A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

Suomalainen, Juha; Anders, Niels; Iqbal, Shahzad; Roerink, G.J.; Franke, Jörg; Wenting, P.F.M.; Hünniger, Dirk; Bartholomeus, Harm; Becker, Rolf; Kooistra, Lammert

doi:10.3390/rs61111013

Cited by 143 publications

(130 citation statements)

References 15 publications

(15 reference statements)

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“…The hyperspectral images of an experimental grassland field at the farm Haus Riswick, near Kleve in Germany (Figure 1), were taken by applying an octocopter UAV, equipped with the Wageningen UR Hyperspectral Mapping System (HYMSY) (Figure 3) [51]. Two different flight campaigns were carried out, one on 15 May 2014 and the other on 14 October 2014.…”

Section: Discussionmentioning

confidence: 99%

“…The assessment of the quality of the model was estimated using R 2 between measured and predicted values from the LOO crossvalidation and the RMSELOO, while the usefulness and goodness-of-fit of calibration models were performed by residual predictive deviation (RPD), calculated as the ratio between the standard deviation of the reference dataset and the RMSE. On the contrary, there is not a specific statistical rule for setting the RPD thresholds and, for these reasons, in this paper, the commonly used classification in literature was adopted [51][52]. It involves three levels of classification: RPD < 3 is not useful because it means low predictive power; RPD > 3 is suitable for screening; RPD > 5 is suitable for detecting the accurate analysis (laboratory analysis).…”

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

“…In order to acquire hyperspectral images, the Wageningen UR Hyperspectral Mapping System (HYMSY) was mounted under the octocopter [51]. HYMSY is a lightweight hyperspectral pushbroom system (2.0 kg) specifically designed for small UAVs and developed at Wageningen University and Research Centre.…”

Section: Uav and Its Equipmentmentioning

confidence: 99%

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Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

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117

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Grassland ecosystems cover around 40% of the entire Earth's surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Rededge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots.

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Section: Discussionmentioning

confidence: 99%

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

See 1 more Smart Citation

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

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117

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“…The further development of these nebkha dunes strongly depends on the balance between summer accumulation of sand and vegetation growth and winter erosion of sand and loss of vegetation (Montreuil et al, 2013). Summer growth and winter erosion depend on weather conditions, such as wind speed, precipitation and storm intensity (Montreuil et al, 2013;van Puijenbroek et al, 2017). As a result, net dune growth can differ from year to year.…”

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confidence: 99%

Exploring the contributions of vegetation and dune size to early dune development using unmanned aerial vehicle (UAV) imaging

et al. 2017

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Abstract. Dune development along highly dynamic land-sea boundaries is the result of interaction between vegetation and dune size with sedimentation and erosion processes. Disentangling the contribution of vegetation characteristics from that of dune size would improve predictions of nebkha dune development under a changing climate, but has proven difficult due to the scarcity of spatially continuous monitoring data.This study explored the contributions of vegetation and dune size to dune development for locations differing in shelter from the sea. We monitored a natural nebkha dune field of 8 ha, along the coast of the island Texel, the Netherlands, for 1 year using an unmanned aerial vehicle (UAV) with camera. After constructing a digital surface model and orthomosaic we derived for each dune (1) vegetation characteristics (species composition, vegetation density, and maximum vegetation height), (2) dune size (dune volume, area, and maximum height), (3) degree of shelter (proximity to other nebkha dunes and the sheltering by the foredune). Changes in dune volume over summer and winter were related to vegetation, dune size and degree of shelter.We found that a positive change in dune volume (dune growth) was linearly related to initial dune volume over summer but not over winter. Big dunes accumulated more sand than small dunes due to their larger surface area. Exposed dunes increased more in volume (0.81 % per dune per week) than sheltered dunes (0.2 % per dune per week) over summer, while the opposite occurred over winter. Vegetation characteristics did not significantly affect dune growth in summer, but did significantly affect dune growth in winter. Over winter, dunes dominated by Ammophila arenaria, a grass species with high vegetation density throughout the year, increased more in volume than dunes dominated by Elytrigia juncea, a grass species with lower vegetation density (0.43 vs. 0.42 (m 3 m −3 ) week −1 ). The effect of species was irrespective of dune size or distance to the sea.Our results show that dune growth in summer is mainly determined by dune size, whereas in winter dune growth was determined by vegetation type. In our study area the growth of exposed dunes was likely restricted by storm erosion, whereas growth of sheltered dunes was restricted by sand supply. Our results can be used to improve models predicting coastal dune development.

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“…The imaging spectrometer data were acquired across two parallel flight lines with 80 % side overlap at a speed of 4 m s −1 and an altitude of 60 m. Shortly prior to take-off, the spectrometer was field calibrated for incident irradiance by taking measurement of a 25 % Spectralon reference panel. The resulting imagery was radiometrically calibrated and geometrically corrected according to the procedures presented in Suomalainen et al (2014). As the geometrical accuracy of HDC was found to be inadequate, an additional georeferencing was performed using Esri ArcMap 10.3.1 and a custom-made reference map of the study site's layout was created to further minimize geometric irregularities.…”

Section: Uav Data Collection and Processingmentioning

confidence: 99%

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

et al. 2017

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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.

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A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

Cited by 143 publications

References 15 publications

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Exploring the contributions of vegetation and dune size to early dune development using unmanned aerial vehicle (UAV) imaging

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

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