Estimation of chlorophyll content in intertidal mangrove leaves with different thicknesses using hyperspectral data

Zhao, Yunxia; Yan, Changrong; Lu, Shan; Wang, Pei; Qiu, Guo Yu; Li, Ruili

doi:10.1016/j.ecolind.2019.105511

Cited by 24 publications

(14 citation statements)

References 59 publications

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“…This helped identifying the most relevant spectral regions for each metal. Then, we tested simple and normalized vegetation indices (see 26 , 77 for the index formulas). Here, we describe only the results for the Normalized Difference Vegetation Indices (NDVI-like indices), which describe the difference of reflectance between two wavelengths.…”

Section: Methodsmentioning

confidence: 99%

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Lassalle

Fabre

Crédoz

et al. 2021

Sci Rep

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Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.

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Section: Methodsmentioning

confidence: 99%

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Lassalle

Fabre

Crédoz

et al. 2021

Sci Rep

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“…Remote sensing, specifically proximal sensing, can provide an effective alternative in assisting nutritional analysis of plants more accurately. The use of proximal sensors has an advantage over traditional agronomic methods since it allows to infer vegetation conditions in a non-invasive and non-destructive manner [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

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“…It made the research more achievable and accurate. Previous studies [20,21,35] can also estimate chlorophyll concentration, but research objects were mostly single crops or densely covered areas with single vegetation type. Therefore, this study is meaningful to the analysis of urban vegetation growth environment and the estimation of important component parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Reflectance spectroscopy technology can better combine pigment concentration and reflectance spectra of vegetation for cross-spatial-scale research. For example, Zulfa et al [20] and Zhao et al [21] identified the relationship between hyperspectral data and chlorophyll of mangrove species. Jesús et al [22] quantified the chlorophyll content of vegetation based on the hyperspectral index.…”

Section: Introductionmentioning

confidence: 99%

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Lin

Xü

et al. 2021

Remote Sensing

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Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. The results showed: (1) Dust deposition had interference effects on pigment concentration, leaf reflectance, and their correlations. Dust was an essential factor that must be considered. (2) The influence of dust deposition on chlorophyll—a concentration estimation was related to the selected vegetation indexes. Different modeling indicators had different sensitivity to dust. The SR705 and CIrededge vegetation indexes based on the red edge band were more suitable for establishing chlorophyll-a prediction models. (3) The leaf chlorophyll concentration prediction can be achieved by using reflectance spectroscopy data. The effect of the chlorophyll estimation model under the levels of “Medium dust” and “Heavy dust” was worse than that of “Less dust”, which meant the accumulation of dust had interference to the estimation of chlorophyll concentration. The quantitative analysis of vegetation spectrum by reflectance spectroscopy shows excellent advantages in the research and application of vegetation remote sensing, which provides an important theoretical basis and technical support for the practical application of plant chlorophyll content prediction.

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Estimation of chlorophyll content in intertidal mangrove leaves with different thicknesses using hyperspectral data

Cited by 24 publications

References 59 publications

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

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