Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends

Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.

Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images

Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges

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. 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. 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 area-based 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.

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

Object-based image analysis and machine-learning classification were applied to multispectral camera array data acquired by a small rotating blade unmanned aerial vehicle (UAV) over a hardwood forest in eastern Ontario. White birch, aspen, and two species of maple were surveyed in the field. Images were segmented and the resulting objects were visually confirmed to correspond with the sampled tree crowns. Following the application of machine-learning classification using the Random Forest algorithm, an independent validation sample of 23 tree crowns was, overall, approximately 78% correct. Aspen and birch were the most distinct species; the two maples appeared to be confused with each other and with immature trees and understory shrubs. Classification accuracy, commission errors, and variable importance were interpreted to be consistent with experience documented in aerial photointerpretation selection and elimination keys for northern hardwoods. Additional tests are recommended to more fully anal...

Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data

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 95th percentile of height (P95) was estimated at 068 ± 005 and 050 m ± 005 m, respectively Similarl

/pdf/unmanned-aerial-systems-for-precision-forest-inventory-2p6g24zuwe.pdf

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

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