Quantifying Lygus (Hemiptera: Miridae) damage in faba bean (Fabaceae) seeds using shortwave-infrared imaging

2019 ◽  
Vol 151 (04) ◽  
pp. 442-455
Author(s):  
A.M. Smith ◽  
B. Rivard ◽  
J. Feng ◽  
H.A. Carcamo
Keyword(s):  
Faba Bean ◽  
Infrared Imaging ◽  
Seed Quality ◽  
Strong Relationship ◽  
Economic Losses ◽  
Spectral Angle Mapper ◽  
Analysis Techniques ◽  
Seed Damage ◽  
Shortwave Infrared ◽  
Spectral Angle

AbstractLygus Hahn (Hemiptera: Miridae) feeding in faba beans (Vicia faba Linnaeus (Fabaceae)) often results in a reduction in seed quality and economic losses. Traditionally, seed damage is assessed subjectively through visual examination by a trained individual, but the use of non-destructive imaging to evaluate seed quality is gaining momentum. The focus of this study was to determine the ability to quantify Lygus species damage in faba bean using shortwave-infrared imaging and two analysis techniques: (1) spectral angle mapper and (2) simple reflectance indices. Seed samples were visually assessed for damage before imaging in 242 wavebands between 980 and 2500 nm. Four spectral intervals, involving 102 wavebands, were identified as optimal for the detection of seed damage using spectral angle mapper. A strong relationship was obtained between the area of seed damage derived using spectral angle mapper and visually (R2 = 0.95). Seed damage derived by thresholding of two normalised faba bean damage indices involving reflectance at 1086 and 1313 nm and 2218 and 2342 nm also showed a strong relationship with the visual assessment (R2 = 0.92). The two image analysis techniques provided similar results. The study suggests that imaging in the shortwave-infrared wavelengths and the derivation of simple indices can effectively quantify faba bean damage by Lygus feeding.

scholarly journals Classificação Espectral de Fitofisionomias em Área de Floresta Tropical Utilizando Dados do Sensor Aster

2019 ◽  
Vol 23 (2) ◽  
pp. 132
Author(s):  
Gustavo Manzon Nunes ◽  
Carlos Roberto De Souza Filho ◽  
Laerte Guimarães Ferreira ◽  
Luiz Eduardo Vicente ◽  
Maricéia Tatiana Vilani
Keyword(s):  
Sustainable Development ◽  
Near Infrared ◽  
Thermal Emission ◽  
Matched Filtering ◽  
Spectral Angle Mapper ◽  
Ndvi Index ◽  
Close Relationship ◽  
Spectral Patterns ◽  
Shortwave Infrared ◽  
Spectral Angle

Este artigo pretende avaliar a capacidade dos dados gerados pelo sensor Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)/Terra, na discriminação de fitofisionomias existentes na Reserva de Desenvolvimento Sustentável Amanã (RDSA). Os dados ASTER analisados incluem os intervalos espectrais do visível (0.52-0.69 μm), infravermelho próximo (0.78-0.86 μm) e infravermelho de ondas curtas (1.60 a 2.43 μm), sendo que nas bandas destes intervalos foram aplicadas técnicas de classificação espectral adaptadas para os dados deste sensor como Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF), além do NDVI. Através da técnica SAM foi possível a discriminação de seis fitofisionomias predominantes na RDSA. Através da técnica MTMF, que envolve um algoritmo de classificação mais robusto, informações equivalentes foram obtidas. Foi possível ainda a associação e detecção dos padrões espectrais da cobertura vegetal, mostrando a estreita relação com o índice NDVI. Palavras-chave: Mapeamento. Reserva de Desenvolvimento Sustentável Amanã. Vegetação.  Abstract This article aims to evaluate the data capacity created by a sensor named Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)/Terra, in the phytophysiognomies description of Amanã Sustainable Development Reserve (RDSA). The ASTER data analyzed include the spectral intervals of visible (0.52-0.69 μm), near-infrared (0.78-0.86 μm) and shortwave infrared (1.60 to 2:43 μm), wherein these intervals bands were applied the spectral classification techniques adapted to the data from this sensor as Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF) plus NDVI. By SAM technique was possible to distinguish six predominant phytophysiognomies in the RDSA. By MTMF technique that involves a more robust classification algorithm, equivalent information was obtained. It was also possible to associate and detect spectral patterns of vegetation, showing the close relationship with the NDVI index. Keywords: Amanã Sustainable Development Reserve. Mapping. Vegetation. 

scholarly journals Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 5518
Author(s):  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
False Negative ◽  
Similarity Score ◽  
Majority Voting ◽  
Similarity Analysis ◽  
Spectral Angle Mapper ◽  
Mineral Detection ◽  
Shortwave Infrared

Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

scholarly journals Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

2021 ◽  
Vol 13 (6) ◽  
pp. 1178
Author(s):  
Jordi Cristóbal ◽  
Patrick Graham ◽  
Anupma Prakash ◽  
Marcel Buchhorn ◽  
Rudi Gens ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Pilot Study ◽  
Data Processing ◽  
High Latitude ◽  
Hyperspectral Images ◽  
The Arctic ◽  
Wetland Mapping ◽  
Kappa Index ◽  
Spectral Angle Mapper ◽  
Spectral Angle

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

Yield Estimation from Hyperspectral Imagery Using Spectral Angle Mapper (SAM)

2008 ◽  
Vol 51 (2) ◽  
pp. 729-737 ◽  
Author(s):  
C. Yang ◽  
J. H. Everitt ◽  
J. M. Bradford
Keyword(s):  
Hyperspectral Imagery ◽  
Yield Estimation ◽  
Spectral Angle Mapper ◽  
Spectral Angle

Impact of speckle on laser range-gated shortwave infrared imaging system target identification performance

2002 ◽  
Author(s):  
Ronald G. Driggers ◽  
Richard H. Vollmerhausen ◽  
Nicole M. Devitt ◽  
Carl E. Halford ◽  
Kenneth J. Barnard
Keyword(s):  
Infrared Imaging ◽  
Imaging System ◽  
Target Identification ◽  
Identification Performance ◽  
Laser Range ◽  
Infrared Imaging System ◽  
Shortwave Infrared

scholarly journals Shortwave Infrared Imaging with J-Aggregates Stabilized in Hollow Mesoporous Silica Nanoparticles

2018 ◽  
Author(s):  
Wei Chen ◽  
ChiAn Cheng ◽  
Emily Cosco ◽  
Shyam Ramakrishnan ◽  
Jakob Lingg ◽  
...  
Keyword(s):  
Mesoporous Silica ◽  
Contrast Agents ◽  
Silica Nanoparticles ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Mesoporous Silica Nanoparticles ◽  
J Aggregates ◽  
Hollow Mesoporous Silica ◽  
Shortwave Infrared

Tissue is translucent to shortwave infrared (SWIR) light, rendering optical imaging superior in this region. However, the widespread use of optical SWIR imaging has been limited, in part, by the lack of bright, biocompatible contrast agents that absorb and emit light above 1000 nm. J-aggregation offers a means to transform stable, near-infrared (NIR) fluorophores into red-shifted SWIR contrast agents. Here we demonstrate that hollow mesoporous silica nanoparticles (HMSNs) can template the J-aggregation of NIR fluorophore IR-140 to result in nanomaterials that absorb and emit SWIR light. The J-aggregates inside PEGylated HMSNs are stable for multiple weeks in buffer and enable high resolution imaging <i>in vivo</i>with 980 nm excitation.

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