Foliar spectral properties following leaf clipping and implications for handling techniques

Foley, Sheri; Rivard, Benoît; Sánchez‐Azofeifa, Arturo; Calvo, Julio

doi:10.1016/j.rse.2005.06.014

Cited by 100 publications

(53 citation statements)

References 39 publications

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“…A total of 10 mature sunlit leaves were collected for each species, which were identified in-situ and then transported within one hour to a laboratory where their spectral reflectance was measured. The time elapsed from collection to measurement is within the range recommended by Foley et al (2006). The collection permit from the Panamanian Ministry of the Environment stipulated that a maximum of ten leaves per species could be collected.…”

Section: Collection Of Leaf Level Reflectance Spectramentioning

confidence: 96%

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Kalácska

Bohlman

Sánchez‐Azofeifa

et al. 2007

Remote Sensing of Environment

109

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A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results followed by the wavelet energy feature vector and a form of principal component analysis. For the same dataset our results indicate that none of the pattern recognition classifiers performed the best across all reduction techniques, and also that none of the parametric classifiers had the overall lowest training and testing errors. At the crown level, in addition to higher testing error rates (7%), it was found that there was no optimal data reduction technique. The significant wavebands were also found to be different between the leaf and crown levels. At the leaf level, the visible region of the spectrum was the most important for discriminating between lianas and trees whereas at the crown level the shortwave infrared was also important in addition to the visible and near infrared.

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Section: Collection Of Leaf Level Reflectance Spectramentioning

confidence: 96%

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Kalácska

Bohlman

Sánchez‐Azofeifa

et al. 2007

Remote Sensing of Environment

109

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“…Such a threshold of significance may not be appropriate in difficult classification problems. Combined with the studies from Lee et al (2014) and Foley et al (2006), the research conducted by Summy et al (2011) further reinforces the need to study spectral signatures of samples after they are excised, since every plant species has a different response. The objective of this research is to model the spectrum of corn leaves as a function of storage duration to provide guidance for field sample studies performed on corn crops.…”

Section: Introductionmentioning

confidence: 87%

“…In both techniques, enough change to prevent the leaf samples from characterizing the crops was observed, so the long term advantage was not useful. Foley et al (2006) preserved the leaves of common guava (Psidium guajava), purple guava (Psidium littorale), weeping fig (Ficus benjamina), floss silk (Chorisia speciosa), and coffee (Coffea arabica) by wrapping moist gauze around the petiole. The sample size used for each plant was one leaf for control and one leaf for treatment, but the study showed that response of leaves from different plant species varied significantly.…”

Section: Introductionmentioning

confidence: 99%

Effects of Sample Storage on Spectral Reflectance Changes in Corn Leaves Excised From the Field

Lee¹,

Huang²,

Yao³

et al. 2014

JAS

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Characterization of leaf spectra is useful for estimating spectra at the canopy level when viewed from airborne or space-borne sensors. When excising and transporting leaves to the laboratory, care must be taken so that degradation does not take place and alter the spectral signature. We compared the effect of elapsed time on leaf reflectance when excised corn leaves were stored inside and outside a cooler. Hyperspectral measurements were obtained 15 minutes after excision, then again 1, 2, 3, 4, 5, 6, and 24 hours after excision. Each hyperspectral band was modeled independently using a piecewise function with a linear portion for the first hour after plant excision and exponential portion after the first hour. Results showed that for the first hour, storage technique did not influence the signature. After the first hour, the leaves stored outside the cooler showed less change than leaves stored in the cooler. Furthermore, after approximately 30 minutes a large percentage of hyperspectral bands drifted beyond the level of significance (as determined by the mean plus or minus two standard deviations). These findings are valuable for developing methods for storage and analysis to support field studies and collection of ground-truth data to support remote sensing.

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“…The detached branch method [26,27] was applied for leaf sampling. Leaf sampling for this study was conducted from June 2007 to August 2013 in Mt.…”

Section: Measurements and Field Datasetsmentioning

confidence: 99%

Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Sonobe

Wang

2017

Remote Sensing

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Abstract:The reflectance properties of leaves are influenced by diverse biochemical components including chlorophyll, one of the key indicators related to plant photosynthesis and plant stress. Although a number of hyperspectral indices have been proposed for quantifying leaf chlorophyll concentrations, their applications are largely restricted to where they were developed and can hardly provide satisfactory results in other cases. In this study, universally applicable hyperspectral indices calculated from both original and first-order derivative spectra were identified for quantifying leaf chlorophyll concentrations in deciduous forests. Using the main criteria of the ratio of performance to deviation (RPD) and the widely applicable information criterion (WAIC), new hyperspectral indices were proposed for quantifying chlorophyll concentrations in four independent datasets. The results revealed that the normalized derivative difference between the green peak (520-540 nm) and the end of the red edge (720-740 nm) were effective. The normalized difference type of index using reflectance derivatives at 522 and 728 nm, dND (522, 728), was the most effective index for quantifying chlorophyll concentrations, with an R 2 of 0.807 and a lowest root mean square error of 8.67 µg/cm 2 , n = 816. This index was also validated based on a simulated dataset generated from the model of PROpriétés SPECTrales Version 5 (PROSPECT 5). Its applicability for assessing chlorophyll content in various deciduous forests was hence demonstrated. We foresee its wide application in the future.

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Foliar spectral properties following leaf clipping and implications for handling techniques

Cited by 100 publications

References 39 publications

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Effects of Sample Storage on Spectral Reflectance Changes in Corn Leaves Excised From the Field

Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

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