Forest inventories for small areas using drone imagery without in-situ field measurements

Kotivuori, Eetu; Kukkonen, Mikko; Mehtätalo, Lauri; Maltamo, Matti; Korhonen, Lauri; Packalén, Petteri

doi:10.1016/j.rse.2019.111404

Cited by 37 publications

(23 citation statements)

References 38 publications

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“…In the last few years, unmanned aerial vehicles (UAVs), as a small-scale, low-cost, remote sensing alternative to satellite and airborne platforms, have provided a unique combination of high temporal and spatial resolution and have gradually become one of the most powerful tools for 3D forest mapping [11,12]. The miniaturized laser scanning sensors on UAV platforms have earned a good reputation in the field of forest inventory and surveying due to their various benefits, such as increased point densities, lower cost, flight route flexibility, and simple operation [13,14].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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Section: Introductionmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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“…Total volumes for the 15 m × 15 m validation sub-plots (n = 80) were predicted with a regional model presented in Kotivuori et al (2020). It is a generic area-based model (Eq.…”

Section: Total Volume Predictionmentioning

confidence: 99%

“…Photogrammetric UAS data has been used to predict different forest inventory attributes, such as height (Ota et al 2017;Krause et al 2019), basal area (Alonzo et al 2018) and volume (Goodbody et al 2017;Jayathunga et al 2018;Cao et al 2019;Kotivuori et al 2020) using area-based approaches (ABA). Tree species have been classified, principally using individual tree detection (ITD) methods (Nevalainen et al 2017;Brovkina et al 2018;Franklin and Ahmed 2018) in which multispectral or hyperspectral images are the primary component.…”

Section: Introductionmentioning

confidence: 99%

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Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Kukkonen¹,

Kotivuori²,

Maltamo³

et al. 2021

Silva Fenn.

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Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m Ã 30 m) were 9.0%, and 33.4â62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

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“…Theoretically, a more complete set of forest observations should lead to improved estimates for forest structure. During the recent years, the use of UAVs has become a feasible option for small-scale forest monitoring [18,[39][40][41]. Applying image processing techniques such as Structure from Motion approach for generating point clouds from overlapping images [42], detailed 3D information on forest canopy structure can be acquired even using a consumer-grade UAV equipped with an RGB camera [43].…”

Section: Introductionmentioning

confidence: 99%

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

Yrttimaa¹,

Saarinen²,

Kankare³

et al. 2020

Preprint

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Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably the root mean square error (RMSE) in Hg improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

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Forest inventories for small areas using drone imagery without in-situ field measurements

Cited by 37 publications

References 38 publications

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

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