Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye
Aim of study: To examine the relationships between net primary productivity (NPP) and leaf area index (LAI) and modeling these parameters with stand parameters such as stand median diameter (dg), dominant height (htop), number of trees (N), stand basal area (BA) and stand volume (V). Area of stu...
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| Format: | Article |
| Language: | English |
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Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
2023-02-01
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| Series: | Forest Systems |
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| Online Access: | https://revistas.inia.es/index.php/fs/article/view/19615 |
| _version_ | 1797853395025920000 |
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| author | Sinan BULUT Alkan GÜNLÜ Sedat KELES |
| author_facet | Sinan BULUT Alkan GÜNLÜ Sedat KELES |
| author_sort | Sinan BULUT |
| collection | DOAJ |
| description |
Aim of study: To examine the relationships between net primary productivity (NPP) and leaf area index (LAI) and modeling these parameters with stand parameters such as stand median diameter (dg), dominant height (htop), number of trees (N), stand basal area (BA) and stand volume (V).
Area of study: Pure Anatolian black pine (Pinus nigra J.F. Arnold) stands in semi-arid climatic conditions in the Black Sea backward region of Türkiye.
Material and methods: In this study, the Carnegie-Ames-Stanford Approach model was used to calculate NPP; LAI, dg, htop, N, BA and V were calculated in 180 sample plots. The relations between NPP and LAI with stand parameters were modeled using multiple regression analysis, support vector machines (SVM) and deep learning (DL) techniques. Relationships between NPP and LAI were investigated according to stand developmental stages and crown closure classes.
Main results: The highest level of relations was obtained in the stands containing the a-b developmental stages (r=0.84). The most successful model in modeling NPP with stand parameters was obtained by DL method (model R2=0.64, test R2=0.51). Although DL method had higher success in modeling LAI with stand parameters, SVM method was found to be more successful in terms of model-test fit, and modeling success in independent data set.
Research highlights: Grouping parameters affecting NPP and LAI increased the level of correlation between them. In modeling NPP and LAI in relation to stand parameters, machine learning algorithms performed better than linear approach. The overfitting problem can be eliminated substantially by including arguments such as early stopping, network reduction and regularization in the network structure.
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| first_indexed | 2024-04-09T19:49:57Z |
| format | Article |
| id | doaj.art-9372042d89394794a68155e81cfc8313 |
| institution | Directory Open Access Journal |
| issn | 2171-9845 |
| language | English |
| last_indexed | 2024-04-09T19:49:57Z |
| publishDate | 2023-02-01 |
| publisher | Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria |
| record_format | Article |
| series | Forest Systems |
| spelling | doaj.art-9372042d89394794a68155e81cfc83132023-04-03T09:03:26ZengInstituto Nacional de Investigación y Tecnología Agraria y AlimentariaForest Systems2171-98452023-02-0132110.5424/fs/2023321-19615Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from TürkiyeSinan BULUT0Alkan GÜNLÜ1Sedat KELES2Department of Forest Management and Planning, Faculty of Forestry, Çankırı Karatekin University, 18200 Çankırı, TürkiyeDepartment of Forest Management and Planning, Faculty of Forestry, Çankırı Karatekin University, 18200 Çankırı, TürkiyeDepartment of Forest Management and Planning, Faculty of Forestry, Çankırı Karatekin University, 18200 Çankırı, Türkiye Aim of study: To examine the relationships between net primary productivity (NPP) and leaf area index (LAI) and modeling these parameters with stand parameters such as stand median diameter (dg), dominant height (htop), number of trees (N), stand basal area (BA) and stand volume (V). Area of study: Pure Anatolian black pine (Pinus nigra J.F. Arnold) stands in semi-arid climatic conditions in the Black Sea backward region of Türkiye. Material and methods: In this study, the Carnegie-Ames-Stanford Approach model was used to calculate NPP; LAI, dg, htop, N, BA and V were calculated in 180 sample plots. The relations between NPP and LAI with stand parameters were modeled using multiple regression analysis, support vector machines (SVM) and deep learning (DL) techniques. Relationships between NPP and LAI were investigated according to stand developmental stages and crown closure classes. Main results: The highest level of relations was obtained in the stands containing the a-b developmental stages (r=0.84). The most successful model in modeling NPP with stand parameters was obtained by DL method (model R2=0.64, test R2=0.51). Although DL method had higher success in modeling LAI with stand parameters, SVM method was found to be more successful in terms of model-test fit, and modeling success in independent data set. Research highlights: Grouping parameters affecting NPP and LAI increased the level of correlation between them. In modeling NPP and LAI in relation to stand parameters, machine learning algorithms performed better than linear approach. The overfitting problem can be eliminated substantially by including arguments such as early stopping, network reduction and regularization in the network structure. https://revistas.inia.es/index.php/fs/article/view/19615machine learning algorithmssupport vector machinesdeep learning |
| spellingShingle | Sinan BULUT Alkan GÜNLÜ Sedat KELES Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye Forest Systems machine learning algorithms support vector machines deep learning |
| title | Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye |
| title_full | Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye |
| title_fullStr | Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye |
| title_full_unstemmed | Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye |
| title_short | Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye |
| title_sort | assessment of the interactions among net primary productivity leaf area index and stand parameters in pure anatolian black pine stands a case study from turkiye |
| topic | machine learning algorithms support vector machines deep learning |
| url | https://revistas.inia.es/index.php/fs/article/view/19615 |
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