Extraction of the red edge position from hyperspectral reflectance data for plant stress monitoring

2019 ◽  
Author(s):  
Kalinka Velichkova ◽  
Dora Krezhova
Keyword(s):  
Plant Stress ◽  
Hyperspectral Reflectance ◽  
Stress Monitoring ◽  
Red Edge ◽  
Reflectance Data ◽  
Red Edge Position

scholarly journals LEAF CHLOROPHYLL CONTENT ESTIMATION IN THE MONARCH BUTTERFLY BIOSPHERE RESERVE

2010 ◽  
Vol 33 (2) ◽  
pp. 175
Author(s):  
Ma. Luisa España-Boquera ◽  
Philippe Lobit ◽  
Vilma Castellanos-Morales
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Indices ◽  
Biosphere Reserve ◽  
Monarch Butterfly ◽  
Hyperspectral Reflectance ◽  
Plant Leaves ◽  
Red Edge ◽  
Monarch Butterfly Biosphere Reserve ◽  
Leaf Composition ◽  
Red Edge Position

Chlorophyll is an essential element of photosynthesis and its content in plant leaves indicates their photosynthetic capacity as well as the presence of stress or diseases. The purpose of this work was to evaluate the feasibility of estimating chlorophyll content in the Monarch Butterfly Biosphere Reserve forest (Sierra Chincua sanctuary, México) based on vegetation indices calculated by using hyperspectral reflectance measurements of plant leaves. This study focused on oyamel (Abies religiosa L.) which is the main tree specie of this area. Leaf samples were taken on 140 trees and analyzed for chlorophyll a and b, nitrogen and carbon content. The hyperspectral reflectance spectra were measured on each sample and different vegetation indices were calculated. Results showed that the indices best correlated with chlorophyll content were the red edge position index (r = 0.531) and the red edge position chlorophyll reflectance index (r = 0.506), followed by the MERIS terrestrial chlorophyll index (r = 0.497) and the green chlorophyll reflectance index (r = 0.472). Although there was a significant correlation between nitrogen and chlorophyll content, none of the indices studied here correlated with nitrogen content. The influence of various environmental factors (altitude, slope, vegetation density and aspect) on leaf composition (nitrogen, carbon chlorophyll content and chlorophyll a/b ratio) and on the vegetation indices was studied. Environmental factors had an influence on both leaf composition and vegetation indices. Chlorophyll and nitrogen content were influenced mostly by the altitude and slope of the site while vegetation indices were affected mostly by its orientation.

Monitoring leaf pigment status with hyperspectral remote sensing in wheat

2008 ◽  
Vol 59 (8) ◽  
pp. 748 ◽  
Author(s):  
Wei Feng ◽  
Xia Yao ◽  
Yongchao Tian ◽  
Weixing Cao ◽  
Yan Zhu
Keyword(s):  
Protein Concentration ◽  
Grain Protein ◽  
Growth Stages ◽  
Hyperspectral Reflectance ◽  
Red Edge ◽  
Leaf Pigment ◽  
Lower Estimation ◽  
Pigment Concentrations ◽  
Highly Correlated ◽  
Red Edge Position

Leaf pigment status within a canopy is a key index for evaluating photosynthetic efficiency and nutritional stress in crop plants. Non-destructive and quick assessment of leaf pigment status is needed for growth diagnosis, yield prediction, and nitrogen (N) management in crop production. The objectives of this study were to analyse quantitative relationships of leaf pigment concentration on a dry weight basis and leaf pigment density per unit soil area to ground-based canopy hyperspectral reflectance and derivative parameters, and to establish estimation models for real-time monitoring of leaf pigment status with key hyperspectral bands and indices in wheat (Triticum aestivum L.). Two field experiments were conducted with different N application rates and wheat cultivars across two growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance over 350−2500 nm, leaf pigment concentrations, and leaf dry weights under the various treatments at different growth stages. The results showed that different pigment concentrations and densities of chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chla+b), and carotenoids (Car) in two cultivars, Ningmai 9 (low grain protein concentration) and Yumai 34 (high grain protein concentration), tended to increase with increasing N rates, and differed with genotypes and growth stages. The analyses on the relationships between vegetation indices and leaf pigment concentrations and densities indicated that the pigment concentrations and pigment densities, respectively, were highly correlated with eight spectral parameters selected. The leaf chlorophyll concentrations were highly correlated with red edge position, with highest coefficients of determination (R2) for REPLE, while R2 between Car and spectral indices decreased. The chlorophyll densities were highly correlated with VOG2, VOG3, RVI(810,560), Dr/Db, and SDr/SDb, but the correlation was also reduced for carotenoids. Testing of the monitoring equations with independent datasets indicated that the red edge position was the best hyperspectral parameter to estimate leaf pigment concentrations, with no significant difference between REPLE and REPIG for Chla, Chla+b, and Car, although better performance with REPIG than with REPIE for estimation of Chlb. The VOG2, VOG3, Dr/Db, and SDr/SDb were the best hyperspectral parameters to estimate leaf pigment densities, but with lower estimation accuracy for Chlb and lower estimation precision for Car. The overall results suggested that the pigment concentrations and densities in wheat leaves, especially for Chla and Chla+b, could be reliably estimated with the hyperspectral parameters established in this study.

A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data

2014 ◽  
Vol 35 (4) ◽  
pp. 1432-1449 ◽  
Author(s):  
Prabir Kumar Das ◽  
Karun Kumar Choudhary ◽  
B. Laxman ◽  
S.V.C. Kameswara Rao ◽  
M.V.R. Seshasai
Keyword(s):  
Hyperspectral Data ◽  
Wheat Crop ◽  
Linear Extrapolation ◽  
Stress Monitoring ◽  
Position Detection ◽  
Red Edge ◽  
Red Edge Position

scholarly journals STRESS MONITORING OF MULBERRY PLANTS BY FINDING REP USING HYPERSPECTRAL DATA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 383-386 ◽  
Author(s):  
K. Bhosle ◽  
V. Musande
Keyword(s):  
Linear Interpolation ◽  
Stressed Condition ◽  
Linear Extrapolation ◽  
Stress Monitoring ◽  
First Derivative ◽  
Red Edge ◽  
Hyper Spectral ◽  
Field Samples ◽  
Hyperion Data ◽  
Red Edge Position

Stressed on crop can be monitored using different indices. Red Edge position is good estimator for stress monitoring. The red edge position (REP) is strongly correlated with foliar chlorophyll content. Strong chlorophyll absorption causes the abrupt change in the 680–800 nm region of reflectance spectra of green vegetation. The red edge consist the point of maximum slope between red and near-infrared wavelengths. REP can be used to recognize green zone of the observation area. The REP is present in spectra for vegetation recorded by remote sensing methods. REP is clearer and significant in hyper spectral data as hyper spectral consist of more and continuous bands data. In this paper experiments were carried out for mulberry crop using USGS EO-1 Hyperion data. Atmospheric corrected data is used for classification. Classification is carried out on small cluster of 14 field samples. Ground truth is verified and classified by comparing with Hyperion data. REP is different for different stressed condition of crops and shows healthy and diseased crop condition. Nutritional stresses, diseases, drought of plants are detected using REP. Stressed and healthy field of mulberry are estimated by calculating REP using maximum first derivative, linear interpolation, linear extrapolation method. Finally REP is compared using above methods. It is noticeable difference of REP for healthy and stressed crop. The research indicates that overall accuracy using maximum first derivative was 92.85 % and it was more compared to other methods. Linear extrapolation gives less accuracy compared to linear interpolation method.

Analysis of hyperspectral reflectance data for monitoring growth and development of lesquerella

2011 ◽  
Vol 33 (2) ◽  
pp. 524-531 ◽  
Author(s):  
K.R. Thorp ◽  
D.A. Dierig ◽  
A.N. French ◽  
D.J. Hunsaker
Keyword(s):  
Growth And Development ◽  
Hyperspectral Reflectance ◽  
Reflectance Data

scholarly journals Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco

2021 ◽  
Author(s):  
Austin Hayes ◽  
T. David Reed
Keyword(s):  
Machine Learning ◽  
Broad Band ◽  
Phytophthora Nicotianae ◽  
Hyperspectral Reflectance ◽  
Black Shank ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Band Index ◽  
Intensively Managed ◽  
Reflectance Data

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.

Differentiation of turfgrass and common weed species using hyperspectral radiometry

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 335-339 ◽  
Author(s):  
Kendall C. Hutto ◽  
David R. Shaw ◽  
John D. Byrd ◽  
Roger L. King
Keyword(s):  
Data Collection ◽  
Classification Accuracy ◽  
Spectral Characteristics ◽  
Creeping Bentgrass ◽  
Hyperspectral Reflectance ◽  
Weed Species ◽  
Spectral Bands ◽  
Virginia Buttonweed ◽  
Reflectance Data

Hand-held hyperspectral reflectance data were collected in the summers of 2002, 2003, and 2004 to differentiate unique spectral characteristics of common turfgrass and weed species. Turfgrass species evaluated were: bermudagrass, ‘Tifway 419’; zoysiagrass, ‘Meyer’; St. Augustinegrass, ‘Raleigh’; common centipedegrass; and creeping bentgrass, ‘Crenshaw’. Weed species evaluated were: dallisgrass, southern crabgrass, eclipta, and Virginia buttonweed. Reflectance data were collected from greenhouse and field locations. An overall classification accuracy of 85% was achieved for all species in the field. A total of 21 spectral bands between 378 and 1,000 nm that were consistent over the three data collection periods were used for analysis. Only centipedegrass, zoysiagrass, and dallisgrass were correctly classified less than 80% of the time. An overall classification accuracy of 69% was achieved for the greenhouse species. Spectral bands used in this analysis ranged from 353 to 799 nm. Creeping bentgrass and Virginia buttonweed were classified correctly at 96 and 92%, respectively.

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