Aerial imagery and UAV-LiDAR data fusion for quantifying aboveground carbon stock of teak plantation

Soraya, Emma; Umarhadi, Deha Agus; Wardhana, Wahyu; Senawi; Ardiansyah, Fiqri

doi:10.12912/27197050/197151

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Tytuł artykułu

Aerial imagery and UAV-LiDAR data fusion for quantifying aboveground carbon stock of teak plantation

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Soraya Emma , Umarhadi Deha Agus , Wardhana Wahyu , Senawi , Ardiansyah Fiqri

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DOI

10.12912/27197050/197151

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Abstrakty

This study examines four regression models to estimate tree-level above-ground carbon stock (AGC) in the clonal teak plantations of Wanagama Forest, Indonesia using a data fusion of UAV-LiDAR and RGB aerial imagery. Data collected in the field were diameter at breast height and tree height. Vegetation indices were derived from the visible bands of a georeferenced orthomosaic captured by DJI Mavic 2 Pro. The LiDAR data was obtained using GeoSun GS-100M device mounted on a quadcopter drone. Spectral features included RGB bands and vegetation indices, while vertical features consisted of a canopy height model (rasterized of normalized LiDAR point clouds) and relative height of LiDAR data. Additionally, the teak crown diameter was derived from object-based segmentation. Our study shows that crown diameter, Z max, and one of the vegetation indices are the three most influential features in estimating the AGC of individual tree in all models. Multiple linear regression (MLR) with eleven independent variables outperformed other models, showing a determination coefficient of 0.73 and providing the most accurate predictions of individual tree AGC, with MAE = 28.35 kg and WAPE = 34.13%. The teak stand predicted using MLR shows an average AGC of 64.15 Mg ha^-1. The regression models developed are accurate for clonal teak plantations and provide a non-destructive way to monitor and manage carbon stocks as the basis for forest management and conservation strategies. However, the LiDAR and RGB spectral features may not capture all relevant tree characteristics, since this study acquired the airborne data during a teak leaf-off season, which may be challenging to distinguish between individual tree causing overestimated tree numbers. Teak sheds its leaves during drought thus similar studies at leaf-on conditions might adequately represent the characteristics of the teak tree.

Słowa kluczowe

regression point clouds visible vegetation indices clonal teak spectral

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2025

Tom

Vol. 26, iss. 2

Strony

178--192

Opis fizyczny

Bibliogr. 58 poz., rys., tab.

Twórcy

autor

Soraya Emma

esoraya@ugm.ac.id

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

https://orcid.org/0000-0001-9764-3800

autor

Umarhadi Deha Agus

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia
Department of Biology, Ludwig Maximilian University of Munich, Munich, Germany

https://orcid.org/0000-0001-9478-9986

autor

Wardhana Wahyu

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

https://orcid.org/0000-0003-2721-268X

autor

Senawi

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

https://orcid.org/0009-0005-3051-8606

autor

Ardiansyah Fiqri

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

https://orcid.org/0009-0001-8617-8461

Bibliografia

1. Afafi, S. N., K. I. Supartha, H. Fatmawati, N. H. E. Sari, F. Y. Al-Husna, F. D. Himawan, M. Aulia, M. B. Ardiansyah, and B. Mulyana. (2022). Carbon Storage of Superior Clonal Teak Stand in Special Purpose Forest Area of Wanagama, Special Region of Yogyakarta. Jurnal Galam 2(2), 66–76.
2. Awad, Mariette, and Rahul Khanna. (2015). Support Vector Regression. 67–80 in Efficient Learning Machines. Berkeley, CA: Apress.
3. Baatz, M., and A. Schäpe. (2000). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. 12–23 in Angewandte Geographische Informations-Verarbeitung XII, edited by J. Strobl, T. Blaschke, and G. Griesebner. Wichmann Verlag, Heidelberg.
4. Balestra, M., Marselis, S., Sankey, T.T., Cabo, C., Liang, X., Mokroš, M., Peng, X., Singh, A., Stereńczak, K., Vega, C., Vincent, G. and Hollaus, M. (2024). LiDAR data fusion to improve forest attribute estimates: A review. Current Forestry Reports 10(4), 281–97. https://doi.org/10.1007/ s40725-024-00223-7
5. Behera, M., and Mohapatra, N.P. (2015). Biomass accumulation and carbon stocks in 13 different clones of teak (Tectona Grandis Linn. F.) in Odisha, India. Current World Environment 10(3), 1011–16. https://doi.org/10.12944/CWE.10.3.33
6. Benoit, K. (2011). Linear Regression Models with Logarithmic Transformations.
7. Cao, L., Liu, H., Fu, X., Zhang, Z., Shen, X. and Ruan, H. (2019). Comparison of UAV LiDAR and digital aerial photogrammetry point clouds for estimating forest structural attributes in subtropical planted forests. Forests 10(2), 145. https://doi.org/10.3390/f10020145
8. Chave, J., Davies, S.J., Phillips, O.L., Lewis, S.L., Sist, P., Schepaschenko, D., Armston, J., Baker, T.R., Coomes, D., Disney, M., Duncanson, L., Hérault, B., Labrière, N., Meyer, V., Réjou-Méchain, M.,
Scipal, K. and Saatchi, S. (2019). Ground data are essential for biomass remote sensing missions. Surveys in Geophysics 40(4), 863–80. https://doi.org/10.1007/s10712-019-09528-w
9. Chayaporn, P., Sasaki, N., Venkatappa, M. and Abe, I. 2021. Assessment of the overall carbon storage in a teak plantation in Kanchanaburi Province, Thailand – Implications for carbon-based incentives. Cleaner Environmental Systems 2, 100023. https://doi.org/10.1016/j.cesys.2021.100023
10. Chen, Q., Gao, T., Zhu, J., Wu, F., Li, X., Lu, D. and Yu, F. (2022). Individual tree segmentation and tree height estimation using leaf-off and leaf-on UAV-lidar data in dense deciduous forests. Remote Sensing 14(12), 2787. https://doi.org/10.3390/rs14122787
11. Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. 785–94 in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA: ACM.
12. Costa, L., Nunes, L. and Ampatzidis, Y. (2020). A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture 172, 105334. https://doi.org/10.1016/j. compag.2020.105334
13. Darmawan, W., Nandika, D., Sari, R.K., Sitompul, A., Rahayu, I., and Gardner, D. (2015). Juvenile and mature wood characteristics of short and long rotation teak in Java. IAWA Journal 36(4), 428–42. https://doi.org/10.1163/22941932-20150112
14. Du, L., Pang, Y., Wang, Q., Huang, C., Bai, Y., Chen, D., Lu, W. and Kong, D. (2023). A LiDAR Biomass Index-Based Approach for Tree- and Plot-Level Biomass Mapping over Forest Farms Using 3D Point Clouds. Remote Sensing of Environment 290, 113543. https://doi.org/10.1016/j.rse.2023.113543
15. Evans, J.S., and Hudak, A.T. (2007). A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45(4), 1029–38. https://doi.org/10.1109/TGRS.2006.890412
16. Fekry, R., Yao, W., Cao, L. and Shen, X. (2022). Ground-Based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest. Forest Ecosystems 9, 100065. https://doi.org/10.1016/j.fecs.2022.100065
17. Fraser, B., and Congalton, R. (2018). Issues in unmanned aerial systems (UAS) data collection of complex forest environments. Remote Sensing 10(6), 908. https://doi.org/10.3390/rs10060908
18. Fu, H., Zhao, H., Jiang, J., Zhang, Y., Liu, Ge., Xiao, W., Du, S., Guo, W. and Liu, X. (2024). Automatic detection tree crown and height using mask R-CNN based on unmanned aerial vehicles images for biomass mapping. Forest Ecology and Management 555, 121712. https://doi.org/10.1016/j. foreco.2024.121712
19. Gitelson, Anatoly A., Yoram J. Kaufman, Robert Stark, and Don Rundquist. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9
20. Gleason, C.J., and Im, J. (2012). Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment 125, 80–91. https://doi.org/10.1016/j. rse.2012.07.006
21. Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z. and Yang. (2019). Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods 15(1), 10. https://doi.org/10.1186/ s13007-019-0394-z
22. Heurich, M. (2008). Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the bavarian forest national park. Forest Ecology and Management 255(7), 2416–33. https://doi.org/10.1016/j.foreco.2008.01.022
23. Hidayati, F., Fajrin, I. T., Ridho, M. R., Nugroho, W. D., Marsoem, S. N. and Na’iem, M. (2016). Sifat fisika dan mekanika kayu jati unggul ‘Mega’ dan kayu jati konvensional yang ditanam di hutan Pendidikan Wanagama, Gunungkidul, Yogyakarta. Jurnal Ilmu Kehutanan 10(2).
24. Kamal, M., Hidayatullah, M.F., Mahyatar, P. and Ridha, S.M. (2022). Estimation of aboveground mangrove carbon stocks from WorldView-2 imagery based on generic and species-specific allometric equations. Remote Sensing Applications: Society and Environment 26: 100748. https://doi.org/10.1016/j.rsase.2022.100748
25. Larjavaara, M., and Muller-Landau, H.C. (2013). Measuring tree height: A quantitative comparison of two common field methods in a moist tropical forest edited by J. Metcalf. Methods in Ecology and Evolution 4(9), 793–801. https://doi.org/10.1111/2041-210X.12071
26. Laurin, G.V., Chen, Q., Lindsell, J.A., Coomes, D.A., Del Frate, F., Guerriero, L., Pirotti, F. and Valentini, R. (2014). Above ground biomass estimation in an african tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 89, 49–58. https://doi.org/10.1016/j.isprsjprs.2014.01.001
27. Lefsky, M.A., Cohen, W.B., Harding, D.J., Parker, G.G., Acker, S.A. and Gower, S.T. (2002). Lidar remote sensing of above‐ground biomass in three biomes. Global Ecology and Biogeography 11(5), 393–99. https://doi.org/10.1046/j.1466-822x.2002.00303.x
28. Lian, X., Zhang, H., Xiao, W., Lei, Y., Ge, L., Qin, K., He, Y., Dong, Q., Li, L., Han, Y., Fan, H., Li, Y., Shi, L. and Chang, J. (2022). biomass calculations of individual trees based on unmanned aerial vehicle multispectral imagery and laser scanning combined with terrestrial laser scanning in complex stands. Remote Sensing 14(19), 4715. https://doi.org/10.3390/rs14194715
29. Lin, J., Wang, M., Ma, M., and Lin, Y. (2018). Aboveground tree biomass estimation of sparse subalpine coniferous forest with UAV oblique photography. Remote Sensing 10(11), 1849. https://doi.org/10.3390/rs10111849
30. Lu, D., Chen, Q., Wang, G., Liu, L., Li, G. and Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth 9(1), 63–105. https://doi.org/10.1080/17538947.2 014.990526
31. Maesano, M., Santopuoli, G., Moresi, F., Matteucci, G., Lasserre, B. and Mugnozza G.S. (2022). Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in southern Italy. iForest - Biogeosciences and Forestry 15(6), 451–57. https://doi.org/10.3832/ ifor3781-015
32. Margono, B.A., Potapov, P.V., Turubanova, S., Stolle, F. and Hansen, M.C. (2014). Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change 4(8), 730–35. https://doi.org/10.1038/ nclimate2277
33. Meyer, G.E., and Neto, J.C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture 63(2), 282–93. https://doi.org/10.1016/j. compag.2008.03.009
34. Mitchard, E.T.A. (2018). The tropical forest carbon cycle and climate change. Nature 559(7715), 527– 34. https://doi.org/10.1038/s41586-018-0300-2
35. Na’iem, M., Rudian, P.A., Hasibuan, S.M., Idhom, A.M., Mustaqim, A., Sutriyati, and CahyonoM.F. (2020). Wanagama: kisah terciptanya hutan pendidikan, konservasi dan kesejahteraan sosial ekonomi bagi rakyat sekitar. Yogyakarta, Indonesia: Samudra Biru.
36. Narine, Lana L., Sorin C. Popescu, and Lonesome Malambo. (2020). Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example. Remote Sensing 12(11), 1824. https://doi.org/10.3390/rs12111824
37. Nurbaya, S. (2023). Folu net sink: Indonesia’s Climate Actions Towards 2030. Jakarta, Indonesia: The Ministry of Environment and Forestry of the Republic of Indonesia.
38. Pérez, A.J., López, F., Benlloch, J.V. and Christensen, S. (2000). Colour and Shape Analysis Techniques for Weed Detection in Cereal Fields. Computers and Electronics in Agriculture 25(3):197–212. https://doi.org/10.1016/S0168-1699(99)00068-X
39. Popescu, Sorin C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy 31(9), 646–55. https://doi.org/10.1016/j. biombioe.2007.06.022
40. Potapov, P., Hansen, M.C., Pickens, A., Hernandez- Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, S.-P., Baggett, A., Kommareddy, I. and Kommareddy, A. (2022). The global 2000–2020 land cover and land use change dataset derived from the landsat archive: First results. Frontiers in Remote Sensing 3, 856903. https://doi.org/10.3389/frsen.2022.856903
41. Purnamasari, E., Kamal, M. and Wicaksono, P. (2021). Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using planet scope image. Regional Studies in Marine Science 44, 101730. https://doi.org/10.1016/j. rsma.2021.101730
42. Purwanto, R.H., and Silaban, M. (2011). Inventore biomasa dan karbon jenis jati (Tectona Grandis L.f.) di hutan rakyat Desa Jatimulyo, Karanganyar. Jurnal Ilmu Kehutanan 5(1), 40. https://doi.org/10.22146/jik.581
43. Qin, H., Zhou, W., Yao, Y. and Wang, W. (2021). Estimating aboveground carbon stock at the scale of individual trees in subtropical forests using UAV LiDAR and hyperspectral data. Remote Sensing 13(24), 4969. https://doi.org/10.3390/rs13244969
44. Ruengvirayudh, P., and Brooks, G.P. (2016). Comparing stepwise regression models to the best-subsets models, or, the art of stepwise. General Linear Model Journal 42(1), 1–14.
45. Sarre, A., ed. (2020). Global forest resources assessment, 2020: Main Report. Rome: Food and Agriculture Organization of the United Nations.
46. Schlund, M., Von Poncet, F., Kuntz, S., Schmullius, C. and Hoekman, D.H. (2015). TanDEM-X data for aboveground biomass retrieval in a tropical peat swamp forest. Remote Sensing of Environment 158, 255–66. https://doi.org/10.1016/j.rse.2014.11.016
47. Segal, M.R. (2004). Machine learning benchmarks and random forest regression. San Francisco: University of California.
48. Seta, G.W., Widiyatno, Hidayati, F., and Na’iem, M. (2021). Impact of thinning and pruning on tree growth, stress wave velocity, and pilodyn penetration response of clonal teak ( Tectona Grandis) plantation. Forest Science and Technology 17(2), 57–66. https://doi.org/10.1080/21580103.2021.1911865
49. Su, R., Du, W., Ying, H., Shan, Y. and Liu, Y. (2023). Estimation of aboveground carbon stocks in forests based on LiDAR and multispectral images: A case study of duraer coniferous forests. Forests 14(5), 992. https://doi.org/10.3390/f14050992
50. Sudarma, I. Made, Moh Saifulloh, I. Wayan Diara, and Abd. Rahman As-syakur. (2024). Carbon stocks dynamics of urban green space ecosystems using time-series vegetation indices. Ecological Engineering & Environmental Technology 25(9), 147– 62. https://doi.org/10.12912/27197050/190515
51. Tranmer, M., Murphy, J., Elliot, M. and Pampaka, M. (2020). Multiple Linear Regression. 2nd Edition.
52. Tsujino, R., Yumoto, T., Kitamura, S., Djamaluddin, I. and Darnaedi, D. (2016). History of forest loss and degradation in Indonesia. Land Use Policy 57, 335– 47. https://doi.org/10.1016/j.landusepol.2016.05.034
53. Umarhadi, D.A., Senawi, Wardhana, W., Soraya, E., Jihad, A.N. and Ardiansyah, F. (2023). Can iPhone/ iPad LiDAR data improve canopy height model derived from UAV? edited by M. Z. Abidin, R. Shamsudin, P. Tongdeenok, S. T. Sobue, A. Young, and A. Hoque. BIO Web of Conferences 80, 03003. https://doi.org/10.1051/bioconf/20238003003
54. Wang, L., Ju, Y., Ji, Y., Marino, A., Zhang, W. and Jing, Q. (2024). Estimation of forest above-ground biomass in the study area of greater khingan ecological station with integration of airborne LiDAR, Landsat 8 OLI, and hyperspectral remote sensing data. Forests 15(11), 1861. https://doi.org/10.3390/f15111861
55. Wirabuana, P.Y.A.P., Hendrati, R.L., Baskorowati, L., Susanto, M., Mashudi, Sulistiadi, H.B.S., Setiadi, D., Sumardi, and Alam, S. (2022a). Growth performance, biomass accumulation, and energy production in age series of clonal teak plantation. Forest Science and Technology 18(2), 67–75. https://doi.org/10.1080/21580103.2022.2063952
56. Zheng, C., Abd-Elrahman, A., Vance Whitaker, V. and Dalid, C. (2022). Prediction of strawberry dry biomass from UAV multispectral imagery using multiple machine learning methods. Remote Sensing 14(18), 4511. https://doi.org/10.3390/rs14184511
57. Zhou, L., Pan, S., Wang, J., and Vasilakos, A.V. (2017). Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–61. https://doi.org/10.1016/j.neucom.2017.01.026
58. Zylshal, S.S., Yulianto, F., Nugroho, J.T. and Sofan, P. (2016). A support vector machine object based image analysis approach on urban green space extraction using Pleiades-1A imagery. Modeling Earth Systems and Environment 2(2), 54. https://doi.org/10.1007/s40808-016-0108-8

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