Estimating above ground biomass for eucalyptus plantation using data from unmanned aerial vehicle imagery

Srestasathiern, Panu; Siripon, Suramongkon; Wasuhiranyrith, Rattawat; Kooha, Phalakorn; Moukomla, Sitthisak

doi:10.1117/12.2323963

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The use of photogrammetry and SfM for modelling tree attributes in forestry has been widely researched in recent years. Studies using SfM have covered a diverse range of forest environments including coniferous plantations [59][60][61][62][63][64][65], eucalyptus plantations [63,66], temperate coniferous forests [67][68][69][70][71][72], temperate deciduous forests [73][74][75][76][77][78], boreal forests [79][80][81], tropical rainforests [82], urban trees [83,84] and palm plantations [85,86]. Using SfM data, height has been accurately predicted for both large [60,71,87] and smaller trees [59,63,88].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

et al. 2020

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The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R2 = 0.99, RMSE = 5.91%, and R2 = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R2 = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials.

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Section: Introductionmentioning

confidence: 99%

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

et al. 2020

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“…Non‐destructive measures are required in projects analyzing temporal patterns in states and processes of ecosystems, measuring ecosystem health (McIntyre et al, 2015), and identifying ecological processes (Moukomla et al, 2018). Nonetheless, some destructive sampling to quantify allometric relationships for calibration is desirable if absolute values rather than comparative of biomass are required.…”

Section: Discussionmentioning

confidence: 99%

Measuring plant biomass remotely using drones in arid landscapes

McCann

Keith

2022

Ecology and Evolution

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Measurement of variation in plant biomass is essential for answering many ecological and evolutionary questions. Quantitative estimates require plant destruction for laboratory analyses, while field studies use allometric approaches based on simple measurement of plant dimensions. We estimated the biomass of individual shrub‐sized plants, using a low‐cost unmanned aerial system (drone), enabling rapid data collection and non‐destructive sampling. We compared volume measurement (a surrogate for biomass) and sampling time, from the simple dimension measurements and drone, to accurate laboratory‐derived biomass weights. We focused on three Australian plant species which are ecologically important to their terrestrial and floodplain ecosystems: porcupine grass Triodia scariosa , Queensland bluebush Chenopodium auricomum , and lignum Duma florulenta . Estimated volume from the drone was more accurate than simple dimension measurements for porcupine grass and Queensland bluebush, compared to estimates from laboratory analyses but, not for lignum. The latter had a sparse canopy, with thin branches, few vestigial leaves and a similar color to the ground. Data collection and analysis consistently required more time for the drone method than the simple dimension measurements, but this would improve with automation. The drone method promises considerable potential for some plant species, allowing data to be collected over large spatial scales and, in time series, increasing opportunities to answer complex ecological and evolutionary questions and monitor the state of ecosystems and plant populations.

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“…In this instance, UAS LIDAR provided independent measurement showing photogrammetric occlusion of upper canopy branches and sub-canopy ground features [19]. Other studies of canopy structure in Eucalyptus examine forestry plantations where tree canopy is light-limited and conical in shape and UAV photogrammetry products are compared to independent methods at a single time [21,53,54]. Appropriate minimum specifications for sensor, environment, and processing to achieve reproducible Eucalyptus canopy models are currently uncertain.…”

Section: Introductionmentioning

confidence: 99%

Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

Fletcher

Mather

2020

Remote Sensing

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Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

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Estimating above ground biomass for eucalyptus plantation using data from unmanned aerial vehicle imagery

Cited by 6 publications

References 15 publications

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

Measuring plant biomass remotely using drones in arid landscapes

Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

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