Calibration of nationwide airborne laser scanning based stem volume models

Kotivuori, Eetu; Maltamo, Matti; Korhonen, Lauri; Packalén, Petteri

doi:10.1016/j.rse.2018.02.069

Cited by 19 publications

(16 citation statements)

References 15 publications

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“…Astrup et al (2019) constructed a similar map product for Norway, employing photogrammetric point clouds instead of ALS data. Other studies merged data from several ALS inventory projects to construct large area prediction models for forest attributes such as volume, biomass and dominant height (Gopalakrishnan et al 2015;Kotivuori et al 2016Kotivuori et al , 2018. Tree age is one of the accurately measured sample tree attributes in NFI field data, but it has not been included so far in large scale ALS modelling studies.…”

Section: Introductionmentioning

confidence: 99%

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

et al. 2020

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Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

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Section: Introductionmentioning

confidence: 99%

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

et al. 2020

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“…Numerous efforts have been made to reduce costs of field data acquisition. These include, for example, use of existing plots from previous inventories (Kotivuori et al 2018) and more effective use of the field plot data by applying properties of the ALS data as prior information when selecting locations of the field plots to be measured in field (Hawbaker et al 2009), which is a topic related to model-based sampling.…”

Section: Introductionmentioning

confidence: 99%

Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning

Maltamo¹,

Hauglin²,

Næsset³

et al. 2019

Silva Fenn.

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Accurately positioned single-tree data obtained from a cut-to-length harvester were used as training harvester plot data for k-nearest neighbor (k-nn) stem diameter distribution modelling applying airborne laser scanning (ALS) information as predictor variables. Part of the same harvester data were also used for stand-level validation where the validation units were stands including all the harvester plots on a systematic grid located within each individual stand. In the validation all harvester plots within a stand and also the neighboring stands located closer than 200 m were excluded from the training data when predicting for plots of a particular stand. We further compared different training harvester plot sizes, namely 200 m, 400 m, 900 m and 1600 m. Due to this setup the number of considered stands and the areas within the stands varied between the different harvester plot sizes. Our data were from final fellings in Akershus County in Norway and consisted of altogether 47 stands dominated by Norway spruce. We also had ALS data from the area. We concentrated on estimating characteristics of Norway spruce but due to the k-nn approach, species-wise estimates and stand totals as a sum over species were considered as well. The results showed that in the most accurate cases stand-level merchantable total volume could be estimated with RMSE values smaller than 9% of the mean. This value can be considered as highly accurate. Also the fit of the stem diameter distribution assessed by a variant of Reynoldâs error index showed values smaller than 0.2 which are superior to those found in the previous studies. The differences between harvester plot sizes were generally small, showing most accurate results for the training harvester plot sizes 200 m and 400 m.222222

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“…Sometimes the information obtained from an earlier model may nevertheless be useful in decision making, because several studies have shown that the decreases in accuracy are often moderate (Uuttera et al 2006;Karjalainen et al 2019;Tompalski et al 2019). Another approach is to combine several ALS inventory areas to construct general large-area models for the prediction of stand attributes (Naesset and Gobakken 2008;Nilsson et al 2017;Kotivuori et al 2018;Noordermeer et al 2019). Such models already incorporate data from multiple inventories and ALS sensors, which could result in more reliable estimates as the model coefficients are less dependent on individual sites and sensors.…”

Section: Introductionmentioning

confidence: 99%

Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines

Korhonen¹,

Repola²,

Karjalainen³

et al. 2019

Silva Fenn.

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Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines Korhonen L., Repola J., Karjalainen T., Packalen P., Maltamo M. (2019). Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines. Silva Fennica vol. 53 no. 3 article id 10179. 18 p. https://doi.org/10.14214/ sf.10179 Highlights• Attributes of individual sawlog-sized pines estimated by transferring ALS-based models between sites. • Mixed effects models were more accurate than k-NN imputation tested earlier.• Calibration with a small number of field measured trees improved the accuracy. AbstractAirborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestris L.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1-3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2-6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4-6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications.

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Calibration of nationwide airborne laser scanning based stem volume models

Cited by 19 publications

References 15 publications

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning

Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines

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