The popularity of using UAV in image-taking for the production of 3D models and orthophotos has increased over time. Karlskoga Municipality has recently acquired an UAV to produce their own 3D models and orthophotos. This project paper aims to study the geospatial accuracy of the orthophotos and DEM files produced using the images taken with their UAV. The flight takes only a few minutes but a considerable time is spent in the production processes. Difficulty is experienced in determining the right center point for most GCPs. Produced orthophotos in the software Photoscan have a resolution from 1.7 to 2.4 centimeters while DEM files have a resolution from 3.4 to 4.8 centimeters. Four orthophotos and four DEM files are produced where GCPs are used and not used and at two different flight heights, 76 and 105 meters. The spatial data of the ten GCPs are identified on the orthophotos and DEM files in ArcMap and compared with GNSS NRTK measurements and Lantmäteriet's data. A visual control in terms of completeness of data, alignment, residual tilt and scale is also done. Standard deviations in plane for orthophotos there GCPs are not used are greater than 2 meters, while there GCPs are used are around 0.7 meters. Standard deviations for DEM files are observed at 0.8 meters.

The forest industry needs an up-to-date overview of certain areas of a forest, to either estimate damages after a storm or assess its overall health. Today, the use of unmanned aerial vehicles (UAV) have exploded. Almost anyone can own one and they are very easy to operate. They are often equipped with accurate sensors and cameras that can be used for several productive applications. This paper investigates if a UAV equipped with positional sensors and a high resolution camera can be used for taking aerial photographs and together with a mobile devices create a coherent orthophoto in real- or near real-time. Three different seam stitching algorithms are tested and evaluated based on speed, accuracy and visual appearance. The results of using a DJI Matrice 100 UAV together with an iPad Air is presented both qualitative and quantitative.

Image classification based on remotely sensed data is the primary domain of automatic mapping research. With the increasing of urban development, keeping geographic database updating is imminently needed. Automatic mapping of land cover types in urban area is one of the most challenging problems in remote sensing. Traditional database updating is time consuming and costly. It has usually been performed by manual observation and visual interpretation, In order to improve the efficiency as well as the accuracy, new technique in the data collection and extraction becomes increasingly necessary. This paper studied an object-based decision tree classification based on orthophoto and lidar data, both alone and integrated. Four land cover types i.e. Forest, Water, Openland as well as Building were successfully extracted. Promising results were obtained with the 89.2% accuracy of orthophoto based classification and 88.6% accuracy of lidar data based classification. Both lidar data and orthophoto showed enough capacity to classify general land cover types alone. Meanwhile, the combination of orthophoto and lidar data demonstrated a prominent classification results with 95.2% accuracy. The results of integrated data revealed a very high agreement. Comparing the process of using orthophoto or lidar data alone, it reduced the complexity of land cover type discrimination. In addition, another classification algorithm, support vector machines (SVM) classification was preformed. Comparing to the decision tree classification, it obtained the same accuracy level as decision tree classification in orthophoto dataset (89.2%) and integration dataset (97.3%). However, the SVM results of lidar dataset was not satisfactory. Its overall accuracy only reached 77.1%. In brief, object-based land cover classification demonstrated its effectiveness in land cover map generation. It could exploit spectral and spatial features from input data efficiently and classifying image with high accuracy.

There is currently no effective tool available to quickly and economically measure a change in landmass in the setting of biomedical professionals and environmental specialists. The purpose of this study is to structure and demonstrate a statistical change-detection method using remotely sensed data that can detect the presence of an infectious land borne disease. Data sources included the Texas Department of Health database, which provided the types of infectious land borne diseases and indicated the geographical area to study. Methods of data collection included the gathering of images produced by digital orthophoto quadrangle and aerial videography and Landsat. Also, a method was developed to identify statistically the severity of changes of the landmass over a three-year period. Data analysis included using a unique statistical detection procedure to measure the severity of change in landmass when a disease was not present and when the disease was present. The statistical detection method was applied to two different remotely sensed platform types and again to two like remotely sensed platform types. The results indicated that when the statistical change detection method was used for two different types of remote sensing mediums (i.e.-digital orthophoto quadrangle and aerial videography), the results were negative due to skewed and unreliable data. However, when two like remote sensing mediums were used (i.e.- videography to videography and Landsat to Landsat) the results were positive and the data were reliable.

Submitted by HENRIQUE CANDIDO DE OLIVEIRA null (henrique.cartografia@gmail.com) on 2016-06-20T14:13:55Z No. of bitstreams: 1 [2016]OLIVEIRA_HC_tese.pdf: 4404064 bytes, checksum: 0993dde10f9daa72330a53e2281c1c45 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-06-22T16:34:05Z (GMT) No. of bitstreams: 1 oliveria_hc_dr_prud.pdf: 4404064 bytes, checksum: 0993dde10f9daa72330a53e2281c1c45 (MD5) / Made available in DSpace on 2016-06-22T16:34:05Z (GMT). No. of bitstreams: 1 oliveria_hc_dr_prud.pdf: 4404064 bytes, checksum: 0993dde10f9daa72330a53e2281c1c45 (MD5) Previous issue date: 2016-04-29 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / A utilização de ortoimagem verdadeira, em projetos executados em áreas urbanas, é essencial para certas aplicações. Para a geração de tal produto é necessária a detecção de áreas de oclusão, assim como a compensação destas áreas, usando-se imagens adjacentes. Este trabalho apresenta um novo método de detecção de oclusão denominado SGBM (Surface- Gradient-Based Method), por meio da identificação de gradientes de superfície sobre representação poliédrica. A diferença desse método para os demais encontrados na literatura, como estado da arte, é a utilização de um conjunto de pontos, organizados de acordo com a estrutura de dados TIN (Triangulated Irregular Network). Essa representação, por ser formada por pontos tridimensionais irregularmente espaçados, permite o uso de uma nuvem de pontos com diferentes densidades e, em determinadas situações, a obtenção de bons resultados por meio do uso de um modelo digital de superfície composto por uma quantidade menor de pontos, o que implica menor tempo de processamento. Nesta tese, apresentam-se diferentes experimentos para validar a metodologia proposta, por meio de análise quantitativa (índice de completude, alinhamento de feições lineares e tempo de processamento) e qualitativa (coerência visual dos resultados obtidos). Foram utilizados três conjuntos de dados aéreos obtidos por plataformas à baixa altura de voo. Esses dados são oriundos de sistemas acoplados a veículos aéreos não tripulados e helicóptero. Os conjuntos de pontos tridimensionais foram obtidos por sistemas de varredura a LASER (Light Amplification by Stimulated Emission of Radiation) aerotransportado e por técnicas fotogramétricas. Selecionou-se esse conjunto de dados, devido à grande presença de áreas de oclusão, ao aumento da utilização destas plataformas em levantamentos fotogramétricos e à atual qualidade da extração de nuvem de pontos via correspondência densa de imagens. Os resultados, assim como comparações com métodos existentes, indicam que o método proposto apresenta contribuição ao estado da arte na detecção de oclusão visando à geração de ortoimagem verdadeira, visto que a utilização de uma malha triangular, para análise da superfície, evita certas limitações encontradas nos métodos atuais, assim como, permite uma representação mais eficiente dos elementos presentes na área de interesse. / The usage of true-orthophoto in projects performed over urban areas is essential for certain applications. To generate this type of product it is necessary a procedure for occlusion detection, as well as, a proper radiometric compensation for the occluded areas, using adjacent images from the aerial image block. The main objective of this thesis is to present a new method for occlusion detection named SGBM (Surface-Gradient-Based Method), using a surface gradient identification over a polyhedral surface. The main difference among this method and the others, presented as state of the art, is the usage of a point cloud arranged as a triangular data structure (TIN: Triangulated Irregular Network). This surface representation is formed by an irregularly-spaced 3D point cloud, which allows the use of different point density along the surface. This characteristic supports obtaining good results by using a less dense point cloud - situation that requires a reduced computational effort. Several experiments were carried out, seeking for the validation of the proposed method, by using quantitative analyzes (completeness index, mosaic alignment and time of processing) and qualitative analyzes (visual coherence of the obtained results). Three datasets were used, being all acquired by a low-altitude flight configuration (helicopter and unmanned aerial vehicles), which make possible the identification of large occlusion areas. The point cloud was obtained by airborne LASER (Light Amplification by Stimulated Emission of Radiation) scanning system and by dense image matching. These dataset were used due to the incidence of occlusions and the growth of unmanned platform usage. The results indicate that the proposed method has some contribution to the state of the art in occlusion detection, aiming the generation of true-orthophoto. The use of an irregularly-spaced point cloud avoids some limitations found on the other methodologies, along with, a better representation of objects in the surface of interest. / FAPESP: 2013/21647-2

This thesis deals with the application of unmanned aerial vehicles (UAV) in photogrammetric purposes useful in geodesy. The UAV that was used for imaging is described in the first part, also there are introduced two programs which were used for data processing. A system of mission planning and data collecting is shown on the ortofoto creating as an example. The second part of this thesis deals with testing of the accuracy through terrestrially independently measured points. The main aim of the work is to demonstrate the high potential of UAV devices for geodesy purposes.

This study demonstrates the value of historical aerial photographs as a source for monitoring long-term landslide evolution, which can be unlocked by using appropriate photogrammetric methods. The understanding of landslide mechanisms requires extensive data records; a literature review identified quantitative data on surface movements as a key element for their analysis. It is generally acknowledged that, owing to the flexibility and high degree of automation of modern digital photogrammetric techniques, it is possible to derive detailed quantitative data from aerial photographs. In spite of the relative ease of such techniques, there is only scarce research available on data quality that can be achieved using commonly available material, hence the motivation of this study. In two landslide case-studies (the Mam Tor and East Pentwyn landslides) the different types of products were explored, that can be derived from historical aerial photographs. These products comprised geomorphological maps, automatically derived elevation models (DEMs) and displacement vectors. They proved to be useful and sufficiently accurate for monitoring landslide evolution. Comparison with independent survey data showed good consistency, hence validating the techniques used. A wide range of imagery was used in terms of quality, media and format. Analysis of the combined datasets resulted in improvements to the stochastic model and establishment of a relationship between image ground resolution and data accuracy. Undetected systematic effects provided a limiting constraint to the accuracy of the derived data, but the datasets proved insufficient to quantify each factor individually. An important advancement in digital photogrammetry is image matching, which allows automation of various stages of the working chain. However, it appeared that the radiometric quality of historical images may not always assure good results, both for extracting DEMs and vectors using automatic methods. It can be concluded that the photographic archive can provide invaluable data for landslide studies, when modern photogrammetric techniques are being used. As ever, independent and appropriate checks should always be included in any photogrammetric design.

Every year, thousands of people all over the world are loosing their lives in natural disasters. As a second most widespread hazard, landslides are still a disaster problem for Turkey. The long-term monitoring studies of instability phenomena have a paramount importance for Turkey to reduce its both direct and indirect effects. The objective of this thesis is to monitor the activity of the B&uuml / lb&uuml / lderesi and Bakacak landslides in Asarsuyu Catchment for 42 years period by the digital aerial photogrammetric techniques while evaluating the possible use of archive aerial photography in such analysis. To achieve the purpose of the study an orthophoto map was generated by aerial photographs belonging to 1994 year. The orthophoto map was utilized as a base map for aerial photo interpretation of different sets of aerial photographs corresponding to 1952, 1972, 1984 and 1994 years. As a result of this, 4 landslide activity maps are obtained. In addition, the characteristics of these landslides are analyzed by utilizing digital elevation model (DEM) created from stereo photographs of 1994. As a result of the study, no considerable variation is detected in the position of main boundaries of B&uuml / lb&uuml / lderesi and Bakacak landslides except some minor differences. However, within the landslides many topographical changes were observed between 1952 and 1994 period. Based on the profiles from toe to crest of the B&uuml / lb&uuml / lderesi landslide, the approximate length was measured as 4773m whereas the approximate width was about 2614m. The areal extent was calculated as ~12km2 having an approximate slope gradient ranging between 10-15&deg / with local variations. On the other hand, the approximate length of Bakacak landslide was 4420m and the approximate width was 832m from toe to crest with an area of ~4km2 and with an approximate slope angle 9-14&deg / .

Submitted by Claudia Rocha (claudia.rocha@udesc.br) on 2017-12-12T16:07:14Z No. of bitstreams: 1 PGEF17MA074.pdf: 2480663 bytes, checksum: 26fb5ceef2b21f799b8a35512121fad5 (MD5) / Made available in DSpace on 2017-12-12T16:07:14Z (GMT). No. of bitstreams: 1 PGEF17MA074.pdf: 2480663 bytes, checksum: 26fb5ceef2b21f799b8a35512121fad5 (MD5) Previous issue date: 2017-02-22 / The objective of this work is to extract different dendrometric variable such as tottal forest height (h), stem diameter at the breast height (DBH), volume (V) and stem diameter at regular height intervals (Hx) directly from cloud points data derived from a sensor coupled to Unmanned Aircraft Veicle System (UAVS) alone. / Este trabalho tem como objetivo a extração de variáveis dendrométricas tais como a altura total (h), diâmetro a altura do peito (DAP) e diâmetro em diferentes alturas do tronco a partir de dados derivados de TLS, sensores embarcados em VANT bem como a sua integração. A área de estudo consiste em um plantio misto, com espécies dos gêneros Pinus spp. e Eucapyptus spp., e área aproximada de 4.200 m². Os dados TLS foram coletados à campo, empregando o método de varreduras múltiplas. O recobrimento foi realizado com VANT a uma máxima de 120 metros. Todos os dados foram referenciados ao Sistema Geodésico Brasileiro mediante a coleta de observações para o campo. Para a validação dos resultados foram coletados dados utilizando técnicas e equipamentos tradicionais. O pré-processamento e processamento dos dados foram realizados empregando os aplicativos computacionais Scene, CloudCompare e Photoscan/Agisoft. Para a extração das variáveis dendrométrica empregaram-se os aplicativos Python e DetecTree. A detecção das árvores a partir de dados TLS obteve um acerto de 98,98%. Por outro lado, a detecção das árvores individual de árvores, usando somente a ortoimagem não obteve bons resultados. Quando comparada a verdade de campo, os diâmetros obtidos a 1,30 m (DAP) e a 3,3 metros de altura, apresentaram igualdade estatística ao nível de significância de 5%. No entanto, a metodologia usada para extração da altura total neste estudo, não apresentou igualdade estatística ao nível de significância de 5%

Det finns ett fåtal studier som berör objekthöjders betydelse för bildövertäckning vid UAV-fotografering. Därför är det intressant att undersöka hur objekthöjder, bildövertäckningar, och flyghöjder påverkar varandra samt hur de gemensamt påverkar kvaliteten på data. Syfte är att undersöka hur bildövertäckningen, och flyghöjder påverkar kvaliteten på ortofoton och digitala ytmodeller. Samt undersöka hur objekthöjder påverkar bildövertäckningen. Kriteriet för studieområdet var att det skulle finnas ett högt objekt. Studien utfördes därför i Inre hamn i Karlstad som omfattar Löfbergsskrapan på ca 42 m. Studien avser UAVs som begränsas enligt Transportstyrelsens regler (TSFS 2017:110). Målet var att presentera referenstabeller till företag eller privatpersoner som samlar in mätdata med UAV. Data samlades in på flyghöjderna; 120 m och 90 m, med bildövertäckningarna; 60/60 %, 80/80 %, och 90/90 %. Höjddata för Löfbergsskrapan mättes in med Satlab GNSS. Resultatet visade att bildövertäckningen 60/60 % inte var användbart inom ett område med maximal objekthöjd 42 m. Det behövdes ≥80/80 % bildövertäckning för att få en bra markupplösning. I studien undersöktes även hur mycket bildövertäckningen kan förändras när objekt ligger under markytans plan. Resultatet visar att bildövertäckningen ökar när objektet eller ytan avviker tillräckligt mycket ifrån markytans plan och minskar om det går tillräckligt högt ovanför markytan. Slutsatsen föreslår att bildövertäckningen ska vara minst 80/80 % för flyghöjderna 120 m och 90 m. De inställningarna innebär att byggnader på ca 42 m höjd ska representeras med 2-3 cm markupplösning i ortofoton och digitala ytmodeller samt omfatta en resursvänlig metod. Slutsatsen menar att förändringen av bildövertäckningen varierar mellan 10-50 % när markytan ligger ca 42 m lägre än starthöjden för en UAV. / There are a few available studies purely focusing on the object heights significance on image overlap in UAV-photogrammetry. Therefore, it is interesting to examine how object heights, image overlaps, and altitudes affect each other and how they jointly affect data quality. The purpose is to examine how image overlap, and altitude affect the quality of orthophotos and digital elevation models. And also examine how object heights affect image overlap. The study area was selected with the criterion of including a high rise building. Therefore, the study area was Inre hamn in Karlstad City covering Löfbergsskrapan, a 42 m high coffee roasting house. The study refers to UAVs restricted according to rules set by Transportstyrelsen (TSFS 2017:110). The objective was to present useful reference tables for companies and individuals working with UAV-data. Data was collected on the altitudes; 120 m and 90 m with an image overlap of; 60/60 %, 80/80 %, and 90/90 %. Elevation values was collected with Satlab GNSS. Results showed that using an image overlap of 60/60 % for both altitudes was not viable in an area covering an object of 42 m high. The image overlap should be at least ≥80/80 % to cover objects of 42 m high. The objective was also to examine how image overlap differ when an object is below ground level. Results showed that image overlap increases when an object or surface differ 42 m from ground level and decreases if the object is above ground level. The conclusion suggests that in order to include objects of 42 m high in an area the image overlap should be at least 80/80 % for both altitudes (120 m and 90 m). With those settings the ground resolution in orthophotos and digital elevations models should be 2-3 cm. It was also estimated that the image overlap may alter from settings anywhere between 10-50 % when ground level is 42 m below the point of departure of the UAV.