Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia

Aneece, Itiya; Epstein, Howard E.

doi:10.1080/01431161.2016.1259682

Cited by 10 publications

(15 citation statements)

References 99 publications

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“…Heavy metal stress can produce some changes in plant morphological and biochemical characteristics [15]. This is because the leaf spectral response is mainly affected by plant structural and morphological characteristics; i.e., the leaf's intracellular and extracellular structure, and biochemical parameters such as nitrogen, pigments, and water contents [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

et al. 2019

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Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

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Section: Introductionmentioning

confidence: 99%

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

et al. 2019

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“…This study 721 Schmidt and Skidmore [14] No wavelength Vaiphasa et al [71] 720 Adam and Mutanga [34] 745, 746 Lehmann et al [66] 675 to 710 Aneece and Epstein [45] 700 to 749…”

Section: Spectral Regions (Nm) Reference Optimal Selected Wavelengthsmentioning

confidence: 75%

“…This study 1064, 1388 Schmidt and Skidmore [14] 771 Adam and Mutanga [34] 892, 932, 934, 958, 961, and 989 Aneece and Epstein [45] 900 to 949 Short wave infrared (1300-2500)…”

Section: Spectral Regions (Nm) Reference Optimal Selected Wavelengthsmentioning

confidence: 88%

“…This indicates a good quality of the device (ASD FieldSpect) used for the spectral acquisition and stable conditions in the laboratory environment [69]. A field spectroradiometer has been used in several studies to determine the optimal wavelengths for separation plant species at in situ and in vitro measurements [34,45,66,[71][72][73][74]. One of the most important issues in this field is data reduction and introducing optimal wavelengths.…”

Section: Discussionmentioning

confidence: 99%

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Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques

et al. 2019

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Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.

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“…In some cases, this is not difficult because data can be obtained, at least partly, through remote photointerpretation or temporal analysis of digital orthophotos. However, this method is only useful for some species of trees, shrubs, and large perennials forming compact monospecific patches [11]. In other cases, on-ground reference data acquisition is necessary, especially for herbaceous plants characterized by high phenological variability and growing in heterogenous, species-rich ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Using Airborne Hyperspectral Imaging Spectroscopy to Accurately Monitor Invasive and Expansive Herb Plants: Limitations and Requirements of the Method

Kopeć

Zakrzewska

Halladin-Dabrowska

et al. 2019

Sensors

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Remote sensing (RS) is currently regarded as one of the standard tools used for mapping invasive and expansive plants for scientific purposes and it is increasingly widely used in nature conservation management. The applicability of RS methods is determined by its limitations and requirements. One of the most important limitations is the species percentage cover at which the classification result is correct and useful for nature conservation. The primary objective, carried out in 2017 in three areas of Poland, was to determine the minimum percentage cover from which it is possible to identify a target species by RS methods. A secondary objective of this research, related to the requirements of the method, was to optimize the set of training polygons for a target species in terms of the number of polygons and abundance percentage cover of the target species. Our method has to be easy to use, effective, and applicable, therefore the analysis was carried out using the basic set of rasters—the first 30 channels after the Minimum Noise Fraction (MNF) transformation (the mosaic of hyperspectral data from HySpex sensors with spectral range 0.4–2.5 µm) and commonly used Random Forest algorithm. The analysis used airborne hyperspectral data with a spatial resolution of 1 m to perform classification of one invasive and three expansive plants—two grasses and two large perennials. On-ground training and validation data sets were collected simultaneously with airborne data collection. When testing different classification scenarios, only the set of training polygons for a target species was changed. Classification results were evaluated based on three methods: accuracy measures (Kappa and F1), true-positive pixels in subclasses with different species cover and compatibility with field mapping. The classification results indicate that to classify the target plant species at the accepted level, the training dataset should contain polygons with a species cover ranging from 80–100%. Training performed only using polygons with a species characterized by a variable, but lower, cover (20–70%) and missing samples in the 80–100% range, led to a map which was not acceptable because of a high overestimation of target species. We achieved effective identification of species in areas where the species cover is above 50%, considering that ecosystems are heterogeneous. The results of these studies developed a methodology of field data acquisition and the necessity of synchronization in the acquisition of airborne data, and training and validation of on-ground sampling.

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Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia

Cited by 10 publications

References 99 publications

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques

Using Airborne Hyperspectral Imaging Spectroscopy to Accurately Monitor Invasive and Expansive Herb Plants: Limitations and Requirements of the Method

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