Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning
Abstract 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 se...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2020-07-01
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| Series: | Forest Ecosystems |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40663-020-00254-z |
| _version_ | 1797969043361103872 |
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| author | Matti Maltamo Hermanni Kinnunen Annika Kangas Lauri Korhonen |
| author_facet | Matti Maltamo Hermanni Kinnunen Annika Kangas Lauri Korhonen |
| author_sort | Matti Maltamo |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T02:55:57Z |
| format | Article |
| id | doaj.art-17ba044dcee6466d879a43d5cd0a4c59 |
| institution | Directory Open Access Journal |
| issn | 2197-5620 |
| language | English |
| last_indexed | 2024-04-11T02:55:57Z |
| publishDate | 2020-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Forest Ecosystems |
| spelling | doaj.art-17ba044dcee6466d879a43d5cd0a4c592023-01-02T15:09:43ZengKeAi Communications Co., Ltd.Forest Ecosystems2197-56202020-07-017111110.1186/s40663-020-00254-zPredicting stand age in managed forests using National Forest Inventory field data and airborne laser scanningMatti Maltamo0Hermanni Kinnunen1Annika Kangas2Lauri Korhonen3University of Eastern Finland, School of Forest SciencesUniversity of Eastern Finland, School of Forest SciencesNatural Resources Institute Finland, Bioeconomy and EnvironmentUniversity of Eastern Finland, School of Forest SciencesAbstract 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.http://link.springer.com/article/10.1186/s40663-020-00254-zForest stock ageLiDARNFINationwide modelGrowth conditions |
| spellingShingle | Matti Maltamo Hermanni Kinnunen Annika Kangas Lauri Korhonen Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning Forest Ecosystems Forest stock age LiDAR NFI Nationwide model Growth conditions |
| title | Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning |
| title_full | Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning |
| title_fullStr | Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning |
| title_full_unstemmed | Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning |
| title_short | Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning |
| title_sort | predicting stand age in managed forests using national forest inventory field data and airborne laser scanning |
| topic | Forest stock age LiDAR NFI Nationwide model Growth conditions |
| url | http://link.springer.com/article/10.1186/s40663-020-00254-z |
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