Journal articles: 'Near infrared imagery'

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Aden, S. T., J. P. Bialas, Z. Champion, E. Levin, and J. L. McCarty. "Low cost infrared and near infrared sensors for UAVs." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 1–7. http://dx.doi.org/10.5194/isprsarchives-xl-1-1-2014.

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Thermal remote sensing has a wide range of applications, though the extent of its use is inhibited by cost. Robotic and computer components are now widely available to consumers on a scale that makes thermal data a readily accessible resource. In this project, thermal imagery collected via a lightweight remote sensing Unmanned Aerial Vehicle (UAV) was used to create a surface temperature map for the purpose of providing wildland firefighting crews with a cost-effective and time-saving resource. The UAV system proved to be flexible, allowing for customized sensor packages to be designed that could include visible or infrared cameras, GPS, temperature sensors, and rangefinders, in addition to many data management options. Altogether, such a UAV system could be used to rapidly collect thermal and aerial data, with a geographic accuracy of less than one meter.

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Adamopoulos, Efstathios, and Fulvio Rinaudo. "Enhancing Image-Based Multiscale Heritage Recording with Near-Infrared Data." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 269. http://dx.doi.org/10.3390/ijgi9040269.

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Passive sensors, operating in the visible (VIS) spectrum, have widely been used towards the trans-disciplinary documentation, understanding, and protection of tangible cultural heritage (CH). Although, many heritage science fields benefit significantly from additional information that can be acquired in the near-infrared (NIR) spectrum. NIR imagery, captured for heritage applications, has been mostly investigated with two-dimensional (2D) approaches or by 2D-to-three-dimensional (3D) integrations following complicated techniques, including expensive imaging sensors and setups. The availability of high-resolution digital modified cameras and software implementations of Structure-from-Motion (SfM) and Multiple-View-Stereo (MVS) algorithms, has made the production of models with spectral textures more feasible than ever. In this research, a short review of image-based 3D modeling with NIR data is attempted. The authors aim to investigate the use of near-infrared imagery from relatively low-cost modified sensors for heritage digitization, alongside the usefulness of spectral textures produced, oriented towards heritage science. Therefore, thorough experimentation and assessment with different software are conducted and presented, utilizing NIR imagery and SfM/MVS methods. Dense 3D point clouds and textured meshes have been produced and evaluated for their metric validity and radiometric quality, comparing to results produced from VIS imagery. The datasets employed come from heritage assets of different dimensions, from an archaeological site to a medium-sized artwork, to evaluate implementation on different levels of accuracy and specifications of texture resolution.

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Pisano, Andrea, Francesco Bignami, and Rosalia Santoleri. "Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery." Remote Sensing 7, no. 1 (January 19, 2015): 1112–34. http://dx.doi.org/10.3390/rs70101112.

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Mantecón, Tomás, Carlos R. del-Blanco, Fernando Jaureguizar, and Narciso García. "A real-time gesture recognition system using near-infrared imagery." PLOS ONE 14, no. 10 (October 3, 2019): e0223320. http://dx.doi.org/10.1371/journal.pone.0223320.

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Stutte, G. W., and C. A. Stutte. "Use of Near-Infrared video for Localizing Nitrogen Stress in Peach Orchards." HortTechnology 2, no. 2 (April 1992): 224–27. http://dx.doi.org/10.21273/horttech.2.2.224.

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Computer analysis of airborne, broad-band, near-infrared (NIR, 710 to 1100 nm) video imagery of peach tree canopies was used to determine spatial variability of cumulative stress in two peach orchards. A significant quadratic correlation was found between leaf-N content and the normalized mean pixel intensity (MPI) of the digital imagery of NIR canopy reflectance. This correlation was used to establish MPI estimates of N-stressed trees in the orchard. The relationship was used to localize site-specific spatial variability in a commercial peach orchard. The underlying soil type was found to be closely associated with the spatial variability in NIR imagery in the commercial peach orchard. Assessing spatial variability in the orchard with NIR video permits early localization of potentially low productivity regions within an orchard.

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Coustenis, Athena, Emmanuel Lellouch, Michel Combes, Renald Wittemberg, Christopher P. McKay, and Jean-Pierre Maillard. "Titan’s Atmosphere and Surface from Infrared Spectroscopy and Imagery." International Astronomical Union Colloquium 161 (January 1997): 227–34. http://dx.doi.org/10.1017/s0252921100014743.

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AbstractWe have performed observations of Titan in the near-infrared since 1991, using both spectroscopy and imagery. A higher geometric albedo is observed near the Greatest Eastern Elongation than near the Greatest Western Elongation or Conjunctions. The surface is within reach for telescopes at 1.075, 1.28, 1.6 and 2.0 micron, so we simulated Titan’s geometric albedo in order to derive the surface albedo at all longitudes. Titan’s leading hemisphere appears brighter than the trailing one by 20-25% at 1.08; 1.28 and 1.6 μm, and by 35% at 2.0 μm, with spectra compatible with H2O everywhere. We have then simulated the presence of a mountain on Titan covering 50% of the leading hemisphere disk, varying its height and introducing a higher surface albedo when required. We find that a relief can not reproduce the whole geometric albedo differences observed between the two hemispheres. Some other additional component bright at 1.0 and 2.0 μm must then be invoked. Candidates are tholins or CH4, which can exist on the top of a mountain if the temperature is lower than 90 K.

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Tempest, Gavin, and Gaynor Parfitt. "Imagery Use and Affective Responses During Exercise: An Examination of Cerebral Hemodynamics Using Near-Infrared Spectroscopy." Journal of Sport and Exercise Psychology 35, no. 5 (October 2013): 503–13. http://dx.doi.org/10.1123/jsep.35.5.503.

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Imagery, as a cognitive strategy, can improve affective responses during moderate-intensity exercise. The effects of imagery at higher intensities of exercise have not been examined. Further, the effect of imagery use and activity in the frontal cortex during exercise is unknown. Using a crossover design (imagery and control), activity of the frontal cortex (reflected by changes in cerebral hemodynamics using near-infrared spectroscopy) and affective responses were measured during exercise at intensities 5% above the ventilatory threshold (VT) and the respiratory compensation point (RCP). Results indicated that imagery use influenced activity of the frontal cortex and was associated with a more positive affective response at intensities above VT, but not RCP to exhaustion (p < .05). These findings provide direct neurophysiological evidence of imagery use and activity in the frontal cortex during exercise at intensities above VT that positively impact affective responses.

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Everitt, James H., James V. Richerson, Mario A. Alaniz, David E. Escobar, Ricardo Villarreal, and Michael R. Davis. "Light Reflectance Characteristics and Remote Sensing of Big Bend Loco (Astragalus mollissimusvar.earlei) and Wooton Loco (Astragalus wootonii)." Weed Science 42, no. 1 (March 1994): 115–22. http://dx.doi.org/10.1017/s0043174500084265.

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The high near-infrared reflectance (0.76 to 0.90 μm) of Big Bend loco and Wooton loco contributed significantly to their orange-red and red image tonal responses, respectively, on color-infrared aerial photographs making them distinguishable from associated vegetation and soil. Big Bend loco could also be distinguished on color-infrared and near-infrared black-and-white video imagery where it had distinct red and whitish tonal responses, respectively. Computer analyses of photographic and videographic images showed that Big Bend loco and Wooton loco populations could be quantified from other landscape features. A global positioning system was integrated with the video imagery that permitted latitude-longitude coordinates to appear on each image. The latitude-longitude data were integrated with a geographical information system to map Big Bend loco populations.

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Everitt, J. H., D. E. Escobar, D. N. Appel, W. G. Riggs, and M. R. Davis. "Using Airborne Digital Imagery for Detecting Oak Wilt Disease." Plant Disease 83, no. 6 (June 1999): 502–5. http://dx.doi.org/10.1094/pdis.1999.83.6.502.

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Color-infrared (CIR) digital imagery was evaluated as a remote sensing tool for detecting oak wilt disease in live oak (Quercus fusiformis). Aerial CIR digital imagery and CIR photography were obtained concurrently of a live oak forested area in south-central Texas affected by oak wilt. Dead, diseased, and healthy live oak trees could generally be delineated as well in the digital imagery as in the CIR photography. Light reflectance measurements obtained in the field showed that dead, diseased, and healthy trees had different visible and near-infrared reflectance values.

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Bochko, Vladimir, Petri Välisuo, Toni Harju, and Jarmo Alander. "Lower extremity ulcer image segmentation of visual and near-infrared imagery." Skin Research and Technology 16, no. 2 (May 2010): 190–97. http://dx.doi.org/10.1111/j.1600-0846.2009.00415.x.

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Schmit, Timothy J., Paul Griffith, Mathew M. Gunshor, Jaime M. Daniels, Steven J. Goodman, and William J. Lebair. "A Closer Look at the ABI on the GOES-R Series." Bulletin of the American Meteorological Society 98, no. 4 (April 1, 2017): 681–98. http://dx.doi.org/10.1175/bams-d-15-00230.1.

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Abstract The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) is America’s next-generation geostationary advanced imager. GOES-R launched on 19 November 2016. The ABI is a state-of-the-art 16-band radiometer, with spectral bands covering the visible, near-infrared, and infrared portions of the electromagnetic spectrum. Many attributes of the ABI—such as spectral, spatial, and temporal resolution; radiometrics; and image navigation/registration—are much improved from the current series of GOES imagers. This paper highlights and discusses the expected improvements of each of these attributes. From ABI data many higher-level-derived products can be generated and used in a large number of environmental applications. The ABI’s design allows rapid-scan and contiguous U.S. imaging automatically interleaved with full-disk scanning. In this paper the expected instrument attributes are covered, as they relate to signal-to-noise ratio, image navigation and registration, the various ABI scan modes, and other parameters. There will be several methods for users to acquire GOES-R imagery and products depending on their needs. These include direct reception of the imagery via the satellite downlink and an online-accessible archive. The information from the ABI on the GOES-R series will be used for many applications related to severe weather, tropical cyclones and hurricanes, aviation, natural hazards, the atmosphere, the ocean, and the cryosphere. The ABI on the GOES-R series is America’s next-generation geostationary advanced imager and will dramatically improve the monitoring of many phenomena at finer time and space scales.

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Nagata, Masaki, Toshiki Hirogaki, Eiichi Aoyama, Takahiro Iida, Yasuhiro Uenishi, Masami Matsubara, and Yoshitaka Usui. "Quality Control Method Based on Gear Tooth Contact Evaluation Using Near-Infrared Ray Imagery." Key Engineering Materials 447-448 (September 2010): 569–73. http://dx.doi.org/10.4028/www.scientific.net/kem.447-448.569.

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Conventionally, tooth contact evaluation has been performed visually by machine operators in gear manufacturing fields when finishing a gear or during assembly. With automation, the contact area’s boundary is unclear due to scattered light when visible light is used to obtain an image for tooth contact evaluation. We therefore focused on using near-infrared to prevent scattered light. First, we confirmed that the tooth contact image obtained by image binarization is hardly affected by the image threshold. Second, we propose a new method to extract the boundary part of the tooth contact by differential calculation of the fine near-infrared image. These methods allow automatic division of near-infrared images into the contact area, the boundary, and the non-contact area. Finally, the obtained result is compared with the tooth contact calculated from the measured tooth surface. We demonstrated that the near-infrared image method is effective for automatic tooth contact evaluation.

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Zhang, Hua, Paul V. Zimba, and Emmanuel U. Nzewi. "A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery." Remote Sensing 11, no. 16 (August 17, 2019): 1931. http://dx.doi.org/10.3390/rs11161931.

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The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

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Fletcher, Andrew, and Richard Mather. "Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models." Remote Sensing 12, no. 8 (April 13, 2020): 1238. http://dx.doi.org/10.3390/rs12081238.

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Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

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Joachim, L., and T. Storch. "CLOUD DETECTION FOR NIGHT-TIME PANCHROMATIC VISIBLE AND NEAR-INFRARED SATELLITE IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 853–60. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-853-2020.

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Abstract. Cloud detection for night-time panchromatic visible and near-infrared (VNIR) satellite imagery is typically performed based on synchronized observations in the thermal infrared (TIR). To be independent of TIR and to improve existing algorithms, we realize and analyze cloud detection based on VNIR only, here NPP/VIIRS/DNB observations. Using Random Forest for classifying cloud vs. clear and focusing on urban areas, we illustrate the importance of features describing a) the scattering by clouds especially over urban areas with their inhomogeneous light emissions and b) the normalized differences between Earth’s surface and cloud albedo especially in presence of Moon illumination. The analyses substantiate the influences of a) the training site and scene selections and b) the consideration of single scene or multi-temporal scene features on the results for the test sites. As test sites, diverse urban areas and the challenging land covers ocean, desert, and snow are considered. Accuracies of up to 85% are achieved for urban test sites.

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Cox, Pierre, Jean-Pierre Maillard, P. J. Huggins, T. Forveille, R. Bachiller, S. Guilloteau, and A. Omont. "K’-Band Spectro-imagery of AFGL 2688 and NGC 7027." International Astronomical Union Colloquium 149 (1995): 332–35. http://dx.doi.org/10.1017/s0252921100023265.

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HIROGAKI, Toshiki, Eiichi AOYAMA, Yoshitaka USUI, and Masaki NAGATA. "1305 Automatic Tooth Contact Evaluation Method Based on Near-Infrared Ray Imagery." Proceedings of the Machine Design and Tribology Division meeting in JSME 2010.10 (2010): 99–102. http://dx.doi.org/10.1299/jsmemdt.2010.10.99.

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Holtkamp, Agnes, Karim Elhennawy, José E. Cejudo Grano de Oro, Joachim Krois, Sebastian Paris, and Falk Schwendicke. "Generalizability of Deep Learning Models for Caries Detection in Near-Infrared Light Transillumination Images." Journal of Clinical Medicine 10, no. 5 (March 1, 2021): 961. http://dx.doi.org/10.3390/jcm10050961.

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Objectives: The present study aimed to train deep convolutional neural networks (CNNs) to detect caries lesions on Near-Infrared Light Transillumination (NILT) imagery obtained either in vitro or in vivo and to assess the models’ generalizability. Methods: In vitro, 226 extracted posterior permanent human teeth were mounted in a diagnostic model in a dummy head. Then, NILT images were generated (DIAGNOcam, KaVo, Biberach), and images were segmented tooth-wise. In vivo, 1319 teeth from 56 patients were obtained and segmented similarly. Proximal caries lesions were annotated pixel-wise by three experienced dentists, reviewed by a fourth dentist, and then transformed into binary labels. We trained ResNet classification models on both in vivo and in vitro datasets and used 10-fold cross-validation for estimating the performance and generalizability of the models. We used GradCAM to increase explainability. Results: The tooth-level prevalence of caries lesions was 41% in vitro and 49% in vivo, respectively. Models trained and tested on in vivo data performed significantly better (mean ± SD accuracy: 0.78 ± 0.04) than those trained and tested on in vitro data (accuracy: 0.64 ± 0.15; p < 0.05). When tested in vitro, the models trained in vivo showed significantly lower accuracy (0.70 ± 0.01; p < 0.01). Similarly, when tested in vivo, models trained in vitro showed significantly lower accuracy (0.61 ± 0.04; p < 0.05). In both cases, this was due to decreases in sensitivity (by −27% for models trained in vivo and −10% for models trained in vitro). Conclusions: Using in vitro setups for generating NILT imagery and training CNNs comes with low accuracy and generalizability. Clinical significance: Studies employing in vitro imagery for developing deep learning models should be critically appraised for their generalizability. Applicable deep learning models for assessing NILT imagery should be trained on in vivo data.

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Molthan, Andrew L., Lori A. Schultz, Kevin M. McGrath, Jason E. Burks, J. Parks Camp, Kelsey Angle, Jordan R. Bell, and Gary J. Jedlovec. "Incorporation and Use of Earth Remote Sensing Imagery within the NOAA/NWS Damage Assessment Toolkit." Bulletin of the American Meteorological Society 101, no. 3 (March 1, 2020): E323—E340. http://dx.doi.org/10.1175/bams-d-19-0097.1.

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Abstract Severe weather events including tornadoes, damaging winds, hail, and their combination produce changes in land surface vegetation and urban settings that are frequently observed through remote sensing. Capabilities continue to improve through a growing constellation of governmental and commercial assets, increasing the spatial resolution of visible, near to shortwave infrared, and thermal infrared remote sensing. Here, we highlight cases where visual interpretation of imagery benefitted severe weather damage assessments made within the NOAA/NWS Damage Assessment Toolkit. Examples demonstrate utility of imagery in assessing tracks and changes in remote areas where staffing limitations or access prevent a ground-based assessment.

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Millán, Luis, M. Lebsock, E. Fishbein, P. Kalmus, and J. Teixeira. "Quantifying Marine Boundary Layer Water Vapor beneath Low Clouds with Near-Infrared and Microwave Imagery." Journal of Applied Meteorology and Climatology 55, no. 1 (January 2016): 213–25. http://dx.doi.org/10.1175/jamc-d-15-0143.1.

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AbstractThis study investigates the synergy of collocated microwave radiometry and near-infrared imagery to estimate the marine boundary layer water vapor beneath uniform cloud fields. Microwave radiometry provides the total column water vapor, while the near-infrared imagery provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor. In combining the two datasets, we apply several flags as well as proximity tests to remove pixels with high clouds and/or intrapixel heterogeneity. Comparisons against radiosonde and ECMWF reanalysis data demonstrate the robustness of these boundary layer water vapor estimates. Last, it is shown that the measured AMSR-MODIS boundary layer water vapor can be analyzed using sea surface temperature and cloud-top pressure information by employing simple equations based on the Clausius–Clapeyron relationship.

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Tanamachi, Robin L., Howard B. Bluestein, Stephen S. Moore, and Robert P. Madding. "Infrared Thermal Imagery of Cloud Base in Tornadic Supercells." Journal of Atmospheric and Oceanic Technology 23, no. 11 (November 1, 2006): 1445–61. http://dx.doi.org/10.1175/jtech1942.1.

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Abstract During the spring seasons of 2003 and 2004, an infrared thermal camera was deployed in and around supercell thunderstorms in an attempt to retrieve the temperature at the cloud base of a mesocyclone prior to tornadogenesis. The motivation for this exercise was to obtain temperature information that might indicate the thermal structure, timing, and extent of the rear-flank downdraft (RFD) and possibly elucidate its relationship to tornadogenesis. An atmospheric transmissivity study was conducted to account for the effects of atmospheric transmission on the measured temperatures, and to determine an ideal range of distances from which infrared images of a wall cloud or a tornado could be safely captured while still retrieving accurate cloud temperatures. This range was found to be 1.5–3 km. Two case days are highlighted in which the infrared camera was deployed within 1.5–3 km of a tornado; the visible and infrared images are shown side by side for comparison. On the single occasion on which the tornadogenesis phase was captured, the infrared images show no strong horizontal temperature gradients. From the infrared images taken of tornadoes, it can be inferred that the infrared signal from the tornado consisted primarily of infrared emissions from lofted dust particles or cloud droplets, and that the infrared signal from the tornado condensation funnel was easily obscured by infrared emissions from lofted dust particles or intervening precipitation curtains. The deployment of the infrared camera near supercell thunderstorms and the analysis of the resulting images proved challenging. It is concluded that the infrared camera is a useful tool for measuring cloud-base temperature gradients provided that distance and viewing angle constraints are met and that the cloud base is unobscured by rain or other intervening infrared emission sources. When these restrictions were met, the infrared camera successfully retrieved horizontal temperature gradients along the cloud base and vertical temperature gradients (close to the moist adiabatic lapse rate) along the tornado funnel.

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Gowravaram, Saket, Haiyang Chao, Andrew Molthan, Tiebiao Zhao, Pengzhi Tian, Harold Flanagan, Lori Schultz, and Jordan Bell. "Spectral Reflectance Estimation of UAS Multispectral Imagery Using Satellite Cross-Calibration Method." Photogrammetric Engineering & Remote Sensing 87, no. 10 (October 1, 2021): 735–46. http://dx.doi.org/10.14358/pers.20-00091r2.

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This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield.

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Collin, A., D. James, A. Mury, M. Letard, and B. Guillot. "PREDICTING THE INFRARED UAV IMAGERY OVER THE COAST." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2021 (June 28, 2021): 149–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2021-149-2021.

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Abstract. The infrared (IR) imagery provides additional information to the visible (red-green-blue, RGB) about vegetation, soil, water, mineral, or temperature, and has become essential for various disciplines, such as geology, hydrology, ecology, archeology, meteorology or geography. The integration of the IR sensors, ranging from near-IR (NIR) to thermal-IR through mid-IR, constitutes a baseline for Earth Observation satellites but not for unmanned airborne vehicles (UAV). Given the hyperspatial and hypertemporal characteristics associated with the UAV survey, it is relevant to benefit from the IR waveband in addition to the visible imagery for mapping purposes. This paper proposes to predict the NIR reflectance from RGB digital number predictors collected with a consumer-grade UAV over a structurally and compositionally complex coastal area. An array of 15 000 data, distributed into calibration, validation and test datasets across 15 representative coastal habitats, was used to build and compare the performance of the standard least squares, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models. The NN family surpassed the four other ones, and the best NN model (R2 = 0.67) integrated two hidden layers provided, each, with five nodes of hyperbolic tangent and five nodes of Gaussian activation functions. This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints. At the habitat scale, sedimentary and dry vegetation environments were satisfactorily predicted (R2 > 0.6), contrary to the healthy vegetation (R2 < 0.2). Those innovative findings will be useful for scientists and managers tasked with hyperspatial and hypertemporal mapping.

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Dalm, M., M. W. N. Buxton, and F. J. A. van Ruitenbeek. "Ore–Waste Discrimination in Epithermal Deposits Using Near-Infrared to Short-Wavelength Infrared (NIR-SWIR) Hyperspectral Imagery." Mathematical Geosciences 51, no. 7 (July 16, 2018): 849–75. http://dx.doi.org/10.1007/s11004-018-9758-6.

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Holper, Lisa, and Martin Wolf. "Motor imagery in response to fake feedback measured by functional near-infrared spectroscopy." NeuroImage 50, no. 1 (March 2010): 190–97. http://dx.doi.org/10.1016/j.neuroimage.2009.12.055.

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Mihara, Masahito, Ichiro Miyai, Noriaki Hattori, Megumi Hatakenaka, Hajime Yagura, Teiji Kawano, Masaki Okibayashi, et al. "Neurofeedback Using Real-Time Near-Infrared Spectroscopy Enhances Motor Imagery Related Cortical Activation." PLoS ONE 7, no. 3 (March 2, 2012): e32234. http://dx.doi.org/10.1371/journal.pone.0032234.

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HUETE, A. R., and C. J. TUCKER. "Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery." International Journal of Remote Sensing 12, no. 6 (June 1991): 1223–42. http://dx.doi.org/10.1080/01431169108929723.

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Holper, Lisa, Diego E. Shalóm, Martin Wolf, and Mariano Sigman. "Understanding inverse oxygenation responses during motor imagery: a functional near-infrared spectroscopy study." European Journal of Neuroscience 33, no. 12 (June 2011): 2318–28. http://dx.doi.org/10.1111/j.1460-9568.2011.07720.x.

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Miesch, C., L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher. "Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery." IEEE Transactions on Geoscience and Remote Sensing 43, no. 7 (July 2005): 1552–62. http://dx.doi.org/10.1109/tgrs.2005.847793.

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Olsoy, P. J., S. N. Barrett, B. C. Robb, J. S. Forbey, T. T. Caughlin, M. D. Blocker, C. Merriman, et al. "SCALING UP SAGEBRUSH CHEMISTRY WITH NEAR-INFRARED SPECTROSCOPY AND UAS-ACQUIRED HYPERSPECTRAL IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 127–32. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-127-2021.

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Abstract. Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas to conserve. Near-infrared spectroscopy (NIRS) has proven effective in predicting plant chemistry under laboratory conditions in a variety of ecosystems, including the sagebrush steppe. Our objectives were to demonstrate the scalability of these models from the laboratory to the field, and in the air with a hyperspectral sensor on an unoccupied aerial system (UAS). Sagebrush leaf samples were collected at a study site in eastern Idaho, USA. Plants were scanned with an ASD FieldSpec 4 spectroradiometer in the field and laboratory, and a subset of the same plants were imaged with a SteadiDrone Hexacopter UAS equipped with a Rikola hyperspectral sensor (HSI). All three sensors generated spectral patterns that were distinct among species and morphotypes of sagebrush at specific wavelengths. Lab-based NIRS was accurate for predicting crude protein and total monoterpenes (R2 = 0.7–0.8), but the same NIRS sensor in the field was unable to predict either crude protein or total monoterpenes (R2 < 0.1). The hyperspectral sensor on the UAS was unable to predict most chemicals (R2 < 0.2), likely due to a combination of too few bands in the Rikola HSI camera (16 bands), the range of wavelengths (500–900 nm), and small sample size of overlapping plants (n = 28–60). These results show both the potential for scaling NIRS from the lab to the field and the challenges in predicting complex plant chemistry with hyperspectral UAS. We conclude with recommendations for next steps in applying UAS to sagebrush ecosystems with a variety of new sensors.

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Johnson, Kathleen E., and Krzysztof Koperski. "VNIR-SWIR Superspectral Mineral Mapping: An Example from Cuprite, Nevada." Photogrammetric Engineering & Remote Sensing 86, no. 11 (November 1, 2020): 695–700. http://dx.doi.org/10.14358/pers.86.11.695.

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Cuprite, Nevada, is a location well known for numerous studies of its hydrothermal mineralogy. This region has been used to validate geological interpretations of airborne hyperspectral imagery (AVIRIS HSI ), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) imagery, and most recently eight-band WorldView-3 shortwave infrared (SWIR ) imagery. WorldView-3 is a high-spatial-resolution commercial multispectral satellite sensor with eight visible-to-near-infrared (VNIR ) bands (0.42–1.04 μm) and eight SWIR bands (1.2–2.33 μm). We have applied mineral mapping techniques to all 16 bands to perform a geological analysis of the Cuprite, Nevada, location. Ground truth for the training and validation was derived from AVIRIS hyperspectral data and United States Geological Survey mineral spectral data for this location. We present the results of a supervised mineral-mapping classification applying a random-forest classifier. Our results show that with good ground truth, WorldView-3 SWIR + VNIR imagery produces an accurate geological assessment.

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Ito, Tomotaka, Satoshi Ushii, Takafumi Sameshima, Yoshihiro Mitsui, Shohei Ohgi, and Chihiro Mizuike. "Design of Brain-Machine Interface Using Near-Infrared Spectroscopy." Journal of Robotics and Mechatronics 25, no. 6 (December 20, 2013): 1000–1010. http://dx.doi.org/10.20965/jrm.2013.p1000.

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In recent years, the fields of robotics and medical science have been paying close attention to brainmachine interface (BMI) systems. BMI observes human cerebral activity and use the collected data as the input to various instruments. If such a systemcould be effectively realized, it could be used as a new intuitive input interface for application to human-robot interactions, welfare scenarios, etc. In this paper, we discussed a design problem related to a BMI system using near-infrared spectroscopy (NIRS). We developed a brain state classifier based on the learning vector quantization (LVQ) method. The proposed method classifies the cerebral blood flow patterns and outputs the brain state estimate. The classification experiments showed that the proposed method can successfully classify not only human physical motions and motor imageries, but also human emotions and human mental commands issued to a robot. Especially, in the classification of “the mental commands to a robot,” we successfully realized the imagery classification of five different mental commands. The results point to the potential of NIRS-based brain machine interfaces.

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Xu, Rudong, Jin Liu, and Jianhui Xu. "Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis." Sensors 18, no. 9 (August 31, 2018): 2873. http://dx.doi.org/10.3390/s18092873.

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This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

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Surový, P., N. A Ribeiro, A. C Oliveira, and Ľ. Scheer. "Discrimination of vegetation from the background in high resolution colour remote sensed imagery." Journal of Forest Science 50, No. 4 (January 11, 2012): 161–70. http://dx.doi.org/10.17221/4611-jfs.

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Different transformations of RGB colour space were compared to develop the best method for discrimination of vegetation from the background in open pure cork oak stands in southern Portugal in high-resolution colour imagery. Normalised difference index, i1i2i3 colour space and other indices developed for classic band imagery were recalculated for near infrared imagery and tested. A new method for fully automated thresholding was developed and tested. The newly developed index shows the equal accuracy performance but provides the smallest overestimation error and retains the largest scale of grey levels for <br />a subsequent shape analysis.

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Chen, X., T. A. Warner, and D. J. Campagna. "Integrating visible, near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data." International Journal of Remote Sensing 28, no. 11 (May 29, 2007): 2415–30. http://dx.doi.org/10.1080/01431160600702624.

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Everitt, James H., David E. Escobar, Mario A. Alaniz, Ricardo Villarreal, and Michael R. Davis. "Distinguishing Brush and Weeds on Rangelands Using Video Remote Sensing." Weed Technology 6, no. 4 (December 1992): 913–21. http://dx.doi.org/10.1017/s0890037x00036472.

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This paper describes the application of a relatively new remote sensing tool, airborne video imagery, for distinguishing weed and brush species on rangelands. Plant species studied were false broomweed, spiny aster, and Chinese tamarisk. A multispectral video system that acquired color-infrared (CIR) composite imagery and its simultaneously synchronized three-band [near-infrared (NIR), red, and yellow-green] narrowband images was used for the false broomweed and spiny aster experiments. A conventional color camcorder video system was used to study Chinese tamarisk. False broomweed and spiny aster could be detected on CIR composite and NIR narrowband imagery, while Chinese tamarisk could be distinguished on conventional color imagery. Quantitative data obtained from digitized video images of the three species showed that their digital values were statistically different (P = 0.05) from those of associated vegetation and soil. Computer analyses of video images showed that populations of the three species could be quantified from associated vegetation. This technique permits area estimates of false broomweed, spiny aster, and Chinese tamarisk populations on rangeland and wildland areas.

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Quang Dang Khoa, Truong, and Vo Van Toi. "Multifractals Properties on the Near Infrared Spectroscopy of Human Brain Hemodynamic." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/670761.

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Nonlinear physics presents us with a perplexing variety of complicated fractal objects and strange sets. Naturally one wishes to characterize the objects and describe the events occurring on them. Moreover, most time series found in “real-life” applications appear quite noisy. Therefore, at almost every point in time, they cannot be approximated either by the Taylor series or by the Fourier series of just a few terms. Many experimental time series have fractal features and display singular behavior, the so-called singularities. The multifractal spectrum quantifies the degree of fractals in the processes generating the time series. A novel definition is proposed called full-width Hölder exponents that indicate maximum expansion of multifractal spectrum. The obtained results have demonstrated the multifractal structure of near-infrared spectroscopy time series and the evidence for brain imagery activities.

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Ducart, Diego Fernando, Adalene Moreira Silva, Catarina Labouré Bemfica Toledo, and Luciano Mozer de Assis. "Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil." Brazilian Journal of Geology 46, no. 3 (September 2016): 331–49. http://dx.doi.org/10.1590/2317-4889201620160023.

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ABSTRACT: Mapping methods for iron oxides and clay minerals, using Landsat-8/Operational Land Imager (OLI) and Earth Observing 1 (EO-1)/Hyperion imagery integrated with airborne geophysical data, were applied in the N4, N5, and N4WS iron deposits, Serra Norte, Carajás, Brazil. Band ratios were achieved on Landsat-8/OLI imagery, allowing the recognition of the main minerals from iron deposits. The Landsat-8/OLI imagery showed a robust performance for iron oxide exploration, even in vegetated shrub areas. Feature extraction and Spectral Angle Mapper hyperspectral classification methods were carried out on EO-1/Hyperion imagery with good results for mapping high-grade iron ore, the hematite-goethite ratio, and clay minerals from regolith. The EO-1/Hyperion imagery proved an excellent tool for fast remote mineral mapping in open-pit areas, as well as mapping waste and tailing disposal facilities. An unsupervised classification was carried out on a data set consisting of EO-1/Hyperion visible near-infrared 74 bands, Landsat-8/OLI-derived Normalized Difference Vegetation Index, Laser Imaging Detection and Ranging-derived Digital Terrain Model, and high-resolution airborne geophysical data (gamma ray spectrometry, Tzz component of gradiometric gravimetry data). This multisource classification proved to be an adequate alternative for mapping iron oxides in vegetated shrub areas and to enhance the geology of the regolith and mineralized areas.

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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 (March 29, 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 watershed (most juniper removed) and an untreated watershed. Research objectives were to: (1) evaluate the density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed; and, (2) assess the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover. Ground- based measurements were used to assess vegetation features in each watershed and as a means to verify analysis from aerial imagery. Visual imagery (red, green, and blue wavelengths) and multispectral imagery (red, green, blue, near-infrared, and red-edge wavelengths) were captured using a quadcopter-style UAV. Canopy cover in the untreated watershed was estimated using two different methods: vegetation indices and support vector machine classification. Supervised classification was used to assess juniper sapling density and vegetation cover in the treated watershed. Results showed that vegetation indices that incorporated near-infrared reflectance values estimated canopy cover within 0.7% to 4.1% of ground-based calculations. Canopy cover estimates at the untreated watershed using supervised classification were within 0.9% to 2.3% of ground-based results. Supervised classification applied to fall imagery using multispectral bands provided the best estimates of juniper sapling density compared to imagery taken in the summer or to using visual imagery. Study results suggest that low-altitude multispectral imagery obtained using small UAV can be effectively used to assess western juniper density and canopy cover.

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El Oufir, Mohamed Karim, Karem Chokmani, Anas El Alem, Hachem Agili, and Monique Bernier. "Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data." Sensors 21, no. 16 (August 4, 2021): 5259. http://dx.doi.org/10.3390/s21165259.

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This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data.

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Summy, Kenneth R., and Christopher R. Little. "Using Color Infrared Imagery to Detect Sooty Mold and Fungal Pathogens of Glasshouse-propagated Plants." HortScience 43, no. 5 (August 2008): 1485–91. http://dx.doi.org/10.21273/hortsci.43.5.1485.

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Fungi are major biotic constraints for optimum production and quality of glasshouse plants. When plants are infested with sooty mold (Capnodium spp.) or infected with pathogens, the reflected wavelengths of the electromagnetic spectrum are altered. Spectroradiometric measurements and color infrared (CIR) images of control, honeydew-coated, and sooty mold-infested saplings and individual leaves from trifoliate orange (Poncirus trifoliata), sour orange (Citrus aurantium), ‘Valencia’ orange (C. sinensis), and ‘Bo’ tree (Ficus religiosa) were obtained. Grapefruit saplings and individual leaves infected with Mycosphaerella citri (greasy spot) were imaged under glasshouse conditions. Similarly, muskmelon foliage showing low and high levels of powdery mildew (Sphaerotheca fuliginea) disease severity were analyzed. When examining individual leaves, all fungal biotic stressors generally resulted in variable spectral reflectance data, especially in the blue (450 nm) and green (550 nm) wavelengths; however, values in the red (650 nm) tended to increase and values in the near-IR (850 nm) tended to decrease with stress. Near-IR/red image ratios were significantly reduced (P < 0.05) in stressed whole plant foliage and individual leaves relative to healthy controls. The accumulation of insect honeydew (which occurs before sooty mold infestation) significantly increased (P < 0.05) near-IR reflectance values and near-IR/red ratios in ‘Valencia’ orange and near-IR/ratios in ‘Bo’ tree foliage and individual leaves. Image acquisition and enhancement techniques may prove useful in large-scale production greenhouses where existing infrastructure and high plant populations require high throughput data analysis and identification of biotic stressors.

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Hernandez-Jimenez, J. A., H. Dottori, P. Grosbøl, and N. Vera-Villamizar. "The Circumnuclear Spiral Pattern of NGC 5427." Proceedings of the International Astronomical Union 5, S267 (August 2009): 331. http://dx.doi.org/10.1017/s1743921310006617.

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We have studied Hubble Space Telescope archive imagery of the central region of the Seyfert 2 galaxy NGC 5427. The images were taken with F606W (V-band) and F160W (H-band) filters with the Wide Field and Planetary Camera 2 and the Near Infrared Camera and Multi-Object Spectrometer, respectively.

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Bühler, Yves, Marc S. Adams, Andreas Stoffel, and Ruedi Boesch. "Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery." International Journal of Remote Sensing 38, no. 8-10 (January 13, 2017): 3135–58. http://dx.doi.org/10.1080/01431161.2016.1275060.

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Korzeniowska, Karolina, Yves Bühler, Mauro Marty, and Oliver Korup. "Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery." Natural Hazards and Earth System Sciences 17, no. 10 (October 23, 2017): 1823–36. http://dx.doi.org/10.5194/nhess-17-1823-2017.

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Abstract. Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.

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Amemiya, Kaoru, Tomohiro Ishizu, Tomoaki Ayabe, and Shozo Kojima. "Effects of motor imagery on intermanual transfer: A near-infrared spectroscopy and behavioural study." Brain Research 1343 (July 2010): 93–103. http://dx.doi.org/10.1016/j.brainres.2010.04.048.

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Xu, Dandan, Dong Zhang, Dan Shi, and Zhaoqing Luan. "Automatic Extraction of Open Water Using Imagery of Landsat Series." Water 12, no. 7 (July 6, 2020): 1928. http://dx.doi.org/10.3390/w12071928.

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Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.

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Stutte, Gary W. "CLASSIFICATION OF STRESS-INDUCED SPATIAL VARIABILITY IN NEAR-INFRARED CANOPY REFLECTANCE." HortScience 25, no. 9 (September 1990): 1136b—1136. http://dx.doi.org/10.21273/hortsci.25.9.1136b.

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A digital video camera (Panasonic Industrial Co., Secaucus, NJ) with a 1.7 cm charged coupled device detector (574 (h) × 499 (v) pixel elements) was modified with a custom made FRF-700 band pass filter to visualize canopy reflectance in the near-infrared (NIR) from 700 to 1100 nm. Images of canopy reflectance under a range of environmental stresses were obtained from peach and apple trees under greenhouse and field conditions. Individual video frames were digitized with Image Capture and Analysis System (Agri Imaging Systems, Inc., Fayetteville, AR). Image contrast was increased with digital equalization and filtering before classification into one of five stress levels. There was a high correlation (r2 > 0.8) between leaf stress and canopy reflectance in both apple and peach at distances < 5 meters. Spatial variability in stress-induced NIR reflectance could be detected and classified at vertical distances from 150 to 500 M. Analysis of vertical imagery revealed sections of the orchard which were most susceptible to environmental stress.

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Trinh, Hung Le, Ha Thu Thi Le, Loc Duc Le, and Long Thanh Nguyen. "A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images." Journal of Mining and Earth Sciences 62, no. 1 (February 28, 2021): 1–9. http://dx.doi.org/10.46326/jmes.2021.62(1).01.

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Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

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Chen, Xianfeng, Timothy A. Warner, and David J. Campagna. "Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada." Remote Sensing of Environment 110, no. 3 (October 2007): 344–56. http://dx.doi.org/10.1016/j.rse.2007.03.015.

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Zhang, Jieyun, Qingling Zhang, Anming Bao, and Yujuan Wang. "A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space." Remote Sensing 11, no. 4 (February 22, 2019): 456. http://dx.doi.org/10.3390/rs11040456.

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Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing reflectance data at the near-infrared (NIR) and red (R) bands. Considering the distribution regularities of soil moisture in this space, we formulated the Ratio Dryness Monitoring Index (RDMI) as a new dryness monitoring indicator. We compared RDMI values with in situ soil moisture content data measured at 0–10 cm depth. Results showed that there was a strong negative correlation (R = −0.89) between the RDMI values and in situ soil moisture content. We further compared RDMI with existing remote sensing dryness indices, and the results demonstrated the advantages of the RDMI. We applied the RDMI to the Landsat-8 imagery to map dryness distribution around the Fukang area on the Northern slope of the Tianshan Mountains, and to the MODIS imagery to detect the spatial and temporal changes in dryness for the entire Xinjiang in 2013 and 2014. Overall, the RDMI index constructed, based on the NIR–Red spectral space, is simple to calculate, easy to understand, and can be applied to dryness monitoring at different scales.

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Journal articles on the topic 'Near infrared imagery'

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