Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes

Assmann, Jakob J.; Kerby, Jeffrey T.; Cunliffe, Andrew M.; Myers‐Smith, Isla H.

doi:10.1101/334730

Cited by 14 publications

(15 citation statements)

References 68 publications

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“…The Pix4D software (Pix4D SA, Lausanne, Switzerland) was used to perform the different steps of aerial triangulation using the 60 high-precision GCPs, bundle block adjustment, sparse matching, and dense matching to produce orthomosaics and DSMs [ 28 ]. Radiometric corrections were applied to convert images into radiance [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

et al. 2021

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Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass [FM] and dry biomass [DM], respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.

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Section: Methodsmentioning

confidence: 99%

Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

et al. 2021

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“…9‐minute acquisition time. This resulted in 8.1 cm mean ground sampling distance and >80% frontal and lateral image overlap as advocated by other studies using the Parrot Sequoia sensor (Assmann et al, 2019; Tu et al, 2018). Visual reference datasets for crown discrimination were acquired using a consumer‐grade RGB camera (Ricoh GRII), mounted on the same platform and flown at 6 m/s ground speed and 60 m height.…”

Section: Methodsmentioning

confidence: 90%

Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data

Fawcett

Bennie

Anderson

2020

Remote Sens Ecol Conserv

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Quantifying the timing of vegetation phenology is critical for monitoring and modelling ecosystem responses to environmental change. Phenological processes have been studied from landscape to global scales using Earth observing satellite data, and at local scale by in situ surveys of individual plants. Now, data acquired from multi-spectral sensors on drone platforms provide flexible opportunities for monitoring phenology from individual plants to small ecosystem scales efficiently, allowing community and species level information to be derived. We captured a time-series of drone-acquired normalized difference vegetation index (NDVI) data with a multi-spectral sensor (Parrot Sequoia, (Parrot, France)) over a highly heterogeneous ecosystem in Cornwall, UK, during a period of spring green-up. We monitored NDVI trajectories at the individual crown and species' level. For deciduous crowns, we derived metrics representative of spring phenological stages: Start-of-spring (SOS), middle-ofspring green-up (MOG) and start-of-peak greenness (SOP) using a logistic function. While the exact timing of SOS, MOG and SOP appeared susceptible to understorey effects and saturation of the NDVI, relative timing of green-up for a subset of species was plausible in relation to phenological observations from an extended geographic region and in situ plant area index (PAI) measurements. In evergreen vegetation (Pinus spp.) subtle changes were also detected through the growing season. The impact of illumination differences was analysed for image pairs during leaf-off and leaf-on conditions. While significant, these effects were small (mean absolute NDVI deviation of up to 0.034 for leaf-off, 0.013 for leaf-on conditions), meaning that data captured under both constant direct and diffuse irradiance conditions can be used together and that cloudy conditions should not lead to data gaps. We conclude that the capability of drone-mounted multi-spectral instruments for spatio-temporal characterization of crown-level phenology shows great promise for improving the understanding of intra-and inter-species differences in strategy, and offers an efficient means of doing so over areas of a few hectares.

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“…To test the influence of sun elevation on allometric functions, we fitted a LMM to predict total biomass as a function of canopy height and sun elevation as fixed-effects and PFT as a random-effect, using the 'lmerTest' package (v3.1-2) 70 (Supplementary Table 5). We only included observations (n=620) collected under relatively clear sky conditions (sky codes ≤ 5, after 71 ) when scene illumination was minimally modulated by clouds. To illustrate the effect of sun elevation, we simulated the modelled relationship between height and biomass for three levels of sun elevation using the LMM (Fig.…”

Section: Discussionmentioning

confidence: 99%

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

Cunliffe

Anderson

Boschetti

et al. 2020

Preprint

Self Cite

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Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe.

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Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes

Cited by 14 publications

References 68 publications

Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

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