Forest Variable Estimation Using a High Altitude Single Photon Lidar System

Wästlund, André; Holmgren, Johan; Lindberg, Eva; Olsson, Håkan

doi:10.3390/rs10091422

Cited by 26 publications

(17 citation statements)

References 18 publications

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“…The advent of new ALS technologies such as single photon lidar may enable cost-effective landscape-level characterization of the ground surface with sufficient accuracy to support DAP normalization. Single photon systems have the ability to fly at greater elevations and faster speeds, acquiring ALS data for less cost than currently standard systems [140]. This raises the potential for DAP data to be used to support forest inventory frameworks, especially areas beyond existing EFI boundaries.…”

Section: Acquisition Planningmentioning

confidence: 99%

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

2019

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Purpose of Review Three-dimensional (3D) data on forest structure have transformed the level of detail and accuracy of forest information. While these 3D data have primarily been derived from airborne laser scanning (ALS), there has been growing interest in the use of 3D data derived from digital aerial photogrammetry (DAP) and image-matching algorithms. In particular, research and operational forestry communities are interested in using DAP data to update existing ALS-derived enhanced forest inventories. Although DAP depends on accurate terrain information provided by ALS to normalize digital surface models to heights above ground, in an inventory update scenario, DAP data currently have cost advantages over repeat ALS acquisitions. Recent Findings Extensive research across a broad range of forest types has demonstrated that DAP data can provide comparable accuracies to ALS for estimating inventory attributes such as volume, basal area, and height when used in an area-based approach with co-located ground plot information. Summary Herein, we review research relevant to the use of DAP for updating area-based forest inventories in subsequent inventory cycles, highlighting issues and opportunities for DAP data in this context. We examine the use of DAP for areabased forest inventory applications, comparing data inputs, algorithms, and outcomes across numerous studies and forest environments. Lastly, we outline outstanding research gaps that require further inquiry including benchmarking of acquisition parameters and image-matching algorithms. Keywords Digital aerial photogrammetry. Image-matching. Airborne laser scanning. Forest inventory update. Digital stereo imagery. Forest structure. Image based point clouds This article is part of the Topical Collection on Remote Sensing

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Section: Acquisition Planningmentioning

confidence: 99%

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

2019

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“…biomass and volume) and the metrics extracted from LiDAR data. Airborne laser scanning (ALS) is the consolidated technique to retrieve tree or stand parameters and to detect forest changes using multitemporal laser surveys, whether single spectral, multi-spectral of single-photon (White et al 2016;Yu et al 2017;Wästlund et al 2018).…”

Section: Forest Productivitymentioning

confidence: 99%

A new generation of sensors and monitoring tools to support climate-smart forestry practices

et al. 2021

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Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in-situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e. a wireless sensor network (WSN), that can be part of a world-wide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in-situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scale of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime-shifts in terms of forest productivity, health and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.

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“…Wästlund et al [21] also found that single-photon LiDAR beams penetrate better through the canopy in hemiboreal forests than linear-mode LiDAR beams. Wästlund et al [21] used the Leica SPL100 instrument, whereas Swatantran et al [17] applied an experimental airborne single-photon LiDAR known as the High-Resolution Quantum LiDAR System. Mandlburger et al [23] reported that linear-mode LiDAR data can better characterize the vegetation below the dominant canopy surface than Leica SPL100 data.…”

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confidence: 98%

“…They observed that single-photon and linear-mode LiDAR data are able to characterize the canopy surface similarly, but that the pulses of linear-mode LiDAR penetrate deeper inside the canopy. Wästlund et al [21] and Yu et al [22] studied the estimation of forest variables using the Leica SPL100 and found that selected forest attributes can be predicted with comparable error rates with both linear-mode LiDAR and single-photon LiDAR data. Wästlund et al [21] also found that single-photon LiDAR beams penetrate better through the canopy in hemiboreal forests than linear-mode LiDAR beams.…”

mentioning

confidence: 99%

“…Wästlund et al [21] and Yu et al [22] studied the estimation of forest variables using the Leica SPL100 and found that selected forest attributes can be predicted with comparable error rates with both linear-mode LiDAR and single-photon LiDAR data. Wästlund et al [21] also found that single-photon LiDAR beams penetrate better through the canopy in hemiboreal forests than linear-mode LiDAR beams. Wästlund et al [21] used the Leica SPL100 instrument, whereas Swatantran et al [17] applied an experimental airborne single-photon LiDAR known as the High-Resolution Quantum LiDAR System.…”

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confidence: 99%

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A Comparison of Linear-Mode and Single-Photon Airborne LiDAR in Species-Specific Forest Inventories

Räty

Varvia

Korhonen

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Single-photon airborne light detection and ranging (LiDAR) systems provide high-density data from high flight altitudes. We compared single-photon and linear-mode airborne LiDAR for the prediction of species-specific volumes in boreal coniferous-dominated forests. The LiDAR data sets were acquired at different flight altitudes using Leica SPL100 (single-photon, 17 points • m −2 ), Riegl VQ-1560i (linear-mode, 11 points • m −2 ), and Leica ALS60 (linear-mode, 0.6 points • m −2 ) LiDAR systems. Volumes were predicted at the plot-level using Gaussian process regression with predictor variables extracted from the LiDAR data sets and aerial images. Our findings showed that the Leica SPL100 produced a greater mean root-meansquared error (RMSE) value (41.7 m 3 • ha −1 ) than the Leica ALS60 (39.3 m 3 • ha −1 ) in the prediction of species-specific volumes. Correspondingly, the Riegl VQ-1560i (mean RMSE = 33.0 m 3 • ha −1 ) outperformed both the Leica ALS60 and the Leica SPL100. We found that the cumulative distributions of the first echo heights > 1.3 m were rather similar among the data sets, whereas the last echo distributions showed larger differences. We conclude that the Leica SPL100 data set is suitable for area-based LiDAR inventory by tree species although the prediction errors are greater than with data obtained using the modern linear-mode LiDAR, such as Riegl VQ-1560i. Index Terms-Airborne laser scanning, Gaussian processes (GPs), light detection and ranging (LiDAR) intensity, photon-counting LiDAR. I. INTRODUCTION A IRBORNE light detection and ranging (LiDAR) has established its role in forest inventories [1]-[3]. LiDAR systems have developed rapidly during the last decade, and the development has mainly focused on linear-mode LiDAR systems. These conventional linear-mode LiDAR systems can operate in discrete return or full-waveform modes. Discrete return LiDARs record distinct echoes, whereas full-waveform systems can store the continuous shape of both the outgoing Manuscript

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Forest Variable Estimation Using a High Altitude Single Photon Lidar System

Cited by 26 publications

References 18 publications

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

A new generation of sensors and monitoring tools to support climate-smart forestry practices

A Comparison of Linear-Mode and Single-Photon Airborne LiDAR in Species-Specific Forest Inventories

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