The use of fixed–wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer–broadleaf forest

Jayathunga, Sadeepa; Owari, Toshiaki; Tsuyuki, Satoshi

doi:10.1016/j.jag.2018.08.017

Cited by 45 publications

(56 citation statements)

References 37 publications

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“…Several studies achieved promising results in the volume (V) estimation. Cao et al [84] (UAV-LiDAR; UAV-RGB), Ota et al [85] and Jayathunga et al [92] achieved a strong correlation in the estimation of this parameter (R 2 respectively 0.78, 0.70, 0.75 and 0.84). Considering these results, Cao et al [84] obtained, once again, a higher correlation using LiDAR system.…”

Section: Stand-level Studiesmentioning

confidence: 96%

“…Considering these results, Cao et al [84] obtained, once again, a higher correlation using LiDAR system. On the other hand, Jayathunga et al [92] achieved a strong correlation thanks to digital photogrammetry of UAV imagery, combined with LiDAR-DTM, which can be feasible for the estimation of V of uneven-aged mixed conifer-broadleaf forest.…”

Section: Stand-level Studiesmentioning

confidence: 99%

“…Gini et al [65,119] applied the ISODATA and maximum likelihood (MaxL) methods. Jayathunga et al [92] applied maximum likelihood (MaxL) method. Laliberte et al [121] applied hierarchical image classification (HIC) method.…”

Section: Tree Species Mapping and Classificationmentioning

confidence: 99%

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Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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Section: Stand-level Studiesmentioning

confidence: 96%

Section: Stand-level Studiesmentioning

confidence: 99%

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Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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“…In the case of ALS data terrain representations can be extracted with relative accuracies of between 0.1 and 1 m depending on the environment and the properties of the data capture (i.e., sensor, footprint size, point density and flying height) [16,17]. While using novel approaches to coregister the two datasets, studies such as Jayathunga et al [18], and Puliti et al [19] have successfully provided measures of forests metrics, timber volume, Lorey's mean height, stem number and basal area. Although the use of alternative sources of terrain information can be successful, often this data is not available and when it is, the co-registration of two datasets is complicated by the use of low cost positioning sensors onboard UAS [13].…”

Section: Describing the Terrainmentioning

confidence: 99%

Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Wallace

Bellman

Hally

et al. 2019

Forests

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Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.

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“…Finally, FVC was utilized to calculate the ratio of the number of vegetation pixels to the total number of pixels in each plot. 39…”

Section: Forest Vegetation Coveragementioning

confidence: 99%

Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

et al. 2019

J. Appl. Rem. Sens.

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Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle," Abstract. To better assess spatial variabilities in subtropical secondary forest biomass, the goal of the present study was to employ small-footprint, discrete-return light detection and ranging and unmanned aerial vehicles, integrated with structure from motion (UAV-SFM) data, to accurately estimate the stand characteristic parameters of subtropical forests. The Lorey's height, density, basal area, volume, and aboveground biomass (AGB) of 30 plots (∅10 m) were modeled using an array of SFM point cloud metrics and image color metrics (forestry vegetation coverage and color index set). Further, the individual models developed using stepwise multiregression analysis and a branch-and-bound algorithm were employed to examine the best models. Overall, the results indicated that the coefficient of determination (R 2 ) for Lorey's height (R 2 ¼ 0.58 to 0.95), volume (R 2 ¼ 0.29 to 0.71), and AGB (R 2 ¼ 0.27 to 0.64) were significantly enhanced compared with the density (R 2 ¼ 0.18 to 0.48), and basal area (R 2 ¼ 0.24 to 0.52). Utilizing independent stand-level data from ground inventory, our results revealed that, overall, the model fit was significant for most stand characteristic parameters, with relationships close to a 1:1 line, thereby indicating no significant bias [relative root mean square error ðrRMSEÞ ¼ 5.32% to 27.04%]. Our image metrics had a statistically significant association with Lorey's height (rRMSE decreased by 0.08%), volume (rRMSE decreased by 0.58%), and AGB (rRMSE decreased by 3.11%), which provided additional explanatory power in the regression analysis. This research demonstrated the potential of UAV-SFM as a technology to accurately assess subtropical forest carbon while providing an improved elucidation of the positive effects of image color metrics.

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The use of fixed–wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer–broadleaf forest

Cited by 45 publications

References 37 publications

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

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