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Journal articles on the topic 'Multispectral aerial video imagery'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Morozov, A. N., A. L. Nazolin, and I. L. Fufurin. "Optical and Spectral Methods for Detection and Recognition of Unmanned Aerial Vehicles." Radio Engineering, no. 2 (May 17, 2020): 39–50. http://dx.doi.org/10.36027/rdeng.0220.0000167.

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The paper considers a problem of detection and identification of unmanned aerial vehicles (UAVs) against the animate and inanimate objects and identification of their load by optical and spectral optical methods. The state-of-the-art analysis has shown that, when using the radar methods to detect small UAVs, there is a dead zone for distances of 250-700 m, and in this case it is important to use optical methods for detecting UAVs.The application possibilities and improvements of the optical scheme for detecting UAVs at long distances of about 1-2 km are considered. Location is performed by int

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Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. "ACCURACY ASSESSMENT OF DIRECT GEOREFERENCING FOR PHOTOGRAMMETRIC APPLICATIONS ON SMALL UNMANNED AERIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 77–83. http://dx.doi.org/10.5194/isprs-archives-xl-3-w4-77-2016.

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Efficient mapping from unmanned aerial platforms cannot rely on aerial triangulation using known ground control points. The cost and time of setting ground control, added to the need for increased overlap between flight lines, severely limits the ability of small VTOL platforms, in particular, to handle mapping-grade missions of all but the very smallest survey areas. Applanix has brought its experience in manned photogrammetry applications to this challenge, setting out the requirements for increasing the efficiency of mapping operations from small UAVs, using survey-grade GNSS-Inertial techn

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Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. "ACCURACY ASSESSMENT OF DIRECT GEOREFERENCING FOR PHOTOGRAMMETRIC APPLICATIONS ON SMALL UNMANNED AERIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 77–83. http://dx.doi.org/10.5194/isprsarchives-xl-3-w4-77-2016.

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Efficient mapping from unmanned aerial platforms cannot rely on aerial triangulation using known ground control points. The cost and time of setting ground control, added to the need for increased overlap between flight lines, severely limits the ability of small VTOL platforms, in particular, to handle mapping-grade missions of all but the very smallest survey areas. Applanix has brought its experience in manned photogrammetry applications to this challenge, setting out the requirements for increasing the efficiency of mapping operations from small UAVs, using survey-grade GNSS-Inertial techn

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Jayroe, Clinton W., William H. Baker, and Amy B. Greenwalt. "Using Multispectral Aerial Imagery to Evaluate Crop Productivity." Crop Management 4, no. 1 (2005): 1–7. http://dx.doi.org/10.1094/cm-2005-0205-01-rs.

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Bruce, Robert W., Istvan Rajcan, and John Sulik. "Classification of Soybean Pubescence from Multispectral Aerial Imagery." Plant Phenomics 2021 (August 4, 2021): 1–11. http://dx.doi.org/10.34133/2021/9806201.

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The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescen

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Yang, Bo, Timothy L. Hawthorne, Hannah Torres, and Michael Feinman. "Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data." Drones 3, no. 3 (2019): 60. http://dx.doi.org/10.3390/drones3030060.

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have qu

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Kramber‡, W. J., A. J. Richardson§, P. R. Nixon§, and K. Lulla†. "Principal component analysis of aerial video imagery†." International Journal of Remote Sensing 9, no. 9 (1988): 1415–22. http://dx.doi.org/10.1080/01431168808954949.

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Zhang, Yanchao, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, and Wenbo Zhang. "Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification." Remote Sensing 13, no. 8 (2021): 1411. http://dx.doi.org/10.3390/rs13081411.

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Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine lear

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Yang, Chenghai, Charles P. C. Suh, and John K. Westbrook. "Early identification of cotton fields using mosaicked aerial multispectral imagery." Journal of Applied Remote Sensing 11, no. 1 (2017): 016008. http://dx.doi.org/10.1117/1.jrs.11.016008.

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Soni, Ayush, Alexander Loui, Scott Brown, and Carl Salvaggio. "High-quality multispectral image generation using Conditional GANs." Electronic Imaging 2020, no. 8 (2020): 86–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.8.imawm-086.

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In this paper, we demonstrate the use of a Conditional Generative Adversarial Networks (cGAN) framework for producing high-fidelity, multispectral aerial imagery using low-fidelity imagery of the same kind as input. The motivation behind is that it is easier, faster, and often less costly to produce low-fidelity images than high-fidelity images using the various available techniques, such as physics-driven synthetic image generation models. Once the cGAN network is trained and tuned in a supervised manner on a data set of paired low- and high-quality aerial images, it can then be used to enhan

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Csatho, Bea M., Cornelis J. Van Der Veen, and Catherine M. Tremper. "Trimline Mapping from Multispectral Landsat ETM+ Imagery." Géographie physique et Quaternaire 59, no. 1 (2006): 49–62. http://dx.doi.org/10.7202/013736ar.

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AbstractMultispectral Landsat ETM+ imagery is used to study the ice-marginal region in the vicinity of Jakobshavn Isfjord, west Greenland. In particular, the trimline indicating margin retreat since the maximum stand attained during the Little Ice Age maximum is reconstructed, and compared with earlier maps based on aerial photogrammetry and ground surveys. Applying supervised classification, fourteen different surface types were identified, ranging from snow and ice, debris-covered ice and water with differing turbidities, to different types of vegetative landcover. After similar classes were

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Yang, Chenghai, and J. H. Everitt. "Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed." International Journal of Remote Sensing 31, no. 20 (2010): 5423–38. http://dx.doi.org/10.1080/01431160903369626.

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Olivetti, Diogo, Henrique Roig, Jean-Michel Martinez, et al. "Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs." Remote Sensing 12, no. 11 (2020): 1855. http://dx.doi.org/10.3390/rs12111855.

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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

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Pourazar, Hossein, Farhad Samadzadegan, and Farzaneh Dadrass Javan. "Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment." European Journal of Remote Sensing 52, sup3 (2019): 17–31. http://dx.doi.org/10.1080/22797254.2019.1642143.

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Borgogno-Mondino, E., A. Lessio, L. Tarricone, V. Novello, and L. de Palma. "A comparison between multispectral aerial and satellite imagery in precision viticulture." Precision Agriculture 19, no. 2 (2017): 195–217. http://dx.doi.org/10.1007/s11119-017-9510-0.

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Su, Jinya, Cunjia Liu, Matthew Coombes, et al. "Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery." Computers and Electronics in Agriculture 155 (December 2018): 157–66. http://dx.doi.org/10.1016/j.compag.2018.10.017.

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Prior, Elizabeth M., Frances C. O’Donnell, Christian Brodbeck, Wesley N. Donald, George Brett Runion, and Stephanie L. Shepherd. "Measuring High Levels of Total Suspended Solids and Turbidity Using Small Unoccupied Aerial Systems (sUAS) Multispectral Imagery." Drones 4, no. 3 (2020): 54. http://dx.doi.org/10.3390/drones4030054.

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Due to land development, high concentrations of suspended sediment are produced from erosion after rain events. Sediment basins are commonly used for the settlement of suspended sediments before discharge. Stormwater regulations may require frequent sampling and monitoring of these basins, both of which are time and labor intensive. Potential remedies are small, unoccupied aerial systems (sUAS). The goal of this study was to demonstrate whether sUAS multispectral imagery could measure high levels of total suspended solids (TSS) and turbidity in a sediment basin. The sediment basin at the Aubur

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Sangeetha, A. "Damaged Building Detection from Satellite Multispectral Imagery." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3367–73. http://dx.doi.org/10.22214/ijraset.2021.35706.

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Damaged Building footprint detection in satellite and aerial imagery is crucial in city management. Building detection is a fundamental but a challenging problem mainly because it requires correct recovery of building footprints from high-resolution images. Buildings are one of the key pieces of cadastral information related to population and cities, and are fundamental to urban planning & policymaking. Critical infrastructures, such as public transport, electricity, water distribution networks, or postal and delivery services, rely heavily on accurate population and building maps. On top

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Khodaei, B., F. Samadzadegan, F. Dadras Javan, and H. Hasani. "3D SURFACE GENERATION FROM AERIAL THERMAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 401–5. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-401-2015.

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Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targe

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Khaliq, Aleem, Lorenzo Comba, Alessandro Biglia, Davide Ricauda Aimonino, Marcello Chiaberge, and Paolo Gay. "Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment." Remote Sensing 11, no. 4 (2019): 436. http://dx.doi.org/10.3390/rs11040436.

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In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation betwee

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Grulke, Nancy, Jason Maxfield, Phillip Riggan, and Charlie Schrader-Patton. "Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery." Remote Sensing 12, no. 14 (2020): 2338. http://dx.doi.org/10.3390/rs12142338.

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Drought, ozone (O3), and nitrogen deposition (N) alter foliar pigments and tree crown structure that may be remotely detectable. Remote sensing tools are needed that pre-emptively identify trees susceptible to environmental stresses could inform forest managers in advance of tree mortality risk. Jeffrey pine, a component of the economically important and widespread western yellow pine in North America was investigated in the southern Sierra Nevada. Transpiration of mature trees differed by 20% between microsites with adequate (mesic (M)) vs. limited (xeric (X)) water availability as described

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Li, Yundong, Antonios Kontsos, and Ivan Bartoli. "Automated Rust-Defect Detection of a Steel Bridge Using Aerial Multispectral Imagery." Journal of Infrastructure Systems 25, no. 2 (2019): 04019014. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000488.

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Mcnally, Adrian J. D., and Samuel J. P. Mckenzie. "Combining multispectral aerial imagery and digital surface models to extract urban buildings." Journal of Maps 7, no. 1 (2011): 51–59. http://dx.doi.org/10.4113/jom.2011.1152.

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Melville, Bethany, Adrian Fisher, and Arko Lucieer. "Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery." International Journal of Applied Earth Observation and Geoinformation 78 (June 2019): 14–24. http://dx.doi.org/10.1016/j.jag.2019.01.013.

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Chrétien, L. P., J. Théau, and P. Ménard. "WILDLIFE MULTISPECIES REMOTE SENSING USING VISIBLE AND THERMAL INFRARED IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 241–48. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-241-2015.

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Wildlife aerial surveys require time and significant resources. Multispecies detection could reduce costs to a single census for species that coexist spatially. Traditional methods are demanding for observers in terms of concentration and are not adapted to multispecies censuses. The processing of multispectral aerial imagery acquired from an unmanned aerial vehicle (UAV) represents a potential solution for multispecies detection. The method used in this study is based on a multicriteria object-based image analysis applied on visible and thermal infrared imagery acquired from a UAV. This proje

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Zhao, Yuan, Song, et al. "Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging." Sensors 19, no. 18 (2019): 3859. http://dx.doi.org/10.3390/s19183859.

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Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged an

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Suo, McGovern, and Gilmer. "Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS." Remote Sensing 11, no. 15 (2019): 1814. http://dx.doi.org/10.3390/rs11151814.

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Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the

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Easterday, Kelly, Chippie Kislik, Todd Dawson, Sean Hogan, and Maggi Kelly. "Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)." Remote Sensing 11, no. 16 (2019): 1853. http://dx.doi.org/10.3390/rs11161853.

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Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference veg

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Fletcher, Reginald S., and Allan T. Showler. "Surveying kaolin-treated cotton plots with airborne multispectral digital video imagery." Computers and Electronics in Agriculture 54, no. 1 (2006): 1–7. http://dx.doi.org/10.1016/j.compag.2006.06.004.

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Vlachopoulos, O., B. Leblon, J. Wang, A. Haddadi, A. LaRocque, and G. Patterson. "MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2021 (June 28, 2021): 203–8. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2021-203-2021.

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Abstract. Unmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with extensive lodging damages in two aerial surveys. A Random Forests classification model was trained and tested for the discrimination of lodged barley with an overall accuracy of 99.7% on the validation dataset. The crop areas with lodging were automatically delineated by vector analysis and co

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Durfee, Nicole, Carlos Ochoa, and Ricardo Mata-Gonzalez. "The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands." Forests 10, no. 4 (2019): 296. http://dx.doi.org/10.3390/f10040296.

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Monitoring vegetation characteristics and ground cover is crucial to determine appropriate management techniques in western juniper (Juniperus occidentalis Hook.) ecosystems. Remote-sensing techniques have been used to study vegetation cover; yet, few studies have applied these techniques using unmanned aerial vehicles (UAV), specifically in areas of juniper woodlands. We used ground-based data in conjunction with low-altitude UAV imagery to assess vegetation and ground cover characteristics in a paired watershed study located in central Oregon, USA. The study was comprised of a treated waters

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Mathews, Adam J. "A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards." International Journal of Applied Geospatial Research 6, no. 4 (2015): 65–87. http://dx.doi.org/10.4018/ijagr.2015100104.

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This paper explores the use of compact digital cameras to remotely estimate spectral reflectance based on unmanned aerial vehicle imagery. Two digital cameras, one unaltered and one altered, were used to collect four bands of spectral information (blue, green, red, and near-infrared [NIR]). The altered camera had its internal hot mirror removed to allow the sensor to be additionally sensitive to NIR. Through on-ground experimentation with spectral targets and a spectroradiometer, the sensitivity and abilities of the cameras were observed. This information along with on-site collected spectral

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Wallhead, Matthew, Heping Zhu, John Sulik, and William Stump. "A Workflow for Extracting Plot-level Biophysical Indicators From Aerially Acquired Multispectral Imagery." Plant Health Progress 18, no. 2 (2017): 95–96. http://dx.doi.org/10.1094/php-04-17-0025-ps.

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Advances in technologies associated with unmanned aerial vehicles (UAVs) have allowed researchers, farmers and agribusinesses to incorporate UAVs coupled with imaging systems into data collection and decision making. Multispectral imagery allows for a quantitative assessment of biophysical indicators or plant health status. What is needed now is a standardized workflow for the quantitative assessment of plant biophysical indicators using low-altitude, high-resolution, UAV-acquired multispectral imagery. With this need in mind, the authors developed and proposed a workflow for extracting plot-l

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Zhou, Jing, Dennis Yungbluth, Chin Nee Vong, Andrew Scaboo, and Jianfeng Zhou. "Estimation of the Maturity Date of Soybean Breeding Lines Using UAV-Based Multispectral Imagery." Remote Sensing 11, no. 18 (2019): 2075. http://dx.doi.org/10.3390/rs11182075.

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Physiological maturity date is a critical parameter for the selection of breeding lines in soybean breeding programs. The conventional method to estimate the maturity dates of breeding lines uses visual ratings based on pod senescence by experts, which is subjective by human estimation, labor-intensive and time-consuming. Unmanned aerial vehicle (UAV)-based phenotyping systems provide a high-throughput and powerful tool of capturing crop traits using remote sensing, image processing and machine learning technologies. The goal of this study was to investigate the potential of predicting maturit

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Zhang, Suming, Gengxing Zhao, Kun Lang, et al. "Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage." Sensors 19, no. 7 (2019): 1485. http://dx.doi.org/10.3390/s19071485.

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Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hek

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Albetis, Johanna, Sylvie Duthoit, Fabio Guttler, et al. "Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery." Remote Sensing 9, no. 4 (2017): 308. http://dx.doi.org/10.3390/rs9040308.

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Brodie, Juliet, Lauren V. Ash, Ian Tittley, and Chris Yesson. "A comparison of multispectral aerial and satellite imagery for mapping intertidal seaweed communities." Aquatic Conservation: Marine and Freshwater Ecosystems 28, no. 4 (2018): 872–81. http://dx.doi.org/10.1002/aqc.2905.

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Marston, Zachary P. D., Theresa M. Cira, Erin W. Hodgson, Joseph F. Knight, Ian V. Macrae, and Robert L. Koch. "Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles." Journal of Economic Entomology 113, no. 2 (2019): 779–86. http://dx.doi.org/10.1093/jee/toz306.

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Abstract Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by

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MAUSEL, P. W., M. A. KARASKA, C. Y. MAO, D. E. ESCOBAR, and J. H. EVERITT. "Insights into secchi transparency through computer analysis of aerial multispectral video data." International Journal of Remote Sensing 12, no. 12 (1991): 2485–92. http://dx.doi.org/10.1080/01431169108955282.

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Boon, M. A., and S. Tesfamichael. "WETLAND VEGETATION INTEGRITY ASSESSMENT WITH LOW ALTITUDE MULTISPECTRAL UAV IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 55–62. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-55-2017.

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The use of multispectral sensors on Unmanned Aerial Vehicles (UAVs) was until recently too heavy and bulky although this changed in recent times and they are now commercially available. The focus on the usage of these sensors is mostly directed towards the agricultural sector where the focus is on precision farming. Applications of these sensors for mapping of wetland ecosystems are rare. Here, we evaluate the performance of low altitude multispectral UAV imagery to determine the state of wetland vegetation in a localised spatial area. Specifically, NDVI derived from multispectral UAV imagery

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Chancia, Robert, Jan van Aardt, Sarah Pethybridge, Daniel Cross, and John Henderson. "Predicting Table Beet Root Yield with Multispectral UAS Imagery." Remote Sensing 13, no. 11 (2021): 2180. http://dx.doi.org/10.3390/rs13112180.

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Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root cou

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Suo, C., E. McGovern, and A. Gilmer. "VEGETATION MAPPING OF A COASTAL DUNE COMPLEX USING MULTISPECTRAL IMAGERY ACQUIRED FROM AN UNMANNED AERIAL SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 421–27. http://dx.doi.org/10.5194/isprs-archives-xlii-1-421-2018.

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<p><strong>Abstract.</strong> Vegetation mapping, identifying the distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environment changes on vegetation, and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper represents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an U

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Stavrakoudis, Dimitris, Dimitrios Katsantonis, Kalliopi Kadoglidou, Argyris Kalaitzidis, and Ioannis Gitas. "Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery." Remote Sensing 11, no. 5 (2019): 545. http://dx.doi.org/10.3390/rs11050545.

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The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). T

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Heim, René, Ian Wright, Peter Scarth, Angus Carnegie, Dominique Taylor, and Jens Oldeland. "Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation." Drones 3, no. 1 (2019): 25. http://dx.doi.org/10.3390/drones3010025.

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Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (B

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Song, Bonggeun, and Kyunghun Park. "Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index." Remote Sensing 12, no. 3 (2020): 387. http://dx.doi.org/10.3390/rs12030387.

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In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalize

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Benjamin, Adam R., Amr Abd-Elrahman, Lyn A. Gettys, Hartwig H. Hochmair, and Kyle Thayer. "Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery." Remote Sensing 13, no. 4 (2021): 830. http://dx.doi.org/10.3390/rs13040830.

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This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than

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d'Angelo, P., G. Kuschk, and P. Reinartz. "Evaluation of Skybox Video and Still Image products." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 95–99. http://dx.doi.org/10.5194/isprsarchives-xl-1-95-2014.

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The SkySat-1 satellite lauched by Skybox Imaging on November 21 in 2013 opens a new chapter in civilian earth observation as it is the first civilian satellite to image a target in high definition panchromatic video for up to 90 seconds. The small satellite with a mass of 100 kg carries a telescope with 3 frame sensors. Two products are available: Panchromatic video with a resolution of around 1 meter and a frame size of 2560 &times; 1080 pixels at 30 frames per second. Additionally, the satellite can collect still imagery with a swath of 8 km in the panchromatic band, and multispectral im

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Baiocchi, V., A. Bianchi, C. Maddaluno, and M. Vidale. "PANSHARPENING TECHNIQUES TO DETECT MASS MONUMENT DAMAGING IN IRAQ." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5/W1 (May 15, 2017): 121–26. http://dx.doi.org/10.5194/isprs-archives-xlii-5-w1-121-2017.

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The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons of safety. For the same reasons, it’s not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images is called pan-sharpening and it can be carried on using different algorithms

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Wan, Liang, Jiangpeng Zhu, Xiaoyue Du, et al. "A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles." Journal of Experimental Botany 72, no. 13 (2021): 4691–707. http://dx.doi.org/10.1093/jxb/erab194.

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Abstract Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four ma

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Thelen, Kurt D., A. N. Kravchenko, and Chad D. Lee. "Use of Optical Remote Sensing for Detecting Herbicide Injury in Soybean." Weed Technology 18, no. 2 (2004): 292–97. http://dx.doi.org/10.1614/wt-03-049r2.

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Experiments were conducted from 2000 to 2002 at two locations each year to determine if lactofen and imazethapyr injury to soybean could be detected using digital aerial imagery and ground-based optical remote sensing. Lactofen and imazethapyr were applied at base rates of 105 and 71 g/ha, respectively, and at 0, 2X, and 4X rates. Treated plots were evaluated between 7 and 21 d after treatment for crop injury using a ground-based radiometer and a system using computer analysis of digital aerial imagery. Both the ground-based radiometer and the digital aerial imagery were effective in detecting

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