Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model
Accurate predictions of forest plantation growth provide forest managers with improved forest inventory estimates, forest valuation, and timely harvest schedules. Forest process-based models are increasingly used for quantifying current and potential productivity, yield gaps, and factors limiting gr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Forests and Global Change |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/ffgc.2023.1181049/full |
| _version_ | 1797776199408156672 |
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| author | Peter Taylor Auro C. Almeida Ernst Kemmerer Rafael Olivares de Salles Abreu |
| author_facet | Peter Taylor Auro C. Almeida Ernst Kemmerer Rafael Olivares de Salles Abreu |
| author_sort | Peter Taylor |
| collection | DOAJ |
| description | Accurate predictions of forest plantation growth provide forest managers with improved forest inventory estimates, forest valuation, and timely harvest schedules. Forest process-based models are increasingly used for quantifying current and potential productivity, yield gaps, and factors limiting growth, such as climate variability, soil characteristics, and water deficit. Improvements in the availability and resolution of spatial and temporal data combined with advancements in machine learning algorithms provide new opportunities to improve model predictions. This study shows how interpretable machine learning (ML) can be used to independently predict site soil fertility rating (FR) and incorporate these results into the 3-PG forest process-based model to accurately predict plantation growth. Four ensemble decision tree machine learning models—random forest trees, extremely randomized trees, gradient boost, and XG boost—were trained and compared using spatial cross-validation across the study area. FR predictions were estimated in relation to the influencing soil type and terrain characteristics, and interpretable ML methods were used to show how input feature permutations may relate to the soil fertility predictions. The results show the explanatory variables are similar across the selected ML models, with the strongest influencing variables being water leaching index, site aspect, and the silt and sand soil texture properties. The extremely randomized tree models showed the overall best performance, with only a small variation in performance across the four ML models. The method was applied to Eucalyptus nitens plantations covering over 63,000 ha in north-west Tasmania, Australia. The results using the predicted FR spatial grid with 3-PG show a strong correlation with observed growth for tree diameter and stand volume (R2 tree diameter at breast height = 0.97, RMSE = 0.85 m; R2 stand volume = 0.96, RMSE = 23.1 m3 ha−1) obtained from 161 permanent sample inventory plots ranging from 3 to 31 years old. This method has practical utility for other study sites to calibrate forest plantation soil fertility rating, in both the spatial and point-scale 3-PG model, where spatial data of soil characteristics are available. The derived soil fertility grid can provide valuable insights into the spatial variability of soil fertility in unknown areas. |
| first_indexed | 2024-03-12T22:46:31Z |
| format | Article |
| id | doaj.art-c96fcd7e6e4648579211129e7547538d |
| institution | Directory Open Access Journal |
| issn | 2624-893X |
| language | English |
| last_indexed | 2024-03-12T22:46:31Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Forests and Global Change |
| spelling | doaj.art-c96fcd7e6e4648579211129e7547538d2023-07-21T05:01:08ZengFrontiers Media S.A.Frontiers in Forests and Global Change2624-893X2023-07-01610.3389/ffgc.2023.11810491181049Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG modelPeter Taylor0Auro C. Almeida1Ernst Kemmerer2Rafael Olivares de Salles Abreu3CSIRO, Sandy Bay, TAS, AustraliaCSIRO, Sandy Bay, TAS, AustraliaForico Pty Limited, Kings Meadows, TAS, AustraliaForico Pty Limited, Kings Meadows, TAS, AustraliaAccurate predictions of forest plantation growth provide forest managers with improved forest inventory estimates, forest valuation, and timely harvest schedules. Forest process-based models are increasingly used for quantifying current and potential productivity, yield gaps, and factors limiting growth, such as climate variability, soil characteristics, and water deficit. Improvements in the availability and resolution of spatial and temporal data combined with advancements in machine learning algorithms provide new opportunities to improve model predictions. This study shows how interpretable machine learning (ML) can be used to independently predict site soil fertility rating (FR) and incorporate these results into the 3-PG forest process-based model to accurately predict plantation growth. Four ensemble decision tree machine learning models—random forest trees, extremely randomized trees, gradient boost, and XG boost—were trained and compared using spatial cross-validation across the study area. FR predictions were estimated in relation to the influencing soil type and terrain characteristics, and interpretable ML methods were used to show how input feature permutations may relate to the soil fertility predictions. The results show the explanatory variables are similar across the selected ML models, with the strongest influencing variables being water leaching index, site aspect, and the silt and sand soil texture properties. The extremely randomized tree models showed the overall best performance, with only a small variation in performance across the four ML models. The method was applied to Eucalyptus nitens plantations covering over 63,000 ha in north-west Tasmania, Australia. The results using the predicted FR spatial grid with 3-PG show a strong correlation with observed growth for tree diameter and stand volume (R2 tree diameter at breast height = 0.97, RMSE = 0.85 m; R2 stand volume = 0.96, RMSE = 23.1 m3 ha−1) obtained from 161 permanent sample inventory plots ranging from 3 to 31 years old. This method has practical utility for other study sites to calibrate forest plantation soil fertility rating, in both the spatial and point-scale 3-PG model, where spatial data of soil characteristics are available. The derived soil fertility grid can provide valuable insights into the spatial variability of soil fertility in unknown areas.https://www.frontiersin.org/articles/10.3389/ffgc.2023.1181049/fullinterpretable machine learningforestry modeling3-PG modelEucalyptus nitenssoil fertility ratingplantation growth and yield |
| spellingShingle | Peter Taylor Auro C. Almeida Ernst Kemmerer Rafael Olivares de Salles Abreu Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model Frontiers in Forests and Global Change interpretable machine learning forestry modeling 3-PG model Eucalyptus nitens soil fertility rating plantation growth and yield |
| title | Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model |
| title_full | Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model |
| title_fullStr | Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model |
| title_full_unstemmed | Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model |
| title_short | Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model |
| title_sort | improving spatial predictions of eucalypt plantation growth by combining interpretable machine learning with the 3 pg model |
| topic | interpretable machine learning forestry modeling 3-PG model Eucalyptus nitens soil fertility rating plantation growth and yield |
| url | https://www.frontiersin.org/articles/10.3389/ffgc.2023.1181049/full |
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