Dissertations / Theses on the topic 'Multispectral aerial video imagery'

Cotton crop condition was assessed from an analysis of multispectral aerial video imagery. Visible-near infrared imagery of two cotton fields was collected towards the end of the 1990 crop. The digital analysis was based on image classification, and the accuracies were assessed using the Kappa coefficient of agreement. The earliest of three images proved to be best for distinguishing plant variety. Vegetation index images were better for estimating potential yield than the original multispectral image; so too were multi-channel images that were transformed using vegetation indices or principal component analysis. The seedbed preparation rig used, the nitrogen application rate and three plant varieties, a weed species and two cotton cultivars, could all be discriminated from the imagery. Accuracies were moderate for the discrimination of plant variety, tillage treatment and nitrogen treatment, and low for the estimation of potential yield.

Video monitoring of beaches is becoming the preferred method for observing changes to nearshore morphology. Consequently this work investigates a new technique for predicting the probability of inundation that is based on measuring runup variation using video. Runup is defined as the water-level elevation maxima on the foreshore relative to the still water level and the waterline is defined as the position where the MWL intersects the beach face. Tairua, and Pauanui Beaches, on the north east coast of the North Island of New Zealand, were used as the field site in this study and represent two very different beaches with the same incoming wave and meteorological conditions. Tairua is most frequently in an intermediate beach state, whereas Pauanui is usually flatter in nature. In order to rectify runup observations, an estimate of the runup elevation was needed (Z). This was estimated by measuring the variation of the waterline over a tidal cycle from time-averaged video images during a storm event and provided beach morphology statistics (i.e. beach slope (α) and beach intercept (b)) used in the rectification process where Z=aX+b. The maximum swash excursions were digitized from time-stacks, and rectified to provide run-up timeseries with duration 20 minutes. Field calibrations revealed a videoed waterline that was seaward of the surveyed waterline. Quantification of this error gave a vertical offset of 0.33m at Tairua and 0.25m at Pauanui. At Tairua, incident wave energy was dominant in the swash zone, and the runup distributions followed a Rayleigh distribution. At Pauanui, the flatter beach, the runup distributions were approximately bimodal due to the dominance of infragravity energy in the swash signal. The slope of the beach was a major control on the runup elevation; runup at Pauanui was directly affected by the deepwater wave height and the tide, while at Tairua there was no correlation. Overall, the results of the study indicate realistic runup measurements, over a wide range of time scales and, importantly, during storm events. However, comparisons of videoed runup and empirical runup formulae revealed larger deviations as the beach steepness increased. Furthur tests need to be carried out to see if this is a limitation of this technique, used to measure runup. The runup statistics are consistently higher at Tairua and suggests that swash runs up higher on steeper beaches. However, because of the characteristics of flatter beaches (such as high water tables and low drainage efficiencies) the impact of extreme runup elevations on such beaches are more critical in regards to erosion and/ or inundation. The coastal environment is of great importance to Māori. Damage to the coast and coastal waahi tapu (places of spiritual importance) caused by erosion and inundation, adversely affects the spiritual and cultural well-being of Māori. For this reason, a chapter was dedicated to investigating the practices used by Māori to protect and preserve the coasts in accordance with tikanga Māori (Māori protocols). Mimicking nature was and still is a practice used by Māori to restore the beaches after erosive events, and includes replanting native dune plants and using natural materials on the beaches to stabilize the dunes. Tapu and rahui (the power and influence of the gods) were imposed on communities to prohibit and prevent people from free access to either food resources or to a particular place, in order to protect people and/ or resources. Interpretations of Māori oral histories provide insights into past local hazards and inform about the safety and viability of certain activities within an area. Environmental indicators were used to identify and forecast extreme weather conditions locally. Māori knowledge of past hazards, and the coastal environment as a whole, is a valuable resource and provides a unique source of expertise that can contribute to current coastal hazards management plans in New Zealand and provide insights about the areas that may again be impacted by natural hazards.

This is a master thesis of the Master of Science degree program in Applied Physics and Electrical Engineering (Y) at Linköping University. The goal of the projectis to develop an application for creating a map in real time from a video camera on a miniature unmanned aerial vehicle. This thesis project and report is a first exploratory study for this application. It implements a prototype method and evaluates it on sample sequences from an on-board video camera. The method first looks for good points to follow in the image and then tracks them in a sequence.The image is then pasted, or merged, together with previous images so that points from the different images align. Two methods to find good points to follow are examined with focus on real-time performance. The result is that the much faster FAST detector method yielded satisfactory results good enough to replace the slower standard method of the Harris-Stephens corner detector. It is also examined whether it is possible to assume that the ground is a flat surface in this application or if a computationally more expensive method estimating altitude information has to be used. The result is that at high altitudes or when the ground is close to flat in reality and the camera points straight downwards a two-dimensional method will do. If flying lower or with high objects in the picture, which is often the case in this application, it must to be taken into account that the points really are at different heights, hence the ground can not be assumed to be flat.

Airborne multispectral video was evaluated as a tool for obtaining urban land cover information for hydrological simulations. Land cover data was obtained for a small urban watershed in Tucson, Arizona using four methods: multispectral aerial video (2 meter and 4 meter pixel resolution), National High Altitude Photography (NHAP), multispectral satellite imagery from Systeme Pour l'Observation de la Terre (SPOT), and by conventional survey. A semi-automated land cover classification produced four classes: vegetation, buildings, pavement, and bare soil. The land cover data from each classification was used as input to a runoff simulation model. Runoff values generate by each simulation were compared to observed runoff. A chi-square goodness-of-fit test indicated that SPOT produced landcover data most similar to the conventional classification. In the curve number model, the SPOT data produced simulated runoff values most similar to observed runoff.

Due to the changing climate and inherent atypically occurring meteorological events in the Arctic regions, more accurate snow quality predictions are needed in order to support the Sámi reindeer herding communities in northern Sweden that struggle to adapt to the rapidly changing Arctic climate. Spatial snow depth distribution is a crucial parameter not only to assess snow quality but also for multiple environmental research and social land use purposes. This contrasts with the current availability of affordable and efficient snow monitoring methods to estimate such an extremely variable parameter in both space and time. In this thesis, a novel approach to determine spatial snow depth distribution in challenging alpine terrain is presented and tested during a field campaign performed in Tarfala, Sweden in April 2019. A multispectral camera capturing five spectral bands in wavelengths between 470 and 860 nanometers on board of a small Unmanned Aerial Vehicle is deployed to derive 3D snow surface models via photogrammetric image processing techniques. The main advantage over conventional photogrammetric surveys is the utilization of accurate RTK positioning technology that enables direct georeferencing of the images, and thus eliminates the need for ground control points and dangerous and time-consuming fieldwork. The continuous snow depth distribution is retrieved by differencing two digital surface models corresponding to the snow-free and snow-covered study areas. An extensive error assessment based on ground measurements is performed including an analysis of the impact of multispectral imagery. Uncertainties and non-transparencies due to a black-box environment in the photogrammetric processing are, however, present, but accounted for during the error source analysis. The results of this project demonstrate that the proposed methodology is capable of producing high-resolution 3D snow-covered surface models (&lt; 7 cm/pixel) of alpine areas up to 8 hectares in a fast, reliable and cost-efficient way. The overall RMSE of the snow depth estimates is 7.5 cm for data acquired in ideal survey conditions. The proposed method furthermore assists in closing the scale gap between discrete point measurements and regional-scale remote sensing, and in complementing large-scale remote sensing data by providing an adequate validation source. As part of the Swedish cooperation project ’Snow4all’, the findings of this project are used to support and validate large-scale snow models for improved snow quality prediction in northern Sweden.<br>På grund av klimatförändringar och naturliga meteorologiska händelser i arktis behövs mer exakta snökvalitetsprognoser för att stödja samernas rensköttsamhällen i norra Sverige som har problem med att anpassa sig till det snabbt föränderliga arktiska klimatet. Rumslig snödjupsfördelning är en avgörande parameter för att inte bara bedöma snökvaliteten utan även för flera miljöforskning och sociala markanvändningsändamål. Detta står i motsats till den nuvarande tillgången till överkomliga och effektiva metoder för snöövervakning för att uppskatta sådan extremt varierande parameter i tid och rum. I detta arbete presenteras och testas en ny metod för att bestämma rumslig snödjupssdistribution i utmanande alpin terräng under en fältstudie som genomfördes i Tarfala i norra Sverige i april 2019. Via fotogrammetrisk bildbehandlingsteknik hämtades snöytemodeller i 3D med hjälp av en multispektral kamera monterad på en liten obemannad drönare. En viktig fördel, i jämförelse med konventionella fotogrammetriska undersökningar, är användningen av exakt RTK-positioneringsteknik som möjliggör direkt georeferencing och eliminerar behovet av markkontrollpunkter. Den kontinuerliga snödjupfördelningen hämtas genom att ytmodellerna delas upp i snöfria respektive snötäckta undersökningsområden. En omfattande felsökning som baseras på markmätningar utförs, inklusive en analys av effekten av multispektrala bilder. Resultaten från denna studie visar att den famtagna metoden kan producera högupplösta snötäckta höjdmodeller i 3D (&lt; 7 cm/pixel) av alpina områden på upp till 8 hektar på ett snabbt, pålitligt och kostnadseffektivt sätt. Den övergripande RMSE för det beräknade snödjupet är 7,5 cm för data som förvärvats under idealiska undersökningsförhållanden. Som ett led i det svenska projektet “Snow4all” används resultaten från projektet för att förbättra och validera storskaliga snömodeller för att bättre förutse snökvaliteten i norra Sverige.

Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1999.<br>"September 1999". Thesis advisor(s): Orin E. Marvel, William Haga, Brad Naegle. Includes bibliographical references (p. 159-162). Also avaliable online.

Approved for public release; distribution is unlimited<br>The Department of Defense Joint Technical Architecture has mandated a migration from analog to digital technology in the Command, Control, Communication, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) community. The Tactical Unmanned Aerial Vehicle (TUAV) and Tactical Control System (TCS) are two brigade imagery intelligence systems that the Army will field within the next three years to achieve information superiority on the modern digital battlefield. These two systems provide the brigade commander with an imagery collection and processing capability never before deployed under brigade control. The deployment of the Warfighter Information Network (WIN), within three to five years, will ensure that a digital dissemination network is in place to handle the transmission bandwidth requirements of large digital video files. This thesis examines the storage and dissemination capabilities of this future brigade imagery system. It calculates a minimum digital! storage capacity requirement for the TCS Imagery Product Library, analyzes available storage media based on performance, and recommends a high capacity storage architecture based on modern high technology fault tolerance and performance. A video streaming technique is also recommended that utilizes the digital interconnectivity of the WIN for dissemination of video imagery throughout the brigade.

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).

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.

Aircraft-mounted cameras have potential to greatly increase the effectiveness of wilderness search and rescue efforts by collecting photographs or video of the search area. The more data that is collected, the more difficult it becomes to process it by visual inspection alone. This work presents a method for automatically detecting unusual objects in aerial video to assist people in locating signs of missing persons in wilderness areas. The detector presented here makes use of anomaly detection methods originally designed for hyperspectral imagery. Multiple anomaly detection methods are considered, implemented, and evaluated. These anomalies are then aggregated into spatiotemporal objects by using the video's inherent spatial and temporal redundancy. The results are therefore summarized into a list of unusual objects to enhance the search technician's video review interface. In the user study reported here, unusual objects found by the detector were overlaid on the video during review. This increased participants' ability to find relevant objects in a simulated search without significantly affecting the rate of false detection. Other effects and possible ways to improve the user interface are also discussed.

<p> Remotely sensed data has been discussed as a possible alternative to the standard precision agriculture systems of combine-mounted yield monitors because of the burden, cost, end of season use, and inherent errors that are associated with these systems. Due to the potential quantitative use of remote sensing in precision agriculture, the primary focus of this study was to test the relationship between multitemporal/multispectral digital aerial imagery with corn (<i>Zea mays</i> L.) and soybean (<i>Glycine max </i> L.) yield. Digital aerial imagery was gathered on nine different dates throughout the 2015 growing season from two fields (one corn and one soybean) located on a farm in Story County, Iowa. To begin assessing this relationship, the digital aerial imagery was radiometrically calibrated. The radiometric calibration process used calibration tarps with known reflectance values (3, 6, 12, 22, 44, and 56 percent). The calibrated imagery was then used to calculate and output 12 different vegetation indices (VIs) and three calibrated wavebands (red, green, and near-infrared). </p><p> Next, the calibrated VIs and wavebands from the 2015 growing season were used to examine their relationship with the corn and soybean yield data collected from a combine yield monitor system. This relationship between multitemporal/multispectral digital aerial imagery with corn and soybean yield was investigated with principal component analysis and spatial modeling techniques. The results from spatial modeling of corn revealed that VIs utilizing the green waveband performed strongly. VIs such as, chlorophyll index-green, chlorophyll vegetation index, and green normalized difference vegetation index accounted for 81.6, 83.0, and 82.4 percent of the yield variability, respectively. Strong modeling relationships were also found in soybean using just the near-infrared waveband or VIs that utilized the near-infrared waveband. The near-infrared waveband captured 89.1 percent of the yield variation, while VIs such as, difference vegetation index, triangular vegetation index, soil adjusted vegetation index, and optimized soil adjusted vegetation index accounted for 87.3, 87.3, 83.9, and 83.8 percent of soybean yield variability, respectively. The temporal assessment of the remotely sensed data also identified certain VIs and wavebands that captured pivotal growth stages for detecting potential yield limiting factors. These specific growth stages varied for different VIs and wavebands for both corn and soybean. Overall, the results from this study identified that mid-to-late vegetative growth stages (prior to tasseling) and late-season reproductive stages were important parameters that provided unique information in the modeling of corn yield variability, while the later reproductive stages (just prior to senescence) were essential to capturing soybean yield variability. </p><p> Lastly, this research produced corn and soybean yield maps from the digital aerial imagery. The digital aerial imagery yield maps were then compared with maps that used kriging interpolation of the combine yield monitor data gathered from the same corn and soybean fields. The results indicated that both corn and soybean yield maps produced with multitemporal/multispectral digital aerial imagery were comparable with a standard method of kriging interpolation from yield monitor data.</p><p>

Nowadays constant technological evolution cover several necessities and daily tasks in our society. In particular, drones usage, given its wide vision to capture the terrain surface images, allows to collect large amounts of information with high efficiency, performance and accuracy. This master dissertation’s main purpose is the analysis, classification and respective mapping of different terrain types and characteristics, using multispectral imagery. Solar radiation flow reflected on the surface is captured by the used multispectral camera’s different lenses (RedEdge-M, created by Micasense). Each one of these five lenses is able to capture different colour spectrums (i.e. Blue, Green, Red, Near-Infrared and RedEdge). It is possible to analyse the various spectrum indices from the collected imagery, according to the fusion of different combinations between coloured bands (e.g. NDVI, ENDVI, RDVI. . . ). This project engages a ROS (Robot Operating System) framework development, capable of correcting different captured imagery and, hence, calculating the implemented spectral indices. Several parametrizations of terrain analysis were carried throughout the project, and this information was represented in semantic maps by layers (e.g. vegetation, water, soil, rocks). The obtained experimental results were validated in the scope of several projects incorporated in PDR2020, with success rates between 70% and 90%. This framework can have multiple technical applications, not only in Precision Agriculture, but also in vehicles autonomous navigation and multi-robot cooperation.

Master of Science<br>Department of Biological & Agricultural Engineering<br>Ajay Sharda<br>Discovered in 1971, sudden death syndrome (SDS), caused by the fungus Fusarium virguliforme, has spread from the US to South American and European countries. It has potential to infect soybean crops worldwide, causing yield losses of 10% to 15% and even 70% in extreme cases. There is a need for rapid spatial assessment of SDS. Currently, the extent and severity of SDS are scored using visual symptoms as indicators. This method can take hours to collect and is subject to human bias and changing environmental conditions. Color infrared (CIR) and thermal infrared (TIR) imagery detect changes in light reflectance (visible and near-infrared bands) and emittance (canopy temperature), respectively. Stressed crops may show deviations in light reflectiveness, as well as elevated canopy temperatures. The use of CIR and TIR imagery and flexible aerial remote sensing platforms offer an alternative for SDS detection and diagnosis compared to hand scoring methods. Crop stress and diseases have been detected using manned and unmanned aerial systems previously. Yet, to date, SDS has not been remotely assessed using CIR or TIR imagery collected with aerial platforms. The following research utilizes high throughput CIR and TIR imagery collected using a small unmanned aerial system (sUAS) to detect and assess SDS. A comparative evaluation of ground-based and aerial CIR methods for assessing SDS was conducted to understand the effectiveness of novel aerial SDS detection methods. Furthermore, a TIR case study investigating the use of potential thermal canopy changes for SDS detection was conducted to investigate the possibility of using TIR as an SDS indicator. CIR reflectance measured from a ground-based spectrometer and sUAS was collected data over a two-year period. Ground-based spectrometer data were collected weekly, while a sUAS collected aerial imagery late in the growing season each year before plant maturity. Pigment index (PI) values were derived from ground-based and aerial data. Results showed a strong negative correlation between SDS score and PI values. Aerial and ground-based data both showed strong correlations to SDS score, however, aerial data displayed a stronger relationship possibly due to minimal changes in environmental conditions. High SDS scores correlated strongly to aerial derived PI (R2 = 0.8359). Rapidly assessed high SDS allows for accurate screening of SDS critical for soybean breeding. The second year of the study investigated each component of SDS score, severity, and incidence. PI proved to have the best correlation with severity (R2 = 0.6313 and ρ = -0.8016) rather than incidence or SDS score. PI also correlated to SDS scores with R2 = 0.6159 and ρ = -0.7916. A sUAS mounted TIR camera collected imagery four times during the growing season when SDS foliar symptoms were just starting to appear. At the start of the study period, the correlation between canopy temperature and SDS is low (ρ = -0.2907), but increases over the growing season as SDS prevalence increases ending with a strong correlation (ρ = -0.7158). Early identification of SDS leads to the implementation of mitigation practices and changes in irrigation scheduling before the disease reaches severe symptoms. Early mitigation of SDS reduces yield loses for farmers. The use of both CIR and TIR aerial imagery captured using sUAS can provide rapid spatial assessments of SDS, which is required by both producers and plant breeders. PI derived from CIR imagery showing strong correlations to SDS score reinforce the idea of replacing the time-consuming traditional ground-based systems with the more flexible, faster, sUAS methods. TIR imagery was shown to be reliable in assessing SDS in soybeans further establishing another possible aerial method for early detection of SDS.

碩士<br>國立屏東科技大學<br>土壤與水工程國際碩士學位學程<br>105<br>Chlorophyll content, a good indicator for plant healthy state and important biophysical parameters, is important significance for precision agriculture. To estimate the spatial variability of chlorophyll content over fields, traditional method using chlorophyll meter requires many point samples. Because of relationship between chlorophyll content and spectral reflectance of certain bands, remote sensing techniques have the potential to predict the chlorophyll content over large fields. In this study, the use of multispectral resolution imagery using unmanned aerial vehicle called UAV is to select the vegetation indices sensitive to chlorophyll content using regression model. The goal of our study is to investigate the performance of multispectral camera for estimation of chlorophyll content. The application of remote sensing techniques on paddy rice were conducted on six dates from January to May during all stage growth with four narrow band sensors (Green, Red, Red Edge and Near Infrared) in order to estimate the chlorophyll content. Nine physiological indices were determined to estimate the chlorophyll content. Normalized Vegetation index (NDVI), Modified Triangular vegetation index (MTVI), Normalized Green-Red Difference Index (NGRDI), Red Edge NDVI (REGNDVI), Chlorophyll Vegetation Index (CVI) were found to be accurate and linear estimators of chlorophyll content measured in the fields.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies<br>It is becoming more present in agriculture’s practices the use of Unmanned Aerial Systems with sensors capable of capturing light, in the visible and in longer wavelengths of the electromagnetic spectrum once reflected on the field. These sensors have been used to perform Remote Sensing also in other knowledge fields, describing phenomenon without the risk, cost and the time consuming processes associated with in site samples collection and analysis by a technician or satellite imagery acquisition. The Vegetation Indexes developed can explain the vigor of the cultivation and its data collection processes are more cost and time efficient, allowing farmers to monitor plant grow in every critical stage. These Vegetation Indexes started by being calculated from satellite and airborne imagery, one of the main source for crop management tools, however UAS is becoming more present in Precision Agriculture, achieving better spatial and temporal resolution. This gap in spatial resolution when studying low density cultivations like olive groves and vineyards, creates Vegetation Index’s maps polluted with noise caused by the soil and therefore difficult to interpret and analyse. Hence, when the agriculture has spaced and low density vegetation becomes challenging to understand and extract information from these vegetation index’s maps regarding different spatial variability patterns of the tree canopy vigor. In these cases, where vegetation is spaced it is important to filter this noise. A Classification Model was developed with the objective of extracting just the vegetation’s canopy data. The soil was filtered and the canopy data interpolated using spatial analysis tools. The final interpolated maps produced can provide meaningful information regarding the spatial variability and be used to support decision making, identifying critical areas to be intervened and managed, or be used as an input for Variable Rate Technology applications.