Search

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RT model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals.

Plant toxic biochemicals play an important role in defense against natural enemies and often are toxic to humans and livestock. Hyperspectral reflectance is an established method for primary chemical detection and could be further used to determine plant toxicity in the field. In order to make a first step for pyrrolizidine alkaloids detection (toxic defense compound against mammals and many insects) we studied how such spectral data can estimate plant defense chemistry under controlled conditions. In a greenhouse, we grew three related plant species that defend against generalist herbivores through pyrrolizidine alkaloids: Jacobaea vulgaris, Jacobaea erucifolia and Senecio inaequidens, and analyzed the relation between spectral measurements and chemical concentrations using multivariate statistics. Nutrient addition enhanced tertiary-amine pyrrolizidine alkaloids contents of J. vulgaris and J. erucifolia and decreased N-oxide contents in S. inaequidens and J. vulgaris. Pyrrolizidine alkaloids could be predicted with a moderate accuracy. Pyrrolizidine alkaloid forms tertiary-amines and epoxides were predicted with 63% and 56% of the variation explained, respectively. The most relevant spectral regions selected for prediction were associated with electron transitions and CH, OH, and NH bonds in the 1530 and 2100 nm regions. Given the relatively low concentration in pyrrolizidine alkaloids concentration (in the order of mg g-1) and resultant predictions, it is promising that pyrrolizidine alkaloids interact with incident light. Further studies should be considered to determine if such a non-destructive method may predict changes in PA concentration in relation to plant natural enemies. Spectroscopy may be used to study plant defenses in intact plant tissues, and may provide managers of toxic plants, food industry and multitrophic-interaction researchers with faster and larger monitoring possibilities

Spectral reflectance can be used to assess large-scale performances of plants in the field based on plant nutrient balance as well as composition of defence compounds. However, plant chemical composition is known to vary with season – due to its phenology – and it may even depend on the succession stage of its habitat. Here we investigate (i) how spectral reflectance could be used to discriminate successional and phenological stages of Jacobaea vulgaris in both leaf and flower organs and (ii) if chemical content estimation by reflectance is flower or leaf dependent. We used J. vulgaris, which is a natural outbreak plant species on abandoned arable fields in north-western Europe and studied this species in a chronosequence representing successional development during time since abandonment. The chemical content and reflectance between 400 and 2500 nm wavelengths of flowers and leaves were measured throughout the season in fields of different successional ages. The data were analyzed with multivariate statistics for temporal discrimination and estimation of chemical contents in both leaf and flower organs. Two main effects were revealed by spectral reflectance measurements: (i) both flower and leaf spectra show successional and seasonal changes, but the pattern is complex and organ specific (ii) flower head pyrrolizidine alkaloids, which are involved in plant defence against herbivores, can be detected through hyperspectral reflectance.We conclude that spectral reflectance of both leaves and flowers can provide information on plant performance during season and successional stages. As a result, remote sensing studies of plant performance in complex field situations will benefit from considering hyperspectral reflectance of different plant organs. This approach may enable more detailed studies on the link between spectral information and plant defence dynamics both aboveground and belowground.

http://www.sciencedirect.com/science/journal/03032434, http://www.sciencedirect.com/science/journal/03032434

Plant toxic biochemicals play an important role in defense against natural enemies and often are toxic to humans and livestock. Hyperspectral reflectance is an established method for primary chemical detection and could be further used to determine plant toxicity in the field. In order to make a first step for pyrrolizidine alkaloids detection (toxic defense compound against mammals and many insects) we studied how such spectral data can estimate plant defense chemistry under controlled conditions. In a greenhouse, we grew three related plant species that defend against generalist herbivores through pyrrolizidine alkaloids: Jacobaea vulgaris, Jacobaea erucifolia and Senecio inaequidens, and analyzed the relation between spectral measurements and chemical concentrations using multivariate statistics. Nutrient addition enhanced tertiary-amine pyrrolizidine alkaloids contents of J. vulgaris and J. erucifolia and decreased N-oxide contents in S. inaequidens and J. vulgaris. Pyrrolizidine alkaloids could be predicted with a moderate accuracy. Pyrrolizidine alkaloid forms tertiary-amines and epoxides were predicted with 63% and 56% of the variation explained, respectively. The most relevant spectral regions selected for prediction were associated with electron transitions and CH, OH, and NH bonds in the 1530 and 2100 nm regions. Given the relatively low concentration in pyrrolizidine alkaloids concentration (in the order of mg g−1) and resultant predictions, it is promising that pyrrolizidine alkaloids interact with incident light. Further studies should be considered to determine if such a non-destructive method may predict changes in PA concentration in relation to plant natural enemies. Spectroscopy may be used to study plant defenses in intact plant tissues, and may provide managers of toxic plants, food industry and multitrophic-interaction researchers with faster and larger monitoring possibilities, Plant toxic biochemicals play an important role in defense against natural enemies and often are toxic to humans and livestock. Hyperspectral reflectance is an established method for primary chemical detection and could be further used to determine plant toxicity in the field. In order to make a first step for pyrrolizidine alkaloids detection (toxic defense compound against mammals and many insects) we studied how such spectral data can estimate plant defense chemistry under controlled conditions. In a greenhouse, we grew three related plant species that defend against generalist herbivores through pyrrolizidine alkaloids: Jacobaea vulgaris, Jacobaea erucifolia and Senecio inaequidens, and analyzed the relation between spectral measurements and chemical concentrations using multivariate statistics. Nutrient addition enhanced tertiary-amine pyrrolizidine alkaloids contents of J. vulgaris and J. erucifolia and decreased N-oxide contents in S. inaequidens and J. vulgaris. Pyrrolizidine alkaloids could be predicted with a moderate accuracy. Pyrrolizidine alkaloid forms tertiary-amines and epoxides were predicted with 63% and 56% of the variation explained, respectively. The most relevant spectral regions selected for prediction were associated with electron transitions and CH, OH, and NH bonds in the 1530 and 2100 nm regions. Given the relatively low concentration in pyrrolizidine alkaloids concentration (in the order of mg g−1) and resultant predictions, it is promising that pyrrolizidine alkaloids interact with incident light. Further studies should be considered to determine if such a non-destructive method may predict changes in PA concentration in relation to plant natural enemies. Spectroscopy may be used to study plant defenses in intact plant tissues, and may provide managers of toxic plants, food industry and multitrophic-interaction researchers with faster and larger monitoring possibilities

Plants interact with a wide array of aboveground and belowground herbivores, pathogens, mutualists, and their natural enemies. These interactions are important drivers of spatio-temporal changes in vegetation, however, they may be difficult to be revealed without extensive sampling.In this thesis I investigated the potential of visible and near-infrared spectral measurements to detect plant chemical changes that may reflect interactions between plants and biotic or abiotic soil factors. First, I examined the relative contribution of pyrrolizidine alkaloids (PAs; these are defence compounds of Senecio-type plants against generalist herbivores) to the spectral reflectance features in the visible and short-wave infrared region. My hypothesis was that PAs can be predicted from specific spectral features of aboveground plant tissues. Since PA profiles and their relation to spectral features could be species specific I compared three different species, Jacobaea vulgaris, J. erucifolia and S. inaequidens subjected to nutrient and water treatments to stimulate plant chemical variation. Pyrrolizidine alkaloids were predicted best by spectral reflectance features in the case of Jacobaea vulgaris. I related the better results obtained with J. vulgaris to the existence of the correlation between PAs and nitrogen and the presence of the epoxide chemical structure in J. vulgaris. I also examined if different soil microbial communities influenced plant shoot spectral reflectance. I grew the same three plant species as before in sterilized soil and living soil collected from fields with J. vulgaris. I expected that soil biota would change shoot defence content and hyperspectral reflectance in plant species-specific ways. Indeed, the exposure to different soils caused plant chemical profiles to change and both chemical and spectral patterns discriminated plants according to the soil biotic conditions. I studied how primary and secondary plant metabolites varied during the growing se

Recent studies revealed that plant-soil biotic interactions may cause changes in above-ground plant chemistry. It would be a new step in below-ground-above-ground interaction research if such above-ground chemistry changes could be efficiently detected. Here we test how hyperspectral reflectance may be used to study such plant-soil biotic interactions in a nondestructive and rapid way. The native plant species Jacobaea vulgaris and Jacobaea erucifolius, and the exotic invader Senecio inaequidens were grown in different soil biotic conditions. Biomass, chemical content and shoot reflectance between 400 and 2500 nm wavelengths were determined. The data were analysed with multivariate statistics. Exposing the plants to soil biota enhanced the content of defence compounds. The highest increase (400%) was observed for the exotic invader S. inaequidens. Chemical and spectral data enabled plant species to be classified with an accuracy > 85%. Plants grown in different soil conditions were classified with 50-60% correctness. Our data suggest that soil microorganisms can affect plant chemistry and spectral reflectance. Further studies should test the potential to study plant-soil biotic interactions in the field. Such techniques could help to monitor, among other things, where invasive exotic plant species develop biotic resistance or the development of hotspots of crop soil diseases.

Recent studies revealed that plant–soil biotic interactions may cause changes in above-ground plant chemistry. It would be a new step in below-ground–above-ground interaction research if such above-ground chemistry changes could be efficiently detected. Here we test how hyperspectral reflectance may be used to study such plant–soil biotic interactions in a nondestructive and rapid way. The native plant species Jacobaea vulgaris and Jacobaea erucifolius, and the exotic invader Senecio inaequidens were grown in different soil biotic conditions. Biomass, chemical content and shoot reflectance between 400 and 2500 nm wavelengths were determined. The data were analysed with multivariate statistics. Exposing the plants to soil biota enhanced the content of defence compounds. The highest increase (400%) was observed for the exotic invader S. inaequidens. Chemical and spectral data enabled plant species to be classified with an accuracy > 85%. Plants grown in different soil conditions were classified with 50–60% correctness. Our data suggest that soil microorganisms can affect plant chemistry and spectral reflectance. Further studies should test the potential to study plant–soil biotic interactions in the field. Such techniques could help to monitor, among other things, where invasive exotic plant species develop biotic resistance or the development of hotspots of crop soil diseases., Recent studies revealed that plant–soil biotic interactions may cause changes in above-ground plant chemistry. It would be a new step in below-ground–above-ground interaction research if such above-ground chemistry changes could be efficiently detected. Here we test how hyperspectral reflectance may be used to study such plant–soil biotic interactions in a nondestructive and rapid way. The native plant species Jacobaea vulgaris and Jacobaea erucifolius, and the exotic invader Senecio inaequidens were grown in different soil biotic conditions. Biomass, chemical content and shoot reflectance between 400 and 2500 nm wavelengths were determined. The data were analysed with multivariate statistics. Exposing the plants to soil biota enhanced the content of defence compounds. The highest increase (400%) was observed for the exotic invader S. inaequidens. Chemical and spectral data enabled plant species to be classified with an accuracy > 85%. Plants grown in different soil conditions were classified with 50–60% correctness. Our data suggest that soil microorganisms can affect plant chemistry and spectral reflectance. Further studies should test the potential to study plant–soil biotic interactions in the field. Such techniques could help to monitor, among other things, where invasive exotic plant species develop biotic resistance or the development of hotspots of crop soil diseases.

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

We used GPS satellite tracking data and field measurements of vegetation to investigate the effect of food resources, distance to roosts, and the location of refuges on the distribution of Barnacle Geese Branta leucopsis in the northern part of The Netherlands. To deal with spatial dependence among the data, a spatial lag model was used. A significant quadratic effect was found between sward height and goose distribution, indicating that geese prefer patches with intermediate sward heights. The manipulation of sward height can therefore be used to attract geese to refuges and thus reduce goose grazing in agricultural land. No relationship was found between grass nitrogen content and grazing intensity, indicating that geese do not distinguish between areas based on nitrogen content. A higher grazing intensity was observed in areas located within 2 km from roosts. The eight tracked geese spent 80% of their foraging time in refuges, demonstrating the importance of the refuge system., We used GPS satellite tracking data and field measurements of vegetation to investigate the effect of food resources, distance to roosts, and the location of refuges on the distribution of Barnacle Geese Branta leucopsis in the northern part of The Netherlands. To deal with spatial dependence among the data, a spatial lag model was used. A significant quadratic effect was found between sward height and goose distribution, indicating that geese prefer patches with intermediate sward heights. The manipulation of sward height can therefore be used to attract geese to refuges and thus reduce goose grazing in agricultural land. No relationship was found between grass nitrogen content and grazing intensity, indicating that geese do not distinguish between areas based on nitrogen content. A higher grazing intensity was observed in areas located within 2 km from roosts. The eight tracked geese spent 80% of their foraging time in refuges, demonstrating the importance of the refuge system.

Information about the distribution of grass nitrogen (N) concentration is crucial in understanding rangeland vitality and facilitates effective management of wildlife and livestock. A challenge in estimating grass N concentration using remote sensing in savannah ecosystems is that these areas are characterised by heterogeneity in edaphic, topographic and climatic factors. The objective is to test the utility of integrating environmental variables and in situ hyperspectral remote sensing variables for predicting grass N concentration along a land use gradient in the greater Kruger National Park. Data used include i) environmental variables, ii) measured grass N concentration and iii) in situ measured hyperspectral spectra. Non-linear partial least square regression was used. Results showed that several environmental variables were important for N estimation. Integrating environmental variables with in situ hyperspectral variables increased grass N estimation accuracy. The study demonstrated the importance of integrated modelling for savannah ecosystem state assessment.