais importante dentro da ciência, devido à capacidade de transportarem diferentes sensores e ao seu baixo custo de operação. Câmeras fotográficas simples acopladas aos VANTs podem ser combinadas com tecnologias de visão computacional, trazendo novas oportunidades para explorar a geração de modelos digitais de elevação. Algoritmos de visão computacional, como o Structure-from-Motion (SfM), permitem a extração de pontos tridimensionais a partir de imagens sobrepostas obtidas por VANTs. Esses pontos compõem nuvens de pontos capazes de subsidiar a geração de modelos digitais de superfície (MDS), quando combinadas com algoritmos de interpolação de dados. Contudo, os modelos gerados desta maneira nos retornam informações relacionadas à superfície dos objetos presentes sobre o terreno, incluindo por exemplo construções e dosséis vegetais. A filtragem e classificação das nuvens de pontos se faz assim necessária para geração de modelos digitais que descrevam mais fielmente a superfície do terreno, sem estes elementos. Nesta dissertação, avaliamos dois métodos para a filtragem e interpolação de modelos digitais de terreno (MDT) a partir de nuvens de pontos geradas por imageamento ótico baseado em VANTs. A área de estudo escolhida foi a região da Serra do Cipó-MG, caracterizada por relevo acidentado e cobertura vegetal variada. O primeiro método consistiu na filtragem (classificação) direta da nuvem de pontos, e o segundo na filtragem do modelo digital de superfície em formato raster, ambos seguidos de interpolação. Os métodos avaliados se mostraram adequados, com coeficientes de determinação da ordem de R² = 0,98 em relação a dados de referência tomados por DGPS. A filtragem foi bastante eficiente para áreas íngremes e com vegetação baixa, e menos eficiente em áreas de vegetação arbórea densa. Os métodos avaliados no presente trabalho contribuirão para a melhoria da geração de MDTs com base na tecnologia emergente oferecida pelos VANTs, que poderão ser utilizados como subsídios a estudos ambientais diversos. / Digital elevation models are important for producing information on different environmental variables correlated to topography, especially for geomorphometric analyses. Remote sensing can contribute to the generation of digital elevation models, mainly through high spatial resolution sensors and advanced technologies. UAVs - Unmanned Aerial Vehicles - have been increasingly employed in the fields of cartography and topography, and have had an increasingly prominent role in science, as they can carry different sensors and have low-cost operation. Simple cameras attached to UAVs can be combined with computer vision technologies, bringing new opportunities to explore the production of digital elevation models. Computer vision algorithms such as Structure-from-Motion (SfM) allow the extraction of threedimensional points from superimposed images obtained by UAVs. These points make up points clouds capable of supporting the production of digital surface models (DSM) when combined with interpolation algorithms. However, models generated this way give us information related to the surface of objects present on the ground, including buildings and plant canopies. Point cloud filtering and classification is thus necessary for producing digital models that more accurately describe the bare terrain surface. In this dissertation, we evaluated two methods for filtering and interpolating digital terrain models (DTM) from point clouds generated by UAVbased optical imaging. The chosen study area was the Serra do Cipó region (Minas Gerais, Brazil), characterized by rugged relief and heterogeneous vegetation cover. The first method consisted of direct filtering (classification) of the point cloud, and the second method was based on filtering the digital surface model in raster format, both followed by interpolation. The evaluated methods were adequate, with determination coefficients of the order of R² = 0.98 in relation to reference data taken by DGPS. Filtering was quite efficient for steep areas with low vegetation, and less efficient in areas of dense arboreal vegetation. The methods evaluated in the present work will contribute to the improvement and generation of DTMs based on the emerging technology offered by UAVs, which can be used as subsidies to diverse environmental studies.

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.

The simple accumulation-based network identification method (ANIM) in a raster Geographic Information System (GIS) posed by O'Callaghan and Mark (1984) has been criticized for producing a spatially uniform drainage density (Tarboton 2002) at the watershed scale. This criticism casts doubt on the use of ANIMs for deriving properties such as overland flow length for nonpoint source pollution models, without calibrating the accumulation threshold value. However, the basic assumption that underlies ANIMs is that convergent topography will yield a more rapid accumulation of cells, and thus, more extensive flow networks, with divergent, or planar terrain yielding sparser networks. Previous studies have focused on networks that are coarser than the hill-slope scale, and have relied upon visual inspection of drainage networks to suggest that ANIMs lack the ability to produce diverse networks. In this study overland flow lengths were calculated on a sub-watershed basis, with standard deviation, and range calculated for sub-watershed populations as a means of quantifying the diversity of overland flow lengths produced by ANIM at the hill slope scale. Linear regression and Spearman ranking analyses were used to determine if the methods represented trends in overland flow length as suggested by manual delineation of contour lines. Three ANIMs were analyzed: the flow accumulation method (O'Callaghan and Mark, 1984), the terrain curvature method (Tarboton, 2000) and the ridge accumulation method (introduced in this study). All three methods were shown to produce non-zero standard deviations and ranges using a single support area threshold, with the terrain curvature method producing the most diverse networks, followed by the ridge accumulation method, and then the flow accumulation method. At an analysis unit size of 20 ha, the terrain curvature method produced a standard deviation that was most similar to those suggested by the contour crenulations, -13.5%, followed by the ridge accumulation method, -21.5%, and the flow accumulation method, -61.6%. The ridge accumulation produced the most similar range, -19.1%, followed by terrain curvature, -24.9%, and flow accumulation, -65.4%. While the flow accumulation networks had a much narrower range of predicted flow lengths, it had the highest Spearman ranking coefficient, Rs=0.722, and linear regression coefficient, R2=0.602. The terrain curvature method was second, Rs=0.641, R2=0.469, and then ridge accumulation, Rs=0.602, R2=0.490. For all methods, as threshold values were varied, areas of dissimilar morphology (as evidenced by the common stream metric stream frequency) experienced changes in overland flow lengths at different rates. This results in an inconsistency in ranking of sub-watersheds at different thresholds. When thresholds were varied to produce average overland flow lengths from 75 m to 150 m, the terrain curvature method showed the lowest incidence of rank change, 16.05%, followed by the ridge accumulation method, 16.73%, then flow accumulation, 25.18%. The results of this investigation suggest that for all three methods, a causal relationship exists between threshold area, underlying morphology, and predicted overland flow length. This causal relationship enables ANIMs to represent contour network trends in overland flow length with a single threshold value, but also results in the introduction of rank change error as threshold values are varied. Calibration of threshold value (varying threshold in order to better match observed overland flow lengths) is an effective means of increasing the accuracy of ANIM predictions, and may be necessary when comparing areas with different stream frequencies. It was shown that the flow accumulation method produces less diverse networks than the terrain curvature and ridge accumulation methods. However, the results of rank and regression analyses suggest that further investigation is required to determine if these more diverse ANIM are in fact more accurate than the flow accumulation method. / Master of Science

Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.

No description available.

No description available.

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2007. / Due to the relative scarcity of weather stations, the climate conditions of large areas are not adequately represented by a weather station. This is especially true for regions with complex topographies or low population densities. Various interpolation techniques and software packages are available with which the climate of such areas can be calculated from surrounding weather stations’ data. This study investigates the possibility of using the software package ANUSPLIN to create accurate climate maps for the Western Cape, South Africa. ANUSPLIN makes use of thin plate smoothing splines and a digital elevation model to convert point data into grid format to represent an area’s climatic conditions. This software has been used successfully throughout the world, therefore a large body of literature is available on the topic, highlighting the limitations and successes of this interpolation method. Various factors have an effect on a region’s climate, the most influential being location (distance from the poles or equator), topography (height above sea level), distance from large water bodies, and other topographical factors such as slope and aspect. Until now latitude, longitude and the elevation of a weather station have most often been used as input variables to create climate grids, but the new version of ANUSPLIN (4.3) makes provision for additional variables. This study investigates the possibility of incorporating the effect of the surrounding oceans and topography (slope and aspect) in the interpolation process in order to create climate grids with a resolution of 90m x 90m. This is done for monthly mean daily maximum and minimum temperature and the mean monthly rainfall for the study area for each month of the year. Not many projects where additional variables have been incorporated in the interpolation process using ANUSPLIN are to be found in the literature, thus further investigation into the correct transformation and the units of these variables had to be done before they could be successfully incorporated. It was found that distance to oceans influences a region’s maximum and minimum temperatures, and to a lesser extent rainfall, while aspect and slope has an influence on a region’s rainfall. In order to assess the accuracy of the interpolation process, two methods were employed, namely statistical values produced during the spline function calculations by ANUSPLIN, and the removal of a selected number of stations in order to compare the interpolated values with the actual measured values. The analysis showed that more accurate maps were obtained when additional variables were incorporated into the interpolation process. Once the best transformations and units were identified for the additional variables, climate maps were produced in order to compare them with existing climate grids available for the study area. In general the temperatures were higher than those of the existing grids. For the rainfall grids ANUSPLIN’s produced higher rainfall values throughout the study region compared to the existing grids, except for the Southwestern Cape where the rainfall values were lower on north-facing slopes and high-lying area

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The requirement for quantitative soil information has increased as a consequence of the global scenarios. The digital soil mapping (DSM) seeks to produce consistent data with the current needs through the generation of functional soil maps in multi-scales. The aim of this study was to evaluate the altimetry quality and limitations of digital elevation model (DEM) in order to assist in choosing the most suitable DEM to derive terrain attributes (TA) to develop spatial prediction functions to be applied to digital mapping of soil organic carbon (SOC) of farm scale in the Plateau of the state of Rio Grande do Sul (RS). The study was conducted on a 937 ha area located in the municipality of Giruá, RS, Brazil. They collected 243 sampling points in the 0-5 cm layer and an accomplished planialtimetric survey (PS) considered in this study the truth of the ground of altitude values. DEM evaluated were: DEM-PS (generated from the PS), DEM-LETTER (generated by interpolating the level of topographic map curves), DEM-ASTER, DEM- SRTM and DEM-TOPODATA. The DEM were evaluated for precision altimetry through statistical test value of the square root of the mean square error (RMSE) and application of the Brazilian Cartographic Standard for defining the scale of each DEM based on the accuracy of the altitude. TA derived from each DEM were faced with the AT derived from the DEM-PS. The results showed that the DEM-PS presented the best quality of elevation data (RMSE = 1.93 m), followed by DEM-SRTM (RMSE = 5.95 m), DEM- (RMSE = 8.28 m), DEM-TOPODATA (RMSE = 9.78 m) and DEM-ASTER (RMSE = 15.57 m). The size of the area and gently rolling relief were the main factors that influenced the results. The DEM-PS is suited in scale 1: 10,000 Class D, while DEM-LETTER and DEM-SRTM are suited in scale 1: 50,000 class B, the DEM-TOPODATA the scale 1: 50,000 class D and the DEM-ASTER scale 1: 100,000 Class B. With regard to TA, the DEM-SRTM and DEM-TOPODATA present results closer to the DEM-PS and smaller RMSE values for each TA assessed. The prediction function constructed from the DEM-PS derived from the TA and vegetation index Landasat-7 obtained images explained only 16% of the variance in SOC area. The small spatial resolution of DEM-PS and images associated with the use only of these predictors were the main factors that influenced the results. Based on these results, the DEM-SRTM and DEM-TOPODATA can be used in DSM semi-detailed soil classes. In the case of the SOC DSM suggest the use of these DEM associated with field control points to verify the precision altimetry and the inclusion of variables related to soil management practices. / A demanda por informações quantitativas de solos em nível detalhado de bacias hidrográficas vêm aumentado em decorrência dos cenários globais. O mapeamento digital de solos (MDS) visa gerar dados compatíveis com essas necessidades por meio da geração de mapas funcionais de solos em multi-escalas. O objetivo desse trabalho foi avaliar a qualidade altimétrica e limitações de MDE com a finalidade de auxiliar na escolha do MDE mais adequados para derivar atributos do terreno (AT) para desenvolver funções de predição espacial para serem aplicadas ao mapeamento digital do carbono orgânico do solo em escala de propriedade rural no Planalto do Estado do Rio Grande do Sul (RS). O estudo foi realizado em uma área de 937 ha localizada no município de Giruá, RS, Brasil. Foram coletados 243 pontos amostrais na camada de 0-5 cm e realizado um levantamento planialtimétrico (LP) considerado neste estudo a verdade do terreno dos valores de altitude. Os MDE avaliados foram: MDE-LP (gerado a partir do LP), MDE-CARTA (gerado pela interpolação das curvas de nível da carta topográfica), MDE-ASTER, MDE-SRTM e MDE-TOPODATA. Os MDE foram avaliados quanto à precisão altimétrica por meio de teste estatísticos, valor da raiz quadrada do erro médio quadrático (RMSE) e aplicação da Norma Brasileira de Cartografia para definição da escala de cada MDE com base na precisão da altitude. Os AT derivados de cada MDE foram confrontados com os AT derivados do MDE-LP. Os resultados mostraram que o MDE-LP apresentou a melhor qualidade dos dados de altitude (RMSE = 1,93 m), seguido dos MDE-SRTM (RMSE = 5,95 m), MDE-CARTA (RMSE = 8,28 m), MDE-TOPODATA (RMSE = 9,78 m) e MDE-ASTER (RMSE = 15,57 m). O tamanho da área e relevo suave ondulado foram os principais fatores que influenciaram nos resultados. O MDE-LP se adequou na escala 1:10.000 classe D, enquanto os MDE-CARTA e MDE-SRTM se adequaram na escala 1:50.000 classe B, o MDE-TOPODATA a escala 1:50.000 classe D e o MDE-ASTER escala 1:100.000 classe B. Em relação aos AT, os MDE- SRTM e MDE-TOPODATA apresentam resultados mais próximos do MDE-LP e os menores valores de RMSE para cada AT avaliado. A função de predição construída a partir dos AT derivados do MDE-LP e índice de vegetação obtido de imagens Landasat-7 explicou apenas 16% da variância do COS na área. A resolução espacial pequena do MDE-LP e das imagens associado ao uso apenas dessas variáveis preditoras foram os principais fatores que influenciaram nos resultados. Com base nesses resultados, os MDE- SRTM e MDE-TOPODATA podem ser utilizados no MDS semidetalhado de classes de solos. No caso do MDS do COS sugere-se o uso desses MDE associado com pontos de controle de campo para verificação da precisão altimétrica e a inclusão de variáveis relacionadas a práticas de manejo do solo.

Abstract Impact cratering is a fundamental geomorphic process on planetary surfaces. More than 60% of known hypervelocity impact craters on Earth are either partially or completely buried beneath post-impact sediments and one-third have been discovered with geophysical methods. In this thesis, geophysical surveys (gravity, magnetics, seismic, bathymetric mapping) were conducted at the deeply buried (>400 m) Holleford impact crater (~2.35 km) and two probable impact structures (Charity Shoal, Skeleton Lake) in southern Ontario, Canada. 3-D potential field models were constructed to determine the subsurface geology and buried crater morphology, and to evaluate evidence for possible impact versus endogenic origins. Holleford Crater is a deeply buried, Late Proterozoic-Early Cambrian (ca. 550 ±100 Ma) simple impact crater (~2.4 km) in southeastern Ontario, Canada. Land-based magnetic and gravity surveys and modelling were conducted in this study, recorded a ~ -3 mGal Bouguer anomaly and small (~30 nT) magnetic anomaly over the crater basin. 3-D gravity modelling revealed a deeply buried simple impact basin in Mesoproterozoic basement with an estimated rim-to-rim diameter (D) of 1.8-2 km, a residual rim height of ~20-30 m and true depth (dt) >400 m. The southeast crater rim is dissected by a 150 m deep, 400 m wide erosional channel produced by fluvial rim dissection. The outflow is infilled by >50 m of Late Cambrian clastic sediments, indicating a probable Late Proterozoic to Early Paleozoic impact event. Charity Shoal is a 1.2-km-diameter, 20 m deep, circular bedrock shoal in eastern Lake Ontario. Marine seismic profiling and total field magnetic surveys (140-line km) were conducted over a 9-km2 area and combined with available multi-beam bathymetric data to evaluate the subsurface geology and structure origin. Seismic surveys revealed ~30 m of Quaternary sediments overlying Middle Ordovician (Trenton Group) carbonates in the central basin and evidence for folding and faulting of the structure rim. Magnetic surveys recorded an annular magnetic high (> 600 nT) and a central magnetic low (~500-600 nT) coincident with a ~-1.7 mGal Bouguer gravity anomaly. The continuity of Middle Ordovician bedrock below the structure rules out a post-Paleozoic intrusion and a pre-Paleozoic intrusion is ruled out with the gravity anomaly. A deeply-buried (> 450 m) impact crater is the only scenario consistent with geophysical evidence. The crater has a rim-to-rim diameter of ~1.2 km, and rim height of ~15-20 m. A 100-m wide breach in the southwestern rim records a possible outflow channel. Skeleton Lake is a suspected (~4.0 km) Paleozoic-age impact structure in Muskoka, Ontario. The lakebed morphology, subsurface structure and possible impact origin were investigated with high-resolution geophysical surveys (magnetics, bathymetry; ~140 line-km) and 3-D magnetic modelling. Bathymetric data reveal a deep (>65 m) central basin with arcuate (Paleozoic?) bedrock ridges that rise >30 m above the southwestern lakebed. Magnetic surveys recorded a >700 nT magnetic low, which truncates northwest-southeast regional magnetic trends. Low-amplitude, northwest-trending magnetic lineaments delineate basement shear zones below the basin centre. Through-going magnetic lineaments and lack of thermal alteration (e.g., dikes, fenitization) in Mesoproterozoic rocks indicate a volcanic origin is unlikely. A 1.2 km diameter volcanic plug with an Early Cambrian remanence (D = 82.2°, I = 82.7°) can reproduce some aspects of the magnetic anomaly but is at odds with the Bouguer gravity anomaly (~ -3 mGal). Forward modelling of a crater-form basin with induction and remanence magnetization yielded an estimated structure depth of ~1200 m. The basement surface model shows a complex basement topography with no apparent rim structure and elevated ‘pinnacles’ that may represent eroded remnants of a central uplift or a highly-dissected basement topography. The structure apparent diameter (> 4.2 km) and complex basement topography suggest a heavily-modified transitional crater, similar with the Gow (Saskatchewan, Canada) and Kärdla (Estonia) impact structures. This thesis demonstrates the subsurface exploration of confirmed and suspected impact structures, integrating seismic, potential field (magnetics, gravity) and digital elevation data within a 3-D geophysical modelling workflow. The approach provides important new insights into the surface and subsurface geology, morphology, and post-emplacement modification of the Holleford impact crater, and new geophysical constraints for evaluating two suspected impact structures. Geophysical data confirm that Charity Shoal and Skeleton Lake are deep-seated, crater-form depressions in Mesoproterozoic basement rocks. The weight of geophysical and geological evidence points to impact cratering processes as opposed to an endogenic (volcanic) origin for both structures. / Thesis / Doctor of Science (PhD)

Topographic depressions are naturally occurring low land areas surrounded by areas of high elevations, also known as “pits” or “sinks”, on terrain surfaces. Traditional watershed modeling often neglects the potential effects of depressions by implementing removal (mostly filling) procedures on the digital elevation model (DEM) prior to the simulation of physical processes. The assumption is that all the depressions are either spurious in the DEM or of negligible importance for modeling results. However, studies suggested that naturally occurring depressions can change runoff response and connectivity in a watershed based on storage conditions and their spatial arrangement, e.g., shift active contributing areas and soil moisture distributions, and timing and magnitude of flow discharge at the watershed outlet. In addition, recent advances in remote sensing techniques, such as LiDAR, allow us to examine this modeling assumption because naturally occurring depressions can be represented using high-resolution DEM. This dissertation provides insights on the effects of depressions on overland flow processes at multiple spatial scales, from internal depression areas to the watershed scale, based on hydrologic connectivity metrics. Connectivity describes flow pathway connectedness and is assessed using geostatistical measures of heterogeneity in overland flow patterns, i.e., connectivity function and integral connectivity scale lengths. A new algorithm is introduced here to upscale connectivity metrics to large gridded patterns (i.e., with > 1,000,000 cells) using GPU-accelerated computing. This new algorithm is sensitive to changes of connectivity directions and magnitudes in spatial patterns and is robust for large DEM grids with depressions. Implementation of the connectivity metrics to overland flow patterns generated from original and depression filled DEMs for a study watershed indicates that depressions typically decrease overland flow connectivity. A series of macro connectivity stages based on spatial distances are identified, which represent changes in the interaction mechanisms between overland flow and depressions, i.e., the relative dominance of fill and spill, and the relative speed of fill and formation of connected pathways. In addition, to study the role of spatial resolutions on such interaction mechanisms at watershed scale, two revised functional connectivity metrics are also introduced, based on depressions that are hydraulically connected to the watershed outlet and runoff response to rainfall. These two functional connectivity metrics are sensitive to connectivity changes in overland flow patterns because of depression removal (filling) for DEMs at different grid resolutions. Results show that these two metrics indicate the spatial and statistical characteristics of depressions and their implications on overland flow connectivity, and may also relate to storage and infiltration conditions. In addition, grid resolutions have a more significant impact on overland flow connectivity than depression removal (filling).