SavedAdd your journal

Journals / Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi / 2026 / Cilt: 27 Sayı: 1

Modeling the Relationship Between Aboveground Biomass and Leaf Area Index in Pure Turkish Red Pine (Pinus brutia Ten.) Stands: A Case Study from Sarıyayla Forest Planning Unit

Saf Kızılçam (Pinus brutia Ten.) Meşcerelerinde Topraküstü Biyokütle ile Yaprak Alan İndeksi Arasındaki İlişkilerin Modellenmesi: Sarıyayla Orman İşletme Şefliği Örneği

Serkan Yayla(Orman Genel Müdürlüğü)0000-0003-1398-8495
Alkan Gunlu(ÇANKIRI KARATEKİN ÜNİVERSİTESİ)0000-0003-4759-3125
Pages
175–188
Publication Date
2026-02-28
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings suggest that the developed regression models hold potential for estimating aboveground biomass in pure Pinus brutia stands; however, further comprehensive and multidisciplinary research is required to enhance model performance and applicability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study was conducted in pure Turkish red pine (Pinus brutia Ten.) stands within the boundaries of Mersin Regional Directorate of Forestry, Anamur Forest Enterprise, Sarıyayla Forest Planning Unit.
📊
Key Findings & Data:Data from a total of 257 sample plots were utilized in the analysis in the study.
💡
Conclusion & Impact:The findings suggest that the developed regression models hold potential for estimating aboveground biomass in pure Pinus brutia stands; however, further comprehensive and multidisciplinary research is required to enhance model performance and applicability.
🔬Design:Modeling & Simulation
📍Scope:2015 · 257 sample
⚙️Variables:Sentinel-2 · Regresyon analizi · Orman kaynakları

Abstract

This study was conducted in pure Turkish red pine (Pinus brutia Ten.) stands within the boundaries of Mersin Regional Directorate of Forestry, Anamur Forest Enterprise, Sarıyayla Forest Planning Unit. Data from a total of 257 sample plots were utilized in the analysis in the study. The aboveground biomass (AGB) of each sample plot was estimated using a single-entry biomass equation developed specifically for pure Calabrian pine stands by Şahin (2015). In addition to inventory data, monthly raster datasets of Leaf Area Index (LAI) were produced using Sentinel-2 satellite imagery, excluding the month of January (due to the lack of sufficiently high-quality imagery). Each sample plot was geographically overlaid onto the monthly LAI images, and for each month, statistical variables including maximum, minimum, mean, total, range, and standard deviation of LAI were calculated for every sample plot. The relationships between AGB values and the LAI-derived variables (both every month and across all months) were modeled using multiple regression analysis. In total, 12 regression models were developed. The coefficient of determination (R²) values of the monthly models ranged from 0.10 to 0.178, while the model incorporating data from all months yielded a substantially higher R² value of 0.444. The findings suggest that the developed regression models hold potential for estimating aboveground biomass in pure Pinus brutia stands; however, further comprehensive and multidisciplinary research is required to enhance model performance and applicability.

Özet

Bu çalışma, Mersin Orman Bölge Müdürlüğü’ne bağlı Anamur Orman İşletme Müdürlüğü, Sarıyayla Orman İşletme Şefliği sınırları içerisinde yayılış gösteren saf kızılçam (Pinus brutia Ten.) meşcerelerinde gerçekleştirilmiştir. Çalışma kapsamında toplam 257 adet örnek alandan elde edilen envanter verileri kullanılmıştır. Her bir örnek alanın topraküstü biyokütle (TÜB) değeri, Şahin (2015) tarafından saf kızılçam meşcereleri için geliştirilmiş olan tek girişli biyokütle denklemi kullanılarak hesaplanmıştır. Envanter verilerine ek olarak, Ocak ayı hariç (yeterli kalitede uydu görüntüsü bulunmadığı için) olmak üzere her ay için Sentinel-2 uydu görüntülerinden yaprak alan indeksi (YAİ) raster verileri türetilmiştir. Her bir örnek alan, aylık YAİ görüntüleri üzerine coğrafi olarak aktarılmış ve buna bağlı olarak her bir örnek alan için aylık bazda YAİ değişkenlerinin maksimum, minimum, ortalama, toplam, değişim aralığı ve standart sapma değerleri hesaplanmıştır. TÜB ile her bir ay ve tüm aylara ilişkin YAİ değişkenleri arasındaki ilişkiler çoğul regresyon analizi ile modellenmiştir. Bu kapsamda toplam 12 adet regresyon modeli geliştirilmiştir. Aylara göre geliştirilen modellerde modellerin belirleme katsayısı (R²) 0,10 ile 0,178 arasında değişirken, tüm aylara ait verilerin birlikte değerlendirildiği modelde ise R² değeri 0,444 olarak bulunmuştur. Elde edilen bulgular, geliştirilen regresyon modellerinin saf kızılçam meşcerelerinde topraküstü biyokütlenin tahmininde potansiyel sunduğunu, ancak model performansının artırılması için daha kapsamlı ve çok yönlü bilimsel araştırmalara ihtiyaç duyulduğunu göstermektedir.

🔖

Cite This Article

Yayla, S., Gunlu, A. (2026). Modeling the Relationship Between Aboveground Biomass and Leaf Area Index in Pure Turkish Red Pine (Pinus brutia Ten.) Stands: A Case Study from Sarıyayla Forest Planning Unit. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, Cilt: 27 Sayı: 1, 175–188. https://doi.org/10.17474/artvinofd.1676490

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (53)

  1. Aksoy H (2023) Sinop Orman Bölge Müdürlüğü saf sarıçam meşcerelerinde farklı uzaktan algılama verileri kullanılarak bazı meşcere parametrelerinin modellenmesi. Çankırı Karatekin Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi, Orman Mühendisliği Anabilim Dalı, Çankırı, 172 s.
  2. Aksoy H, Günlü A (2025) UAV and satellite-based prediction of aboveground biomass in scots pine stands: a comparative analysis of regression and neural network approaches. Earth Science Informatics, 18(1): 1–24. https://doi.org/10.1007/s12145-024-01657-0
  3. Albaugh TJ, Allen HL, Dougherty PM, Kress LW, King JS (1998) Leaf area and above- and belowground growth responses of loblolly pine to nutrient and water additions. Forest Science, 44: 317–328. https://doi.org/10.1093/forestscience/44.2.317
  4. Alemdağ İŞ (1981) Aboveground-mass equations for six hardwood species from natural stands of the research forest at Petawawa. Canadian Forestry Service Information Report, PI-X-6, Canada.
  5. Andalibi L, Ghorbani A, Moameri M, Hazbavi Z, Nothdurft A, Jafari R, Dadjou F (2021) Leaf area index variations in ecoregions of Ardabil province, Iran. Remote Sensing, 13(15):2879. https://doi.org/10.3390/rs13152879
  6. Anonim (2016) Mersin Orman Bölge Müdürlüğü, Anamur Orman İşletme Müdürlüğü, Sarıyayla Orman İşletme Şefliği, Fonksiyonel Orman Amenajman Planı. Orman Genel Müdürlüğü, Orman İdaresi ve Planlama Dairesi, Ankara.
  7. Atwell BJ, Kriedeman PE, Turnbull GN (1999) Plants in Action: Adaptation in Nature, Performance in Cultivation (s. 664). Macmillan, Avustralya.
  8. Baccini A, Laporte N, Goetz SJ, Sun M, Dong H (2008) A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters, 3: 045011. https://doi.org/10.1088/17489326/3/4/045011
  9. Batu F (1995) Uygulamalı istatistik yöntemler. Karadeniz Teknik Üniversitesi Orman Fakültesi Genel Yayın No: 179, Fakülte Yayın No: 22, Trabzon.
  10. Berterretche M, Hudak AT, Cohen WB, Maiersperger TK, Gower ST, Dungan J (2005) Comparison of regression and geostatistical methods for mapping leaf area index (LAI) with Landsat ETM+ data over a boreal forest. Remote Sensing of Environment, 96: 49–61. https://doi.org/10.1016/j.rse.2005.01.014
  11. Boegh E, Soegaard H, Broge N, Hasager CB, Jensen NO, Schelde K, Thomsen A (2002) Airborne multispectral data for quantifying leaf area index nitrogen concentration and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 81(2–3):179–193. https://doi.org/10.1016/S0034-257(01)00342-X
  12. Boyd DS, Wicks TE, Curran PJ (2000) Use of middle infrared radiation to estimate the leaf area index of a boreal forest. Tree Physiology, 20:755–760. https://doi.org/10.1093/treephys/20.11.755
  13. Brown S (2002) Measuring carbon in forests: current status and future challenges. Environmental Pollution, 16:363–372. https://doi.org/10.1016/S02697491(01)00212-3
  14. Bulut S, Günlü A, Sönmez MY (2021) Saf karaçam meşcerelerinde yaprak alan indeksi ile meşcere parametreleri arasındaki ilişkilerin modellenmesi. Bartın Orman Fakültesi Dergisi, 23(3): 980–989. https://doi.org/10.24011/barofd.1002569
  15. Bulut S (2023) Machine learning prediction of above-ground biomass in pure calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region, Türkiye. Ecological Informatics, 74: 101951. https://doi.org/10.1016/j.ecoinf.2022.101951
  16. Bulut S, Günlü A, Çakır G (2023) Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye. Geocarto Internatıonal, 38 (1): 2158238.https://doi.org/10.1080/10106049.2022.2158238
  17. Chen JM, Pavlic G, Brown L, Cihlar J, Leblanc SG, White HP, Hall RJ, Peddle DR, King DJ, Trofymow JA, Swift E, Van Der Sanden J, Pellikka PKE (2002) Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment, 80:165–184. https://doi.org/10.1016/S00344257(01)00300-5
  18. Colombo R, Bellingeri D, Fasolini D, Marino CM (2003) Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, 86:120–131. https://doi.org/10.1016/S0034-4257(03)00094-4
  19. Demir S, Günlü A (2024) Sentinel-2 uydu görüntüsü kullanılarak saf kavak meşcerelerinde meşcere hacmi ve göğüs yüzeyinin tahmin edilmesi. Anadolu Orman Araştırmaları Dergisi, 10(2):78-86. https://doi.org/10.53516/ajfr.1558255
  20. 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. Forest Ecosystems, 5: 1–12. https://doi.org/10.1186/s40663-018-0149-8
  21. Gonçalves AO, Da Silva EHFM, Gasparotto LG, Rosa JM, do Carmo S, Júnior IMF, Marin FR (2020) Improving indirect measurements of the leaf area index using canopy height. Pesqui. Agropecu. Bras., 55:1–9. https://doi.org/10.1590/s16783921.pab2020.v55.01894
  22. Günlü A, Ercanli İ, Başkent EZ, Çakır G (2014) Estimating aboveground biomass using Landsat TM imagery: a case study of Anatolian Crimean pine forests in Turkey. Annals of Forest Research, 57(2):289-298. https://doi.org/10.15287/afr.2014.278
  23. 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. Environmental Monitoring and Assessment, 189:1-11. https://doi.org/10.1007/s10661-017-6254-2
  24. Günlü A, Ercanlı İ (2020) Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey. Geocarto International, 35(1):17-28. https://doi.org/10.1080/10106049.2018.1499817
  25. Günlü A, Bulut S (2023) Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands. International Journal of Environmental Science and Technology, 20(5):5471-5490. https://doi.org/10.1007/s13762-022-04552-7
  26. Heiskanen J (2006) Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, 27(6):1135-1158. https://doi.org/10.1080/01431160500353858
  27. Kalıpsız A (1984) Dendrometri. İstanbul Üniversitesi Orman Fakültesi Yayınları, İÜ Yayın No: 3194, OF Yayın No:354, İstanbul, 407 s.
  28. Liu Q, Huete A (1995) A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE T. Geosci. Remote Sens., 33 (2):457-465. https://doi.org/10.1109/TGRS.1995.8746027
  29. Lu D, Batistella M (2005) Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon. Acta Amozonica, 35(2): 249-257. https://doi.org/10.1590/S0044-59672005000200015
  30. Ma H, Song J, Wang J, Xiao Z, Fu Z (2014) Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LİDAR and remote sensing multi-angle optical data. Agric. For. Meteorol., 189–190:60–70. https://doi.org/10.1016/j.agrformet.2014.01.009
  31. Nguyen TH, Jones S, Soto-Berelov M, Haywood A, Hislop S (2020) Landsat time-series for estimating forest aboveground biomass and its dynamics across space and time: a review. Remote Sens., 12(1):98. https://doi.org/10.3390/rs12010098
  32. Özbayram AK, Çiçek E, Yılmaz F (2015) Kızılçam ve Karaçam meşcerelerinde yaprak alanı indeksi (YAİ) ile bazı meşcere özellikleri arasındaki ilişkiler. Kastamonu University Journal of Forestry Faculty, 15(1):78-85. https://doi.org/10.17475/kuofd.23267
  33. Özdemir İ, Karnieli A (2011) Predicting forest structural parameters using the image texture derived from worldview-2 multispectral imagery in a dryland forest, Israel. International Journal of Applied Earth Observation and Geoinformation, 13: 701-710. https://doi.org/10.1016/j.jag.2011.05.006
  34. Parker GG (2020) Tamm review: leaf area ındex (LAI) is both a determinant and a consequence of important processes in vegetation canopies. Forest Ecology and Management, 477: 118496. https://doi.org/10.1016/j.foreco.2020.118496
  35. Peichl M, Arain MA (2007) Allometry and partitioning of above and belowground tree biomass in an age-sequence of white pine forests. Forest Ecology and Management, 253: 68-80. https://doi.org/10.1016/j.foreco.2007.07.003
  36. Running S, Baldocchi D, Gower S, Bakwin P, Hibbard K (1999) A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS data. Remote Sensing of Environment, 70:108-127. https://doi.org/10.1016/S00344257(99)00061-9
  37. Sakıcı OE, Ercanlı İ, Kahriman A (2004) Klasik biyokütle tahmin yöntemleri ve yeni yaklaşımlar. Kafkas Üniversitesi Artvin Orman Fakültesi Dergisi, 3-4: 165-171.
  38. Sakıcı OE, Günlü A (2018) Artificial intelligence applications for predicting some stand attributes using Landsat 8 OLI satellite data: a case study from Turkey. Appl Ecol Env Res., 16(4):5269–5285. https://doi.org/10.15666/aeer/1604_52695285
  39. Saraçoğlu N (1988) Kızılağaç (Alnus glutinosa var. barbata (C. A. Mey.) Ledeb.) gövde hacim ve biyokütle tablolarının düzenlenmesi. Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi, Trabzon.
  40. SPSS (2007) SPSS Base 270 User’s Guide. 770 s.
  41. Sola I, García-Martín A, Sandonís-Pozo L, Álvarez-Mozos J, Pérez-Cabello F, González-Audícana M, Llovería RM (2018) Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes. International Journal of Applied Earth Observation and Geoinformation, 73:63-76. https://doi.org/10.1016/j.jag.2018.05.020
  42. Song C (2012) Optical remote sensing of forest leaf area index and biomass. Progress in Physical Geography, 237: 98–113. https://doi.org/10.1177/03091333124713
  43. Sun O, Uğurlu S, Özer E (1980) Kızılçam türüne ait biyolojik kütlenin saptanması. OAE Teknik Bülten, No:104, Ankara.
  44. Sun Y, Qin Q, Ren H, Zhang T, Chen S (2020) Red-edge band vegetation ındices for leaf area ındex estimation from Sentinel-2/MSI imagery. IEEE Transactions on Geoscience and Remote Sensing, 58: 826–840. https://doi.org/10.1109/TGRS.2019.2940826
  45. Şahan E, Günlü A (2024) Aktif ve pasif uydu görüntüleri kullanılarak saf karaçam meşcerelerinde topraküstü karbonun tahmin edilmesi. Bartın Orman Fakültesi Dergisi, 26(4):377-391. https://doi.org/10.24011/barofd.1544831
  46. Şahin A (2015) Mersin yöresi saf kızılçam (Pinus brutia Ten.) meşcerelerinde hasılat araştırmaları. Artvin Çoruh Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi, Artvin.
  47. Turgut R, Günlü A (2022) Estimating aboveground biomass using Landsat 8 OLI satellite image in pure Crimean pine (Pinus nigra JF Arnold subsp. pallasiana (Lamb.) Holmboe) stands: a case from Turkey. Geocarto International, 37(3): 720–734. https://doi.org/10.1080/10106049.2020.1737971
  48. Tuzet A, Perrier A, Leuning R (2003) A coupled model of stomatal conductance, photosynthesis and transpiration. Plant, Cell and Environment, 26(7):1097–1116. https://doi.org/10.1046/j.13653040.2003.01035.x
  49. Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185: 46-56. https://doi.org/10.1016/j.rse.2016.04.008
  50. Vogt K (1991) Carbon budgets of temperate forest ecosystems. Tree Physiology, 9:69–86. https://doi.org/10.1093/treephys/9.1-2.69
  51. White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW (2017) A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using landsat time series. Remote Sens Environ., 194:303–321. https://doi.org/10.1016/j.rse.2017.03.035
  52. Xin Q, Dai Y, Li X, Liu X, Gong P, Richardson AD (2018) A steady-state approximation approach to simulate seasonal leaf dynamics of deciduous broadleaf forests via climate variables. Agricultural and Forest Meteorology, 249:44-56.https://doi.org/10.1016/j.agrformet.2017.11.025
  53. Yer MB (2021) Kastamonu yöresi karaçam meşcerelerinde yaprak alan indeksi ile çeşitli meşcere özellikleri arasındaki ilişkilerin belirlenmesi. Kastamonu Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, Kastamonu.