Estimating the Rut Depth by UAV Photogrammetry

Nevalainen, Paavo; Salmivaara, Aura; Ala‐Ilomäki, Jari; Launiainen, Samuli; Hiedanpää, Juuso; Finér, Leena; Pahikkala, Tapio; Heikkonen, Jukka

doi:10.3390/rs9121279

Cited by 24 publications

(27 citation statements)

References 30 publications

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“…This way, UAVs can be used as a cost-effective alternative for post-harvest surveying, constituting a rapid assessment of the disturbance extent and erosion risk mapping in a wide range of sites. Nevalainen et al [161] assessed the rut depth distribution and measurement of a logging site using photogrammetric point clouds form UAV-based RGB imagery. The proposed method can classify rut depths into two categories: insignificant depression and harmful rut depth.…”

Section: Other Applicationsmentioning

confidence: 99%

“…Apart from forest applications with more incidence towards tree development and its status, other applications in forestry contexts were explored using UAVs for: forest canopy assessment (canopy cover [125], canopy gaps [152][153][154], LAI [7,155], foliage clumping [7] and leaf angle distribution [156]), regeneration of forests [126,127,157,158], assessment of soil disturbances in post-harvest areas [159][160][161], monitoring of logging operations [162] and tree-stump detection [163]. Most of the studies rely in the use of RGB sensors mounted on rotary-wing UAVs (apart from the multispectral sensor used in [155]), except for canopy gaps [152][153][154] in which a fixed-wing UAVs were used.…”

Section: Other Applicationsmentioning

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: Other Applicationsmentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

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

et al. 2020

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“…UAVs, in combination with photogrammetric methods, have been successfully used for assessing forested areas for pre- [2] and post-harvest assessment [3][4][5][6][7]. In post-harvest assessment, [3,5,8] explored the possibilities of using UAV-borne photogrammetric three-dimensional (3D) data for quantifying soil disturbance from forest machinery. When compared to ground-based solutions [9], UAVs that are equipped with imaging sensors represent a more efficient tool to acquire highly detailed and spatially continuous 3D data.…”

Section: Introductionmentioning

confidence: 99%

Tree-Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery

Puliti

Talbot

Astrup

2018

Forests

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Unmanned aerial vehicles (UAVs) are increasingly used as tools to perform a detailed assessment of post-harvest sites. One of the potential use of UAV photogrammetric data is to obtain tree-stump information that can then be used to support more precise decisions. This study developed and tested a methodology to automatically detect, segment, classify, and measure tree-stumps. Among the potential applications for single stump data, this study assessed the possibility (1) to detect and map root-and butt-rot on the stumps using a machine learning approach, and (2) directly measure or model tree stump diameter from the UAV data. The results revealed that the tree-stumps were detected with an overall accuracy of 68-80%, and once the stump was detected, the presence of root-and butt-rot was detected with an accuracy of 82.1%. Furthermore, the root mean square error of the UAV-derived measurements or model predictions for the stump diameter was 7.5 cm and 6.4 cm, respectively, and with the former systematically under predicting the diameter by 3.3 cm. The results of this study are promising and can lead to the development of more cost-effective and comprehensive UAV post-harvest surveys.

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“…Talbot et al (2017b) considered only the status of the site after harvesting was complete, without the use of any information on type or number of machine passes, as the study proposed a method for post-harvest sampling. For their purposes, Nevalainen et al (2017) used trails specifically made for the study in simulating real operations, but these were only used as a baseline for method development. Astrup et al (2016) used scanner data from a forwarder that was collected in both operational and controlled field experiment settings.…”

Section: Application In An Operational Settingmentioning

confidence: 99%

A review of Sensors, Sensor-Platforms and Methods Used in 3D Modelling of Soil Displacement after Timber Harvesting

Talbot

Astrup

2020

Croat. j. for. eng. (Online)

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Proximal sensing technologies are becoming widely used across a range of applications in environmental sciences. One of these applications is in the measurement of the ground surface in describing soil displacement impacts from wheeled and tracked machinery in the forest. Within a period of 2–3 years, the use photogrammetry, LiDAR, ultrasound and time-of-flight imaging based methods have been demonstrated in both experimental and operational settings. This review provides insight into the aims, sampling design, data capture and processing, and outcomes of papers dealing specifically with proximal sensing of soil displacement resulting from timber harvesting. The work reviewed includes examples of sensors mounted on tripods and rigs, on personal platforms including handheld and backpack mounted, on mobile platforms constituted by forwarders and skidders, as well as on unmanned aerial vehicles (UAVs). The review further highlights and discusses the benefits, challenges, and some of the shortcomings of the various technologies and their application as interpreted by the authors.The majority of the work reviewed reflects pioneering approaches and innovative applications of the technologies. The studies have been carried out almost simultaneously, building on little or no common experience, and the evolution of standardized methods is not yet fully apparent. Some of the issues that will likely need to be addressed in developing this field are (i) the tendency toward generating apparently excessively high resolution micro-topography models without demonstrating the need for or contribution of such resolutions on accuracy, (ii) the inadequacy of conventional manual measurements in verifying the accuracy of these methods at such high resolutions, and (iii) the lack of a common protocol for planning, carrying out, and reporting this type of study.

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Estimating the Rut Depth by UAV Photogrammetry

Cited by 24 publications

References 30 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

Tree-Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery

A review of Sensors, Sensor-Platforms and Methods Used in 3D Modelling of Soil Displacement after Timber Harvesting

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