Analysing the Change of Aboveground Biomass Density Using Earth Observation and Machine Learning Technology: Alanya Case

Abstract

Aboveground biomass (AGB) is a key parameter in assessing forest carbon stocks, ecosystem productivity, and the global carbon cycle. This study aims to model the annual AGB change between 2019 and 2024 in Alanya, Türkiye, using remote sensing (RS) technologies and open-source datasets. Sentinel-2 surface reflectance data, slope data derived from the Copernicus GLO-30 Digital Elevation Model (DEM), and GEDI L4A biomass data were utilized. As GEDI point data cannot be directly used for mapping, it was employed as a reference for model training. Spectral bands and vegetation indices from Sentinel-2 imagery were modeled using the Random Forest algorithm. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). The highest total AGB was observed during the 2022–2023 period, while the lowest occurred between 2019–2020. The findings indicate that biomass dynamics in the region are influenced not only by climatic conditions but also significantly by anthropogenic activities. The study presents a remote sensing-based approach to support carbon-neutral strategies through accurate biomass monitoring.

Keywords

• Geographic information systems
• Remote sensing
• Earth observation
• Biomass
• Above ground biomass (AGB)

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

1. Aaslyng JM, Lund JB, Ehler N, Rosenqvist E. 2003. IntelliGrow: a greenhouse component-based climate control system. Environ Model Softw. 18(7): 657–666.
2. Al Saud MM. 2022. Space Techniques for Earth Observation. In: Applications of Space Techniques on the Natural Hazards in the MENA Region. 1: 3–14.
3. Askne JI, Dammert PB, Ulander LM, Smith G. 1997. C-band repeat-pass interferometric SAR observations of the forest. IEEE Trans Geosci Remote Sens. 35(1): 25–35.
4. Cao L, Dubayah R, Zhang Z, Armston J. 2023. Validation of GEDI biomass estimates in Western U.S. forests using field inventory data. Remote Sens Environ. 295: 113630.
5. Da Silveira F, Da Silva SLC, Machado FM, Barbedo JGA, Amaral FG. 2023. Farmers' perception of the barriers that hinder the implementation of agriculture 4.0. Agric Syst. 208: 103656.
6. De Araujo V, Pramreiter M, Christoforo A. 2025. A global policy framework for the circular use of forest biomass as building materials. Nat Rev Mater. 3: 1–3.
7. Duncanson L, Kellner JR, Armston J, Dubayah R, Minor DM, Hancock S, Healey S. 2022. Aboveground biomass density models for NASA's GEDI L2A data. Environ Res Lett. 17(9): 095001.
8. Eckert S. 2012. Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sens. 4(4): 810–829.

9. Evrendilek F, Celik I, Kilic S. 2004. Changes in soil organic carbon and other physical soil properties along adjacent Mediterranean forest, grassland, and cropland ecosystems in Turkey. J Arid Environ. 59(4): 743–752.
10. Foody GM, Cutler ME, McMorrow J, Pelz D, Tangki H, Boyd DS, Douglas IA. 2001. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Glob Ecol Biogeogr. 10(4): 379–387.
11. Franklin J, Hiernaux PH. 1991. Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model. Int J Remote Sens. 12(6): 1387–1404.
12. Gallaun H, Zanchi G, Nabuurs GJ, Hengeveld G, Schardt M, Verkerk PJ. 2010. EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements. For Ecol Manag. 260(3): 252–261.
13. Gilreath JP, Jones JP, Overman AJ. 1994. Soil-borne pest control in mulched tomato with alternatives to methyl bromide. 156–159.
14. Günlü A, Göl C, Sariçam F. 2019. The evaluation of temporal and spatial change of aboveground stand carbon: a case study of upstream of the Göksu river basin. 352–359.
15. Güverçin İ. 2022. Estimation of aboveground biomass in pure red pine stands using Sentinel-1A and Landsat 8 OLI satellite images (Anamur forest sub-district directorate example). PhD thesis, Çankırı Karatekin University, Institute of Science, Çankırı, pp: 65-66.
16. Houghton RA, Hall F, Goetz SJ. 2009. Importance of biomass in the global carbon cycle. J Geophys Res Biogeosci. 114(G2).
17. İşler B, Aslan Z, Sunar F, Güneş A, Feoli E, Gabriels D. 2023. Evaluation of prediction performance of vegetation biomass density for two different case study areas in Turkey with hybrid wavelet and artificial neural network method.
18. İşler B, Aslan Z, Sunar F, Güneş A, Feoli E, Gabriels D. 2024. Hybrid model-based prediction of biomass density in case studies in Turkiye. Ecol Inform. 79: 102439.
19. Keleş NN, Tepebaş B, Keleş S. 2024. Analysis of the temporal changes in the amount of carbon stored and oxygen produced in forest trees. Anat J For Res. 10(2): 16–21.
20. Khan MN, Tan Y, Gul AA, Abbas S, Wang J. 2024. Forest aboveground biomass estimation and inventory: Evaluating remote sensing-based approaches. Forests. 15(6): 1055.
21. Kim CK, Chung JD, Park SH, Burrell AM, Kamo KK, Byrne DH. 2004. Agrobacterium tumefaciens-mediated transformation of Rosa hybrida using the green fluorescent protein (GFP) gene. Plant Cell Tissue Organ Cult. 78: 107–111.
22. Le Toan T, Beaudoin A, Riom J, Guyon D. 1992. Relating forest biomass to SAR data. IEEE Trans Geosci Remote Sens. 30(2): 403–411.
23. Lu D, Batistella M. 2005. Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon. Acta Amazon. 35: 249–257.
24. Muukkonen P, Heiskanen J. 2005. Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sens Environ. 99(4): 434–447.
25. Nelson R, Krabill W, Tonelli J. 1988. Estimating forest biomass and volume using airborne laser data. Remote Sens Environ. 24(2): 247–267.
26. Potapov P, Li X, Hernandez-Serna A, Tyukavina A, Hansen MC, Kommareddy A. 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens.
27. Prentice IC, Farquhar GD, Fasham MJ, Goulden ML, Heimann M, Jaramillo VJ, Kheshgi HS, Le Quéré C, Scholes RJ, Wallace DW, Archer D. 2001. The carbon cycle and atmospheric carbon dioxide. In: Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change.
28. Protocol K. 1997. United Nations framework convention on climate change. Kyoto Protocol. Kyoto. 19(8): 1–21.
29. Ravindranath NH, Ostwald M. 2007. Carbon inventory methods: handbook for greenhouse gas inventory, carbon mitigation and roundwood production projects. Springer, pp: 3.
30. Sader SA, Waide RB, Lawrence WT, Joyce AT. 1989. Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data. Remote Sens Environ. 28: 143–198.
31. Santos JR, Freitas CC, Araujo LS, Dutra LV, Mura JC, Gama FF, Soler LS, Sant'Anna SJ. 2003. Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest. Remote Sens Environ. 87(4): 482–493.
32. Sharma RK, Sankhayan PL, Hofstad O. 2008. Forest biomass density, utilization and production dynamics in a western Himalayan watershed. J For Res. 3: 171–180.
33. Sharma V, Ghosh S, Singh S, Vishwakarma DK, Al-Ansari N, Tiwari RK, Kuriqi A. 2022. Spatial variation and relation of aerosol optical depth with LULC and spectral indices. Atmosphere. 13(12): 1992.
34. Steininger MK. 2000. Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. Int J Remote Sens. 21(6-7): 1139–1157.
35. 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 Int. 37(3): 720–734.
36. Watson RT, Noble IR, Bolin B, Ravindranath NH, Verardo DJ, Dokken DJ. 2000. Land use, land use change, and forestry.
37. Zhang P, Liu H, Li H, Yao J, Chen X, Feng J. 2023. Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology. Front For Glob Change. 6: 1257287.
38. Zhao P, Lu D, Wang G, Liu L, Li D, Zhu J, Yu S. 2016. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. Int J Appl Earth Obs Geoinf. 53: 1–5.
39. Zheng D, Rademacher J, Chen J, Crow T, Bresee M, Le Moine J, Ryu SR. 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens Environ. 93(3): 402–411.
40. Zimble DA, Evans DL, Carlson GC, Parker RC, Grado SC, Gerard PD. 2003. Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sens Environ. 87(2-3): 171–182.