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Murundus fields, wet landscapes of the Cerrado biome that have been degraded, are characterized by a set of rounded hills with varying diameters and heights. They are considered APP in DF since March 17, 2020 (Law n° 6.520). Due to the small area they occupy in the geographic space and the decharacterization of their original vegetation they become barely perceptible in mapping or orbital sensor images. Recognizing the need to map them to preserve them, it was decided to use Remotely Piloted Aircraft (RPA), with sensors attached. The Golfinho Murundus Field (FAL, UnB) was selected as study area for its already recognized characteristics, being described in previous studies (BAPTISTA, 2008). The field aerial survey planning relied on the use of Google Earth Pro and DroneDeploy software. Four flights were planned with frontal and lateral overlaps of 70%, with the objective of obtaining more details of the terrain. Agisoft Metashape Professional software was used to process the images obtained. The post-processing was done through the GPPK software, supplied by Guandalini Positioning. It was found that the APP in question is being affected by undue works and changes in the composition of fauna and flora, according to the district legislation in force. The methodology applied, given the objective, proved to be effective and well consolidated. It was possible to identify hillside individuals, as well as the relief features forming a drainage divide with a high level of detail., Os campos de murundus, paisagens úmidas do bioma Cerrado que vem sendo degradadas, caracterizam-se por um conjunto de morrotes arredondados com diâmetros e alturas variados. São considerados APP (Área de Preservação Permanente) no DF desde 17 de março de 2020 (Lei nº 6.520). Devido a reduzida área que ocupam no espaço geográfico e a descaracterização de sua vegetação original tornam-se pouco perceptíveis em mapeamentos ou imagens de sensores orbitais. Reconhecendo a necessidade de mapeá-los para preservá-los, optou-se pelo uso de Aeronaves Remotamente Pilotadas (RPA), com sensores acoplados. O Campo de Murundus Golfinho (FAL, UnB) foi selecionado como área de estudo pelas características já reconhecidas, sendo descritas em estudos anteriores (BAPTISTA, Gustavo Macedo de Mello. 2008). O planejamento para aerolevantamento em campo contou com a utilização dos softwares Google Earth Pro e DroneDeploy. Foram planejados 4 voos com sobreposições frontal e lateral de 70%, com o objetivo de obter mais detalhes do terreno. Para processamento das imagens obtidas foi utilizado o software Agisoft Metashape Professional. O pós-processamento foi realizado através do software GPPK, fornecido pela Guandalini Posicionamento. Foi constatado que a APP em questão está sendo afetada por obras indevidas e alterações na composição de fauna e flora, de acordo com a legislação distrital vigente. A metodologia aplicada, dado o objetivo, se mostrou eficaz e bem consolidada. Foi possível identificar indivíduos de morrotes, bem como as características do relevo formando divisor de drenagem com alto nível de detalhes.

Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires.

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. However, traditional population survey methods can be unreliable and labour‐intensive, which complicates the effective conservation and management of many threatened species. We developed a method of using drone‐acquired thermal orthomosaics to monitor the abundance of grey‐headed flying‐foxes (Pteropus poliocephalus) within tree roosts, an IUCN Red Listed species of bat. We assessed the accuracy and precision of this new method and evaluated the performance of four semi‐automated methods for counting flying‐foxes in thermal orthomosaics, including machine learning and Computer Vision (CV) methods. We found a high concordance between the number of flying‐foxes manually counted in drone‐acquired thermal imagery and the true abundance of flying‐foxes in single roost trees, as obtained from direct on‐ground observation. This indicated that the number of flying‐foxes observed in thermal imagery accurately reflected the true abundance of flying‐foxes. In addition, for thermal orthomosaics of whole roost sites, the number of flying‐foxes manually counted was highly repeatable between the same‐day drone surveys and human counters, indicating that this method produced highly precise abundance estimates independent of the identity/experience of human counters. Finally, the number of flying‐foxes manually counted in drone‐acquired thermal orthomosaics was highly concordant with the counts derived from CV and machine learning‐enabled classification techniques. This indicated that accurate and precise measures of colony abundance can be obtained semi‐automatically, thus greatly reducing the amount of human effort involved for obtaining abundance estimates. Our method is thus valuable for reliably monitoring the abundance of individuals in flying‐fox roosts and will aid in the conservation and management of this globally threatened group of flying‐mammals, as well as other homeothermic arboreal‐roosting species.

Ecosystem trait is a standardized description of biological features of a community, and it bridges individual plants and ecosystem. Conventionally most ecosystem trait data are collected from field survey and the generated data is hard to meet the requirements as set in the concept of ecosystem trait. To a great extent, remotely piloted aircraft systems (RPAS) remote sensing, which is capable of retrieving ecosystem traits across multiple scales, can overcome constraints in field plot survey. In this study, we selected alpine grassland ecosystem on the Tibetan Plateau (TP), which is under‐studied due to scarcity of field monitoring data, as the research target. A new data framework was proposed by integrating field plot and RPAS remote sensing data to map spatial patterns of ecosystem traits for the alpine grasslands. Across four landscapes on the TP, ecosystem traits of vegetation coverage (CVC), species number (CSN), individual number (CIN), above ground biomass (AGB), organic carbon content (OC%) and total nitrogen content (TN%) were retrieved. We also calculated Shannon's Diversity Index and Shannon's Evenness Index for each plot. The results showed that RPAS‐based high spatial resolution RGB image is capable of predicting both physical and chemical ecosystem traits for alpine grasslands on the TP. Remote sensing on physical traits are overall more efficient than on chemical traits, with the highest R2 of 0.86 and 0.48 for physical trait and chemical one, respectively. The bands of Red and Green contributed more to the prediction model than band of Blue did, and the spectral mean value played a greater role than the spectral standard deviation. Based on the retrieved results, a set of spatial patterns on ecosystem traits can be revealed. This study represents an advance on ecosystem trait study and can significantly improve our understanding on ecosystem functions of the alpine ecosystem on the TP.

The research aimed to evaluate the spectral behavior of remotely piloted aircraft system (RPAS) images taken of agricultural crops during the day to answer the following hypotheses: (1) there are no significant differences between spectral reflectance information obtained through RPAS images and data from spectroradiometry; (2) the survey time can be defined as a limiting factor in obtaining accurate reflectance data; and (3) it is possible to characterize homogeneous observation windows throughout the day. With regular intervals of 1 h, 12 surveys were made in rice and soybean cultivars, using a Sequoia® camera, embedded in RPAS, and the FieldSpec®3 spectroradiometer, to validate the image spectra. Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption Reflectance Index (TCARI) derived from aerial images were subjected to Kruskal–Wallis and hierarchical cluster tests to define homogeneous observation times. The Sequoia® system made it possible to obtain reflectance data that met the validation criteria, even operating under varying interaction factors during the day, so the flight schedule did not result in loss of information about the vegetation. The hierarchical cluster analysis allowed the detection of temporal patterns of NDVI, OSAVI, and TCARI throughout the day. For rice and soybeans, three more homogeneous observation windows were characterized. In terms of temporal pattern of NDVI, OSAVI, and TCARI, the Kruskal–Wallis analysis was quite sensitive to the inherent variation of the crops throughout the day, so a hypothesis of homogeneous monitoring windows input throughout the day could not be proven.

The main problem of sea aquatorium monitoring is the surveillance of large areas of the sea surface and the presence of many moving ships with different parameters. A methodology for the optimization of a Remotely Piloted Aircraft’s (RPA) route is presented.

The prediction of the formation, spacing and location of rip currents is a scientific challenge that can be achieved by means of different complementary methods. In this paper the analysis of numerical and experimental data, including UAV observation, allowed to detect the presence of rip currents and rip channels at the mouth of Sele river, in the Gulf of Salerno, southern Italy. The dataset used to analyze these phenomena consisted of two different bathymetric surveys, a detailed sediment 5 analysis and a set of high-resolution wave numerical simulations, completed with satellite and UAV observation. The grain size trend analysis and the numerical simulations allowed to identify the rip current system, forced by topographically constrained channels incised on the seabed, which were detected by high resolution bathymetric surveys. The study evidenced that on the coastal area of the Sele mouth grain-size trends are controlled by the contribution of fine sediments, which exhibit suspended transport pathways due to rip currents and longshore currents. The results obtained were confirmed by satellite and UAV 10 observations in different years.

Frequently exposed to natural agents such as waves, wind, tides, storm activity, seasonal changes and anthropogenic agents, coastal areas are tangibly high energy environments and therefore subject to considerable dynamics. In order to mitigate and reduce the impacts on these areas, different types of coastal protection systems can be implemented. Rockwalls and breakwaters are the most ordinary structures and even if used precisely for coastal protection, these flexible structures can in turn be damaged or ineffective over time. Therefore, like the monitoring of coastal areas in terms of execution frequency and accuracy, the measurement of changes over time of these structures, in particular after significant events, can allow to carry out an economic maintenance service before a serious occurrence and costly damage. However, given the rapid evolution of the preservation state of coastal areas and protection structures, it is therefore essential to plan an equally frequent, practical and accurate structural-coastal monitoring. On the other hand, their accessibility can sometimes be dangerous or uncomfortable such as to compromise operations in the field. In this work, the application of two close-range detection techniques competitor, i.e. from the Terrestrial Laser Scanner and from Remote Piloted Aircraft Systems, aimed at the generation of three-dimensional reconstructions of a protection structure, was analyzed. By performing a cloud-to-cloud comparison, interesting considerations have been obtained on the precision that can be achieved and on the technical limits deriving from the two methodologies. Considering the economy and practicality of use, if used correctly, a Remote Piloted Aircraft Systems supported by a suitable geo-referencing and an optimized data processing, can produce accurate and coherent 3D reconstructions as those derivable from the Terrestrial Laser Scanner. Finally, the results obtained by merging the point clouds generated from the two different techniques were evaluated in order to identify any advantages in the structural maintenance of the systems.

The Cerrado, the most biologically diverse savanna in the world, is threatened by anthropogenic activities, and requires development of effective environmental policies spanning local to global scales. Remotely Piloted Aircraft Systems (RPAS) can dramatically reduce the costs and time of surveys and evaluation of these regions. The objective of this article is to demonstrate the potential of visual (RGB) and Light Detection and Ranging (LIDAR) sensors on RPAS for physical characterization of landscapes in the Cerrado biome. Analyses on vegetation structure were performed, with the number of trees automatically counted. The average height of the trees obtained with the RGB sensor was significantly lower than the obtained by LIDAR, demonstrating the limitation of Structure from Motion data in representing the landscape with denser vegetation. Automatic counting of trees with LIDAR data were equal to 1825 on the whole study area, and 245 inside the ecological study area parcels.

This work describes a pilot study in southern Ontario, Canada evaluating the use of the ‘Headwall Nano-Hyperspec’ hyperspectral imager onboard a Remotely Piloted Aircraft System (RPAS). Hyperspectral imagers are extremely useful for monitoring vegetation health and water quality, among other environmental parameters. However, guidelines on the use of this specific instrument for these applications are not yet available. As such, recommended operational settings and calibration procedures are presented here, based on nearly 50 flight campaigns over water bodies and vineyards. Using these procedures, spectral reflectance was successfully captured using an RPAS.

Mechanized operations on terrain slopes can still lead to considerable errors in the alignment and distribution of plants. Knowing slope interference in semi-mechanized planting quality can contribute to precision improvement in decision making, mainly in regions with high slope. This study evaluates the quality of semi-mechanized coffee planting in different land slopes using a remotely piloted aircraft (RPA) and statistical process control (SPC). In a commercial coffee plantation, aerial images were collected by a remotely piloted aircraft (RPA) and subsequently transformed into a digital elevation model (DEM) and a slope map. Slope data were subjected to variance analysis and statistical process control (SPC). Dependent variables analyzed were variations in distance between planting lines and between plants in line. The distribution of plants on all the slopes evaluated was below expected, the most impacted was the slope between 20–25%, implementing 7.8% fewer plants than projected. Inferences about the spacing between plants in the planting row showed that in slopes between 30–40%, the spacing was 0.53 m and between 0 and 15% was 0.55 m. This denotes the compensation of the speed of the operation on different slopes. The spacing between the planting lines had unusual variations on steep slopes. The SCP quality graphics are of lower quality in operations between 30–40%, as they have an average spacing of 3.65 m and discrepant points in the graphics. Spacing variations were observed in all slopes as shown in the SCP charts, and possible causes and implications for future management were discussed, contributing to improvements in the culture installation stage.

In this paper, a low-cost remotely piloted aircraft system (RPAS) technique is proposed for measurement of the surface velocity in rivers or channels with low surface velocity and small discharge. To verify the reliability of the results obtained with the RPAS, we simultaneously measured the surface velocity with other methods based on total stations and close range photogrammetry. The RPAS was used both with ground control points (GCPs) for orientation of the photographic images and without GCPs. The data analysis showed that the RPAS provides valid results even without GCPs. Use of a RPAS without GCPs, relying solely on flight altitude to determine the water velocity, opens the way for its utilization in emergency conditions when it is impossible to access the river banks for the realization and survey of GCPs.

Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production. peerReviewed

In the last few years, interest in the collection of data using remotely piloted aircraft systems (RPAS) has sharply risen. RPAS technology has a very wide area of use; one of its main advantages is its accuracy, timeliness of data, frequency of collecting data and low operating costs. RPAS can be used for the mapping of small, dangerous and inaccessible areas in contrast with ordinary aerial photogrammetry. In the cadastre of real estates of the Czech Republic, it is possible to map out areas by using aerial photogrammetry, so it has been done in the past. However, this is a relatively expensive and complex technology, and therefore we are looking for new alternatives. An alternative would be to use RPAS technology for data acquisition. The testing of the possibility of using RPAS for the cadastre of real estates of the Czech Republic is the subject of this paper. When evaluating results we compared point coordinates measured by geodetic method, GNSS technology and RPAS technology.

In June and July 2011 the RPAS (Remotely Piloted Aircraft System) SUMO (Small Unmanned Meteorological Observer) performed a total number of 299 scientific flights during the BLLAST (Boundary Layer Late Afternoon and Sunset Turbulence) campaign in southern France. Three different types of missions were performed: vertical profiling of the mean meteorological parameters (temperature, humidity and wind), horizontal surveys of the surface temperature and horizontal transects for the estimation of turbulence. The manuscript provides an introduction to the corresponding SUMO operations, including regulatory issues and the coordination of manned and unmanned airborne operations for boundary-layer research that have been pioneered during the BLLAST campaign. The main purpose of the SUMO flight strategy was atmospheric profiling at high temporal resolution. A total of 168 profile flights were performed during the campaign with typically more than 10 flights per Intensive Observational Period (IOP) day. The collected data allow for a detailed study of boundary-layer structure and dynamics and will be used for further analysis, e.g. the determination of profiles of sensible and latent heat fluxes. First, tests of a corresponding method have shown very promising results and have provided surface-flux values in close agreement with those from ground-based eddy-covariance measurements. In addition, 74 horizontal surveys of the IR emission of the surface were performed at altitudes of around 65 m. Each of those surveys covers a typical area of around 1 km2 and allows for an estimation of the surface-temperature variability, important information for the assessment of the heterogeneity of the surface forcing as a function of soil and vegetation properties. The comparison with other surface-temperature measurements shows that the raw data of the airborne and ground observations can differ considerably, but that even a very simple multiple regression method can reduce those differences to a large degree. Finally, 49 flight missions for the measurement of velocity variance have been realized during the BLLAST campaign. For that, SUMO has been equipped with a 5-hole probe (5HP) sensor for the determination of the flow vector at 100 Hz. In particular, for this application there is still need for further improvement, both with respect to the aircraft and sensor hard- and software, and the algorithms and methods for data analysis and interpretation. Nevertheless, the SUMO operations during the BLLAST campaign have shown the vast potential of small and lightweight RPA systems with low infrastructural demand for atmospheric boundary-layer research.

Simple Summary Remotely piloted aircraft systems (RPAS) have been successfully applied in wildlife monitoring with imaging sensors to improve or to supplement conventional field observations. To effectively utilize this technique, we reviewed previous studies related to wildlife detection with RPAS. First, this study provides an overview of the applications of RPAS for wild animal studies from the perspective of individual detection and population surveys as well as behavioral studies. In terms of the RPAS payload, applying thermal-imaging sensors was determined to be advantageous in detecting homeothermic animals due to the thermal contrast with background habitat using case studies detecting southern elephant seal (Mirounga leonina) using RGB and thermal imaging sensors in King George Island, maritime Antarctica. Abstract In wildlife biology, it is important to conduct efficient observations and quantitative monitoring of wild animals. Conventional wildlife monitoring mainly relies on direct field observations by the naked eyes or through binoculars, on-site image acquisition at fixed spots, and sampling or capturing under severe areal constraints. Recently, remotely piloted aircraft systems (RPAS), also called drones or unmanned aerial vehicles (UAV), were successfully applied to detect wildlife with imaging sensors, such as RGB and thermal-imaging sensors, with superior detection capabilities to those of human observation. Here, we review studies with RPAS which has been increasingly used in wildlife detection and explain how an RPAS-based high-resolution RGB image can be applied to wild animal studies from the perspective of individual detection and population surveys as well as behavioral studies. The applicability of thermal-imaging sensors was also assessed with further information extractable from image analyses. In addition, RPAS-based case studies of acquisition of high-resolution RGB images for the purpose of detecting southern elephant seals (Mirounga leonina) and shape property extraction using thermal-imaging sensor in King George Island, maritime Antarctica is presented as applications in an extreme environment. The case studies suggest that currently available cost-effective small-sized RPAS, which are capable of flexible operation and mounting miniaturized imaging sensors, and are easily maneuverable even from an inflatable boat, can be an effective and supportive technique for both the visual interpretation and quantitative analysis of wild animals in low-accessible extreme or maritime environments.

The aim of the research project &ldquo, Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)&rdquo, is to substantially increase the understanding of the stable atmospheric boundary layer (SBL) through a combination of well-established and innovative observation methods as well as by models of different complexity. During three weeks in February 2017, a first field campaign was carried out over the sea ice of the Bothnian Bay in the vicinity of the Finnish island of Hailuoto. Observations were based on ground-based eddy-covariance (EC), automatic weather stations (AWS) and remote-sensing instrumentation as well as more than 150 flight missions by several different Unmanned Aerial Vehicles (UAVs) during mostly stable and very stable boundary layer conditions. The structure of the atmospheric boundary layer (ABL) and above could be resolved at a very high vertical resolution, especially close to the ground, by combining surface-based measurements with UAV observations, i.e., multicopter and fixed-wing profiles up to 200 m agl and 1800 m agl, respectively. Repeated multicopter profiles provided detailed information on the evolution of the SBL, in addition to the continuous SODAR and LIDAR wind measurements. The paper describes the campaign and the potential of the collected data set for future SBL research and focuses on both the UAV operations and the benefits of complementing established measurement methods by UAV measurements to enable SBL observations at an unprecedented spatial and temporal resolution.

In disaster management activities or conflict, it is of utmost importance to have accurate information about the terrain. Due to the practicality of the Remotely Piloted Aircraft Systems (RPAS), this paper proposes the use of these tools at the identification of Drop Zones (DZs), designated as areas into which troops or supplies are dropped by parachute, and Helicopter Landing Zones (HLZs), that can play an important role in the rescue of victims. The use of digital image processing enables the automatic generation, in a short amount of time and with good precision, of an orthorectified mosaic and a Digital Surface Model (DSM), which are fundamental to the terrain's comprehension. Afterward, new attributes are generated from the original image and such information is used by the decision trees to determine the areas of interest. The obtained results indicate that all the areas extracted really can be useful for the purpose.

Using remote sensing methods to capture environmental contamination is very relevant not only to Lithuania, but also to the whole of Europe. The article examines the Remotely Piloted Aircraft System (RPAS) and its components, in particular aircraft (UAV) mounted camera sensors. From the type of sensor depends what can be identified in the photo. The article presents the geographic informational (GIS) modeling system CALMIM with which the experimental modeling of the landfill territory has been performed. UAV aerial photos captured, modeling described and data analysis carried out.

The number of scientific studies that consider possible applications of Remotely Piloted Aircraft Systems (RPAS) for the management of natural hazards effects and the identification of occurred damages are strongly increased in last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPAS can acquire on-demand ultra-high resolution images that can be used for the identification of active processes like landslides or volcanic activities but also for the definition of effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards.

This is a pre-print of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-017-2109-4. Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multi-core boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.

Unmanned aerial systems (UAS), commonly referred to as drones, are finding applications in several ecological research areas since remotely piloted aircraft (RPA) technology has ceased to be a military prerogative. Fixed-wing RPA have been tested for line transect aerial surveys of geographically dispersed marine mammal species. Despite many advantages, their systematic use is far from a reality. Low altitude, long endurance systems are still highly priced. Regulatory bodies also impose limitations while struggling to cope with UAS rapid technological evolution. In contrast, small vertical take-off and landing (VTOL) UAS have become increasingly affordable but lack the flight endurance required for long-range aerial surveys. Although this issue and civil aviation regulations prevent the use of VTOL UAS for marine mammal abundance estimation on a large scale, recent studies have highlighted other potential applications. The present note represents a general overview on the use of UAS as a survey tool for marine mammal studies. The literature pertaining to UAS marine mammal research applications is considered with special concern for advantages and limitations of the survey design. The use of lightweight VTOL UAS to collect marine mammal behavioral data is also discussed.

Made available in DSpace on 2017-07-21T14:19:29Z (GMT). No. of bitstreams: 1 TIAGO GERKE.pdf: 2494737 bytes, checksum: 62b5d2787dcdebe0e9ccf39bfa75f626 (MD5) Previous issue date: 2017-02-03 Wheat cultivation plays an important role to Brazil and the world economic development, as well as in the human diet. The wheat Brazilian production is insufficient to meet the national demand, making research needed in order to improve the yield of this cereal. The goal of this work was to estimate wheat yield, searching for a predictive model through the data mining techniques, with data obtained from high spatial resolution images collected by unmanned aerial vehicles (UAV). The work was carried out in two experimental areas at Ponta Grossa city, Parana state, where for each area eight images were taken, at different culture development stages, with spatial resolution of 3.4cm/px and two images with resolution 10cm/px and 20cm/px, using an eBee UAV with an RGB and a NIR camera. The image processing was done with the Pix4D software, and resulted in an orthomosaics with reflectance values at different wavelengths: Red, Green and Blue, from the RGB camera and Red, Greed and NIR from the NIR camera, besides an image with NDVI values obtained from the arithmetic of NIR and Red wavelengths. The georeferencing correction of each orthomosaic and the extraction of the reflectance values were done with Quantum GIS geographic information system (GIS). From the extracted reflectance values, databases in different proportions (10%, 20%, 40%, 70% and 100%) were created for data mining, using the SMOReg algorithm, based on a support vector machine (SVM) for regression (SVR). The georreferencing correction using 10 control points provided ortomosaics with mean square error (RSME) of distance of 0.35m, which did not show significant difference compared to the correction with 5 control points (RMSE = 0.38m). The reflectance values were different for each study area, making it difficult to indicate better periods for estimating wheat yield. The highest correlation were obtained with data from RGB camera images, followed by the NIR and NDVI camera, with correlations of 0.6168,0.5423 and 0.5324, respectively. The amount of information extracted from the images, reflected in the proportion of the databases, was not significant to generated predictive models, as well as in the correlation indexes, which were statistically the same. Better correlation indices were obtained from the data extracted from the images with spatial resolution of 20cm/px, which suggests that high spatial resolution images may not be adequate for wheat yield estimation. O cultivo do trigo desempenha um papel importante no desenvolvimento econômico de várias regiões do Brasil e do mundo, bem como na dieta humana. A produção brasileira do trigo é insuficiente para atender à demanda nacional, tornando necessárias pesquisas com intuito de melhorar a produtividade desse cereal. O objetivo desse trabalho foi a estimativa de produtividade do trigo, a partir da criação de modelos preditivos por meio da mineração de dados obtidos em imagens de alta resolução espacial, coletadas por aeronave remotamente pilotada (RPA). O trabalho foi realizado em duas áreas experimentais na cidade de Ponta Grossa – PR, onde para cada área foram feitas oito coletas de imagens, em diferentes estádios de desenvolvimento da cultura, com resolução espacial de 3,4cm/px e duas coletas com resolução 10cm/px e 20cm/px, através de uma RPA eBee utilizando uma câmera RGB e outra NIR. O processamento das imagens foi feito a partir do software Pix4D, e resultou em um ortomosaicos com os valores de refletância em diferentes comprimentos de onda: R, G e B da câmera RGB e R, G e NIR da câmera NIR, além de uma imagem com valores de NDVI obtidos a partir da aritmética das bandas Nir e Red (vermelho). A correção de georreferenciamento de cada ortomosaico e a extração dos valores de refletância foram feitas com auxílio do sistema de informação geográfica (SIG) Quantum GIS. A partir dos valores de refletância extraídos, foram criadas bases de dados em diferentes proporções (10%, 20%, 40%, 70% e 100%) para mineração de dados por meio do algoritmo SMOReg, baseado em máquina de vetor de suporte (SVM) para regressão (SVR). A correção de georreferenciamento utilizando 10 pontos de controle proporcionou ortomosaicos com erro médio quadrático (RSME) de distância de 0,35m, o qual não mostrou diferença significativa para a correção com 5 pontos de controle (RMSE = 0,38m). Os valores de refletância foram diferentes para cada área de estudo, tornando difícil a indicação de melhores períodos para a estimativa de produtividade do trigo. Os maiores índices de correlação da produtividade com os comprimentos de onda, foram obtidos com os dados das imagens da câmera RGB, seguido da câmera NIR e NDVI, com as correlações de 0,6168, 0,5423 e 0,5324, respectivamente. A quantidade de informação extraída das imagens, refletida na proporção das bases de dados, não se mostrou significativa nos modelos preditivos gerados, bem como nos índices de correlação, os quais foram estatisticamente iguais. Índices de correlação melhores foram obtidos a partir dos dados extraídos das imagens com resolução espacial de 20cm/px, o que sugere que imagens de alta resolução espacial podem não ser adequadas para estimativa de produtividade do trigo.

Small unmanned aircraft systems (UASs) are often suited to applications where the cost, resolution, and (or) operational inflexibility of conventional remote sensing platforms is limiting. Remote sensing with small UASs is still relatively new, and there is limited understanding of how the data are acquired and used for scientific purposes and decision making. This paper provides practical guidance about the opportunities and limitations of small UAS-based remote sensing by highlighting a small sample of scientific and commercial case studies. Case studies span four themes: (i) mapping, which includes case studies to measure aggregate stockpile volumes and map river habitat; (ii) feature detection, which includes case studies on grassland image classification and detection of agricultural crop infection; (iii) wildlife and animal enumeration, with case studies describing the detection of fish concentrations during a major salmon spawning event, and cattle enumeration at a concentrated animal feeding operation; (iv) landscape dynamics with a case study of arctic glacier change. Collectively, these case studies only represent a fraction of possible remote sensing applications using small UASs, but they provide insight into potential challenges and outcomes, and help clarify the opportunities and limitations that UAS technology offers for remote sensing of the environment.

Knowledge of the extent and degree of wooded vegetation cover in the arid parts of Australia is essential for land-holders and management agencies. The balance between wooded and ground-cover vegetation is important to livestock production and landscape health. Adequate mapping of changes in wooded vegetation cover allows the assessment of its expansion and contraction as input for improved management of production and conservation. The aim of this study was to develop a method to accurately map the extent and degree of wooded tree and shrub cover across an area of arid rangeland in central Australia. Its open and sparse distribution throughout the landscape and its adaptation to an arid environment present challenges to obtaining both representative field measurements and scale appropriate remotely sensed imagery. Recent advancements and access to high spatial resolution satellite imagery provide opportunities for improved mapping. The rapid development of Remotely Piloted Aircraft Systems (RPAS) or drones also provides further opportunities to improve the accuracy of field measurements used in the classification of wooded vegetation. An optimised machine-learning classification was developed using high resolution Planet Dove cube-sat and Sentinel2 imagery and compared to medium resolution Landsat8 imagery. An efficient method of collecting plot scale (ha) wooded vegetation cover estimates for the training and assessment of the satellite image classification was also developed using the RPAS. It was comparable to other field based measurements. The results of the classifications showed a moderate degree of accuracy in distinguishing wooded cover from non-wooded cover, highest with the Planet Dove imagery. An improved accuracy in distinguishing between wooded cover classes was also seen in the Sentinel2 classification. The mapping and subsequent monitoring of wooded vegetation in these landscapes has been shown to be improved with higher resolution satellite imagery.

Experiences from recent conflicts show the strong need for smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors. Smart sensor suites should be part of a smart sensor network - A network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations such as situation assessment, intelligence, surveillance, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Within the study at hand, an open system architecture was developed in order to increase the efficiency of sensor suites. The open system architecture for smart sensor suites, based on a system-of-systems approach, enables combining different sensors in multiple physical configurations, such as distributed sensors, co-located sensors combined in a single package, tower-mounted sensors, sensors integrated in a mobile platform, and trigger sensors. The architecture was derived from a set of system requirements and relevant scenarios. Its mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc. The presented open architecture is designed in accordance with the NATO Architecture Framework (NAF). The architecture allows smart sensor suites to be part of a surveillance network, linked e.g. to a sensor planning system and a C4ISR center, and to be used in combination with future RPAS (Remotely Piloted Aircraft Systems) for supporting a more flexible dynamic configuration of RPAS payloads. © 2017 SPIE. The Society of Photo-Optical Instrumentation Engineers (SPIE)

Experiences from recent conflicts show the strong need for smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors. Smart sensor suites should be part of a smart sensor network - A network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations such as situation assessment, intelligence, surveillance, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Within the study at hand, an open system architecture was developed in order to increase the efficiency of sensor suites. The open system architecture for smart sensor suites, based on a system-of-systems approach, enables combining different sensors in multiple physical configurations, such as distributed sensors, co-located sensors combined in a single package, tower-mounted sensors, sensors integrated in a mobile platform, and trigger sensors. The architecture was derived from a set of system requirements and relevant scenarios. Its mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc. The presented open architecture is designed in accordance with the NATO Architecture Framework (NAF). The architecture allows smart sensor suites to be part of a surveillance network, linked e.g. to a sensor planning system and a C4ISR center, and to be used in combination with future RPAS (Remotely Piloted Aircraft Systems) for supporting a more flexible dynamic configuration of RPAS payloads. © 2017 SPIE. The Society of Photo-Optical Instrumentation Engineers (SPIE)

In this paper we present two system approaches for perimeter surveillance with radar techniques focused on the detection of Micro Aerial Vehicles (MAVs). The main task of such radars is to detect movements of targets such as an individual or a vehicle approaching a facility. The systems typically cover a range of several hundred meters up to several kilometers. In particular, the capability of identifying Remotely Piloted Aircraft Systems (RPAS), which pose a growing threat on critical infrastructure areas, is of great importance nowadays. The low costs, the ease of handling and a considerable payload make them an excellent tool for unwanted surveillance or attacks. Most platforms can be equipped with all kind of sensors or, in the worst case, with destructive devices. A typical MAV is able to take off and land vertically, to hover, and in many cases to fly forward at high speed. Thus, it can reach all kinds of places in short time while the concealed operator of the MAV resides at a remote and riskless place.

To estimate the carbon uptaken by the aerial biomass of a crop, destructive methods based on random sampling of plants are often used. These methods usually require a great effort of time, work and economic costs, besides the error generated in the interpolation process. On the other hand, close range photogrammetric methods that take advantage of sensors mounted on remotely piloted aircraft (RPAs or drone) can be used easily and quickly to obtain the necessary information in order to estimate the aerial biomass of a crop and therefore the assimilated carbon. In this work a methodology based on these photogrammetry techniques by using RPAs with optical off-the-shelf cameras onboard is proposed. This methodology allows a quick data acquisition and processing, relative low costs and reliable and accurate results.

Measurements of solar and infrared irradiance by instruments rigidly mounted to an aircraft have historically been plagued by the introduction of offsets and fluctuations into the data that are solely due to the pitch and roll movements of the aircraft. Two STabilized RAdiometer Platforms (STRAPs) were developed for the U.S. Navy in the early to mid-2000s to address this problem. The development was a collaborative effort between the Naval Research Laboratory (NRL), the Naval Postgraduate School Center for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS), and the U.S. Department of Energy (DOE) Sandia National Laboratories. The STRAPs were designed and built by L-3 Communications Sonoma EO (formerly the small business Sonoma Design Group).

A remotely piloted aircraft research facility is described that will provide new capabilities for atmospheric and oceanographic measurements. The aircraft can fly up to 24 h over remote ocean regions, at low or high altitude, and in various other challenging mission scenarios. The aircraft will fly research missions at speeds of 40 m s^(−1) and provide high spatial resolution measurements. Data will be transmitted in real time to a ground station for analysis and decision-making purposes. The facility will expand the opportunities for universities to participate in field measurement programs.

The prototype of a remotely piloted aircraft designed for research and operational reconnaissance of tropical cyclones has been developed and successfully test flown. Using modern aerodynamic and materials technology, the operational aircraft will by 1994 be capable of sustained operations at altitudes up to 20 km and of deploying large numbers of frangible dropwindsondes. We discuss the potential of such vehicles for making significant improvements of hurricane forecasts and for enhancing the database used in operational weather forecasts, atmospheric research, and climate monitoring.

Small remotely piloted aircraft systems (RPAS), also known as unmanned aircraft systems (UAS), are expected to provide important contributions to wildland fire operations and research, but their evaluation and use have been limited. Our objectives were to leverage US Air Force-controlled airspace to (1) deploy RPAS in support of the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research (RxCADRE) project campaign objectives, including fire progression at multiple scales and (2) assess tactical deployment of multiple RPAS with manned flights in support of incident management. We report here on planning for the missions, including the logistics of integrating RPAS into a complex operations environment, specifications of the aircraft and their measurements, execution of the missions and considerations for future missions. Deployments of RPAS ranged both in time aloft and in size, from the Aeryon Scout quadcopter to the fixed-wing G2R and ScanEagle UAS. Real-time video feeds to incident command staff supported prescribed fire operations and a concept of operations (a planning exercise) was implemented and evaluated for fires in large and small burn blocks. RPAS measurements included visible and long-wave infrared (LWIR) imagery, black carbon, air temperature, relative humidity and three-dimensional wind speed and direction.

LONG-TERM GOAL. The long-term goals of this project are to develop and test methods for extracting geophysical information about the coastal and global oceans from active microwave instruments. We ultimately envision the coastal measurements to be made from remotely piloted aircraft and the global measurements to be made from satellites. SCIENTIFIC OBJECTIVES. The scientific objectives of this research are to understand the interaction of microwave radiation with the ocean surface at a variety of incidence angles and to apply this understanding to extract information about the air/sea interface.

Early in 1982, B.C. Research began to evaluate the feasibility, advantages and limitations of using Remotely-Piloted Aircraft (RPA) to acquire aerial photography in environmental applications. Advantages associated with these aircraft include low costs, ease of operation, low noise levels, portability, safety and very low speed-low altitude capability. In the first phase of this program, a three-meter span fixed-wing aircraft was constructed from a commercial model kit and modified to carry a remotely-operated 35 mm camera system. In summer 1982, this system was used to acquire aerial photography in a variety of applications including forestry, pollution detection, wildlife habitat assessment, site mapping, publicity, wildlife inventories and shoreline mapping. Various operational limitations were encountered including difficulty of navigation and aircraft control, the fragility of the “model” airframe and its limited payload capability. These limitations are being addressed in Phase II of the program, during which a purpose-built airframe is under development, equipped with automatic flight controllers and emergency systems, and employing digitally-encoded radio signals for secure aircraft command. A black-and-white video camera installed in the airframe provides real-time imagery as an aid to navigation and photoframing.

Highly effective small-scale remote sensing can be carried out using a remotely piloted aircraft as a platform for various instruments. The operation of such a platform is described and details of two sensors (a photographic camera and a multiband radiometer)are given. Some preliminary results are presented and future developments are discussed.

Tree attenuation results are described based on data acquired from an experiment employing UHF transmissions at 869 MHz between a remotely piloted aircraft and a stationary vehicle. The objective of the experiment was directed toward providing input to the land mobile satellite community where the extent of shadowing from roadside trees represents important information to be used for system design. Single trees were found to attenuate between 10 to 20 dB with an average median attenuation of 12 dB.