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
Abstract
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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References
Aertsen W, Kint V, Van Orshoven J, Özkan K, Muys B, 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model 221(8): 1119-1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007
Aiello S, Kraljevic T, Maj P, 2015. Package 'h2o'. dim, 2, 12.
Asner GP, Scurlock JMA, Hicke J, 2003. Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Global Ecol Biogeogr 12(3): 191-205. https://doi.org/10.1046/j.1466-822X.2003.00026.x
Bahrami B, Hildebrandt A, Thober S, Rebmann C, Fischer R, Samaniego L, et al., 2022. Developing a parsimonious canopy model (PCM v1. 0) to predict forest gross primary productivity and leaf area index. Geoscientific Model Development Discussions, 1-40. https://doi.org/10.5194/gmd-2022-87
Bonan GB, 1993. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sens Environ 43(3): 303-314. https://doi.org/10.1016/0034-4257(93)90072-6
Bramer M, 2002. Using J -pruning to reduce overfitting in classification trees. Knowledge-Based Systems 15(5-6): 301-308. https://doi.org/10.1016/S0950-7051(01)00163-0
Bulut S, Şatır O, Günlü A, 2019. Determining the interactions of black pine net primary productivity and forest stand parameters in northern Turkey. Appl Ecol Environ Res 17(2): 4459-4473. https://doi.org/10.15666/aeer/1702_44594473
Candel A, Parmar V, LeDell E, Arora A, 2016. Deep learning with H2O. H2O. ai Inc.
Cenni E, Bussotti F, Galeotti L, 1998. The decline of a Pinus nigra Arn. reforestation stand on a limestone substrate: the role of nutritional factors examined by means of foliar diagnosis. Ann Sci Forest 55(5): 567-576. https://doi.org/10.1051/forest:19980504
Chen JM, Rich PM, Gower ST, Norman JM, Plummer S, 1997. Leaf area index of boreal forests: Theory, techniques, and measurements. J Geophys Res: Atmospheres 102(D24): 29429-29443. https://doi.org/10.1029/97JD01107
Chen Y, Chen L, Cheng Y, Ju W, Chen HY, Ruan H, 2020. Afforestation promotes the enhancement of forest LAI and NPP in China. For Ecol Manage 462: 117990. https://doi.org/10.1016/j.foreco.2020.117990
Chen Y, Jiao S, Cheng Y, Wei H, Sun L, Sun Y, 2022. LAI-NOS: An automatic network observation system for leaf area index based on hemispherical photography. Agr For Meteorol 322: 108999. https://doi.org/10.1016/j.agrformet.2022.108999
Clough B, Tan DT, Buu DC, 2000. Canopy leaf area index and litter fall in stands of the mangrove Rhizophora apiculata of different age in the Mekong Delta, Vietnam. Aquat Bot 66(4): 311-320. https://doi.org/10.1016/S0304-3770(99)00081-9
Çil B, 2014. Bazı meşcere parametrelerinin farklı uydu görüntüleri yardımıyla tahmin edilmesi: Kelkit ve İğdir Planlama Birimi örneği. Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 72 s., Trabzon.
Davidson DP, 2002. Sensitivity of ecosystem net primary productivity models to remotely sensed leaf area index in a Montane Forest environment. PhD Thesis, Univ. Lethbridge, Lethbridge, Alberta, Canada.
Ercanlı İ, Günlü A, Şenyurt M, Keleş S, 2018. Artificial neural network models predicting the leaf area index: a case study in pure even-aged Crimean pine forests from Turkey. For Ecosyst 5(1): 29. https://doi.org/10.1186/s40663-018-0149-8
Ercanlı İ, 2020. Ankara Orman Bölge Müdürlüğü Anadolu karaçamı meşcereleri için tek ağaç gövde çapı ve gövde hacminin uyumlu gövde çapı denklemleri ve yapay sinir ağları ile tahmin edilmesi. Tübitak, 119O061 nolu 1002 projesi.
GDF, 2021. State of Turkey's Forests 2020. Ministry Agr Forest, General Directorate of Forestry, Forest Management and Planning Dept., Ankara, Türkiye.
Gholz HL, 1982. Environmental limits on aboveground net primary production, leaf area, and biomass in vegetation zones of the Pacific Northwest. Ecology 63(2): 469-481. https://doi.org/10.2307/1938964
Gower ST, Kucharik CJ, Norman JM, 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens Environ 70(1): 29-51. https://doi.org/10.1016/S0034-4257(99)00056-5
Gülbeyaz Ö, 2018. Estimating net primary productivity of forest ecosystems over Turkey using remote sensıng approach. Degree of Doctor of Philosophy, METU, Natural and Applied Sciences.
Günlü A, Keleş S, Ercanlı İ, Şenyurt M, 2017. Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey. Environ Monit Assess 189(11): 538. https://doi.org/10.1007/s10661-017-6254-2
Jagodzinski AM, Kalucka I, 2008. Age-related changes in leaf area index of young Scots pine stands. Dendrobiology 59: 57-65.
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F, 2004. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agr For Meteorol 121(1-2): 19-35. https://doi.org/10.1016/j.agrformet.2003.08.027
Khosravi S, Namiranian M, Ghazanfari H, Shirvani A, 2012. Estimation of leaf area index and assessment of its allometric equations in oak forests: northern Zagros, Iran. J For Sci 58(3): 116-122. https://doi.org/10.17221/18/2011-JFS
Kushida K, Isaev AP, Maximov TC, Takao G, Fukuda M, 2007. Remote sensing of upper canopy leaf area index and forest floor vegetation cover as indicators of net primary productivity in a Siberian larch forest. J Geophys Res: Biogeosci 112(G2). https://doi.org/10.1029/2006JG000269
Li Z, Zhou T, 2015. Optimization of forest age-dependent light-use efficiency and its implications on climate-vegetation interactions in China. ISPRS Archiv 40: 449-454. https://doi.org/10.5194/isprsarchives-XL-7-W3-449-2015
Luo T, Pan Y, Ouyang H, Shi P, Luo J, Yu Z, Lu Q, 2004. Leaf area index and net primary productivity along subtropical to alpine gradients in the Tibetan Plateau. Global Ecol Biogeogr 13(4): 345-358. https://doi.org/10.1111/j.1466-822X.2004.00094.x
Maier HR, Dandy GC, 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15(1): 101-124. https://doi.org/10.1016/S1364-8152(99)00007-9
MathWorks Inc, 2019. Improve shallow neural network generalization and avoid overfitting. Software Manual.
Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F, 2015. Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, 2015. R package vers 1.6-7.
Pan S, Tian H, Dangal SR, Ouyang Z, Lu C, Yang J, et al., 2015. Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century. J Geograph Sci 25(9): 1027-1044. https://doi.org/10.1007/s11442-015-1217-4
Pan N, Wang S, Wei F, Shen M, Fu B, 2021. Inconsistent changes in NPP and LAI determined from the parabolic LAI versus NPP relationship. Ecol Ind 131: 108134. https://doi.org/10.1016/j.ecolind.2021.108134
Peng C, Wen X, 1999. Recent applications of artificial neural networks in forest resource management: an overview. Transfer 1(X2): W1. https://doi.org/10.1109/9780470545355.ch0
Potter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA, 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochem Cycl 7: 811-841. https://doi.org/10.1029/93GB02725
Quinto-Mosquera H, Valois-Cuesta H, Moreno-Hurtado F, 2021. Effects of soil fertilization on the allocation of net primary productivity in tropical rainforests of Chocó, Colombia, Preprints, 2021070522 (doi: 10.20944/preprints202107.0522.v1). https://doi.org/10.20944/preprints202107.0522.v1
R Development Core Team, 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
Raskutti G, Wainwright MJ, Yu B, 2014. Early stopping for non-parametric regression: An optimal data-dependent stopping rule. J Machine Learn Res 15: 335-366.
Scurlock JMO, Asner GP, Gower ST, 2001. Worldwide historical estimates of leaf area index, 1932-2000. ORNL/TM-2001/268, 34. https://doi.org/10.2172/814100
Sprintsin M, Cohen S, Maseyk K, Rotenberg E, Grünzweig J, Karnieli A, et al., 2011. Long term and seasonal courses of leaf area index in a semi-arid forest plantation. Agr For Meteorol 151(5): 565-574. https://doi.org/10.1016/j.agrformet.2011.01.001
Sterenczak K, Lisanczuk M, Parkitna K, Mitelsztedt K, Mroczek P, Misnicki S, 2018. The influence of number and size of sample plots on modelling growing stock volume based on airborne laser scanning. Drewno. Prace Naukowe. Doniesienia. Komunikaty, 61(201).
Wang Y, Yue T, 2022. Make forests better play their role in mitigating climate change. Forests 13(2): 249. https://doi.org/10.3390/f13020249
Warde-Farley D, Goodfellow IJ, Courville A, Bengio Y, 2013. An empirical analysis of dropout in piecewise linear networks. Cornell University, arXiv:1312.6197 [stat.ML].
Yan H, Wang SQ, Billesbach D, Oechel W, Zhang JH, Meyers T, et al., 2012. Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model. Remote Sens Environ 124: 581-595. https://doi.org/10.1016/j.rse.2012.06.004
Yan W, He Y, Cai Y, Qu X, Cui X, 2021. Relationship between extreme climate indices and spatiotemporal changes of vegetation on Yunnan Plateau from 1982 to 2019. Global Ecol Conserv 31: e01813. https://doi.org/10.1016/j.gecco.2021.e01813
Ying X, 2019. An overview of overfitting and its solutions. Journal of Physics: Conference Series 1168(2): 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
Zhang M, Lin H, Wang G, Sun H, Cai Y, 2019. Estimation of vegetation productivity using a Landsat 8 time series in a heavily urbanized area, Central China. Remote Sens 11(2): 133. https://doi.org/10.3390/rs11020133
Zhao Q, Yu S, Zhao F, Tian L, Zhao Z, 2019. Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. For Ecol Manage 434: 224-234. https://doi.org/10.1016/j.foreco.2018.12.019
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