Evaluating the accuracy of ALS-based removal estimates against actual logging data

Ville Vähä-Konka,Timo Pukkala,Kalle Kärhä,Matti Maltamo

doi:10.1007/s13595-020-00985-7

Ville Vähä-Konka, Timo Pukkala + Show 2 more

Open Access

https://doi.org/10.1007/s13595-020-00985-7

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Abstract

Key messageWe examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester.ContextAirborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data.AimsThis study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data.MethodsWe examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient.ResultsIn clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals.ConclusionThe study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

Highlights

Combined use of airborne laser scanning (ALS), field plots, and predictive models is today the most important source of information in management-oriented inventories of Finnish forests (Maltamo and Packalén 2014)
We investigated the accuracy of determining the dominant tree species from ALS-based volume estimates since this information is of primary interest in harvesting and silviculture operations
3.1 Accuracy of ALS estimates in clear cutting

Summary

Introduction

Combined use of airborne laser scanning (ALS), field plots, and predictive models is today the most important source of information in management-oriented inventories of Finnish forests (Maltamo and Packalén 2014). Verifications of inventories have shown that inventories based on ALS data (Wallenius et al 2012) can be more accurate than those obtained by using traditional field-based methods (Suvanto et al 2005) Both in the evaluation of tree species-specific attributes (e.g., Packalén and Maltamo 2007; Breidenbach et al 2010) and in the measurement of individual tree attributes (e.g., Korpela et al 2010; Vauhkonen 2010; Yao et al 2012; Silva et al 2016), the accuracy has been at least the same as in traditional field assessments. Further research is needed to improve the accuracy of tree quality estimation in ALS-based forest inventory (Wallenius et al 2012)

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