Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.)

Mırık, Mustafa; Ansley, R. James; Michels, G. J.; Elliott, Norman C.

doi:10.1007/s11119-012-9264-7

Cited by 41 publications

(30 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NIR reflectance from vegetation increases with the increasing green leaf or canopy density and with decreasing gaps between them due to the internal scattering at the cell wall-air interfaces within the leaf [72]. In both April and June, musk thistle had lower reflectance than wheat, brome grass, Johnsongrass and Russian thistle and higher reflectance than tansy mustard and senescent grass in the 763-885 nm wavelengths.…”

Section: Discussionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

View full text Add to dashboard Cite

Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two OPEN ACCESSRemote Sens. 2013, 5 613 distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.

show abstract

Section: Discussionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Water stress in plants causes changes in the chemical-pigment concentrations, area and cellular structure of the affected plant tissues (Mirik, 2012). Thus, the internal structure of the leaves is responsible for the high reflectance in the near infrared region, due to internal scattering (Jensen, 2011).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Time After Leaf Collection As An Influential Factor In Spectroradiometric Measurements

2017

Aust. J. Basic & Appl. Sci.

View full text Add to dashboard Cite

Background: Spectroradiometric studies involving the collection of vegetative material in the field and subsequent radiometric measurements in the laboratory are susceptible to limitations due to the loss of information related to the temporal lapse between collection and analysis. Objective: Thus, the objective of this study is to identify which spectral intervals and which vegetation indices present higher sensitivity to the postharvest time of leaves extracted from an individual of the species Eucalyptus saligna Smith.Material and Methods: The material was collected in the field, and the radiometric measurements were performed in laboratory using a FieldSpec®3 spectroradiometer, with an interval of 15 minutes, during 10 hours of observation. Epectral bands and indexes with frequent use in vegetation studies that evolve both in situ spectroradiometry techniques and orbital sensor surveys were selected. For each of the variables, were computed the averages of the 40 observations over the time and Tukey's test of means comparison was applied pair by pair. Results:In general, bands and indices that respond directly by vegetative vigor and photosynthetic activity showed higher latency on significance in relation to the beginning of the observations, as showed by SR, NDVI and REP, with 5, 5, 25, 9.75 hours respectively. However, the variables related to the internal scattering due to the tissue rupture and water loss were more sensitive to the experiment, especially the TVI index and the NIR band, which demonstrated significant results after 1.25 and 1.5 hours of the beginning of the measurements. Conclusion:The listed variables allowed the detection of foliar degradation over time after collection at different levels of sensitivity. For the latter it is recommended that in similar experiments it is timely to observe the waiting time between the extraction of leaves and the performance of the radiometric measurements in order to avoid possible loss of information.

show abstract

“…Adverse climatic events (abiotic stress) can alter the plants' water status (Auclair 1963), chemical and nutritional composition (Moheb et al 2011, Tariq et al 2012, Walter et al 2013, and lead to a decrease in the concentration of defense compounds (Rhoades et al 1979, Rosenzweig et al 2001, Fuhrer 2003; some or all of which alter the fitness and abundance of pest populations (Kennedy and Booth 1951;Dixon 1971;Tariq et al 2012Tariq et al , 2013. Remote sensing technologies have been used widely to detect aphid infestations in wheat and other crops (Yang et al 2005(Yang et al , 2009Mirik et al 2006Mirik et al , 2012Elliott et al 2009). Such studies, showing that established aphid infestations can be detected based on changes in crop reflectance, are promising and important as part of developing sensitive, reliable, and cost-effective crop surveillance systems to improve input management in large-scale agriculture.…”

Section: Authorsmentioning

confidence: 99%

Increased Susceptibility to Aphids of Flowering Wheat Plants Exposed to Low Temperatures

Lacoste

Nansen

Thompson

et al. 2015

Environmental Entomology

View full text Add to dashboard Cite

Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.)

Cited by 41 publications

References 72 publications

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Evaluation of Time After Leaf Collection As An Influential Factor In Spectroradiometric Measurements

Increased Susceptibility to Aphids of Flowering Wheat Plants Exposed to Low Temperatures

Contact Info

Product

Resources

About