Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression

Qian, Yi; Jiapaer, Guli; Chen, Jingming; Bao, Anming; Wang, Fumin

doi:10.1016/j.isprsjprs.2014.01.004

Cited by 55 publications

(45 citation statements)

References 60 publications

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“…Using the "LinearModel.fit() and LinearModel.stepwise()" functions in Statistics and Machine Learning Toolbox Functions -MATLAB 9.2, the VI that most significantly correlated with the visual disease rankings was identified [36]. Although other more complex statistical methods such as partial least square regression (PLSR) have been used for identifying optimal wavelengths from hyperspectral data, Yi et al [39] demonstrated that once the prediction model is simplified, selecting only the most significant variables (out of 19, 2-band VIs), PLSR did not perform significantly differently to stepwise regression method.…”

Section: Discussionmentioning

confidence: 99%

“…However, the background was bare soil and observed to be uniform over the site of interest To evaluate the spectral responses from the sampled trees against PRR disease severity rankings, 18 vegetation indices described in Table 2 were calculated. These indices (Table 2) were selected on the basis of having being closely related to specific features of plant leaf physiology [21,[37][38][39]. To evaluate the spectral responses from the sampled trees against PRR disease severity rankings, 18 vegetation indices described in Table 2 were calculated.…”

Section: Deriving Vegetation Indices From Canopy Spectral Informationmentioning

confidence: 99%

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Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

et al. 2018

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Phytophthora root rot (PRR) infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, canopy decline, defoliation, and, eventually, tree mortality. Typically, the severity of PRR disease (proportion of canopy decline) is assessed by visually comparing the canopy health of infected trees to a standardised set of photographs and a corresponding disease rating. Although this visual method provides some indication of the spatial variability of PRR disease across orchards, the accuracy and repeatability of the ranking is influenced by the experience of the assessor, the visibility of tree canopies, and the timing of the assessment. This study evaluates two image analysis methods that may serve as surrogates to the visual assessment of canopy decline in large avocado orchards. A smartphone camera was used to collect red, green, and blue (RGB) colour images of individual trees with varying degrees of canopy decline, with the digital photographs then analysed to derive a canopy porosity percentage using a combination of 'Canny edge detection' and 'Otsu's' methods. Coinciding with the on-ground measure of canopy porosity, the canopy reflectance characteristics of the sampled trees measured by high resolution Worldview-3 (WV-3) satellite imagery was also correlated against the observed disease severity rankings. Canopy porosity values (ranging from 20-70%) derived from RGB images were found to be significantly different for most disease rankings (p < 0.05) and correlated well (R 2 = 0.89) with the differentiation of three disease severity levels identified to be optimal. From the WV-3 imagery, a multivariate stepwise regression of 18 structural and pigment-based vegetation indices found the simplified ratio vegetation index (SRVI) to be strongly correlated (R 2 = 0.96) with the disease rankings of PRR disease severity, with the differentiation of four levels of severity found to be optimal.

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Section: Discussionmentioning

confidence: 99%

Section: Deriving Vegetation Indices From Canopy Spectral Informationmentioning

confidence: 99%

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

et al. 2018

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“…The best results are visualised in colour (yellow for July dataset, red for August dataset and green for both). For the indices formulas refer the literature: Thenkabail et al, 2012;Main et al, 2011;le Maire et al, 2004;Hernandez-Clemente et al, 2012;Yi at al., 2014, Zemek et al 2014. The predictive equations modeled by the linear regressions show the similar values for both datasets (July and August) in case of plant cover and fAPAR and the corresponding indices.…”

Section: Resultsmentioning

confidence: 99%

“…Secondly, fifty vegetation indices chosen based on the literature review (Thenkabail et al, 2012;Main et al, 2011;le Maire et al, 2004;Hernandez-Clemente et al, 2012;Yi at al., 2014, Zemek et al 2014 were calculated using the field reflectance spectra. The models using the linear regression between each index and each parameter were built, again separately for July and August dataset.…”

Section: Methods and Softwarementioning

confidence: 99%

Field Spectroscopy for Vegetation Evaluation Along the Nutrient and Elevation Gradient Above the Tree Line in the Krkonoše Mountains National Park

Červená¹,

Kupková²,

Suchá³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper examines the relations between vegetation spectra measured in the field along the nutrient and elevation gradient in the most valuable parts of The Krkonoše Mountains tundra and selected parameters describing vegetation state and condition (fAPAR, plant cover and average vegetation height). The main goal was to find relations and indices based on spectral measurements that could be used for vegetation evaluation and classification in practice and management. The vegetation parameters and spectral properties were also compared for two datasets -one acquired in July and second in August 2015. The best correlations were obtained for plant cover (R 2 above 0.8 for July dataset and above 0.7 for August dataset) and two types of indices -using the wavelengths of red edge, e.g. OSAVI or mND705, and indices for vegetation water content estimates using the wavelengths in shortwave infrared region of the spectra in combination with wavelengths above 800 nm, e. g. NDII. The worst results were found for fAPAR with maximal values of R 2 just above 0.4 with the indices using the wavelengths around 700 nm. For vegetation height the results differ between July and August data -R 2 around 0.62 in July and only 0.47 in August for vegetation indices using the wavelengths of visible and red edge regions.

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“…PLSR, a multivariate extension of the multiple regression model, is the most widely used approach in chemometrics, owing to its ability to analyze data with many noisy, collinear, and even incomplete variables in both input (X) and output (Y) measurements (Wold et al 2001). It has been claimed that PLSR analysis can provide a more useful and accurate estimation tool for plant spectroscopic analysis compared with other empirical approaches; for example, PLSR can improve the prediction of green biomass and leaf nitrogen concentration compared to hyperspectral indices (Cho et al 2007, Hansen & Schjoerring 2003, and carotenoid estimation results based on PLSR were significantly better than those based on stepwise multiple linear regression and vegetation indexes (Yi et al 2014). In addition, the PLSR model is more suitable than the multiple linear regression model for predicting the nitrogen content at the heading stage using the hyperspectral reflectance (Ryu et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis

Wang¹,

Jin²

2016

iForest

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A clear understanding of plant transpiration is a crucial step for water cycle and climate modeling, especially for arid ecosystems in which water is one of the major constraints. Traditional field measurements of leaf scale transpiration are always time-consuming and often unfeasible in the context of large spatial and temporal scales. This study focused on a dominant native plant in the arid land of central Asia, Haloxylon ammondendron, with the aim of deriving the leaf-scale transpiration through hyperspectral reflectance using Partial Least Squares Regression (PLSR) analysis. The results revealed that the PLSR model based on the first-order derivative spectra at wavelengths selected through stepwise regression analysis can closely trace leaf transpiration with a high accuracy (R 2 = 0.78, RMSE = 1.62 µmol g -1 s -1 ). The accuracy is also relatively stable even at a spectral resolution of 10 nm, which is very close to the bandwidths of several running satellite-borne hyperspectral sensors such as Hyperion. The results also proved that the first-order derivative spectra within the shortwave infrared (SWIR) domain, especially at 2435, 2440, 2445, and 2470 nm, were critical for PLSR models to predict leaf transpiration. These findings highlight a promising strategy for developing remote sensing methods to potentially characterize transpiration at broad scales.

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Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression

Cited by 55 publications

References 60 publications

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Field Spectroscopy for Vegetation Evaluation Along the Nutrient and Elevation Gradient Above the Tree Line in the Krkonoše Mountains National Park

Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis

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