Rangeland monitoring is used by land managers for assessing multiple-use management practices on western rangelands. Managers benefit from improved monitoring methods that provide rapid, accurate, cost-effective, and robust measures of rangeland health and ecological trend. In this study, we used a supervised classification image analysis approach to estimate plant cover and bare ground by functional group that can be used to monitor and assess rangeland structure. High-resolution color infrared imagery taken of 40 research plots was acquired with a UltraCam X (UCX) digital camera during summer 2011. Ground estimates of cover were simultaneously collected by the Utah Division of Wildlife Resources' Range Trend Project field crew within these same areas. Image analysis was conducted using supervised classification to determine percent cover from Red, Green, Blue and infrared images. Classification accuracy and mean difference between cover estimates from remote sensed imagery and those obtained from the ground were compared using an accuracy assessment with Kappa statistic and a t-test analysis, respectively. Percent cover estimates from remote sensing ranged from underestimating the surface class (rock, pavement, and bare ground) by 27% to overestimating shrubs by less than 1% when compared to field-based measurements. Overall accuracy of the supervised classification was 91% with a kappa statistic of 0.88. The highest accuracy was observed when classifying surface values (bare ground, rock) which had a user's and producer's accuracy of 92% and 93%, respectively. Although surface cover varied significantly from field-based estimates, plant cover varied only slightly, giving managers an option to assess plant cover effectively and efficiently on greater temporal and spatial extents.

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Small Unmanned Aerial Vehicles (SUAVs) are an attractive choice for many surveillance tasks. However, video from an SUAV can be difficult to use in its raw form. In addition, the limitations inherent in the SUAV platform inhibit the distribution of video to remote users. To solve the problems with using SUAV video, we propose a system to automatically create geo-referenced mosiacs of video frames. We also present three novel techniques we have developed to improve ortho-rectification and geo-location accuracy of the mosaics. The most successful of these techniques is able to reduce geo-location error by a factor of 15 with minimal computational overhead. The proposed system overcomes communications limitations by transmitting the mosaics to a central server where there they can easily be accessed by remote users via the Internet. Using flight test results, we show that the proposed mosaicking system achieves real-time performance and produces high-quality and accurately geo-referenced imagery.

The purpose of this thesis is to test the validity of applications of Remote Sensing and Geographical Information Systems in Anatolian archaeology. The focus of the study is an Iron Age fortress &Ccedil / evre Kale and its associated structures. During the course of the study, 5 km long outer wall enclosing a territory around &Ccedil / evre Kale documented for the first time by employing high altitude aerial imagery. In addition to the GIS analyses, examination of the geology, land use and soil quality data showed that the outer wall is in a way acting to guard and protect inhabitants of the fortress and, perhaps more importantly, the well-watered pasture surrounding the fortress and demarcated by the enclosure wall. Evaluation of the available archaeological and historical evidence suggested that &Ccedil / evre Kale might be of a site with significant military importance at least in the first half of the 6th century BC. As a result, this thesis is underlying the importance of high and low altitude aerial imagery in terms of documentation, evaluation and monitoring of the archaeological sites as part of the archaeological research

The thesis deals with the possible use of optical data for modeling forest area compared with utilization of airborne laser scanning data. At first these two datasets are compared and causes of differences are explained. Then canopy height models are made and object-oriented classification is applied for separation of vegetation stands. Methodical procedure is suggested for delineation and detection individual trees in forest. Then their height is detected. There are summarized and other possibilities for improvement in detection and delineation of trees. The results show that optical data with resolution about 25 cm are suitable for dermining the characteristics of the forest stands up to individual tree level. The outputs of this research can be used for forest inventory. Key words: aerial imagery, image matching, laser scanning, point cloud, forest inventory

The thesis content study of land use in municipality Lelekovice and its connection with software for geographic information system. It describes processing data especially historical cadastre maps, archival aerial imagery and orthophotos which were used for analyzing of this area. Analysis were performed in software ArcGIS. Appendixes of thesis are graphs and visualization of development of study area.

Deep learning has proven a powerful tool for image analysis during the past two decades. With the rise of high resolution overhead imagery, an opportunity for automatic geospatial 3D-recreation has presented itself. This master thesis researches the possibil- ity of 3D-recreation through deep learning based image analysis of overhead imagery. The goal is a model capable of making predictions for three different tasks: heightmaps, bound- ary proximity heatmaps and semantic segmentations. A new neural network is designed with the novel feature of supplying the predictions from one task to another with the goal of improving performance. A number of strategies to ensure the model generalizes to un- seen data are employed. The model is trained using satellite and aerial imagery from a variety of cities on the planet. The model is meticulously evaluated by using four common performance metrics. For datasets with no ground truth data, the results were assessed visually. This thesis concludes that it is possible to create a deep learning network capa- ble of making predictions for the three tasks with varying success, performing best for heightmaps and worst for semantic segmentation. It was observed that supplying estima- tions from one task to another can both improve and decrease performance. Analysis into what features in an image is important for the three tasks was clear in some images, unclear in others. Lastly, validation proved that a number of random transformations during the training process helped the model generalize to unseen data. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

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Miniature Aerial Vehicles (MAVs) are quickly gaining acceptance as a platform for performing remote sensing or surveillance of remote areas. However, because MAVs are typically flown close to the ground (1000 feet or less in altitude), their field of view for any one image is relatively small. In addition, the context of the video (where and at what orientation are the objects being observed, the relationship between images) is unclear from any one image. To overcome these problems, we propose a geo-referenced mosaicing method that creates a mosaic from the captured images and geo-references the mosaic using information from the MAV IMU/GPS unit. Our method utilizes bundle adjustment within a constrained optimization framework and topology refinement. Using real MAV video, we have demonstrated our mosaic creation process on over 900 frames. Our method has been shown to produce the high quality mosaics to within 7m using tightly synchronized MAV telemetry data and to within 30m using only GPS information (i.e. no roll and pitch information).