Submitted by William Justo Figueiro (williamjf) on 2015-07-01T23:37:49Z No. of bitstreams: 1 10.pdf: 1839460 bytes, checksum: 72eb68f5839e6788cf3f08b69ca3d26c (MD5) / Made available in DSpace on 2015-07-01T23:37:49Z (GMT). No. of bitstreams: 1 10.pdf: 1839460 bytes, checksum: 72eb68f5839e6788cf3f08b69ca3d26c (MD5) Previous issue date: 2011 / FAPERGS - Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul / O sistema LIDAR obtém nuvens de pontos georreferenciadas que podem ser convertidas em Modelos Digitais de Afloramentos (MDAs). Os pulsos de laser são gerados e emitidos por um Laser Scanner Terrestre, que atinge a superfície do afloramento em diferentes pontos. Estes objetos refletem o pulso incidente, que volta para o equipamento. Com isso, a distância entre o sensor e o objeto é determinada com base no intervalo de tempo entre a emissão e o retorno do pulso. Para fins de Modelagem Digital de Afloramentos (MDA) o uso dessa técnica é recente e necessita do desenvolvimento de pesquisas. Diante disso, o objetivo desse trabalho foi estabelecer uma sequencia de métodos envolvendo a aquisição e processamento de nuvem de pontos e a visualização e interpretação de superfícies e volumes de um Modelo Digital de Afloramentos (MDA). A Rocha da Pedra Pintada, localizado no Rio Grande do Sul na Cidade de Caçapava do Sul, foi imageado a partir de 7 estações e a nuvem totalizou 17 milhões de pontos, que foi convertida, após processamento, em um MDA. Para a interpretação geológica, utilizou-se a técnica de ortorretificação para sobrepor a nuvem de pontos a fotografias de alta resolução do afloramento, o que agregou qualidade na visualização e interpretação do MDA. Dificuldades nas etapas de tratamento dos dados ocorreram em razão do grande volume de dados, da ausência de recursos de otimização de processamento e da limitação no gerenciamento de banco de dados. Além disso, faz-se necessário desenvolver um aplicativo eficiente de visualização tridimensional com ferramentas especificas de interpretação geológica. / The LIDAR system provides georeferrenced clouds with thousand-to-million of points which can be converted in digital outcrops models. A laser beam is emitted and captured by a laser scanner after reaching an outcrop in many different positions. Based on the travel time between sensor and outcrop it is possible to determine the position of each point with high accuracy. This technique is still a novelty for applied studies in Geology, especially in Digital Outcrop Models (DOMs), being necessary research and development. Thus, the goal of this work was establish a workflow concerning acquisition and processing of point clouds, and visualization and geological interpretation of DOMs. The Pedra Pintada, located in the state Rio Grande do Sul in the city Caçapava do Sul outcrop was imaged from seven different stations and the cloud has 17 million points, converted in a DOM after processing. The geological interpretation was made possible by the orthorectification technique, in which a high resolution photograph overlies the point cloud and the visual quality is obtained. The huge volume of data, the lack of optimized processing resources and the inadequate dataset management became visualization and interpretation of DOMs a difficult task. Furthermore, it is necessary to develop a software with an efficient tridimensional visualization system with specific tools for geological interpretations.

The use of Light Detection and Ranging (LiDAR) information is gaining popularity, however its use has been limited in deciduous forests. This thesis describes two studies using LiDAR data in an Eastern Kentucky deciduous forest. The first study quantifies vertical error of LiDAR derived digital elevation models (DEMs) which describe the forests terrain. The study uses a new method which eliminates Global Positioning System (GPS) error. The study found that slope and slope variability both significantly affect DEM error and should be taken in to account when using LiDAR derived DEMs. The second study uses LiDAR derived forest vegetation and terrain metrics to predict terrestrial Plethodontid salamander abundance across the forest. This study used night time visual encounter surveys coupled with zero-inflation modeling to predict salamander abundance based on environmental covariates. We focused on two salamander species, Plethodon glutinosus and Plethodon kentucki. Our methods produced two different best fit models for the two species. Plethodon glutinosus included vegetation height standard deviation and water flow accumulation covariates, while Plethodon kentucki included only canopy cover as a covariate. These methods are applicable to many different species and can be very useful for focusing management efforts and understanding species distributions across the landscape.

O trabalho se estruturou em torno de dois estudos. O primeiro avaliou o ajuste de um modelo não linear de efeito misto para descrever o afilamento do tronco de árvores clonais de eucalipto. O modelo utilizado para descrever as variações da altura em função do raio foi o logístico de quatro parâmetros que, por integração permitiu a estimação do volume das árvores. A incorporação de funções de variância no processo de ajuste resultou em redução significativa no valor do Critério de informação de Akaike, mas os resíduos não apresentaram melhorias notáveis. Com a finalidade de compatibilizar precisão e parcimônia, o modelo que considera as variações do afilamento como uma função da altura total e do raio à altura do peito mostrou-se como o mais indicado para a estimativa do volume de árvores por funções de afilamento. O segundo estudo analisou uma nova proposta para inventários florestais em plantios clonais de eucalipto que integra modelagem geoestatística, medições de circunferência das árvores em campo e a tecnologia LiDAR aeroembarcada. As estatísticas propostas mostraram que o modelo geoestatístico com função para média foi estatisticamente superior ao modelo com média constante, com erros reduzidos em até 40%. A altura das árvores que compuseram o grid de predição para aplicação do modelo geoestatístico foi obtida pelo processamento da nuvem de pontos dos dados LiDAR. Obtidos os pares de diâmetro e altura, aplicou-se o modelo de afilamento selecionado no primeiro artigo em que se observaram diferenças médias na predição do volume próximas a 0,7%, e 0,18% para contagem de árvores, ambas com tendências de subestimativas. Diante dos resultados obtidos, o método é considerado como promissor e trabalhos futuros visam gerar um banco de parcelas permanentes que propiciem estudos de crescimento e produção florestal. / This study investigates the use of mixed-effect model and the use of LiDAR based model to estimate volume from eucalyptus forest plantation. At the first part, this study evaluates nonlinear mixed-effects to model stem taper of monoclonal Eucalyptus trees. The relation between radius and height variation was described by the four-parameter logistic model that integration returns stem volume. Embedding variance functions to the estimation process decreased significantly the Akaike\'s Information Criterion but did not improve the residual analysis. The best model to estimate stem volume from taper equations explained the stem taper as a function of the commercial height and the radius at breast height. The second part investigated the volume estimation fusing geostatistic derived from field information and airborne laser scanning data. The model based on geostatistic assumptions was statistically superior to the traditional one, with errors 40% lower. Thus, the geostatistical model was applied over tree heights extracted from the laser cloud. To each combination of diameter and height, the taper equation form the first part of this study was used. The volume and the number of trees were underestimated in 0.7% and 0.18%, respectively. The results look promising, and more permanent plots are necessary to allow studies about growth and yield of forest.

As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.

Submitted by William Justo Figueiro (williamjf) on 2015-07-09T22:32:57Z No. of bitstreams: 1 27b.pdf: 4120563 bytes, checksum: 28666d8a39aa4371e2cad8353a3b6fc2 (MD5) / Made available in DSpace on 2015-07-09T22:32:57Z (GMT). No. of bitstreams: 1 27b.pdf: 4120563 bytes, checksum: 28666d8a39aa4371e2cad8353a3b6fc2 (MD5) Previous issue date: 2012-08-01 / Petrobras - Petróleo Brasileiro S. A. / UNISINOS - Universidade do Vale do Rio dos Sinos / O presente estudo visou o desenvolvimento e aplicação de uma metodologia para a detecção e classificação de diferentes respostas espectrais em nuvens de pontos obtidas a partir de escâner a laser terrestre (Laser Scanner Terrestre) com o intuito de identificar a presença de diferentes rochas em afloramentos e a geração de um Modelo Digital de Afloramento. A ferramenta para a classificação de padrões espectrais, denominada K-Clouds, foi desenvolvida com base em análise de agrupamentos (clusters), que a partir de uma indicação do número de classes fornecido pelo usuário através da análise de um histograma dos dados, realiza a classificação da nuvem de pontos. Os dados classificados podem então ser interpretados por geólogos para uma melhor compreensão e identificação das rochas presentes no afloramento. Além da detecção de diferentes rochas, verificouse que é possível detectar pequenas alterações nas características físico-químicas das mesmas, como aquelas causadas por intemperismo e variação composicional. / The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from Terrestrial Laser Scanner in order to identify the presence of different rocks in outcrops and to generate a Digital Outcrop Model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to, beginning with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

This work addresses the deep-learning-based 6 Degree-of-Freedom (DoF) pose estimation utilizing 3D cameras on an iPhone 13 Pro. The task of pose estimation is to estimate the spatial rotation and translation of an object given its 2D or 3D images. During the pose estimation network training process, a common way to expand the training dataset is to generate synthetic images, which requires the 3D mesh of the target object. Although several famous datasets provide the 3D object files, it is still a problem when one wants to generate a customized real-world object. The typical 3D scanners are mainly designed for industrial usage and are usually expensive. We investigated in this project whether the 3D cameras on Apple devices can replace the industrial 3D scanners in the pose estimation pipeline and what might influence the results during scanning. During the data synthesis, we introduced a pose sampling method to equally sample on a sphere. Random transformation and background images from the SUN2012 dataset are applied, and the synthetic image is rendered through Blender. We picked five testing objects with different sizes and surfaces. Each object is scanned both by front TrueDepth camera and rear Light Detection and Ranging (LiDAR) camera with the ‘3d Scanner App’ on iOS. The network we used is based on PVNet, which uses a pixel-wise voting scheme to find 2D keypoints on RGB images and utilizes uncertainty-driven Perspective-n-Point (PnP) to compute the pose. We achieved both quantitative and qualitative results for each instance. i) TrueDepth camera outperforms Light Detection and Ranging (LiDAR) camera in most scenarios, ii) when an object has less reflective surface and high-contrast texture, the advantage of TrueDepth is more obvious. We also picked three baseline objects from Linemod dataset. Although the average accuracy is lower than the original paper, the performance of our baseline instances shows a similar trend to the original paper’s results. In conclusion, we proved that the 3D cameras on iPhone are capable of the pose estimation pipeline. / Detta arbete tar upp den djupinlärningsbaserade 6 Degree-of-Freedom (DoF) poseringsuppskattning med 3D-kameror på en iPhone 13 Pro. Uppgiften med poseuppskattning är att uppskatta den rumsliga rotationen och translationen av ett objekt givet dess 2D- eller 3D-bilder. Ett vanligt sätt att utöka träningsdataup- psättningen under träningsprocessen för positionsuppskattning är att generera syntetiska bilder, vilket kräver 3D-nätet för målobjektet. Även om flera kända datamängder tillhandahåller 3D-objektfilerna, är det fortfarande ett problem när man vill generera ett anpassat verkligt objekt. De typiska 3D-skannrarna är främst designade för industriell användning och är vanligtvis dyra. Vi undersökte i detta projekt om 3D-kamerorna på Apple-enheter kan ersätta de industriella 3D-skannrarna i poseskattningspipelinen och vad som kan påverka resultaten under skanning. Under datasyntesen introducerade vi en posesamplingsmetod för att sampla lika mycket på en sfär. Slumpmässig transformation och bakgrundsbilder från SUN2012-datauppsättningen tillämpas, och den syntetiska bilden renderas genom Blender. Vi valde ut fem testobjekt med olika storlekar och ytor. Varje objekt skannas både av den främre TrueDepth-kameran och den bakre ljusdetektions- och avståndskameran (LiDAR) med "3d-skannerappenpå iOS. Nätverket vi använde är baserat på PVNet, som använder ett pixelvis röstningsschema för att hitta 2D-nyckelpunkter på RGB-bilder och använder osäkerhetsdrivet Perspective-n-Point (PnP) för att beräkna poseringen. Vi uppnådde både kvantitativa och kvalitativa resultat för varje instans. i) TrueDepth-kameran överträffar Light Detection and Ranging-kameran (LiDAR) i de flesta scenarier, ii) när ett objekt har mindre reflekterande yta och högkontraststruktur är fördelen med TrueDepth högre. Vi valde också tre baslinjeobjekt från Linemod dataset. Även om den genomsnittliga noggrannheten är lägre än originalpapperet, visar prestandan för våra baslinjeinstanser en liknande trend som originalpapperets resultat. Sammanfattningsvis bevisade vi att 3D-kamerorna på iPhone är kapabla att göra positionsuppskattning.

Flooding is a worldwide problem that every year causes substantial damage for the environment and stakeholders nearby, and this impact relates to several of the United Nations Sustainable Development Goals. Colombia is specially prone to flooding as 17% of its surface area is at risk of extreme flooding. In addition, there is something called a POT (plan de ordenamiento territorial) for every municipality in Colombia, which states how the territory should be managed. For this project the rivers were of particular interest, and the POT states that no temporary or permanent constructions are allowed within 30 meters on either side of a river. The purpose of this report was to investigate and analyze the possibilities of using UAV (unmanned aerial vehicle) -based photogrammetry and UAV-based LiDAR (light detection and ranging) technology to gather sufficient data for a model that could simulate different flooding scenarios in the examined area. Data from the UAV-based photogrammetry resulted in a complete visual overview of the examined area. The data gathered from the UAV-based light detection and ranging resulted in an accurate point cloud that could be processed into a DTM (digital terrain model) where three different flooding scenarios were simulated. The simulations and the visual model showed that majority of people in theexamined area were disobeying the POT and the 30 meter rule, therefore being in risk of flooding and impacting the natural diversity of the body of water. The simulation also showed that stakeholders close to the body of water were affected for each of the three different water level scenarios. In some cases, it was only vegetation and crops that got affected by the flooding scenario, while in other cases entire structures and buildings were damaged due to the increase of water level. To complement the flooding scenarios, interviews were conducted with people that have good knowledge of the area and of ecology, resulting in a stakeholder analysis. This provided an additional depth to the analysis and showed the complexity in the management of flooding in the area.

Tesis por compendio / [ES] Los humedales son uno de los ecosistemas que reciben mayor atención por parte de la comunidad científica. Su importancia se puede explicar teniendo en cuenta que ayudan a mitigar los efectos de inundaciones, pueden actuar como filtros de agua y constituyen hábitats de valiosas especies de fauna y flora. En los últimos años este tipo de ambientes están cada vez más amenazados como consecuencia de la contaminación, alteraciones de los niveles de agua asociadas a los efectos del cambio climático y usos antrópicos, introducción de especies invasoras y los efectos negativos de algunos cambios de usos del suelo y determinadas prácticas asociados a ellos (agrícolas, pastoreo y urbanización). Para analizar estos ecosistemas, la teledetección se presenta como una herramienta con alto potencial que permite identificar, evaluar y monitorizar estos espacios. En esta tesis se utilizaron imágenes Sentinel-2A/B, Landsat-5 TM y Landsat-8 OLI para extraer láminas de agua libre en dos entornos: humedales mediterráneos costeros (Prat Cabanes-Torreblanca, Marjal de Sagunto, Marjal de La Safor y Marjal Pego-Oliva) y el humedal de llanura aluvial situado en el centro de España las Tablas de Daimiel. Para ello, se realizó un análisis de siete índices de agua (NDWI, mNDWI, Cedex, Re-NDWI, Awei(sh), AWEI (nsh) y B_Blue) y de umbrales para obtener la cartografía de las masas de agua libre en estos espacios. El objetivo principal es definir el índice y el umbral que permitan un uso más amplio de la metodología para su aplicación en otras zonas húmedas. También se utilizó información LiDAR (Laser Imaging Detection and Ranging) en el humedal de La Safor para analizar los efectos de la superficie inundada en diferentes usos del suelo. La evaluación de los resultados se realizó a partir de la validación con un conjunto de muestras obtenidas a partir de imágenes de elevada resolución espacial. Se calcularon la fiabilidad global y el índice kappa en los humedales analizados para distintas fechas y sensores. En el caso de los humedales costeros, el índice de agua NDWI con un umbral de ¿0,30 proporcionó los resultados con mayor precisión obteniendo un valor promedio de 0,89 en fiabilidad global. En el caso del humedal de las Tablas de Daimiel, se seleccionaron el índice MNDWI y umbral ¿0,15 para imágenes Landsat-5 (fiabilidad global 0,88), el índice MNDWI y umbral ¿0,25 para imágenes Landsat-8 (fiabilidad global 0,99) y el índice NDWI y umbral ¿0,20 (fiabilidad global 0,99) en el caso de imágenes Sentinel-2A/B. En el humedal de las Tablas de Daimiel se realizó un análisis temporal desde el año 2000 al 2021 que permitió calcular las anomalías de la superficie de agua, de la precipitación, del nivel piezométrico y del caudal hidrológico. Esta reconstrucción temporal también permitió comparar los resultados derivados a partir de las imágenes Sentinel-2A/B y de las imágenes Landsat 8. Se realizó un análisis de correlación entre los índices de anomalías calculados, que revela una correlación no significativa entre las anomalías precipitación y de superficie de agua. Mientras que el índice de anomalías de superficie de agua si presentó una correlación estadísticamente significativa con los índices de anomalía de caudal y de niveles piezométricos. En cuanto al análisis comparativo entre las imágenes Landsat-8 y Sentinel-2 se obtuvo una relación lineal entre la superficie de agua estimada por ambos sensores con un valor de R2 = 0,87. No obstante, la mayor resolución espacial de Sentinel-2 permite detectar masas de agua más pequeñas contribuyendo a un mejor análisis de los patrones de variabilidad en el área de estudio. La información derivada de esta tesis presenta una aplicabilidad de interés medioambiental para el seguimiento del estado de los humedales ayudando a adaptar planes de gestión que conduzcan a un estado de conservación adecuado. / [CA] Els aiguamolls són un dels ecosistemes que reben major atenció per part de la comunitat científica. La seua importància es pot explicar tenint en compte que ajuden a mitigar els efectes d'inundacions, poden actuar com a filtres d'aigua i constitueixen hàbitats de valuoses espècies de fauna i flora. En els últims anys aquest tipus d'ambients estan cada vegada més amenaçats a conseqüència de la contaminació, alteracions dels nivells d'aigua associades a l'efecte del canvi climàtic i usos antròpics, introducció d'espècies invasores i els efectes negatius d'alguns canvis d'usos del sòl i determinades pràctiques associades a ells (agrícoles, pasturatge i urbanització). Per a analitzar aquests ecosistemes, la teledetecció es presenta com una eina amb alt potencial que permet identificar, avaluar i monitorar aquests espais. En aquesta tesi es van utilitzar imatges Sentinel-2A/B, Landsat-5 TM i Landsat-8 OLI per a extraure làmines d'aigua lliure en dos entorns: aiguamolls mediterranis costaners (Prat Cabanes-Torreblanca, Marjal de Sagunt, Marjal de La Safor i Marjal Pego-Oliva) i l'aiguamoll de plana al·luvial situat en el centre d'Espanya les Taules de Daimiel. Per a això, es va realitzar una anàlisi de set índexs d'aigua (NDWI, mNDWI, Cedex, Re-NDWI, Awei (sh), AWEI (nsh) i B_Blue) i de llindars per a obtindre la cartografia de les masses d'aigua lliure en aquests espais. L'objectiu principal és definir l'índex i el llindar que permeten un ús més ampli de la metodologia per a la seua aplicació en altres zones humides. També es va utilitzar informació LiDAR (Laser Imaging Detection and Ranging) en l'aiguamoll de La Safor per a analitzar els efectes de la superfície inundada en diferents usos del sòl. L'avaluació dels resultats es va realitzar a partir de la validació amb un conjunt de mostres obtingudes a partir d'imatges d'elevada resolució espacial. Es van calcular la fiabilitat global i l'índex kappa en els aiguamolls analitzats per a diferents dates i sensors. En el cas dels aiguamolls costaners, l'índex d'aigua NDWI amb un llindar de ¿0,30 va proporcionar els resultats amb major precisió obtenint un valor mitjà de 0,89 en fiabilitat global. En el cas de l'aiguamoll de les Taules de Daimiel, es van seleccionar l'índex MNDWI i llindar ¿0,15 per a imatges Landsat-5 (fiabilitat global 0,88), l'índex MNDWI i llindar ¿0,25 per a imatges Landsat-8 (fiabilitat global 0,99) i l'índex NDWI i llindar ¿0,20 (fiabilitat global 0,99) en el cas d'imatges Sentinel-2A/B. En l'aiguamoll de les Taules de Daimiel es va realitzar una anàlisi temporal des de l'any 2000 al 2021 que va permetre calcular les anomalies de la superfície d'aigua, de la precipitació, del nivell piezomètric i del cabal hidrològic. Aquesta reconstrucció temporal també va permetre comparar els resultats derivats a partir de les imatges Sentinel-2A/B i de les imatges Landsat 8. Es va realitzar una anàlisi de correlació entre els índexs d'anomalies calculats, que revela una correlació no significativa entre les anomalies precipitació i de superfície d'aigua. Mentre que l'índex d'anomalies de superfície d'aigua si va presentar una correlació estadísticament significativa amb els índexs d'anomalia de cabal i de nivells piezomètrics. Quant a l'anàlisi comparativa entre les imatges Landsat-8 i Sentinel-2 es va obtindre una relació lineal entre la superfície d'aigua estimada per tots dos sensors amb un valor de R2 = 0,87. No obstant això, la major resolució espacial de Sentinel-2 permet detectar masses d'aigua de menor grandària contribuint a una millor anàlisi dels patrons de variabilitat en l'àrea d'estudi. La informació derivada d'aquesta tesi presenta una aplicabilitat d'interés mediambiental per al seguiment de l'estat dels aiguamolls ajudant a adaptar plans de gestió que condueixen a un estat de conservació adequat. / [EN] Wetlands are one of the ecosystems that receive the most attention from the scientific community. Their importance can be explained by the fact that they help mitigate the effects of flooding, can act as water filters, and provide habitats for valuable species of fauna and flora. In recent years, these types of environments are increasingly threatened as a result of pollution, alterations in water levels associated with the effects of climate change and anthropic uses, the introduction of invasive species and the negative effects of some changes in land use and certain practices associated with them (agriculture, grazing and urbanization). To analyze these ecosystems, remote sensing is presented as a tool with high potential to identify, evaluate and monitor these areas. In this thesis, Sentinel-2A/B, Landsat-5 TM and Landsat-8 OLI images were used to extract free water bodies in two environments: coastal Mediterranean wetlands (Prat Cabanes-Torreblanca, Marjal de Sagunto, Marjal de La Safor and Marjal Pego-Oliva) and the alluvial plain wetland located in the center of Spain, the Tablas de Daimiel. For this purpose, an analysis of seven water indices (NDWI, mNDWI, Cedex, Re-NDWI, Awei (sh), AWEI (nsh) and B_Blue) and thresholds were carried out to obtain the mapping of free water bodies in these areas. The main objective is to define the index and threshold that allow a wider use of the methodology for its application in other wetlands. LiDAR (Laser Imaging Detection and Ranging) information was also used in La Safor wetland to analyze the effects of the flooded surface on different land uses. The evaluation of the results was carried out based on the validation with a set of samples obtained from high spatial resolution images. The overall accuracy and the kappa index were calculated for the wetlands analyzed for different dates and sensors. In the case of the coastal wetlands, the NDWI water index with a threshold of ¿0.30 provided the most accurate results with an average value of 0.89 in global accuracy. In the case of Las Tablas de Daimiel wetland, the MNDWI index and threshold ¿0.15 were selected for Landsat-5 images (overall accuracy 0.88), the MNDWI index and threshold ¿0.25 for Landsat-8 images (overall accuracy 0.99) and the NDWI index and threshold ¿0.20 (overall accuracy 0.99) in the case of Sentinel-2A/B images. In Las Tablas de Daimiel wetland, a temporal analysis was carried out from 2000 to 2021 to calculate the anomalies of the water surface, precipitation, piezometric level and hydrological flow. This temporal reconstruction also made it possible to compare the results derived from Sentinel-2A/B images and Landsat 8 images. A correlation analysis was performed between the calculated anomaly indices, which revealed a non-significant correlation between the precipitation and water surface anomalies. However, the water surface anomaly index did show a statistically significant correlation with the flow anomaly and piezometric level indexes. As for the comparative analysis between Landsat-8 and Sentinel-2 images, a linear relationship was obtained between the water surface estimated by both sensors with a value of R2 = 0.87. However, the higher spatial resolution of Sentinel-2 allows the detection of smaller water masses contributing to a better analysis of the variability patterns in the study area. The information derived from this thesis presents an application of environmental interest for monitoring the state of wetlands helping to adapt management plans that lead to an adequate conservation status. / Pena Regueiro, J. (2023). Monitorización de cambios en la lámina libre de agua en humedales mediante teledetección [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196109 / Compendio

This study explores the utilisation of spatial technologies as a tool to analyse and combat crime. The study deals specifically with remote sensing and its potential for being integrated with geographical information systems (GIS). The integrated spatial approach resulted in the understanding of land use class behaviour over time and its relationship to specific crime incidents per police precinct area. The incorporation of spatial technologies to test criminological theories in practice, such as the ecological theories of criminology, provides the science with strategic value. It proves the value of combining multi-disciplinary scientific fields to create a more advanced platform to understand land use behaviour and its relationship to crime. Crime in South Africa is a serious concern and it impacts negatively on so many lives. The fear of crime, the loss of life, the socio-economic impact of crime, etc. create the impression that the battle against crime has been lost. The limited knowledge base within the law enforcement agencies, limited logistical resources and low retention rate of critical staff all contribute to making the reduction of crime more difficult to achieve. A practical procedure of using remote sensing technology integrated with geographical information systems (GIS), overlaid with geo-coded crime data to provide a spatial technological basis to analyse and combat crime, is illustrated by a practical study of the Tshwane municipality area. The methodology applied in this study required multi-skilled resources incorporating GIS and the understanding of crime to integrate the diverse scientific fields into a consolidated process that can contribute to the combating of crime in general. The existence of informal settlement areas in South Africa stresses the socio-economic problems that need to be addressed as there is a clear correlation of land use data with serious crime incidents in these areas. The fact that no formal cadastre exists for these areas, combined with a great diversity in densification and growth of the periphery, makes analysis very difficult without remote sensing imagery. Revisits over time to assess changes in these areas in order to adapt policing strategies will create an improved information layer for responding to crime. Final computerised maps generated from remote sensing and GIS layers are not the only information that can be used to prevent and combat crime. An important recipe for ultimately successfully managing and controlling crime in South Africa is to strategically combine training of the law enforcement agencies in the use of spatial information with police science. The researcher concludes with the hope that this study will contribute to the improved utilisation of spatial technology to analyse and combat crime in South Africa. The ultimate vision is the expansion of the science of criminology by adding an advanced spatial technology module to its curriculum. / Criminology / D.Litt. et Phil. (Criminology)

This study explores the utilisation of spatial technologies as a tool to analyse and combat crime. The study deals specifically with remote sensing and its potential for being integrated with geographical information systems (GIS). The integrated spatial approach resulted in the understanding of land use class behaviour over time and its relationship to specific crime incidents per police precinct area. The incorporation of spatial technologies to test criminological theories in practice, such as the ecological theories of criminology, provides the science with strategic value. It proves the value of combining multi-disciplinary scientific fields to create a more advanced platform to understand land use behaviour and its relationship to crime. Crime in South Africa is a serious concern and it impacts negatively on so many lives. The fear of crime, the loss of life, the socio-economic impact of crime, etc. create the impression that the battle against crime has been lost. The limited knowledge base within the law enforcement agencies, limited logistical resources and low retention rate of critical staff all contribute to making the reduction of crime more difficult to achieve. A practical procedure of using remote sensing technology integrated with geographical information systems (GIS), overlaid with geo-coded crime data to provide a spatial technological basis to analyse and combat crime, is illustrated by a practical study of the Tshwane municipality area. The methodology applied in this study required multi-skilled resources incorporating GIS and the understanding of crime to integrate the diverse scientific fields into a consolidated process that can contribute to the combating of crime in general. The existence of informal settlement areas in South Africa stresses the socio-economic problems that need to be addressed as there is a clear correlation of land use data with serious crime incidents in these areas. The fact that no formal cadastre exists for these areas, combined with a great diversity in densification and growth of the periphery, makes analysis very difficult without remote sensing imagery. Revisits over time to assess changes in these areas in order to adapt policing strategies will create an improved information layer for responding to crime. Final computerised maps generated from remote sensing and GIS layers are not the only information that can be used to prevent and combat crime. An important recipe for ultimately successfully managing and controlling crime in South Africa is to strategically combine training of the law enforcement agencies in the use of spatial information with police science. The researcher concludes with the hope that this study will contribute to the improved utilisation of spatial technology to analyse and combat crime in South Africa. The ultimate vision is the expansion of the science of criminology by adding an advanced spatial technology module to its curriculum. / Criminology and Security Science / D.Litt. et Phil. (Criminology)