The SPECCHIO Spectral Information System

Hueni, Andreas; Chisholm, Laurie; Ong, Cindy; Malthus, Tim J.; Wyatt, Mathew; Trim, Simon A.; Schaepman, Michael E.; Thankappan, Medhavy

doi:10.1109/jstars.2020.3025117

Cited by 17 publications

(13 citation statements)

References 35 publications

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“…The hyperdimensionality of the reflectance spectrum could make it hard to accommodate within existing standards like the Darwin Core (Wieczorek et al 2012), at least without extensions. One could link records to external spectral databases like EcoSIS or SPECCHIO (Hueni et al 2020), which are also designed to store metadata about instrumentation and processing. We would advocate for coordination between herbarium managers and researchers who use reflectance spectroscopy, which could build agreement about best practices for spectral measurement and curation and allow data to be synthesized and compared across research groups.…”

Section: Discussionmentioning

confidence: 99%

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

Kothari

Beauchamp‐Rioux

Laliberté

et al. 2021

Preprint

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More than ever, ecologists seek to employ herbarium collections as tools to estimate plant functional traits from the past or from places that are hard to sample. However, many functional trait measurements are destructive, which may preclude their use on valuable herbarium specimens. Reflectance spectroscopy is increasingly used to estimate traits rapidly from fresh or dried, ground leaves, as well as to differentiate and identify taxa. Here, we extend this body of work to pressed, intact leaves such as those in herbarium collections. Using a dataset with 619 plant samples belonging to 70 woody and herbaceous species, we used partial least-squares regression to build validated models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), carbon (Cmass) and nitrogen (Nmass) concentrations, and carbon fractions such as cellulose and lignin. We compared the accuracy of these trait estimates to those from fresh- or ground-leaf spectra of the same samples. Our pressed-leaf models predicted these traits with moderate-to-high accuracy (R2 = 0.586-0.924; %RMSE = 5.7-11.7%). For estimating chemical traits, pressed-leaf models performed better than fresh-leaf models but slightly worse than ground-leaf models. Pressed-leaf models did not perform as well as fresh-leaf models for estimating LMA and LDMC, but outperformed ground-leaf models for LMA. Accuracy was no worse for pressed leaves that underwent discoloration in storage. Finally, on a subset of common species in the dataset, we used partial least-squares discriminant analysis to classify specimens to species with near-perfect accuracy from pressed-(>97%), and ground-leaf (>96%) spectra and slightly lower accuracy from fresh-leaf spectra (>89%). The success of trait estimation and species classification from pressed-leaf spectra may owe to the fact that they combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of internal leaf structure; like ground leaves, they reveal minor absorption features of chemical constituents that would otherwise be masked by water. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits non-destructively. Our study has far-reaching implications for capturing the wide range of functional, phenotypic, and taxonomic information in the world’s preserved plant collections.

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Section: Discussionmentioning

confidence: 99%

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

Kothari

Beauchamp‐Rioux

Laliberté

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Each sample spectrum is an average of three spectral measurements. The data is publicly available at SPECCHIO database (Hueni et al, 2020), which is administered by Remote Sensing Laboratories, Institute of Geography, University of Zurich. The size of rendered images was 16 pixels (4 × 4) as that is the smallest image used Blender version (2.93.5) can produce.…”

Section: Methodsmentioning

confidence: 99%

Hyperblend: Simulating Spectral Reflectance and Transmittance of Leaf Tissue With Blender

Riihiaho

Rossi

Pölönen

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.

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“…These relationships are defined by registering a spectrum in an UncertaintySpectrumNode object. To facilitate the generation of unique matrices holding uncertainty information for spectral collections we have upgraded the SPECCHIO Space Factory [19]. The Space Factory generates congruent spectral spaces, i.e.…”

Section: B Application Programming Interface and Uncertainty Classesmentioning

confidence: 99%

“…It is thus a natural conclusion that spectral information systems should include uncertainty and traceability information and support their handling [14,19]. The spectral information system SPECCHIO is an advanced software package that was developed by the Remote Sensing Laboratories of the University of Zurich [20][21][22][23] and enhanced in a project funded by the Australian National Data Service [19]. SPECCHIO uses a client-server architecture and a MySQL relational database management system (DBMS) to handle and store point spectrometer data.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we present the concepts and implementations that enable the SPECCHIO system [19] to store and manage traceability chains and uncertainties. A case study serves to demonstrate the function of the system by applying it to the common use case of the calculation of Hemispherical-Conical Reflectance Factors (HCRF) [14,29] from radiance data.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Support in the Spectral Information System SPECCHIO

Hueni¹,

Mason²,

Trim³

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Spectral information system SPECCHIO was updated to support the generic handling of uncertainty information in the form of uncertainty tree diagrams. The updates involve changes to the relations database model as well as dedicated methods provided by the SPECCHIO application programming interface. A case study selected from classic field spectroscopy demonstrates the use of the functionality. In conclusion, a database centric automated uncertainty propagation in combination with measurement protocol standardisation will provide a crucial step towards spectroscopy data accompanied by propagated, traceable uncertainty information.

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The SPECCHIO Spectral Information System

Cited by 17 publications

References 35 publications

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

Hyperblend: Simulating Spectral Reflectance and Transmittance of Leaf Tissue With Blender

Uncertainty Support in the Spectral Information System SPECCHIO

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