scholarly journals Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7192
Author(s):  
Gustavo Togeiro de Alckmin ◽  
Lammert Kooistra ◽  
Richard Rawnsley ◽  
Sytze de Bruin ◽  
Arko Lucieer
Keyword(s):  
Perennial Ryegrass ◽  
Cross Validation ◽  
Parametric Method ◽  
Vegetation Indices ◽  
Performance Comparison ◽  
Hyperspectral Data ◽  
Biomass Estimation ◽  
Canopy Reflectance ◽  
Multispectral Data ◽  
Non Destructive

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.

scholarly journals Gossypium Plant Health Detection from Hyperspectral Data using Various Disease Indices

2019 ◽  
Vol 9 (1) ◽  
pp. 7129-7135
Keyword(s):  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Economic Losses ◽  
Treatment Technique ◽  
Linear Regression Models ◽  
Plant Composition ◽  
Personal Loss ◽  
Spectral Vegetation Indices ◽  
Non Destructive ◽  
Hyperspectral Sensor

Cotton is the world's most prevalent beneficial non-food crop, producing revenue for over 250 million people globally and employing nearly 7% of all workers in developing nations. About half of all fabrics are produced with cotton. In such a case if the cotton plant gets affected due to disease can lead to economic and personal loss. These diseases may be one of the reasons that could significantly reduce the supply of cotton to the market, which result in a low agricultural economy. Faster and more accurate prediction of leaf diseases in crops could help to develop an early treatment technique while significantly reducing economic losses. The traditional monitoring system is time-consuming and expensive. In this paper, we have discussed hyperspectral sensor ASD FieldSpec4 which are less time consuming and non-destructive. Spectral Vegetation Indices (SVI) is strongly linked to the chemical composition of the plant leaf such as chlorophyll, nitrogen, carotenoid, and anthocyanin. The linear regression models were developed for the calculation of correlations between spectral indices and plant composition using MATLAB 2018.

scholarly journals Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

2014 ◽  
Vol II-7 ◽  
pp. 47-53 ◽  
Author(s):  
C. Mundava ◽  
P. Helmholz ◽  
A. G. T. Schut ◽  
R. Corner ◽  
B. McAtee ◽  
...  
Keyword(s):  
Western Australia ◽  
Cross Validation ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Site Group ◽  
Elevation Data ◽  
Annual Grasses ◽  
Group 3 ◽  
Specific Factors ◽  
Good Calibration

The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011&ndash;2013). The sites were divided into three groups (<i>Open plains, Bunch grasses</i> and <i>Spinifex</i>) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the <i>Open plains</i> and <i>Bunch grasses</i> sites, but not for <i>Spinifex</i> sites. When rainfall and elevation data are included, cross validation improved slightly with a Q<sup>2</sup> of 0.49&ndash;0.72 for <i>Open plains</i> and <i>Bunch grasses</i> sites respectively. Cross validation results for total AGB were moderately accurate (Q<sup>2</sup> of 0.41) for <i>Open plains</i> but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors.

scholarly journals Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249351
Author(s):  
Hong Li ◽  
Wunian Yang ◽  
Junjie Lei ◽  
Jinxing She ◽  
Xiangshan Zhou
Keyword(s):  
Plant Species ◽  
Cross Validation ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Leaf Water Content ◽  
Spectral Absorption ◽  
Equivalent Water Thickness ◽  
Leaf Level

The leaf equivalent water thickness (EWT, g cm−2) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI970, SAI1200, and SAI1660) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI1200 was more suitable for estimating the EWT than FMC, whereas SAI970 and SAI1660 were more suitable for estimating the FMC. Second, SAI1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination (Rcv2) of 0.845 and relative cross-validation root mean square error (rRMSEcv) of 8.90%. Third, SAI1660 outperformed the other indices in estimating the FMC at the leaf level, with an Rcv2 of 0.637 and rRMSEcv of 8.56%. Fourth, SAI970 achieved a moderate accuracy in estimating the EWT (Rcv2 of 0.25 and rRMSEcv of 19.68%) and FMC (Rcv2 of 0.275 and rRMSEcv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI1200 to determine the leaf EWT and SAI1660 to obtain the leaf FMC among various plant types.

scholarly journals Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices

2020 ◽  
Vol 22 (1) ◽  
pp. 205-225 ◽  
Author(s):  
Gustavo Togeiro de Alckmin ◽  
Lammert Kooistra ◽  
Richard Rawnsley ◽  
Arko Lucieer
Keyword(s):  
Perennial Ryegrass ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Biomass Estimation ◽  
Pasture Management ◽  
Accuracy And Precision ◽  
Spectral Vegetation Indices ◽  
Regression Techniques

AbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice

2007 ◽  
Vol 58 (11) ◽  
pp. 1077 ◽  
Author(s):  
Yan Zhu ◽  
Dongqin Zhou ◽  
Xia Yao ◽  
Yongchao Tian ◽  
Weixing Cao
Keyword(s):  
Spectral Reflectance ◽  
Vegetation Indices ◽  
Leaf Nitrogen ◽  
Canopy Reflectance ◽  
Rice Cultivars ◽  
Canopy Spectral Reflectance ◽  
Mean Square Errors ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (|r| > 0.85), between LNA and canopy reflectance from 760 to 1100 nm (|r| > 0.79), and from 460 to 710 nm wavelengths (|r| > 0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 341
Author(s):  
Pauliina Salmi ◽  
Matti A. Eskelinen ◽  
Matti T. Leppänen ◽  
Ilkka Pölönen
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Strong Correlations ◽  
Algae Biomass ◽  
Chlorophyll A Concentration ◽  
Cost Efficient ◽  
Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

scholarly journals Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 914
Author(s):  
Adeel Ahmad ◽  
Hammad Gilani ◽  
Sajid Rashid Ahmad
Keyword(s):  
High Resolution ◽  
Literature Review ◽  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Independent Variables ◽  
Non Linear ◽  
Multiple Variables ◽  
Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

scholarly journals Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuai Che ◽  
Guoying Du ◽  
Ning Wang ◽  
Kun He ◽  
Zhaolan Mo ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Multispectral Imaging ◽  
Vegetation Indices ◽  
Spectral Imaging ◽  
Shielding Effect ◽  
Quadratic Model ◽  
Biomass Estimation ◽  
Marine Aquaculture ◽  
Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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