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Resolution dependence in an area-based approach to forest inventory with airborne laser scanning.

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Title: Resolution dependence in an area-based approach to forest inventory with airborne laser scanning.
Authors: Packalen, Petteri1 petteri.packalen@uef.fi, Strunk, Jacob1 jstrunk@fs.fed.us, Packalen, Tuula1 tuula.packalen@luke.fi, Maltamo, Matti1 matti.maltamo@uef.fi, Mehtätalo, Lauri1 lauri.mehtatalo@uef.fi
Source: Remote Sensing of Environment. Apr2019, Vol. 224, p192-201. 10p.
Subjects: Forest surveys, Airborne lasers, Optical scanners, Prediction models, Error rates
Abstract: Abstract In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters. Highlights • We quantify the effect of varying resolution in the context ALS forest inventories. • Irregular point pattern of ALS data hamper achieving resolution invariance. • Very small resolution effect in most real-world cases • Resolution invariance is most relevant in large area strategic inventories. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 135012532
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PubTypeId: academicJournal
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  Data: Resolution dependence in an area-based approach to forest inventory with airborne laser scanning.
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  Data: <searchLink fieldCode="AR" term="%22Packalen%2C+Petteri%22">Packalen, Petteri</searchLink><relatesTo>1</relatesTo><i> petteri.packalen@uef.fi</i><br /><searchLink fieldCode="AR" term="%22Strunk%2C+Jacob%22">Strunk, Jacob</searchLink><relatesTo>1</relatesTo><i> jstrunk@fs.fed.us</i><br /><searchLink fieldCode="AR" term="%22Packalen%2C+Tuula%22">Packalen, Tuula</searchLink><relatesTo>1</relatesTo><i> tuula.packalen@luke.fi</i><br /><searchLink fieldCode="AR" term="%22Maltamo%2C+Matti%22">Maltamo, Matti</searchLink><relatesTo>1</relatesTo><i> matti.maltamo@uef.fi</i><br /><searchLink fieldCode="AR" term="%22Mehtätalo%2C+Lauri%22">Mehtätalo, Lauri</searchLink><relatesTo>1</relatesTo><i> lauri.mehtatalo@uef.fi</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing+of+Environment%22">Remote Sensing of Environment</searchLink>. Apr2019, Vol. 224, p192-201. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Forest+surveys%22">Forest surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Airborne+lasers%22">Airborne lasers</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+scanners%22">Optical scanners</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink>
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  Data: Abstract In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters. Highlights • We quantify the effect of varying resolution in the context ALS forest inventories. • Irregular point pattern of ALS data hamper achieving resolution invariance. • Very small resolution effect in most real-world cases • Resolution invariance is most relevant in large area strategic inventories. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.rse.2019.01.022
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 192
    Subjects:
      – SubjectFull: Forest surveys
        Type: general
      – SubjectFull: Airborne lasers
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      – SubjectFull: Optical scanners
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      – SubjectFull: Prediction models
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      – SubjectFull: Error rates
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      – TitleFull: Resolution dependence in an area-based approach to forest inventory with airborne laser scanning.
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            NameFull: Packalen, Petteri
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            NameFull: Mehtätalo, Lauri
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              Text: Apr2019
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              Y: 2019
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