scholarly journals Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

2018 ◽  
Vol 8 (2) ◽  
pp. 249-259 ◽  
Author(s):  
Miloš Barták ◽  
Kumud Bandhu Mishra ◽  
Michaela Marečková
Keyword(s):  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Structural Features ◽  
Coefficient Of Determination ◽  
Curvilinear Relationship ◽  
Structural Deterioration ◽  
Reflectance Indices

Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

scholarly journals Spectral signature of brown rust and orange rust in sugarcane

2020 ◽  
pp. 9-20
Author(s):  
Eniel Rodríguez-Machado ◽  
Osmany Aday-Díaz ◽  
Luis Hernández-Santana ◽  
Jorge Luís Soca-Muñoz ◽  
Rubén Orozco-Morales
Keyword(s):  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Brown Rust ◽  
Spectral Signature ◽  
Spatial And Temporal Variability ◽  
Mean Values ◽  
Index Value

Precision agriculture, making use of the spatial and temporal variability of cultivable land, allows farmers to refine fertilization, control field irrigation, estimate planting productivity, and detect pests and disease in crops. To that end, this paper identifies the spectral reflectance signature of brown rust (Puccinia melanocephala) and orange rust (Puccinia kuehnii), which contaminate sugar cane leaves (Saccharum spp.). By means of spectrometry, the mean values and standard deviations of the spectral reflectance signature are obtained for five levels of contamination of the leaves in each type of rust, observing the greatest differences between healthy and diseased leaves in the red (R) and near infrared (NIR) bands. With the results obtained, a multispectral camera was used to obtain images of the leaves and calculate the Normalized Difference Vegetation Index (NDVI). The results identified the presence of both plagues by differentiating healthy from contaminated leaves through the index value with an average difference of 11.9% for brown rust and 9.9% for orange rust.

The effect of upper cortex absence on spectral reflectance indices in Antarctic lichens during thallus dehydration

2018 ◽  
Vol 8 (1) ◽  
pp. 107-118 ◽  
Author(s):  
Alla Orekhova ◽  
Michaela Marečková ◽  
Jana Hazdrová ◽  
Miloš Barták
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Hydration Status ◽  
Nostoc Commune ◽  
Response Curves ◽  
Relative Water ◽  
James Ross Island ◽  
Species Specific ◽  
Reflectance Indices

In maritime Antarctica, lichens and mosses represent dominant autotrophs forming community structure of vegetation oases. In our study, we selected 4 most common lichen species (Xanthoria elegans, Rhizoplaca melanophthalma, Leptogium puberulum, Physconia muscigena) and monospecific colony of Nostoc commune typical for James Ross Island (Antarctica) for detailed physiological experiments. We investigated their spectral characteristics in response to hydration status of their thalli. In samples desiccating from fully wet (RWC, relative water content of 100%) to dry state (RWC = 0), photochemical reflectance index (PRI), and normalized difference vegetation index (NDVI) were evaluated for control thalli and those with removed upper cortex. In this way, the effect of presence/absence of the upper cortex on PRI, NDVI was studied. PRI showed either no change or species-specific an increase/decrease with dehydration. Removal of the upper cortex caused both PRI decrease (N. commune, P. muscigena) and increase (R. melanophthalma, L. puberulum). Removal of the upper cortex led to increase in NDVI in all species, typically within the RWC range of 20-100%. Species-specific differences of hydration-response curves of PRI and NDVI are discussed as well as the role of the absence of the upper cortex in the evaluation of spectral characteristics in desiccating lichens.

scholarly journals Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

2020 ◽  
pp. 1-9
Author(s):  
H. R. Naveen ◽  
B. Balaji Naik ◽  
G. Sreenivas ◽  
Ajay Kumar ◽  
J. Adinarayana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Hyperspectral Remote Sensing ◽  
Nitrogen Stress ◽  
Fixed Amount ◽  
Nitrogen Levels ◽  
Stress Environment

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.

scholarly journals Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

2018 ◽  
Vol 10 (10) ◽  
pp. 1528 ◽  
Author(s):  
Liang Han ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Chengquan Zhou ◽  
Hao Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Canopy Structure ◽  
Canopy Cover ◽  
Structural Features ◽  
Discrimination Ability ◽  
Maize Varieties ◽  
Aerial Vehicle

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

scholarly journals Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

2012 ◽  
Vol 30 (2) ◽  
pp. 437-447 ◽  
Author(s):  
A. Merotto JR. ◽  
C. Bredemeier ◽  
R.A. Vidal ◽  
I.C.G.R. Goulart ◽  
E.D. Bortoli ◽  
...  
Keyword(s):  
Weed Control ◽  
Precision Agriculture ◽  
Weed Management ◽  
Dry Matter ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Weed Interference ◽  
Reflectance Indices ◽  
The Relationship

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

scholarly journals Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1180
Author(s):  
Meng Li ◽  
Ronghao Chu ◽  
Xiuzhu Sha ◽  
Feng Ni ◽  
Pengfei Xie ◽  
...  
Keyword(s):  
Scale Effect ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Scale Effects ◽  
Spectral Characteristics ◽  
Summer Maize ◽  
Infrared Band ◽  
Area Index ◽  
Closed Canopy

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.

scholarly journals Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland

2020 ◽  
Vol 12 (17) ◽  
pp. 2677
Author(s):  
Maya Deepak ◽  
Sarita Keski-Saari ◽  
Laure Fauch ◽  
Lars Granlund ◽  
Elina Oksanen ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Chlorophyll Content ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Leaf Traits ◽  
Silver Birch ◽  
Red Edge

The goal of this study was to investigate the variation in the leaf spectral reflectance and its association with other leaf traits from 12 genotypes among three provenances of origin (populations) in a common garden for Finnish silver birch trees in 2015 and 2016. The spectral reflectance was measured in the laboratory from the detached leaves in the wavelength range of visible and near-infrared (VNIR, 400–1000 nm) and shortwave infrared (SWIR, 1000–2500 nm). The variation among the provenance was initially visualized with principal component analysis (PCA) and a clear separation among the provenances was detected with the discriminant analysis of principal components (DAPC) and partial least squares discriminant analysis (PLS-DA) depicting a less strong variation among the genotypes within the provenances. Wavelengths contributing to the separation of the genotypes and provenances were identified from the contribution plot of DAPC and the red edge was strongly related to the differences. Chlorophyll content showed clear provenance variation and was associated with the separation among the genotypes and provenances in the DAPC space. The normalized difference vegetation index (NDVI705,750) and chlorophyll reflectance index (CRI) showed clear significance among the provenances, whereas NDVI670,780 showed no variation. The variation in the chlorophyll content and the CRI and red edge-based NDVI indices indicated seasonal variation as the chlorophyll content starts increasing in early June. The correlation of foliar chlorophyll content and the chlorophyll-related spectral indices for the discrimination of provenances and genotypes are reported for the first time in a naturally occurring tree species consecutively for two years.

Comparison of Spectral Reflectance and Multispectrally Induced Fluorescence to Determine Winter Wheat Nitrogen Deficit

2014 ◽  
Vol 1059 ◽  
pp. 127-133 ◽  
Author(s):  
Jana Galambošová ◽  
Miroslav Macák ◽  
Marek Živčák ◽  
Vladimír Rataj ◽  
Pavol Slamka ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
N Uptake ◽  
Health Effect ◽  
Growth Stages ◽  
Commercial Technology ◽  
Reflectance Indices ◽  
Crop Health

Technical and technological aspects of variable rate nitrogen fertilization receive much attention nowadays. Current commercial technology is based on the use of spectral reflectance of crop. However, these have some limitations as variety dependence, crop health effect and limited use in more developed growth stages. New parameters overcoming these problems need to be assessed and their potential in precision agriculture should be considered. Multispectrally induced fluorescence is a progressive method. In addition to chlorophyll content, it allows to determine phenolic compounds, which is a product of metabolism of the plant under nitrogen deficit and is considered as the most exact indicator of nitrogen deficit. Comparing the spectral reflectance indices (normalized difference vegetation index – NDVI and normalized difference red edge index – NDRE) and multispectral fluorescence index (nitrogen balance index – NBI), these performed similarly in terms of determining the leaves biomass and nitrogen content in %, NDRE and NBI reflected significantly also aboveground N; however, only the correlation of NDVI reflected with N uptake and with leaf area was highly significant.

scholarly journals Remote Sensing for Plant Water Content Monitoring: A Review

2021 ◽  
Vol 13 (11) ◽  
pp. 2088
Author(s):  
Carlos Quemada ◽  
José M. Pérez-Escudero ◽  
Ramón Gonzalo ◽  
Iñigo Ederra ◽  
Luis G. Santesteban ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Wave ◽  
Technological Development ◽  
Water Status ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Plant Water ◽  
Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

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