Derivative vegetation indices as a new approach in remote sensing of vegetation

Kochubey, Svetlana M.; Kazantsev, Taras

doi:10.1007/s11707-012-0325-z

Cited by 48 publications

(14 citation statements)

References 28 publications

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“…First-order derivatives, calculated in ViewSpec Pro 6.1.10 [52] involve the calculation of the slope of the spectrum. First derivative spectra are useful for reducing the effects of multiple scattering of radiation due to sample geometry and surface roughness [53] and for enhancing absorption features and inflection points masked by interfering background absorptions and canopy background effects [54]–[56]. Given the background absorptions of the surrounding water, we expected that indices derived from first derivative spectra would perform well in estimating leaf nitrogen concentration. where R i and R j are the reflectance values of bands i and j .…”

Section: Methodsmentioning

confidence: 99%

Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus acutus

2014

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Coastal marshes depend on belowground biomass of roots and rhizomes to contribute to peat and soil organic carbon, accrete soil and alleviate flooding as sea level rises. For nutrient-limited plants, eutrophication has either reduced or stimulated belowground biomass depending on plant biomass allocation response to fertilization. Within a freshwater wetland impoundment receiving minimal sediments, we used experimental plots to explore growth models for a common freshwater macrophyte, Schoenoplectus acutus. We used N-addition and control plots (4 each) to test whether remotely sensed vegetation indices could predict leaf N concentration, root:shoot ratios and belowground biomass of S. acutus. Following 5 months of summer growth, we harvested whole plants, measured leaf N and total plant biomass of all above and belowground vegetation. Prior to harvest, we simulated measurement of plant spectral reflectance over 164 hyperspectral Hyperion satellite bands (350–2500 nm) with a portable spectroradiometer. N-addition did not alter whole plant, but reduced belowground biomass 36% and increased aboveground biomass 71%. We correlated leaf N concentration with known N-related spectral regions using all possible normalized difference (ND), simple band ratio (SR) and first order derivative ND (FDN) and SR (FDS) vegetation indices. FDN1235, 549 was most strongly correlated with leaf N concentration and also was related to belowground biomass, the first demonstration of spectral indices and belowground biomass relationships. While S. acutus exhibited balanced growth (reduced root:shoot ratio with respect to nutrient addition), our methods also might relate N-enrichment to biomass point estimates for plants with isometric root growth. For isometric growth, foliar N indices will scale equivalently with above and belowground biomass. Leaf N vegetation indices should aid in scaling-up field estimates of biomass and assist regional monitoring.

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Section: Methodsmentioning

confidence: 99%

Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus acutus

2014

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“…Derivative analysis can be particularly useful for remotely biomonitoring heavy metal using reflectance spectra measured from above the vegetation canopy (Wang et al, 2018). Canopy spectra first derivatives eliminate the additive noises (baseline shifts) induced by illumination instability, canopy structural or soil background influences (Demetriades-Shah et al, 1990;Gnyp et al, 2014;Kochubey and Kazantsev, 2012;Pu, 2011), thereby improving the accuracy for quantification of canopy biochemical or physiological changes (Jin and Wang, 2016;O'Connell et al, 2014). Moreover, PLS modeling further facilitates the use of features of the full derivative spectrum for the characterization of vegetation undergoing changes or stresses.…”

Section: Mixed Modelmentioning

confidence: 99%

Vegetation reflectance spectroscopy for biomonitoring of heavy metal pollution in urban soils

Geel

Ceulemans

et al. 2018

Environmental Pollution

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Heavy metals in urban soils may impose a threat to public health and may negatively affect urban tree viability. Vegetation spectroscopy techniques applied to bio-indicators bring new opportunities to characterize heavy metal contamination, without being constrained by laborious soil sampling and labbased sample processing. Here we used Tilia tomentosa trees, sampled across three European cities, as bio-indicators i) to investigate the impacts of elevated concentrations of cadmium (Cd) and lead (Pb) on leaf mass per area (LMA), total chlorophyll content (Chl), chlorophyll a to b ratio (Chla:Chlb) and the maximal PSII photochemical efficiency (Fv/Fm); and ii) to evaluate the feasibility of detecting Cd and Pb contamination using leaf reflectance spectra. For the latter, we used a partial-least-squares discriminant analysis (PLS-DA) to train spectral-based models for the classification of Cd and/or Pb contamination. We show that elevated soil Pb concentrations induced a significant decrease in the LMA and Chla:Chlb, with no decrease in Chl. We did not observe pronounced reductions of Fv/Fm due to Cd and Pb contamination. Elevated Cd and Pb concentrations induced contrasting spectral changes in the red-edge (690e740 nm) region, which might be associated with the proportional changes in leaf pigments. PLS-DA models allowed for the classifications of Cd and Pb contamination, with a classification accuracy of 86% (Kappa ¼ 0.48) and 83% (Kappa ¼ 0.66), respectively. PLS-DA models also allowed for the detection of a collective elevation of soil Cd and Pb, with an accuracy of 66% (Kappa ¼ 0.49). This study demonstrates the potential of using reflectance spectroscopy for biomonitoring of heavy metal contamination in urban soils.

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“…[23][24][25][26], and many studies have analyzed the potential of using spectral reflectance indices in wheat starting from the early years of the century (e.g., [27]) and up to the present day (e.g., [28]). High-resolution VIs may detect changes of wheat crop status and it might help to improve crop monitoring [29], nitrogen management [30], and crop yield estimation [31,32]. Furthermore, it is indeed known that yield prediction while using VIs in wheat can be accurate two months prior to harvest, because yield estimates stabilize and especially during the flowering period, significant correlations between UAV-VIs and yield components were found [33,34].…”

Section: Introductionmentioning

confidence: 99%

Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping

Polivova¹,

Brook²

2022

Vegetation Index and Dynamics

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Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. These technologies have great importance for plant state monitoring, especially for agriculture. The main aim is to assess the performance level of the selected VIs calculated from space-borne multispectral imagery and point-based field spectroscopy in application to crop state estimation. The results obtained indicate that space-borne VIs react on phenology. This feature makes it an appropriate data source for monitoring crop development, crop water needs and yield prediction. Field spectrometer VIs were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability of the calculated values. The results obtained in the second part of the presented study were reported on crop state estimated by 17 VIs known as sensitive to plant drought. An alternative approach for identification early stress by VIs proposed in this study is Principal Component Analysis (PCA). The results show that PCA has identified the degree of similarity of the different states and together with reference stress states from the control plot clearly estimated stress in the actual irrigated field, which was hard to detect by VIs values only.

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Derivative vegetation indices as a new approach in remote sensing of vegetation

Cited by 48 publications

References 28 publications

Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus acutus

Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus acutus

Vegetation reflectance spectroscopy for biomonitoring of heavy metal pollution in urban soils

Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping

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