The aim of this thesis is to devise a method for objective classification of floodplain based on spatially accurate data from UAV that allows identification of the fundamental features of floodplain and channel arising from or affecting by the floods activities. Background research is focused on floodplain forming processes; types of flood on our territory and its geomorphological effects, as well as a brief description unmanned aerial vehicle and their applicability in natural science and the flood. Proposed method was carried out on the test section - a part of meander of Javoří stream in Šumava Mountain - then was tested on complex meander belt of the same stream. Proposed method is based on applicability of standard objective classification. Elementary products from photogrammetric analysis - 2D orthophoto and 3D digital surface model - are used as basic input data. Another aim of theses is to discuss applicability of this method for assessment of fluvial form, its limits and potential development. The results indicated that success of classification will increase significantly the involvement of 3D data to classification, which from standard data from the UAV, despite the lack of absence multispectral bands doing a very valuable source of information for mapping and analysis, for example, the...

In this research we aim to achieve cybernetic cohesion information flow in precision agriculture, integrating machine learning methods, computer vision, geographical information system and UAV-photogrammetry in an irrigated area with slaughterhouse wastewater, under five treatments (W100 - irrigation with superficial water and 100% of nitrogen mineral fertilization, E0, E33, E66 and E100 - irrigation with treated effluent from slaughterhouse and addition of 0, 33, 66 and 100% of nitrogen mineral fertilization, respectively) and four replications on grassland (Cynodon dactylon (L.) Pers.). Several images (between one hundred and two hundred) with red, green, blue (RGB) color model were captured using a quadcopter flying at 20 meter altitude and obtaining spatial resolution of 1 centimeter on a surface of approximately 0.5 ha. The images were orthorectified together with nine ground control points done by differential global positioning system (GPS), both processed in the Agisoft PhotoScan software. Thirteen photogrammetric projects were done over time with 30-day revisit, the root mean squared error (RMSE) was used as accuracy measurement, and reached values lower than 5 centimeters for x, y and z axis. The orthoimage obtained with unmanned aerial vehicle (UAV) photogrammetry was changed from RGB to hue, saturation, value (HSV) color model, and the hue color space was chosen due to independence of illumination, beyond it has a good description of exposure of soil and vegetation, but it is dependent of light source temperature, so difficult to estabilish a static threshold, so we selected an unsupervised classification method, K-Means, to classify the unknown patterns along the area. Polygons were drawn delimiting the area represented by each portion and a supervised classification method based on entropy was used, the decision tree, to explore and find patterns that recognize each treatment. These steps are also displayed in forms of georeferenced thematic maps and were executed in the open source softwares Python, QGIS and Weka. The rules defined on the hue color space reached an accuracy of 100% on the training set, and provided a better understanding about the distribution of soil and vegetation on the parcels. This methodology shows a great potential for analysis of spectral data in precision agriculture. / Nesta pesquisa pretendemos alcançar a coesão cibernética no fluxo de informações dentro da agricultura de precisão, integrando métodos de aprendizagem de máquinas, visão computacional, sistema de informação geográfica e aerofotogrametria em uma área irrigada com efluente de matadouro, sob cinco tratamentos (W100 - irrigação com água superficial e 100 % de adubação mineral nitrogenada, E0, E33, E66 e E100 - irrigação com efluente tratado de abatedouro e adição de 0, 33, 66 e 100% de adubação mineral nitrogenada, respectivamente) e quatro repetições em pastagem (Cynodon dactylon (L.) Pers.) Várias imagens (entre cem e duzentas) com modelo de cor vermelho, verde e azul (RGB) foram capturadas por um quadricóptero voando a 20 metros de altitude, e obtendo resolução espacial de 1 centímetro em uma superfície de aproximadamente 0.5 ha. As imagens foram ortorretificadas juntamente com nove pontos de controle, realizados pelo sistema de posicionamento global diferencial (GPS), ambos processados no software Agisoft PhotoScan. Treze projetos fotogramétricos foram realizados ao longo do tempo com revisita de 30 dias, a raiz do erro quadrático médio (RMSE) foi usada como medida de acurácia e atingiu valores menores que 5 centímetros para os eixos x, y e z. A ortoimagem obtida com a fotogrametria do veículo aéreo não tripulado (UAV) foi alterada de RGB para matiz, saturação, valor (HSV) e o espaço de cor matiz foi escolhido devido a independência da iluminação, além de ter boa descrição da exposição do solo e vegetação. Entretanto este é dependente da temperatura da fonte de luz, portanto difícil de se estabelecer um limiar estático, logo selecionamos um método de classificação não supervisionado, o K-Means, para classificar os padrões desconhecidos ao longo da área. Polígonos foram traçados delimitando a área representada por cada parcela e um método supervisionado de classificação baseado na entropia foi utilizado, a árvore de decisão, para explorar e encontrar padrões que reconheçam cada tratamento. Essas etapas também são exibidas em formas de mapas temáticos georeferenciados e foram executadas nos softwares de código aberto Python, QGIS e Weka. As regras definidas no espaço de cor matiz atingiram uma acurácia de 100% no conjunto de treinamento e proporcionaram um melhor entendimento sobre a distribuição do solo e da vegetação nas parcelas. Esta metodologia mostra um grande potencial para análise de dados na agricultura de precisão.

In this research we aim to achieve cybernetic cohesion information flow in precision agriculture, integrating machine learning methods, computer vision, geographical information system and UAV-photogrammetry in an irrigated area with slaughterhouse wastewater, under five treatments (W100 - irrigation with superficial water and 100% of nitrogen mineral fertilization, E0, E33, E66 and E100 - irrigation with treated effluent from slaughterhouse and addition of 0, 33, 66 and 100% of nitrogen mineral fertilization, respectively) and four replications on grassland (Cynodon dactylon (L.) Pers.). Several images (between one hundred and two hundred) with red, green, blue (RGB) color model were captured using a quadcopter flying at 20 meter altitude and obtaining spatial resolution of 1 centimeter on a surface of approximately 0.5 ha. The images were orthorectified together with nine ground control points done by differential global positioning system (GPS), both processed in the Agisoft PhotoScan software. Thirteen photogrammetric projects were done over time with 30-day revisit, the root mean squared error (RMSE) was used as accuracy measurement, and reached values lower than 5 centimeters for x, y and z axis. The orthoimage obtained with unmanned aerial vehicle (UAV) photogrammetry was changed from RGB to hue, saturation, value (HSV) color model, and the hue color space was chosen due to independence of illumination, beyond it has a good description of exposure of soil and vegetation, but it is dependent of light source temperature, so difficult to estabilish a static threshold, so we selected an unsupervised classification method, K-Means, to classify the unknown patterns along the area. Polygons were drawn delimiting the area represented by each portion and a supervised classification method based on entropy was used, the decision tree, to explore and find patterns that recognize each treatment. These steps are also displayed in forms of georeferenced thematic maps and were executed in the open source softwares Python, QGIS and Weka. The rules defined on the hue color space reached an accuracy of 100% on the training set, and provided a better understanding about the distribution of soil and vegetation on the parcels. This methodology shows a great potential for analysis of spectral data in precision agriculture. / Nesta pesquisa pretendemos alcançar a coesão cibernética no fluxo de informações dentro da agricultura de precisão, integrando métodos de aprendizagem de máquinas, visão computacional, sistema de informação geográfica e aerofotogrametria em uma área irrigada com efluente de matadouro, sob cinco tratamentos (W100 - irrigação com água superficial e 100 % de adubação mineral nitrogenada, E0, E33, E66 e E100 - irrigação com efluente tratado de abatedouro e adição de 0, 33, 66 e 100% de adubação mineral nitrogenada, respectivamente) e quatro repetições em pastagem (Cynodon dactylon (L.) Pers.) Várias imagens (entre cem e duzentas) com modelo de cor vermelho, verde e azul (RGB) foram capturadas por um quadricóptero voando a 20 metros de altitude, e obtendo resolução espacial de 1 centímetro em uma superfície de aproximadamente 0.5 ha. As imagens foram ortorretificadas juntamente com nove pontos de controle, realizados pelo sistema de posicionamento global diferencial (GPS), ambos processados no software Agisoft PhotoScan. Treze projetos fotogramétricos foram realizados ao longo do tempo com revisita de 30 dias, a raiz do erro quadrático médio (RMSE) foi usada como medida de acurácia e atingiu valores menores que 5 centímetros para os eixos x, y e z. A ortoimagem obtida com a fotogrametria do veículo aéreo não tripulado (UAV) foi alterada de RGB para matiz, saturação, valor (HSV) e o espaço de cor matiz foi escolhido devido a independência da iluminação, além de ter boa descrição da exposição do solo e vegetação. Entretanto este é dependente da temperatura da fonte de luz, portanto difícil de se estabelecer um limiar estático, logo selecionamos um método de classificação não supervisionado, o K-Means, para classificar os padrões desconhecidos ao longo da área. Polígonos foram traçados delimitando a área representada por cada parcela e um método supervisionado de classificação baseado na entropia foi utilizado, a árvore de decisão, para explorar e encontrar padrões que reconheçam cada tratamento. Essas etapas também são exibidas em formas de mapas temáticos georeferenciados e foram executadas nos softwares de código aberto Python, QGIS e Weka. As regras definidas no espaço de cor matiz atingiram uma acurácia de 100% no conjunto de treinamento e proporcionaram um melhor entendimento sobre a distribuição do solo e da vegetação nas parcelas. Esta metodologia mostra um grande potencial para análise de dados na agricultura de precisão.

Topographic mapping of rock formations using GIS methods Abstract This thesis deals with issues of creating 3D models of rock formations with data from terrestrial laser scanning, close range photogrammetry and UAV photogrammetry. The theoretical part focuses on explaining functioning and usage of those methods. Beside that there is described issues of 3D point cloud filtering. Practical part of this work describes data collecting and processing procedure. Further there is proposed filtering process which aim to remove noise points from point clouds and remove vegetation with combination of vegetation index ExG, clustering algorithm DBSCAN and Hough Transform. The proposed method is tested on the selected rock formation in Bohemian Switzerland National Park. The evaluation of the proposed method is based on comparison of models filtered with proposed method with reference models, which are filtered manually. Finally, the achieved accuracy of the models is evaluated using geodetic measurements. key words laser scanning, photogrammetry, UAV, point cloud, data filtering

UAV (Unmanned Aircraft Vehicle) har revolutiontionerat ortofotoframställningen med sitt bidrag till ökad säkerhet, lägre kostnader samt effektivare arbetsgång vid framställning av ortofoton. Den traditionella flygfotogrammetrin med flygplan och utplacering av flygsignaler har varit den givna metoden i många år. Att flyga med UAV istället för flygplan sparar tid och pengar däremot är utplacering och inmätning av flygsignaler fortfarande tidskrävande och därför kostsamt. Företaget DJI har tagit fram en ny UAV med namnet DJI Phantom 4 RTK vilken stödjer möjligheten att använda satellitbaserad positionering för direkt georeferering. Den här studien har jämfört två olika georefereringsmetoder för framställning av ortofoton med UAV: direkt georeferering med NRTK (satellitbaserad positionering och nätverks-RTK) samt indirekt georeferering med olika antal markstödspunkter. Studien utfördes vid Högskolan i Gävle på en yta av åtta hektar. En undersökning av avvikelser i plan och höjd resulterade i acceptabla värden enligt de riktlinjer som följdes i HMK – Ortofoto (2017) samt de kontroller som genomfördes enligt SIS-TS 21144:2016. RMS-värdet i plan för den indirekta georefereringsmetoden ligger på 0,0102m. För den direkta georefereringsmetoden ligger RMS-värdet i plan vid användning av markstödpunkter mellan 0,0132 och 0,0148 m. Slutligen för den direkta georefereringsmetoden utan markstödpunkter är RMS-värdet i plan på 0,0136 m. RMS i höjd ligger inom intervallet 0,008-0,025 m. Det som redovisas i studien visar att en accepterad kvalitet av ortofoton går att erhålla baserat på de RMS-värden i plan och höjd med samtliga georefereringsmetoder som testats. Efter genomförda kontroller och utvärdering av de resultat kan det konstateras att de olika georefereringsmetoderna skiljer inte mycket åt varandra kvalitetsmässigt.Dock är den direkta georefereringsmetoden utan markstödpunkter mycket effektivare ur ett tidsperspektiv. Phantom 4 RTK är ny på marknaden och det behöver utföras mer forskning för att få en större insikt av dess potential. Dock krävs det mer forskning kring direkt georeferering för utvärdering av orotofotons kvalitet. / UAV (Unmanned Aircraft Vehicle) has revolutionized the creation of orthophotos with its contribution to increased safety, lower costs and more effective ways when making orthophotos. The traditional aerial photogrammetry with airplanes and placement of flight signals has been the standard method for years. To fly with UAV instead of an airplane is cheaper and saves time, however, the placement and measurements of flight signals is still time consuming and therefore expensive. The company DJI has developed a new UAV called Phantom 4 RTK that supports satellite based technology for direct georeferercing. This study compared two different measuring methods when producing orthophotos with UAV: direct georeferencing with NRTK (Network Real Time Kinematic) and indirect georeferencing when using different number of Ground Control Points (GCP). The study was conducted at the University of Gävle over an area of eight hectares. An investigation of the deviation in plane and height resulted in acceptable units based on the guidelines that were followed in HMK – Ortofoto and the controls that were followed from SIS- TS 21144:2016. The RMS value in plane for the indirect georeferencing method is 0,0102 m. For the direct georeferencing method the RMS value in plane when using ground control points is between 0,0132 and 0,0148 m. At last the RMS value for the direct georeferencing method without ground control points is 0,0136m. The RMS value in height is between the intervals 0,008-0,025 m. The data presented in this study show that an accepted quality in the orthophotos can be acquired based on the RMS values in plane and height for every georeferencing that was tested. After accomplished controls and evaluation the results show that the different georeferencing methods doesn´t differantiate too much from each other based on their quality. However, the direct georeferencing method with ground control points is more effective from a time perspective. Phantom 4 RTK is new on the market and more research is necessary in order to understand the potential of this technology and its posibility to integrate into society. More research is recquired for the direct georeferencing method in order to evaluate the quality of orthophotos.

This study aims at increasing the amount of data available on recent past Swedish glacier front variations, at improving the knowledge on the present behavior of these glaciers, and at contributing to the defnition of the guideline for future of glacier front observations in Sweden. To do so, the study proposes Sentinel-2 based yearly front variation measurements for 22 Swedish glaciers between 2015 and 2022, calculated based on the multicentreline approach of the MaQiT tool. It also assesses the uncertainty of Sentinel-2 based mapping by comparing it to 0.48 m spatial resolution aerial imagery based mapping and to field based mapping conducted on four northern Sweden glaciers during the end of the summer 2022: Kaskasatj SE, Kebnepakteglaciären, Mårmaglaciären, and Storglaciären. The fieldwork included handheld GNSS, UAV photogrammetry, and total station survey in order to compare the three methods in the mapping of glacier fronts. This study also compares the measured glaciers front variations to climatic factors and glaciers boundary conditions. The resulting glacier front variations in Sweden between 2015 and 2022, averaged over all glaciers studied, is −10.28 m yr−1. Small glaciers retreated on average 0.51 % of their length per year, while large glaciers retreated on average 0.35 % per year. This study highlights the importance of recording yearly front positions of a large amount of glaciers, and therefore encourages for the future the use of satellite imagery to observe all Swedish glaciers fronts on a yearly basis. It also supports the conduction of regular UAV photogrammetry surveys to provide high resolution mapping of a sample of glacier fronts chosen for their vicinity with the Tarfala Research Station, the Swedish field centre for glaciological and alpine research.

The master´s thesis deals with the application of unmanned aerial vehicle (UAV) in photogrammetry and mapping. The first part describes the UAV that was used for imaging, legislative restrictions resulting from its operations, planning and realization of the flight. The second part of this thesis is focused on processing results, especially on evaluation the accuracy of the results gained by UAV with and without a GNSS module. The data are evaluated in the 3rd accuracy rating class (ČSN 01 3410). The theoretical principles are explained as well. The main aim of the thesis is to demonstrate the effective use of the GNSS module Emlid Reach and the unmanned aerial vehicle in geodesy.

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.