Enhancing a eucalypt crown condition indicator driven by high spatial and spectral resolution remote sensing imagery

Evans, Bradley J.; Lyons, Thomas; Barber, P.A.; Stone, Christine; Hardy, G.

doi:10.1117/1.jrs.6.063605

Cited by 13 publications

(5 citation statements)

References 59 publications

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“…The spectral bands are located in the visible and near-infrared (NIR) region of the electromagnetic spectrum using filters centred at 450 nm (blue), 550 nm (green), 675 nm (red), and 780 nm (NIR) [32].…”

Section: High Spatial Resolution Remote Sensing Datamentioning

confidence: 99%

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: High Spatial Resolution Remote Sensing Datamentioning

confidence: 99%

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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“…Between 2012 and 2015, high-resolution digital multi-spectral imagery (DMSI) was captured annually with a fixed-wing aircraft across all urban bushlands in Joondalup, as four narrow spectral bands of data (red, green, blue, and near infrared) at a spatial resolution of 0.5 m pixels. The Red Edge Extrema Index (REEI) was calculated using the ratio of near infrared: red bands, and a difference in pixel values from 2015 to 2012 was calculated by subtracting the 2015 image from the 2012 image [ 27 , 28 ]. An increase in pixel values represent an increase in vegetation condition over this period, while a decrease in pixel values indicates a decline in health.…”

Section: Methodsmentioning

confidence: 99%

Association of Phytophthora with Declining Vegetation in an Urban Forest Environment

et al. 2020

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Urban forests consist of various environments from intensely managed spaces to conservation areas and are often reservoirs of a diverse range of invasive pathogens due to their introduction through the nursery trade. Pathogens are likely to persist because the urban forest contains a mixture of native and exotic plant species, and the environmental conditions are often less than ideal for the trees. To test the impact of different land management approaches on the Phytophthora community, 236 discrete soil and root samples were collected from declining trees in 91 parks and nature reserves in Joondalup, Western Australia (WA). Sampling targeted an extensive variety of declining native trees and shrubs, from families known to be susceptible to Phytophthora. A sub-sample was set aside and DNA extracted for metabarcoding using Phytophthora-specific primers; the remaining soil and root sample was baited for the isolation of Phytophthora. We considered the effect on the Phytophthora community of park class and area, soil family, and the change in canopy cover or health as determined through sequential measurements using remote sensing. Of the 236 samples, baiting techniques detected Phytophthora species from 24 samples (18 parks), while metabarcoding detected Phytophthora from 168 samples (64 parks). Overall, forty-four Phytophthora phylotypes were detected. Considering only sampling sites where Phytophthora was detected, species richness averaged 5.82 (range 1–21) for samples and 9.23 (range 2–24) for parks. Phytophthora multivora was the most frequently found species followed by P. arenaria, P. amnicola and P. cinnamomi. While park area and canopy cover had a significant effect on Phytophthora community the R2 values were very low, indicating they have had little effect in shaping the community. Phytophthora cinnamomi and P. multivora, the two most invasive species, often co-occurring (61% of samples); however, the communities with P. multivora were more common than those with P. cinnamomi, reflecting observations over the past decade of the increasing importance of P. multivora as a pathogen in the urban environment.

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“…There have been several reviews which have evaluated the potential role of remote sensing (in particular, hyper-spectral, hyper-spatial and hyper-temporal optical data and active airborne laser scanning or radar data) for vegetation condition assessment (e.g., [19][20][21][22]). These reviews demonstrate a broad range of remote sensing approaches used to estimate various indicators and then infer vegetation or habitat condition; for example, measures of forest extent or canopy cover [23,24], composition or diversity [25,26], structure, physiology or function [24,[27][28][29][30], health, stress or disease [31][32][33][34][35], seasonal dynamics [36], disturbance, degradation, vulnerability or recovery [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

2022

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Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts.

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Enhancing a eucalypt crown condition indicator driven by high spatial and spectral resolution remote sensing imagery

Cited by 13 publications

References 59 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

Association of Phytophthora with Declining Vegetation in an Urban Forest Environment

Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

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