Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

Berra, Elias Fernando; Gaulton, Rachel; Barr, SL

doi:10.1016/j.rse.2019.01.010

Cited by 111 publications

(84 citation statements)

References 93 publications

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“…NDVI UAV had an R-square value of 0.93 and 0.86 with NDVIs evaluated from ground and Landsat-8 data (hereafter NDVI Landsat-8 ), respectively, in mixed deciduous and coniferous woodland [26]. In particular, the relationship between NDVI UAV and NDVI Landsat-8 at the beginning of spring was relatively weak (R-square < 0.5), and the authors noted that this was due to a temporal gap between the Landsat and UAV data [27]. In fact, according to Battude et al [28], high correlation coefficients between NDVIs using different satellites were evaluated if there was no temporal gap between the measurements.…”

Section: Discussionmentioning

confidence: 99%

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

Ryu

Cho

2020

Remote Sensing

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Remote sensing techniques using visible and near-infrared wavelengths are useful for monitoring terrestrial vegetation. The normalized difference vegetation index (NDVI) is a widely used proxy of vegetation conditions, and it has been measured at various footprint sizes using satellite, unmanned aerial vehicle (UAV), and ground-installed sensors. The goal of this study was to analyze the spatial characteristics of NDVI data by comparing the values obtained at different footprint sizes. In particular, the NDVI was evaluated in garlic and onion fields that featured ridges and furrows. The evaluation was performed using data from a leaf spectrometer, field spectrometers, ground-installed spectral reflectance sensors, a multispectral camera onboard a UAV, and Sentinel-2 satellites. The correlation coefficients between NDVIs evaluated from the various sensors (excluding the satellite-mounted sensors) ranged from 0.628 to 0.944. The UAV-based NDVI (NDVIUAV) exhibited the lowest root mean square error (RMSE = 0.088) when compared with field spectrometer data. On the other hand, the satellite-based NDVI data (NDVISentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVIUAV data to the pixel size of Sentinel-2, the comparison was improved, and the following statistics were obtained: correlation coefficient: 0.504–0.785; absolute bias: 0.048–0.078; RMSE: 0.063–0.094. According to the aforementioned results, ground-based NDVI data can be used to validate NDVIUAV data without further processing and NDVIUAV data can be used to validate NDVISentinel-2 data after upscaling to the Sentinel-2 pixel size. Overall, the results presented in this study may be helpful to understand and integrate NDVI data at different spatial scales.

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Section: Discussionmentioning

confidence: 99%

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

Ryu

Cho

2020

Remote Sensing

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“…Despite these observed biases, there was a strong correspondence between drone-derived VIs and the coarser-grained HyPlant and S2 datasets (NDVI R 2 = 0.91, CHL R 2 = 0.75-0.9, Figures 9 and 10), indicating that the drone VIs can reflect variations within maize canopy cover and can be compared across scales. This highlights the potential for integrating drone-based VI measurements within or as an alternative to coarser resolution workflows either as validation or additional measurements at desired time steps for monitoring purposes, such as identifying water limitation or phenology of vegetation canopies [22,49]. This remains feasible and cost-effective for study areas <5 ha [50].…”

Section: Multi-scale VI Consistency and Sensitivitymentioning

confidence: 99%

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

et al. 2020

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Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

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“…At the same time, UAV technology carries its own challenges requiring the development of best practice procedures and protocols for reliable data acquisition and interpretation (Aasen et al , 2018). To date, the few studies that have investigated the use of UAV‐based imaging to capture tree physiological responses to seasonality and phenology have concentrated on the green chromatic coordinate index and the normalized difference vegetation index (NDVI) in mixed forest stands dominated by deciduous species (Lisein et al , 2015; Klosterman & Richardson, 2017; Klosterman et al , 2018; Berra et al , 2019; Park et al , 2019). These UAV‐based indices were used to detect differences in phenophases that allowed species discrimination in temperate (Lisein et al , 2015; Klosterman et al , 2018) and, used in combination with texture information, in tropical deciduous forests (Park et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

High‐throughput drone‐based remote sensing reliably tracks phenology in thousands of conifer seedlings

et al. 2020

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Phenology is an important indicator of environmental variation and climate change impacts on tree responses. In conifers, monitoring phenology of photosynthesis through remote sensing has been unreliable, because needle foliage varies little throughout the year. This is challenging for modelling ecosystem carbon uptake and monitoring phenology for enhanced breeding (genomic selection) and forest health.Here, we demonstrate that drone-based carotenoid-sensitive spectral indices, such as the Chl/carotenoid index (CCI), can be used to track phenology in conifers by taking advantage of the close relationship between seasonally changing carotenoid levels and the variation of photosynthetic activity.Physiological ground measurements, including photosynthetic pigments and maximum quantum yield of Chl fluorescence, indicated that CCI tracked the variation of photosynthetic activity better than other vegetation indices for 30 white spruce seedlings measured over 1 yr. A machine-learning approach, using CCI derived from drone-based multispectral imagery, was used to model phenology of photosynthesis for the entire pedigree population (6000 seedlings).This high-throughput drone-based phenotyping approach is suitable for studying climate change impacts and environmental variation on the physiological status of thousands of fieldgrown conifers at unprecedented speed and scale.

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Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

Cited by 111 publications

References 93 publications

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

High‐throughput drone‐based remote sensing reliably tracks phenology in thousands of conifer seedlings

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