Using excess greenness and green chromatic coordinate colour indices from aerial images to assess lodgepole pine vigour, mortality and disease occurrence

Reid, Anya M.; Chapman, William K.; Prescott, Cindy E.; Nijland, Wiebe

doi:10.1016/j.foreco.2016.05.006

Cited by 42 publications

(38 citation statements)

References 29 publications

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“…GCC has also been found to be a good indicator of vegetation health and phenology in other studies [18,40], as GCC best represented differences in healthy vigour and mortality of vegetation. Meanwhile, from the different relationship of GCC between plot and individual plot scales, it can be concluded that the best performance of GCC requires vegetation classification, suggesting that model precision is determined by the accuracy of vegetation classification.…”

Section: Indicators Of Carbon Stocks (C T )mentioning

confidence: 81%

“…The green chromatic coordinate (GCC) was used in this study to test the performance of general optical bands for estimating saltbush biomass. GCC has been used as an indicator of plant condition and phenology [18,40]. The GCC was chosen as it can be readily calculated from spectral bands found in standard digital cameras, and so has the potential to be available from a variety of sensor platforms.…”

Section: Vegetation Index Formula Referencementioning

confidence: 99%

“…Although the use of pixel-based vegetation indices as proxies for estimating vegetation biomass is well established [17,18], including examples such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) [19], and the ratio vegetation index (RVI) on forests [20], grass [21] and woodland [22,23], these techniques have been infrequently used to estimate carbon stocks in shrublands. However, the broader use of remote sensing for calibrating vegetation indices to biomass (e.g., Asner [24]) shows that the relationship between vegetation indices and biomass can differ between species, season, and the scale of the vegetation and pixels.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

et al. 2017

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Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant-and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R 2 : 0.76-0.89) and spectral-based vegetation indices (R 2 : 0.77-0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands.

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Section: Indicators Of Carbon Stocks (C T )mentioning

confidence: 81%

Section: Vegetation Index Formula Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

et al. 2017

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“…In this workflow, the amount of human involvement is limited to the choice of the dynamic scale (s) and curvature tolerance (t) values for ground filtering, and it thus has potential for upscaling and/or repeat monitoring of an area, on the assumption that the GCC threshold value can be held constant across space and time, a question we have not investigated in this study. A study of multiple vegetation indices for tracking phenological events [59] examined the influences of camera type, weather, and time of day on the observed GCC values in forested environments and although they suggested using the GCC over alternative vegetation indices to follow canopy greenness due to its capacity to suppress most variation, these influences were not completely removed [58]. Unwanted variability due to scene illumination in imagery obtained during overcast vs. sunny days, or in sunlit vs. shaded areas, could potentially make comparisons more difficult.…”

Section: Discussionmentioning

confidence: 99%

“…The GCC has been used mostly to follow phenological events of various plants and trees [37,52,[54][55][56]. Another application of indices based on RGB colors is to accurately discriminate plants from non-plant surfaces for agriculture purposes [57], or to measure forest health by detecting disease and mortality occurrence in pine forests [58]. To our knowledge, no studies have used the GCC to distinguish plant types.…”

Section: Remote Sensing Data Acquisition and Processingmentioning

confidence: 99%

Mapping Wild Leek through the Forest Canopy Using a UAV

Leduc

Knudby

2018

Remote Sensing

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Abstract:Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species.

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UAV‐derived greenness and within‐crown spatial patterning can detect ash dieback in individual trees

Flynn,

Grieve,

Henshaw

et al. 2024

Ecol Sol and Evidence

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Ash Dieback (ADB) has been present in the UK since 2012 and is expected to kill up to 80% of UK ash trees. Detecting and quantifying the extent of ADB in individual tree crowns (ITCs), which is crucial to understanding resilience and resistance, currently relies on visual assessments which are impractical over large scales or at high frequency. The improved imaging capabilities and declining cost of consumer UAVs, together with new remote sensing methods such as structure from motion photogrammetry (SfM) offers potential to quantify the fine‐scale structural and spectral metrics of ITCs that are indicative of ADB, rapidly, and at low‐cost. We extract high‐resolution 3D RGB point clouds derived from SfM of canopy ash trees taken monthly throughout the growing season at Marden Park, Surrey, UK, a woodland impacted by ADB. We segment ITCs, extract green chromatic coordinate (gcc), and test the relationship with visual assessments of crown health. Next, we quantify spatial patterning of dieback within ITCs by testing the relationship between internal variation of gcc and path length, a measure of the distance from foliage to trunk, for small clusters of foliage. We find gcc correlates with visual assessments of crown health throughout the growing season, but the strongest relationships are in measurements taken after peak greenness, when the effects of ADB on foliage are likely to be most prevalent. We also find a negative relationship between gcc and path length in infected trees, indicating foliage loss is more severe at crown extremities. We demonstrate a new method for identifying ADB at scale using a consumer‐grade 3D RGB UAV system and suggest this approach could be adopted for widespread rapid monitoring. We recommend the optimum time of year for data acquisition, which we find to be an important factor for detecting ADB. Although here applied to ADB, this framework is applicable to a multitude of drivers of crown dieback, presenting a method for identifying spectral‐structural relationships which may be characteristic of disturbance type.

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Using excess greenness and green chromatic coordinate colour indices from aerial images to assess lodgepole pine vigour, mortality and disease occurrence

Cited by 42 publications

References 29 publications

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Mapping Wild Leek through the Forest Canopy Using a UAV

UAV‐derived greenness and within‐crown spatial patterning can detect ash dieback in individual trees

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