Local detrended fluctuation analysis for spectral red-edge parameters extraction

Jiang, Shan; Wang, Fang; Shen, Luming; Liao, Guiping

doi:10.1007/s11071-018-4241-y

Cited by 16 publications

(9 citation statements)

References 34 publications

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“…Therefore, the spectral response of the red-edge to chlorophyll is also very strong. The red-edge parameters became one of the most important index for the growth of crops [7], as well as for estimating chlorophyll content in plants [8]. And then, the optimal red-edge parameters were screened out by detecting the hyperspectral values and chlorophyll content, and a relationship model was established for them [9].…”

Section: Introductionmentioning

confidence: 99%

Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

Zhang

Zhao

Lang

et al. 2019

Sensors

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Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R2 = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R2 = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.

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Section: Introductionmentioning

confidence: 99%

Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

Zhang

Zhao

Lang

et al. 2019

Sensors

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“…As comparison, the five generalized Hurst exponents of original spectral are also computed and combined as a feature (denoted as h o ). Since the simple linear as well as high-order polynomial models cannot exactly portray the relationship between the spectrum information and rapeseeds biochemical index [6], here, an intelligent method, namely, random decision forest (RDF) is employed [33] to do this job. In addition, three indicators, namely root-mean-square error (Rmse) [6], correlation coefficient (R) [6], and relative error (Re) [6], are employed to evaluate the models, defined as in Eqs.…”

Section: Regression Model For Oleic Acidmentioning

confidence: 99%

“…Compared with traditional multispectral remote sensing, it can not only present the shape and spatial information of matter at different wavelengths in multiple spectral channels, but also access the spectral information of its components [2]. Due to the powerful data acquisition and analysis capabilities, the hyperspectral technique is naturally applied in intelligent agriculture [3][4][5][6][7][8][9]. Nevertheless, since that the hyperspectral remote sensing has the characteristics of multi band, narrow band width and large amount of data, the analysis of dynamic structure of hyperspectral signal become a critical premise to promote its effective utilization [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Because of the ability to deal with nonstationary measure, the method of DFA together with its extension have widely been applied into various fields [18][19][20][21][22][23][24]. As a microcosmic generalization of the DFA method, local DFA [6,[25][26][27] will provide the long-term correlated measurement for each considering windows, which is exactly what we need, i.e, to explore the correlation at each band.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we attempt to propose a method to detect rapeseeds hyperspectral redundancy by the local DFA. In addition, researches show that the crop hyperspectrum expresses multifractal nature due to its self-similarity and singularity [4][5][6][28][29][30]. Accordingly, to validate our method, we apply the multifractal DFA (MF-DFA) [18] to extract the fractal features after removing redundant band, which are used as argument to construct an oleic acid content prediction model.…”

Section: Introductionmentioning

confidence: 99%

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A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent

Li¹,

Li²,

Wang³

et al. 2021

Preprint

Self Cite

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Hyperspectrum reflectance is a curve in a certain wavelength range. Its complex dynamic structure reflects rich information of the object at variable bands. However, the potential redundancy will seriously affect accurate extraction of spectral features, therefore, information redundancy detection is a critical pretreatment for spectral analysis. In this paper, by using the local detrended fluctuation analysis, we propose a new method to detect the redundant bands. The method focuses on the spectral auto-correlation represented by local Hurst exponent in moving windows. Thus, the redundant band can be determined by the comparison of auto-correlation between two adjacent windows. To test our method, using the fractal feature of the removing redundant bands as augment, rapeseed oleic acid's prediction model is constructed based on random decision forest method. As comparison, the same feature of the original spectrum is also employed as augment for the model. The result shows that the feature of removing the redundant bands will bring better model performance than the feature of original spectrum does.

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The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible‐near infrared band feature fusion

Liu

et al. 2023

Biotechnology Journal

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Hyperspectral remote sensing technology is becoming increasingly popular in various fields due to its ability to provide detailed information about crop growth and nutritional status. The use of hyperspectral technology to predict SPAD (Soil and Plant Analyzer Development) values during cotton growth and adopt precise fertilization management measures is crucial for achieving high yield and fertilizer efficiency. To detect the nitrogen nutrition in cotton canopy leaves quickly, a non‐destructive nitrogen nutrition retrieval model was proposed based on the spectral fusion features of the cotton canopy. The hyperspectral vegetation index and multifractal features were fused to predict the SPAD value and identify the amount of fertilizer applied at different levels. The random decision forest algorithm was used as the model predictor and classifier. A method was introduced which was widely used in the fields of finance and stocks (MF‐DFA) into the field of agriculture to extract fractal features of cotton spectral reflectance. Comparing the fusion feature with multi‐fractal feature and vegetation index, the results showed that the fusion feature parameters had higher accuracy and better stability than using a single feature or feature combination. The R2 was as high as 0.8363, and the RMSE was 1.8767%. Our intelligent model provides a new idea for detecting nitrogen nutrition in cotton canopy leaves rapidly.

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Local detrended fluctuation analysis for spectral red-edge parameters extraction

Cited by 16 publications

References 34 publications

Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent

The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible‐near infrared band feature fusion

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