Documentation of landslides and inaccessible parts of a mine using an unmanned uav system and methods of digital terrestrial photogrammetry

Quite a big boom has recently been experienced in the technology of unmanned aerial vehicles (UAV). In conjunction with dense matching system, it gives one a powerful tool for the creation of digital terrain models and orthophotomaps. This system was used for the documentation of landslides and inaccessible parts of the Nástup Tušimice mine in the North Bohemian Brown Coal Basin (Czech Republic). The images were taken by the GATEWING X100 unmanned system that automatically executed photo flights an area of interest. For detailed documentation of selected parts of the mine, we used the method of digital terrestrial photogrammetry. The main objective was to find a suitable measurement technology for operational targeting of landslides and inaccessible parts of the mine, in order to prepare the basics for remediation work

COMBINED GEOMETRIC AND THERMAL ANALYSIS FROM UAV PLATFORMS FOR ARCHAEOLOGICAL HERITAGE DOCUMENTATION

The aim of this work is to study the value and potential of UAV technology as an instrument for documenting and analyzing a heritage site on both the detailed scale and the wider territorial scale. In particular, this paper will focus on the application of an UAV platform on the archeological site of Isola Comacina (Comacina Island), in the Lago di Como (Lake Como, Lombardy, Northern Italy). The work considers the advantages of different metric scales and the use of both RGB and thermal imagery, along with other terrestrial data (total station measurements and laser scans), in order to arrive at a working heritage information model. In particular, the archaeological remains on Isola Comacina have been intensively studied before by standard techniques but unfortunately no wider context is provided. A part of the research is the investigation of new methodologies offered by accurate geometric reconstructions combined with thermal imagery acquired by means of UAV platforms, e.g. the support of this type of imagery to discover rock formations partially buried

Implementation of Unmanned aerial vehicles (UAVs) for assessment of transportation infrastructure - Phase II

Technological advances in unmanned aerial vehicle (UAV) technologies continue to enable these tools to become easier to use, more economical, and applicable for transportation-related operations, maintenance, and asset management while also increasing safety and decreasing cost. This Phase 2 project continued to test and evaluate five main UAV platforms with a combination of optical, thermal, and lidar sensors to determine how to implement them into MDOT workflows. Field demonstrations were completed at bridges, a construction site, road corridors, and along highways with data being processed and analyzed using customized algorithms and tools. Additionally, a cost-benefit analysis was conducted, comparing manual and UAV-based inspection methods. The project team also gave a series of technical demonstrations and conference presentations, enabling outreach to interested audiences who gained understanding of the potential implementation of this technology and the advanced research that MDOT is moving to implementation. The outreach efforts and research activities performed under this contract demonstrated how implementing UAV technologies into MDOT workflows can provide many benefits to MDOT and the motoring public; such as advantages in improved cost-effectiveness, operational management, and timely maintenance of Michigan’s transportation infrastructure

Unmanned Aerial Vehicle – Efficient mapping tool available for recent research in polar regions

Unmanned Aerial Vehicles (UAV) have technical capabilities to extended usage in various fields ofscience. The existing UAVs are to be relatively easily accessible in the near future. It is possible to equip them with different sensors but there are still some usage limitations. This paper focuses ondemonstrating UAVs usage for research in polar regions. The research in polar regions is very specific and, due to harsh climate, limits the field work with UAVs. The options and limitations are presented in a case study performed in the Nordenskiöldbreen area, Svalbard Archipelago. In the end some derived products suitable for further analysis are presented

Extraction Landscape Elements from Remote Sensing Data

V této práci je popsán postup pro automatickou detekci krajinných prvků z dat pořízených bezkontaktními dálkovými metodami. Tato interpretace dat byla provedena v softwaru eCognition Developer prostřednictvím procesu klasifikace. Pro klasifikaci byla využita matoda obektově orientované analýzy, která dělí data takovým způsobem, že přiřazuje informaci o příslušnosti k nějaké třídě, například krajinnému typu, skupinám pixelů - objektům. Klasifikace byla provedena se současným využitím produktů dvou různých mapovacích technik - ortofot pořízených z leteckého snímkování a normalizovaného digitálního modelu povrchu, který byl určen pomocí LiDARU. Bylo identifikováno a klasikováno pět typů krajinných prvků.In this thesis, an approach to automatically derive information about land cover from the remotely sensed data is presented. The data interpretation was done with classification process and performed in software eCognition Developer. The Object-based image analysis, which assignes the classes - for example land cover types, to clusters of pixels (=objects), was used. For the classification, products of two different data sources were combined - the orthophotos generated from aerial imagery and Normalized Digital surface model derived from LiDAR data. Five types of landscape elements were identified and classified.

Mapping surface features of an Alpine glacier through multispectral and thermal drone surveys

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier’s energy budget and melt rates

Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV

Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m(2)). Model validation resulted in the correlation coefficient-R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor

UAV Deployment Exercise for Mapping Purposes: Evaluation of Emergency Response Applications

Exploiting the decrease of costs related to UAV technology, the humanitarian community started piloting the use of similar systems in humanitarian crises several years ago in different application fields, i.e., disaster mapping and information gathering, community capacity building, logistics and even transportation of goods. Part of the author’s group, composed of researchers in the field of applied geomatics, has been piloting the use of UAVs since 2006, with a specific focus on disaster management application. In the framework of such activities, a UAV deployment exercise was jointly organized with the Regional Civil Protection authority, mainly aimed at assessing the operational procedures to deploy UAVs for mapping purposes and the usability of the acquired data in an emergency response context. In the paper the technical features of the UAV platforms will be described, comparing the main advantages/disadvantages of fixed-wing versus rotor platforms. The main phases of the adopted operational procedure will be discussed and assessed especially in terms of time required to carry out each step, highlighting potential bottlenecks and in view of the national regulation framework, which is rapidly evolving. Different methodologies for the processing of the acquired data will be described and discussed, evaluating the fitness for emergency response applications

Comparison of DSMs Generated Using High Resolution UAV Images in an Archaeological Site

Unmanned Ariel Vehicles (UAVs) are increasingly used for topographic mapping. The use of UAVs in the photogrammetric survey of archaeological sites provides extensive opportunities for the creation of documentation. By using this technology, a detailed and precise digital map of historical and cultural areas can be produced, digital terrain model, orthophotos of the whole area can be produced and inaccessible parts of the historical area such as towers, walls, steep slopes can be documented. For this study, 542 high-resolution images were captured with a UAV from approximately 20 m high. The high-resolution images were processed using Agisoft Photoscan and Pix4Dmapper Pro software to generate point clouds and Digital Surface Models (DSMs). Both software packages produced GSD values are between 0,401 - 0.425 cm/pixel. When comparing the cross sections obtained from the DSMs obtained from the two software packages, it was seen that the Pix4D software was more successful, especially in the sections produced from surfaces, such as ducts and pits