Uncertainty Quantification in ALS-Based Species-Specific Growing Stock Volume Estimation

Varvia, Petri; Lähivaara, Timo; Maltamo, Matti; Packalén, Petteri; Tokola, Timo; Seppänen, Aku

doi:10.1109/tgrs.2016.2628960

Cited by 9 publications

(15 citation statements)

References 30 publications

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“…The same test data as in [13] is used in this study. In this section, the data set is briefly summarized, for detailed description, see e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Plot or cell level prediction uncertainty has garnered some research interest in recent years and several methods of predicting plot/cell level variance have been proposed [10]- [12]. Recently, a Bayesian inference approach to quantify uncertainty within the framework of the ABA was proposed by Varvia et al [13]. The main shortcoming in the method proposed in [13] is that it is computationally costly: wall-to-wall uncertainty quantification of a large forest area would require considerable computer resources.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a Bayesian inference approach to quantify uncertainty within the framework of the ABA was proposed by Varvia et al [13]. The main shortcoming in the method proposed in [13] is that it is computationally costly: wall-to-wall uncertainty quantification of a large forest area would require considerable computer resources.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data

Varvia

Lähivaara

Maltamo

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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While the analysis of airborne laser scanning (ALS) data often provides reliable estimates for certain forest stand attributes -such as total volume or basal area -there is still room for improvement, especially in estimating species-specific attributes. Moreover, while information on the estimate uncertainty would be useful in various economic and environmental analyses on forests, a computationally feasible framework for uncertainty quantifying in ALS is still missing. In this article, the species-specific stand attribute estimation and uncertainty quantification (UQ) is approached using Gaussian process regression (GPR), which is a nonlinear and nonparametric machine learning method. Multiple species-specific stand attributes are estimated simultaneously: tree height, stem diameter, stem number, basal area, and stem volume. The cross-validation results show that GPR yields on average an improvement of 4.6% in estimate RMSE over a state-of-the-art k-nearest neighbors (kNN) implementation, negligible bias and well performing UQ (credible intervals), while being computationally fast. The performance advantage over kNN and the feasibility of credible intervals persists even when smaller training sets are used.

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“…The same test data as in [13] is used in this study. In this section, the data set is briefly summarized, for detailed description, see e.g.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data

Varvia

Lähivaara

Maltamo

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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“…An additional benefit of these methods is that both Kalman filter and Bayesian techniques, commonly used for data assimilation, provide uncertainty estimates as part of the estimation framework (e.g. Varvia et al 2017). …”

Section: Estimating Uncertaintiesmentioning

confidence: 99%

Remote sensing and forest inventories in Nordic countries – roadmap for the future

Kangas

Astrup

Breidenbach

et al. 2018

Scandinavian Journal of Forest Research

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147

103

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The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field. ARTICLE HISTORY

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“…If this is the case, then asymptotic unbiasedness is tested by cross-validation methods, such as Leave-One-Out (LOO). Some recent Bayesian studies of forest inventory that have adopted this approach include [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon

et al. 2017

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Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme-or LAMP-for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution.

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Uncertainty Quantification in ALS-Based Species-Specific Growing Stock Volume Estimation

Cited by 9 publications

References 30 publications

Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data

Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data

Remote sensing and forest inventories in Nordic countries – roadmap for the future

LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon

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