Academic literature on the topic 'High definition color aerial photography'

The threatened malleefowl (Leipoa ocellata) constructs a large (often >3 m) incubator mound (nest) that is considered a useful proxy for surveying its presence and abundance in the context of an environmental impact assessment. Here we report on the effectiveness and relative cost of using high-definition aerial photography to search in 3D for malleefowl mounds by comparing results to those of earlier ground-based searches. High-definition colour aerial photography was taken of an area of ~7014 ha and searched in 3D for malleefowl mounds. All 24 active (i.e. in use) malleefowl mounds known before the examination of aerial photography were detected using the new assessment technique. Of the 108 total mounds (active and inactive) known from earlier on-ground surveys, 94 (87%) were recorded using the new technique. Mounds not detected were all old and weathered, many barely above ground level and some with vegetation growing in the crater. Approximately 6.3% of the identifications considered ‘confident’ and ~35.0% considered ‘potential’ based on the aerial photography proved to be false positives. The cost of detecting malleefowl mounds using the interpretation of high-definition 3D colour aerial photography and then subsequently examining these areas on the ground is appreciably cheaper than on-ground grid searches.

Abstract. The quality of cartographic products, obtained on base of aerial images, depends on their quality. The image‘s quality can be defined in terms of measurement characteristics and in it’s visual properties. The object of the paper is an aerial image’s quality, concerning its visual perception. The process of topographic aerial images quality assessing is currently not regulated, therefore, this problem becomes particularly relevant today. The article is devoted to development the quality criteria system for aerial images, obtained for mapping purposes, and definition the requirements for them. The analysis of factors affecting the quality of aerial images is carried out, they include natural conditions, technical and technological parameters of aerial surveying and images post-processing. The main imperfections of image quality, appearing as a result of these factors influence are determined – blurring, high level of random image noise, haze, color imbalance, loss of information in shadows and illumination. Methods of identifying these imperfections and assessing their influence on aerial images quality are shown.

Mangrove migration, or transgression in response to global climatic changes or sea-level rise, is a slow process; to capture it, understanding both the present distribution of mangroves at individual patch (single- or clumped trees) scale, and their rates of change are essential. In this study, a new method was developed to delineate individual patches and to estimate mangrove cover from very high-resolution (0.08 m spatial resolution) true color (Red (R), Green (G), and Blue (B) spectral channels) aerial photography. The method utilizes marker-based watershed segmentation, where markers are detected using a vegetation index and Otsu’s automatic thresholding. Fourteen commonly used vegetation indices were tested, and shadows were removed from the segmented images to determine their effect on the accuracy of tree detection, cover estimation, and patch delineation. According to point-based accuracy analysis, we obtained adjusted overall accuracies >90% in tree detection using seven vegetation indices. Likewise, using an object-based approach, the highest overlap accuracy between predicted and reference data was 95%. The vegetation index Excess Green (ExG) without shadow removal produced the most accurate mangrove maps by separating tree patches from shadows and background marsh vegetation and detecting more individual trees. The method provides high precision delineation of mangrove trees and patches, and the opportunity to analyze mangrove migration patterns at the scale of isolated individuals and patches.

ABSTRACT The January 2007 release of over 8,000 barrels of a condensate crude oil from a damaged well in Bayou Perot, Louisiana resulted in intermittent oiling of remote mud flats and salt marshes over a 30 square mile area. NOAA'S National Geodetic Survey collected aerial vertical digital photography 17 days after the spill to assist in locating and quantifying areas of oiling. The effective pixel size was 34 centimeters, however, the data were processed to 40 centimeter resolution. Useful products were posted to the web within two days after acquisition. Standard supervised and unsupervised image processing techniques were used in conjunction with oblique photography and field knowledge to define the oiling signatures. Time constraints required that the classification be conducted on mosaiced, non-color balanced images (ideally each image would be classified independently to account for differences in illumination and/or processing). However, the strong visible signature of the oiled areas and ground-truth data from field surveys resulted in high confidence levels for several oil types which in turn were used to enhance the identification of the remaining classes. Five oil types were identified: Black (218,000 ft2), Red (81,000 ft2), Orange (154,000 ft2), Yellow (38,000 ft2), and Light Yellow (349,000 ft2) corresponding to the color and attributes of the oil. The total conservative estimate of oiled area was 840,000 ft2 or nearly 20 acres. Based on estimated thicknesses of the different oils, the total volume of oil present at the time of imagery acquisition was 3,330 barrels. This value was close to the actual amount of oil recovered over the time period between the date of imagery acquisition and the end of cleanup.

This contribution promotes 3D photography as an important tool to obtain objective object information. Keeping mainly in mind World Heritage documentation as well as Heritage protection, it is another intention of this paper, to stimulate the interest in applications of 3D photography for professionals as well as for amateurs. In addition this is also an activity report of the international CIPA task group 3. The main part of this paper starts with “Digging the treasure of existing international 3D photography”. This does not only belong to tangible but also to intangible Cultural Heritage. 3D photography clearly supports the recording, the visualization, the preservation and the restoration of architectural and archaeological objects. Therefore the use of 3D photography in C.H. should increase on an international level. The presented samples in 3D represent a voluminous, almost partly “forgotten treasure” of international archives for 3D photography. <br><br> The next chapter is on “Promoting new 3D photography in Cultural Heritage”. Though 3D photographs are a well-established basic photographic and photogrammetric tool, even suited to provide “near real” documentation, they are still a matter of research and improvement. Beside the use of 3D cameras even single lenses cameras are very much suited for photographic 3D documentation purposes in Cultural Heritage. <br><br> Currently at the Faculty of Civil Engineering of the University of Applied Sciences Magdeburg-Stendal, low altitude aerial photography is exposed from a maximum height of 13m, using a hand hold carbon telescope rod. The use of this “huge selfie stick” is also an (international) recommendation, to expose high resolution 3D photography of monuments under expedition conditions. In addition to the carbon rod recently a captive balloon and a hexacopter UAV- platform is in use, mainly to take better synoptically (extremely low altitude, ground truth) aerial photography. Additional experiments with respect to “easy geometry” and to multistage concepts of 3D photographs in Cultural Heritage just started. Furthermore a revised list of the 3D visualization principles, claiming completeness, has been carried out. Beside others in an outlook <br><br> *It is highly recommended, to list every historical and current stereo view with relevance to Cultural Heritage in a global Monument Information System (MIS), like in google earth. <br><br> *3D photographs seem to be very suited, to complete and/or at least partly to replace manual archaeological sketches. In this concern the still underestimated 3D effect will be demonstrated, which even allows, e.g., the spatial perception of extremely small scratches etc... <br><br> *A consequent dealing with 3D Technology even seems to indicate, currently we experience the beginning of a new age of “real 3DPC- screens“, which at least could add or even partly replace the conventional 2D screens. Here the spatial visualization is verified without glasses in an all-around vitreous body. In this respect nowadays widespread lasered crystals showing monuments are identified as “Early Bird“ 3D products, which, due to low resolution and contrast and due to lack of color, currently might even remember to the status of the invention of photography by Niepce (1827), but seem to promise a great future also in 3D Cultural Heritage documentation. <br><br> *Last not least 3D printers more and more seem to conquer the IT-market, obviously showing an international competition.

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg) onboard a DJI Phantom 4 UAV (~1.4 kg) during dry and rainy seasons over inlet areas of both reservoirs. Field measurements of total suspended solids (TSS) and water clarity were made jointly with the airborne survey campaigns. Field hyperspectral radiometry data were also collected during two field surveys. Bio-optical models for TSS were tested for all spectral bands of the Sequoia camera. The near-infrared single band was found to perform the best (R2: 0.94; RMSE: 7.8 mg L−1) for a 0–180 mg L−1 TSS range and was used to produce time series of TSS concentration maps of the study areas. This flexible platform enabled monitoring of the increase of TSS concentration at a ~13 cm spatial resolution in urban and rural drainages in the rainy season. Aerial surveys allowed us to map TSS load fluctuations in a 1 week period during which no satellite images were available due to continuous cloud coverage in the rainy season. This work demonstrates that a low-cost configuration allows dense TSS monitoring at the inlet areas of reservoirs and thus enables mapping of the sources of sediment inputs, supporting the definition of mitigation plans to limit the siltation process.

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.

Thermokarst waterbodies caused by permafrost thawing and degradation are ubiquitous in many subarctic and Arctic regions. They are globally important components of the biogeochemical carbon cycle and have potential feedback effects on climate. These northern waters are mostly small lakes and ponds, and although they may be mapped using very high-resolution satellites or aerial photography, these approaches are generally not suitable for monitoring purposes, due to the cost and limited availability of such images. In this study we evaluated the potential use of widely available high-resolution imagery from Sentinel-2 (S2) for the characterization of the spectral reflectance of thermokarst lakes and ponds. Specifically, we aimed to define the minimum lake area that could be reliably imaged, and to identify challenges and solutions for remote sensing of such waters in the future. The study was conducted in subarctic Canada, in the vicinity of Whapmagoostui-Kuujjuarapik (Nunavik, Québec), an area in the sporadic permafrost zone with numerous thermokarst waterbodies that vary greatly in size. Ground truthing lake reflectance data were collected using an Unmanned Aerial System (UAS) fitted with a multispectral camera that collected images at 13 cm resolution. The results were compared with reflectance from Sentinel-2 images, and the effect of lake area on the reflectance response was assessed. Our results show that Sentinel-2 imagery was suitable for waterbodies larger than 350 m2 once their boundaries were defined, which in the two test sites would allow monitoring from 11% to 30% of the waterbodies and 73% to 85% of the total lake area. Challenges for remote sensing of small lakes include the confounding effects of water reflection (both direct radiation and diffuse), wind and shadow. Given the small threshold area and frequent revisit time, Sentinel-2 provides a valuable approach towards the continuous monitoring of waterbodies, including ponds and small lakes such as those found in thermokarst landscapes. UASs provide a complementary approach for ground truthing and boundary definition.

Le Ministère des Ressources Naturelles et de la Faune (MRNF) a mandaté la compagnie de géomatique SYNETIX inc. de Montréal et le laboratoire de télédétection de l’Université de Montréal dans le but de développer une application dédiée à la détection automatique et la mise à jour du réseau routier des cartes topographiques à l’échelle 1 : 20 000 à partir de l’imagerie optique à haute résolution spatiale. À cette fin, les mandataires ont entrepris l’adaptation du progiciel SIGMA0 qu’ils avaient conjointement développé pour la mise à jour cartographique à partir d’images satellitales de résolution d’environ 5 mètres. Le produit dérivé de SIGMA0 fut un module nommé SIGMA-ROUTES dont le principe de détection des routes repose sur le balayage d’un filtre le long des vecteurs routiers de la cartographie existante. Les réponses du filtre sur des images couleurs à très haute résolution d’une grande complexité radiométrique (photographies aériennes) conduisent à l’assignation d’étiquettes selon l’état intact, suspect, disparu ou nouveau aux segments routiers repérés. L’objectif général de ce projet est d’évaluer la justesse de l’assignation des statuts ou états en quantifiant le rendement sur la base des distances totales détectées en conformité avec la référence ainsi qu’en procédant à une analyse spatiale des incohérences. La séquence des essais cible d’abord l’effet de la résolution sur le taux de conformité et dans un second temps, les gains escomptés par une succession de traitements de rehaussement destinée à rendre ces images plus propices à l’extraction du réseau routier. La démarche globale implique d’abord la caractérisation d’un site d’essai dans la région de Sherbrooke comportant 40 km de routes de diverses catégories allant du sentier boisé au large collecteur sur une superficie de 2,8 km2. Une carte de vérité terrain des voies de communication nous a permis d’établir des données de référence issues d’une détection visuelle à laquelle sont confrontés les résultats de détection de SIGMA-ROUTES. Nos résultats confirment que la complexité radiométrique des images à haute résolution en milieu urbain bénéficie des prétraitements telles que la segmentation et la compensation d’histogramme uniformisant les surfaces routières. On constate aussi que les performances présentent une hypersensibilité aux variations de résolution alors que le passage entre nos trois résolutions (84, 168 et 210 cm) altère le taux de détection de pratiquement 15% sur les distances totales en concordance avec la référence et segmente spatialement de longs vecteurs intacts en plusieurs portions alternant entre les statuts intact, suspect et disparu. La détection des routes existantes en conformité avec la référence a atteint 78% avec notre plus efficace combinaison de résolution et de prétraitements d’images. Des problèmes chroniques de détection ont été repérés dont la présence de plusieurs segments sans assignation et ignorés du processus. Il y a aussi une surestimation de fausses détections assignées suspectes alors qu’elles devraient être identifiées intactes. Nous estimons, sur la base des mesures linéaires et des analyses spatiales des détections que l’assignation du statut intact devrait atteindre 90% de conformité avec la référence après divers ajustements à l’algorithme. La détection des nouvelles routes fut un échec sans égard à la résolution ou au rehaussement d’image. La recherche des nouveaux segments qui s’appuie sur le repérage de points potentiels de début de nouvelles routes en connexion avec les routes existantes génère un emballement de fausses détections navigant entre les entités non-routières. En lien avec ces incohérences, nous avons isolé de nombreuses fausses détections de nouvelles routes générées parallèlement aux routes préalablement assignées intactes. Finalement, nous suggérons une procédure mettant à profit certaines images rehaussées tout en intégrant l’intervention humaine à quelques phases charnières du processus.
In order to optimize and reduce the cost of road map updating, the Ministry of Natural Resources and Wildlife is considering exploiting high definition color aerial photography within a global automatic detection process. In that regard, Montreal based SYNETIX Inc, teamed with the University of Montreal Remote Sensing Laboratory (UMRSL) in the development of an application indented for the automatic detection of road networks on complex radiometric high definition imagery. This application named SIGMA-ROUTES is a derived module of a software called SIGMA0 earlier developed by the UMRSL for optic and radar imagery of 5 to 10 meter resolution. SIGMA-ROUTES road detections relies on a map guided filtering process that enables the filter to be driven along previously known road vectors and tagged them as intact, suspect or lost depending on the filtering responses. As for the new segments updating, the process first implies a detection of potential starting points for new roads within the filtering corridor of previously known road to which they should be connected. In that respect, it is a very challenging task to emulate the human visual filtering process and further distinguish potential starting points of new roads on complex radiometric high definition imagery. In this research, we intend to evaluate the application’s efficiency in terms of total linear distances of detected roads as well as the spatial location of inconsistencies on a 2.8 km2 test site containing 40 km of various road categories in a semi-urban environment. As specific objectives, we first intend to establish the impact of different resolutions of the input imagery and secondly establish the potential gains of enhanced images (segmented and others) in a preemptive approach of better matching the image property with the detection parameters. These results have been compared to a ground truth reference obtained by a conventional visual detection process on the bases of total linear distances and spatial location of detection. The best results with the most efficient combination of resolution and pre-processing have shown a 78% intact detection in accordance to the ground truth reference when applied to a segmented resample image. The impact of image resolution is clearly noted as a change from 84 cm to 210 cm resolution altered the total detected distances of intact roads of around 15%. We also found many roads segments ignored by the process and without detection status although they were directly liked to intact neighbours. By revising the algorithm and optimizing the image pre-processing, we estimate a 90% intact detection performance can be reached. The new segment detection is non conclusive as it generates an uncontrolled networks of false detections throughout other entities in the images. Related to these false detections of new roads, we were able to identify numerous cases of new road detections parallel to previously assigned intact road segments. We conclude with a proposed procedure that involves enhanced images as input combined with human interventions at critical level in order to optimize the final product.