Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring

Zhang, Jian; Hu, Jianbo; Lian, Juyu; Fan, Zongji; Ouyang, Xing; Ye, Wanhui

doi:10.1016/j.biocon.2016.03.027

Cited by 199 publications

(116 citation statements)

References 67 publications

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“…The PPC errors estimated for the remaining eight plots are comparable to PPC errors listed by Dandois and Ellis [33], and slightly higher than those reported by Wallace et al [36] and Zhang et al [46]. Our PPC z errors were under 0.10, which are either comparable to or lower than the vertical PPC errors reported in these studies.…”

Section: Uav Data Collection and Processingsupporting

confidence: 85%

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

et al. 2017

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Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted by field personnel in order to assess the capacity of PPC data to contribute to vegetation-reclamation surveys. We found good statistical agreement between key structural vegetation parameters, such as mean and maximum vegetation height, with PPC metrics successfully predicting most height and tree-diameter metrics using multivariate linear regression. However, PPC metrics were not as useful for estimating ground-measured vegetation cover. We believe that part of the issue lies in the mismatch between PPC-and ground-based measurement approaches, including subjective judgement on behalf of ground crews: a topic that requires more investigation. Our work highlights the emerging value of UAV-based PPCs to complement, and in some cases supplement, traditional ground-based sources of measured vegetation structure.

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Section: Uav Data Collection and Processingsupporting

confidence: 85%

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

et al. 2017

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“…1; Pajares 2015). UAS can operate at much lower altitudes and achieve higher spatial resolutions than conventional aerial and satellite surveying techniques (Zhang et al 2016). Research and development of UAS have focused on the ability to miniaturize, automate and enhance navigation, improve payload potential, and maximize flight times.…”

Section: Uas Reviewmentioning

confidence: 99%

“…Tang et al (2015) and Siebert and Teizer (2014) indicate that the collection of high resolution imagery using UAS-DAP should be encouraged for forestry purposes because it allows measurement of canopy and individual tree heights at lower costs than ALS systems. Depending on UAS flight parameters, ortho-mosaic imagery of forested areas can have spatial resolutions of 3-cm or less, providing managers with ultra-high definition images of operational areas and a 3D point cloud in a single flight plan (Zhang et al 2016). Apart from studies such as Zarco-Tejada et al (2014), which demonstrated that optical imagery can be used to measure canopy heights at operational scales, UAS-DAP point clouds have been used for many other forestry purposes, including spatial quantification of riparian areas and determining vegetation composition (Dunford et al 2009), mapping bark beetle damage at the individual tree level (Näsi et al 2015), long-term forest monitoring (Zhang et al 2016), and monitoring tropical forest recovery (Zahawi et al 2015).…”

Section: )mentioning

confidence: 99%

Unmanned aerial systems for precision forest inventory purposes: A review and case study

Goodbody

CoopsNicholas

MarshallPeter

et al. 2017

The Forestry Chronicle

144

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Unmanned Aerial Systems (UAS) are capable of improving the efficiency of acquisition and providing fine spatial scale data for sustainable resource management. In this paper we begin by describing differences between UAS airframes, their successes and limitations, and list contemporary research applications. UAS compatible sensor technologies are discussed, including passive and active sensors. Finally, we detail a case study where UAS updated an Enhanced Forest Inventory (EFI) for a study area in interior British Columbia. Airborne Laser Scanning (ALS) from 2013 and Digital Aerial Photogrammetric (DAP) point clouds acquired using a UAS from 2015 were used to estimate individual tree height and volume increments. A total of 246 trees were detected using Canopy Height Models (CHMs) with 70% of these trees being matched in the ALS and DAP data sets. Mean tree growth between 2013 and 2015 from the CHM and 95 th percentile of height (P95) was estimated at 0.68 ± 0.05 and 0.50 m ± 0.05 m, respectively. Similarly, mean gross tree volume increments (m 3 ) were computed as 0.05 m 3 ± 0.005 m 3 and 0.03 m 3 ± 0.005 m 3 for the CHM and P95, respectively. The results indicate that information from UAS-DAP point clouds can generate spatially and temporally accurate inventories and have potential to inform a number of sustainable forest management activities.

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“…Lisein et al (2015) reported classification of deciduous trees with UAV imagery with high classification accuracy. 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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Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring

Cited by 199 publications

References 67 publications

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Unmanned aerial systems for precision forest inventory purposes: A review and case study

Estimating tree heights with images from an unmanned aerial vehicle

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