Inventory of Small Forest Areas Using an Unmanned  Aerial System

Puliti, Stefano; Ørka, Hans Ole; Gobakken, Terje; Næsset, Erik

doi:10.3390/rs70809632

Cited by 313 publications

(359 citation statements)

References 39 publications

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“…It is also possible to carry out near-infrared remote sensing with UAVs using modified off-the-shelf digital cameras [19,21,23] or custom light-weight multi-spectral cameras [16,77,78] and future work should consider the potential of using such sensors for SFM mapping of canopy NDVI (Normalized Difference Vegetation Index) at high spatial resolution and in 3D, providing links between canopy structure, optical properties, and biophysical parameters. Future research should also consider the influence of different spectral bands on reconstructions of canopy structure from SFM, for example this and prior studies used RGB imagery for modeling canopy height [14,22], while other studies achieved comparable results using RG-NIR imagery [21,23], and it is not clear what, if any, effect the NIR information has on estimates of canopy structure compared to RGB alone.…”

Section: The Role Of the Camera Sensor; Multi And Hyperspectral Strucmentioning

confidence: 99%

“…Personal remote sensing systems have enabled accurate mapping of canopy height and biomass density as well as the discrimination of individual tree structural, spectral, and phenological traits [14,[21][22][23]. Similar systems have also been used for mapping stream channel geomorphology [24,25], vineyard and orchard plant structure [26][27][28], the topography of bare substrates [29][30][31][32][33][34], and lichen and moss extent and coverage [16].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

2015

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Ecological remote sensing is being transformed by three-dimensional (3D), multispectral measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision structure from motion (SFM) algorithms. Yet applications of this technology have out-paced understanding of the relationship between collection method and data quality. Here, UAV-SFM remote sensing was used to produce 3D multispectral point clouds of Temperate Deciduous forests at different levels of UAV altitude, image overlap, weather, and image processing. Error in canopy height estimates was explained by the alignment of the canopy height model to the digital terrain model (R 2 = 0.81) due to differences in lighting and image overlap. Accounting for this, no significant differences were observed in height error at different levels of lighting, altitude, and side overlap. Overall, accurate estimates of canopy height compared to field measurements (R 2 = 0.86, RMSE = 3.6 m) and LIDAR (R 2 = 0.99, RMSE = 3.0 m) were obtained under optimal conditions of clear lighting and high image overlap (>80%). Variation in point cloud quality appeared related to the behavior of SFM 'image features'. Future research should consider the role of image features as the fundamental unit of SFM remote sensing, akin to the pixel of optical imaging and the laser pulse of LIDAR.

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Section: The Role Of the Camera Sensor; Multi And Hyperspectral Strucmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

2015

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“…We used Agisoft PhotoScan Professional Edition 1.3.2 (Agisoft, St. Petersburg, Russia) to generate 3D dense point clouds, as it has proven effective for the production of dense and accurate point clouds over forested areas [38,51]. PhotoScan offers a user-friendly workflow that combines proprietary algorithms based on computer vision SfM and stereo-matching for image alignment and 3D reconstruction [61].…”

Section: Photogrammetric Processingmentioning

confidence: 99%

“…Although fixed-wing UAVs have great potential for use in forestry applications, very few studies have involved detailed analyses of point clouds or canopy surface models built from fixed-wing UAV imagery at comparatively large scales, such as the forest management compartment level, and even fewer studies have used fixed-wing UAV imagery to estimate forest structural attributes (e.g., [19,38,55]). Applications of fixed-wing UAV imagery and the robustness of digital photogrammetry have also not been studied intensively over a range of forest types, such as mixed conifer-broadleaf forests.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

2018

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Unmanned aerial vehicles (UAVs) and digital photogrammetric techniques are two recent advances in remote sensing (RS) technology that are emerging as alternatives to high-cost airborne laser scanning (ALS) data sources. Despite the potential of UAVs in forestry applications, very few studies have included detailed analyses of UAV photogrammetric products at larger scales or over a range of forest types, including mixed conifer-broadleaf forests. In this study, we assessed the performance of fixed-wing UAV photogrammetric products of a mixed conifer-broadleaf forest with varying levels of canopy structural complexity. We demonstrate that fixed-wing UAVs are capable of efficiently collecting image data at local scales and that UAV imagery can be effectively utilized with digital photogrammetric techniques to provide detailed automated reconstruction of the three-dimensional (3D) canopy surface of mixed conifer-broadleaf forests. When combined with an accurate digital terrain model (DTM), UAV photogrammetric products are promising for producing reliable structural measurements of the forest canopy. However, the performance of UAV photogrammetric products is likely to be influenced by the structural complexity of the forest canopy. Furthermore, we highlight the potential of fixed-wing UAVs in operational forest management at the forest management compartment level, for acquiring high-resolution imagery at low cost. A future direction of this research would be to address the issue of how well the photogrammetric products can predict the actual structure of mixed conifer-broadleaf forests.

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“…Zhang et al (2016) showed that long-term ecological monitoring can be effectively done by UAV systems with high resolution data. Puliti et al (2015) reported how small forest inventories can be created by UAV systems and provided highly accurate results.…”

Section: Introductionmentioning

confidence: 99%

Estimating tree heights with images from an unmanned aerial vehicle

Birdal

Avdan

Türk

2017

Geomatics, Natural Hazards and Risk

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Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

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Inventory of Small Forest Areas Using an Unmanned Aerial System

Cited by 313 publications

References 39 publications

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

Estimating tree heights with images from an unmanned aerial vehicle

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