Field spectroradiometer data : acquisition, organisation, processing and analysis on the example of New Zealand native plants : a thesis presented in fulfilment of the requirements for the degree of Master of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand

The purpose of this research was to investigate the acquisition, storage, processing and analysis of hyperspectral data for vegetation applications on the example of New Zealand native plants. Data covering the spectral range 350nm-2500nm were collected with a portable spectroradiometer. Hyperspectral data collection results in large datasets that need pre-processing before any analysis can be carried out. A review of the techniques used since the advent of hyperspectral field data showed the following general procedures were followed: 1. Removal of noisy or uncalibrated bands 2. Data smoothing 3. Reduction of dimensionality 4. Transformation into feature space 5. Analysis techniques Steps 1 to 4 which are concerned with the pre-processing of data were found to be repetitive procedures and thus had a high potential for automation. The pre-processing had a major impact on the results gained in the analysis stage. Finding the ideal pre-processing parameters involved repeated processing of the data. Hyperspectral field data should be stored in a structured way. The utilization of a relational database seemed a logical approach. A hierarchical data structure that reflected the real world and the setup of sampling campaigns was designed. This structure was transformed into a logical data model. Furthermore the database also held information needed for pre-processing and statistical analysis. This enabled the calculation of separability measurements such as the JM (Jeffries Matusila) distance or the application of discriminant analysis. Software was written to provide a graphical user interface to the database and implement pre-processing and analysis functionality. The acquisition, processing and analysis steps were applied to New Zealand native vegetation. A high degree of separability between species was achieved and using independent data a classification accuracy of 87.87% was reached. This outcome required smoothing, Hyperion synthesizing and principal components transformation to be applied to the data prior to the classification which used a generalized squared distance discriminant function. The mixed signature problem was addressed in experiments under controlled laboratory conditions and revealed that certain combinations of plants could not be unmixed successfully while mixtures of vegetation and artificial materials resulted in very good abundance estimations. The combination of a relational database with associated software for data processing was found to be highly efficient when dealing with hyperspectral field data

Uncertainty support in the spectral information System SPECCHIO

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 standardization will provide a crucial step toward spectroscopy data accompanied by propagated, traceable, uncertainty information

Uncertainty budget for a traceable operational radiometric calibration of field spectroradiometers, calibration of the heliosphere

To measure the distinct interaction of the Earth’smaterials with solar electromagnetic radiation, field spectroradiometers are commonly utilized. These are used to validate spectroradiometers deployed on various platforms through comparison exercises. Following metrology standards, the inclusion of uncertainties is required. Thus, field spectroradiometers need to be calibrated regularly against traceable radiance sources. In this article, we present a laboratory radiometric calibration protocol for the calibration of a heliosphere integrating sphere to make it traceable to the International System of Units as well as to establish an uncertainty budget. We adopted a transfer radiometer approach including four spectroradiometers that were calibrated at the Deutsches Zentrum für Luft und Raumfahrt Radiometric Standard facility before transferring that calibration to the heliosphere. After considering various sources of uncertainty by employing an uncertainty tree diagram approach, we arrive at an overall propagated uncertainty of approximately 1.5%. In future publications, we will present how to extend the traceability to other attenuations provided by the heliosphere. Its application to the calibration of a field spectroradiometer will be the focus of a future publication

Spectral information system for Australian spectroscopy data

Abstract presented at 2013 Fall Meeting, AGU, San Francisco, California, USA, 9-13 Dec

Optimized Spectrometers Characterization Procedure for Near Ground Support of ESA FLEX Observations: Part 1 Spectral Calibration and Characterisation

The paper presents two procedures for the wavelength calibration, in the oxygen telluric absorption spectral bands (O2-A, λc = 687 nm and O2-B, λc = 760.6 nm), of field fixed-point spectrometers used for reflectance and Sun-induced fluorescence measurements. In the first case, Ne and Ar pen-type spectral lamps were employed, while the second approach is based on a double monochromator setup. The double monochromator system was characterized for the estimation of errors associated with different operating configurations. The proposed methods were applied to three Piccolo Doppio-type systems built around two QE Pros and one USB2 + H16355 Ocean Optics spectrometers. The wavelength calibration errors for all the calibrations performed on the three spectrometers are reported and potential methodological improvements discussed. The suggested calibration methods were validated, as the wavelength corrections obtained by both techniques for the QE Pro designed for fluorescence investigations were similar. However, it is recommended that a neon emission line source, as well as an argon or mercury-argon source be used to have a reference wavelength closer to the O2-B feature. The wavelength calibration can then be optimised as close to the O2-B and O2-A features as possible. The monochromator approach could also be used, but that instrument would need to be fully characterized prior to use, and although it may offer a more accurate calibration, as it could be tuned to emit light at the same wavelengths as the absorption features, it would be more time consuming as it is a scanning approach

Genetic constraints on temporal variation of airborne reflectance spectra and their uncertainties over a temperate forest

Remote sensing enhances large-scale biodiversity monitoring by overcoming temporal and spatial limitations of ground-based measurements and allows assessment of multiple plant traits simultaneously. The total set of traits and their variation over time is specific for each individual and can reveal information about the genetic composition of forest communities. Measuring trait variation among individuals of one species continuously across space and time is a key component in monitoring genetic diversity but difficult to achieve with ground-based methods. Remote sensing approaches using imaging spectroscopy can provide high spectral, spatial, and temporal coverage to advance the monitoring of genetic diversity, if sufficient relation between spectral and genetic information can be established. We assessed reflectance spectra from individual Fagus sylvatica L. (European beech) trees acquired across eleven years from 69 flights of the Airborne Prism Experiment (APEX) above the same temperate forest in Switzerland. We derived reflectance spectra of 68 canopy trees and correlated differences in these spectra with genetic differences derived from microsatellite markers among the 68 individuals. We calculated these correlations for different points in time, wavelength regions and relative differences between wavelength regions. High correlations indicate high spectral-genetic similarities. We then tested the influence of environmental variables obtained at temporal scales from days to years on spectral-genetic similarities. We performed an uncertainty propagation of radiance measurements to provide a quality indicator for these correlations. We observed that genetically similar individuals had more similar reflectance spectra, but this varied between wavelength regions and across environmental variables. The short-wave infrared regions of the spectrum, influenced by water absorption, seemed to provide information on the population genetic structure at high temperatures, whereas the visible part of the spectrum, and the near-infrared region affected by scattering properties of tree canopies, showed more consistent patterns with genetic structure across longer time scales. Correlations of genetic similarity with reflectance spectra similarity were easier to detect when investigating relative differences between spectral bands (maximum correlation: 0.40) than reflectance data (maximum correlation: 0.33). Incorporating uncertainties of spectral measurements yielded improvements of spectral-genetic similarities of 36% and 20% for analyses based on single spectral bands, and relative differences between spectral bands, respectively. This study highlights the potential of dense multi-temporal airborne imaging spectroscopy data to detect the genetic structure of forest communities. We suggest that the observed temporal trajectories of reflectance spectra indicate physiological and possibly genetic constraints on plant responses to environmental change

2nd generation of RSL’s spectrum database SPECCHIO

The organised storage of spectral data described by according metadata is important for long term use and data sharing with other scientists. The recently redesigned SPECCHIO system acts as a repository for spectral field campaign and reference signatures. An analysis of metadata space has resulted in a non-redundant relational data model and efficient graphical user interfaces with underlying processing mechanisms minimizing the required user interaction during data capture. Data retrieval is based on imposing restrictions on metadata space dimensions and the resulting dataset can be visualised on screen or exported to files. The system is based on a relational database server with a Java application providing the user interface. This architecture facilitates the operation of the system in a heterogeneous computing environment

Sun-induced chlorophyll fluorescence I:Instrumental considerations for proximal spectroradiometers

Growing interest in the proximal sensing of sun-induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes„ (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions

Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers

Growing interest in the proximal sensing of sun‐induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions