A toolbox for studying thermal heterogeneity across spatial scales: from unmanned aerial vehicle imagery to landscape metrics

Faye, Émile; Rebaudo, François; Yánez‐Cajo, Danilo; Cauvy‐Fraunié, Sophie; Dangles, Olivier

doi:10.1111/2041-210x.12488

Cited by 74 publications

(80 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We were able to pick up significant differences between the treatments both on the in situ measured and on the remote-sensing-based modelled values. These results provide scope for unmanned aerial vehicles (UAVs) and imaging spectroscopy as an enabling means to transfer PSF studies and related studies on legacy effects in soil to outdoor field environments (Fiorani and Schurr, 2013;Faye et al, 2016).…”

Section: Plant Traits and Plant Physiological Stagementioning

confidence: 93%

“…hand-held) means, while limiting the intrusion of changing atmospheric conditions to affect measurements (Chapman et al, 2014). Moreover, UAVs enable operational resilience, besides adequate scaling of spatial detail and temporal revisiting times without the need for destructive sampling to measure and monitor ecological phenomena such as successional physiological vegetation processes over time (Faye et al, 2016).…”

Section: Plant Traits and Plant Physiological Stagementioning

confidence: 99%

“…It is argued here that advancements in remote sensing platforms (i.e. Unmanned Aerial Vehicles) and imaging spectroscopy combined may offer novel opportunities for non-invasive assessment of plant trait responses to soil legacies at enhanced spatial-temporal resolutions (Faye et al, 2016) and unprecedented detail (Fiorani and Schurr, 2013), with potential use in both fundamental and applied research in natural and agro-ecosystems.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

View full text Add to dashboard Cite

Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are shortterm and small-scale due to practical constraints for fieldscale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R 2 = 0.79), chlorophyll content (RMSE = 0.11 g m −2 , R 2 = 0.80), Ncontent (RMSE = 1.94 g m −2 , R 2 = 0.68), and fresh biomass (RMSE = 0.72 kg m −2 , R 2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m −2 , respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m −2 , respectively), while the lowest values (76 cm, 0.41 g m −2 , respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAVbased optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

show abstract

Section: Plant Traits and Plant Physiological Stagementioning

confidence: 93%

Section: Plant Traits and Plant Physiological Stagementioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This list is not exhaustive, but our aim is to prioritize actions that we consider are needed to improve our ability to accurately quantify the effects of global climate changes on insects associated with plants. Furthermore, technological progresses should make it easier in the near future to collect a large amount of microclimatic data, either via high-resolution remote sensing (e.g., satellites, unmanned aerial vehicles; Faye et al, 2016) or microrobotics (Floreano & Wood, 2015). Although the microclimatic conditions established by plants have been measured and modelled for decades, lack of coherent and comprehensive databases on the temperature of leaves or other plant organs across latitudes, altitudes, and biomes is currently a major limitation (but see Michaletz et al, 2015).…”

Section: Future Promises and Challengesmentioning

confidence: 99%

Promises and challenges in insect–plant interactions

Giron

Dubreuil

Bennett

et al. 2018

Entomologia Exp Applicata

View full text Add to dashboard Cite

There is tremendous diversity of interactions between plants and other species. These relationships range from antagonism to mutualism. Interactions of plants with members of their ecological community can lead to a profound metabolic reconfiguration of the plants' physiology. This reconfiguration can favour beneficial organisms and deter antagonists like pathogens or herbivores. Determining the cellular and molecular dialogue between plants, microbes, and insects, and its ecological and evolutionary implications is important for understanding the options for each partner to adopt an adaptive response to its biotic environment. Moving forward, understanding how such ecological interactions are shaped by environmental change and how we potentially mitigate deleterious effects will be increasingly important. The development of integrative multidisciplinary approaches may provide new solutions to the major ecological and societal issues ahead of us. The rapid evolution of technology provides valuable tools and opens up novel ways to test hypotheses that were previously unanswerable, but requires that scientists master these tools, understand potential ethical problems flowing from their implementation, and train new generations of biologists with diverse technical skills. Here, we provide brief perspectives and discuss future promise and challenges for research on insect-plant interactions building on the 16th International Symposium on Insect-Plant interactions (SIP) meeting that was held in Tours, France (2-6 July 2017). Talks, posters, and discussions are distilled into key research areas in insect-plant interactions, highlighting the current state of the field and major challenges, and future directions for both applied and basic research.

show abstract

“…Temperature varies across time and space in many ways that can easily be captured by thermal images; it is important to exploit the information provided without becoming overwhelmed. Both Shi, Wen, Paull, and Guo () and Faye et al () provide a useful summary of some important metrics, and Faye et al () introduce a spatial component by borrowing metrics from landscape ecology, such as Shape Index and Cohesion Index (McGarigal, Cushman, & Ene, ). Extending this approach reveals other relevant techniques, such as hot spot analysis (Getis & Ord, ) and connectivity (McGuire, Lawler, McRae, & Theobald, ).…”

Section: Introductionmentioning

confidence: 99%

ThermStats: An R package for quantifying surface thermal heterogeneity in assessments of microclimates

2019

View full text Add to dashboard Cite

Variation in temperature at a fine spatial scale creates critically important microclimates for many organisms. Quantifying thermal heterogeneity at this scale is challenging and, until recently, has been largely restricted to the use of dataloggers to record air temperature. Thermography is becoming an increasingly viable alternative. A single photo from a thermal imaging camera contains thousands of spatially explicit surface temperature measurements, making thermal cameras ideal for rapidly assessing temperature variation at fine scale. Here, we present an R package—ThermStats—for processing thermal images and other gridded temperature data. The package addresses current constraints on applying thermography in ecology, by speeding up and simplifying the extraction of data from thermal images, and by facilitating the calculation of different metrics of thermal heterogeneity. The metrics capture both the frequency distribution and spatial patterns of temperature, and the package functions are designed to accommodate different sampling strategies and data in either matrix or raster format. We demonstrate how ThermStats can be used to capture temperature variation at fine spatial scales in structurally complex habitat, such as tropical rainforest. Using thermal images collected in the field (~0.5 cm2 resolution), we found that thermal hetereogeneity varied little between primary and logged forest, but did vary with time of day. Comparing temperature extremes in a microclimate layer estimated from LIght Detection And Ranging data (LIDAR; 2,500 m2 resolution) , we found that both hot and cold extremes were hotter inside oil palm plantations than in the neighbouring forest. Our package simplifies the processing of thermal data, and our metrics capture key spatiotemporal temperature trends that underpin physiological, behavioural and demographic responses to environmental change. As such, ThermStats can advance a wide range of studies requiring fine‐scale surface temperature data for microclimate investigations.

show abstract

A toolbox for studying thermal heterogeneity across spatial scales: from unmanned aerial vehicle imagery to landscape metrics

Cited by 74 publications

References 46 publications

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

Promises and challenges in insect–plant interactions

ThermStats: An R package for quantifying surface thermal heterogeneity in assessments of microclimates

Contact Info

Product

Resources

About