Applied Machine Learning Algorithms and Landsat 8 for Estimating Aboveground Carbon Stock in Evergreen Broadleaf Forest in Binh Phuoc Province
Main Article Content
Abstract
Abstract: The assessment of carbon stocks is one of the key measurements to support climate change mitigation policies. The research applied Landsat 8 satellite imagery combined with field-measurements using four machine learning methods (random forest - RF, artificial neural networks - NNET, support vector machines – SVM, and linear regression - LM) to estimate aboveground carbon in evergreen broadleaf forest in Binh Phuoc province. The field sample plots were randomly divided into training (96 plots) and testing (24 plots) data. The results showed that RF yielded the greatest precision with an R2 value above 0,9 and RMSE below 6 ton/ha on the training data, with an R2 value of 0,41 and RMSE of 11,04 ton/ha on the testing data. The estimate of forest carbon stock increased distinctly from the mean value of 59,80 ton/ha in the very poor forest to 87,78 ton/ha in the rich forest. The results found in the present study demonstrated that Landsat 8 imagery in conjunction with RF has the appropriate to estimate aboveground carbon stock in evergreen broadleaf forest-leaved in Binh Phuoc province.
Keywords: Random forest, aboveground carbon, REDD+, forest carbon estimation.
Keywords:
Random forest, aboveground carbon, REDD , forest carbon estimation.
References
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[2] V. H. Than, L. T. Truong, V.T. Hien, REDD+ Training Material, RECOFTC, 2017, pp. 1-7
(in Vietnamese).
[3] T. Q. Bao, Indentifying Carbon Sequestration of Forest Through Remote Sensing in Kim Boi District, Hoa Binh Province, Vietnam, Journal of Forestry Science and Technology, No. 1, 2013,
pp. 14-21 (in Vietnamese).
[4] V. L. Nguyen, Estimation of Biomass for Calculating Carbon Storage and CO2 Sequestration Using Remote Sensing Technology in Yok Don National Park, Central Highlands of Vietnam, Journal of Vietnamese Environment, Vol. 3, No. 1, 2012, pp. 14-18, https://doi.org/10.13141/jve.vol3.no1.pp14-18
(in Vietnamese).
[5] T. D. Pham, N. N. Le, N. T. Ha, L. V. Nguyen,
J. Xa, N. Yokoya, T. T. To, H. X. Trinh, L. Q. Kieu, W. Takeuchi, Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam, Remote Sensing, Vol. 12, No. 5, 2020, pp. 777-789, https://doi.org/10.3390/rs12050777.
[6] H. H. Nguyen, T. T. H. Nguyen, Above-Ground Biomass Estimation Models of Mangrove Forests Based on Remote Sensing and Field-Surveyed Data: Implications for C-PFES Implementation in Quang Ninh Province, Vietnam, Regional Studies in Marine Science, Vol. 48, 2021, pp. 101985, https://doi.org/10.1016/j.rsma.2021.101985.
[7] R. Erika, M. Herold, L. Kooistra, D. Murdiyarso, L. Verchot, Assessing Capacities of non-Annex I Countries for National Forest Monitoring in The Context of REDD+, Environmental Science & Policy, Vol. 19, 2012, pp. 33-48, https://doi.org/10.1016/j.envsci.2012.01.005.
[8] B. Gizachew, S. Solberg, E. Næsset, T. Gobakken, O. M. Bollandsås, J. Breidenbach, E. W. Mauya. Mapping and Estimating the Total Living Biomass and Carbon in Low-Biomass Woodlands Using Landsat 8 CDR Data, Carbon Balance and Management, Vol. 11, No. 1, 2026, pp. 1-14, https://doi.org/10.1186/s13021-016-0055-8.
[9] M. A. Marelign, D. T. Mekonen, Estimating and Mapping Woodland Biomass and Carbon Using Landsat 8 Vegetation Index: A Case Study in Dirmaga Watershed, Ethiopia, Computational Ecology and Software, Vol. 12, No. 2, 2022,
pp. 67-79.
[10] J. Zhou, Z. Zhou, Q. Zhao, Z. Han, P. Wang, J. Xu, Y. Dian, Valuation of Different Algorithms for Estimating the Growing Stock Volume of Pinus Massoniana Plantations Using Spectral and Spatial Information from a SPOT6 Image, Forests,
Vol. 11, No. 5, pp. 540-554, https://doi.org/10.3390/f11050540.
[11] P. V. Duan, V. T. Thin, Determinate Estimate Biomass and Volume Forests from Satellite Images, Journal of Forestry Science and Technology, No. 3, 2015, pp. 17-28 (in Vietnamese).
[12] D. T. A. Ngoc, N. T. T. Van, N. P. Anh, Monitoring of Rice Paddy and Estimating Biomass Based on Machine Learning Algorithms to Multi-temporal Sentinel-1a Data, Journal of Geodesy and Cartography, No. 49, 2021, pp. 52-64 (in Vietnamese).
[13] Binh Phuoc Sub-Department of Forest Protection, Survey and Assessment of Forest Area and Forest Status in Binh Phuoc Province, 2020, pp. 1-145
(in Vietnamese).
[14] P. H. Ho, An Illustrated Flora of Vietnam, Young Publisher, Hanoi, 2003 (in Vietnamese).
[15] T. Hop, Trees of Vietnam, Agricultural Publisher, Hanoi, 2002 (in Vietnamese).
[16] B. Huy, K. Kralicek, K. P. Poudel, V. T. Phuong, P. V. Khoa, N. D. Hung, H. Temesgen, Allometric Equations for Estimating Tree Above-Ground Biomass in Evergreen Broadleaf Forests of Vietnam, Forest Ecology and Management,
Vol. 382, 2016, pp. 193-205, https://doi.org/10.1016/j.foreco.2016.10.021.
[17] A. E. Zanne, G. L. Gonzalez, D. A. Coomes, J. Ilic, S. Jansen, S. L. Lewis, R. B. Miller, N. G. Swenson, M. C. Wiemann, J. Chave. Data from: Towards a Worldwide Wood Economics Spectrum, https://doi.org/10.5061/dryad.234.
[18] IPCC, Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, Editors, Published: IGES, Japan, 2006.
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[20] A. R. Huete, A Comparison of Vegetation Indices Global Set of TM Images for EOS-MODIS, Remote Sensing of Environment, Vol. 59, No. 3, 1997, pp. 440-451, https://doi.org/10.1016/S0034-4257(96)00112-5.
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[22] G. Rondeaux, M. Steven, F. Baret, Optimization of Soil-Adjusted Vegetation Indices, Remote Sensing of Environment, Vol. 55, No. 2, 1996,
pp. 95-107,
https://doi.org/10.1016/0034-4257(95)00186-7.
[23] B. C. Gao, NDWI- a Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, Vol. 58, No. 3, 1996, pp. 257-266, https://doi.org/10.1016/S0034-4257(96)00067-3.
[24] J. D. Miller, A. E. Thode, Quantifying Burn Severity in a Heterogeneous Landscape with a Relative Version of the Delta Normalized Burn Ratio (dNBR), Remote Sensing of Environment, Vol. 109, No. 1, 2007, pp. 66-80, https://doi.org/10.1016/j.rse.2006.12.006.
[25] N. Ahmed, C. Atzberger, W. Zewdie, Species Distribution Modelling Performance and its Implication for Sentinel-2-Based Prediction of Invasive Prosopis juliflora in Lower Awash River Basin, Ethiopia, Ecological Processes, Vol. 10,
No. 1, 2021, pp. 1-16, https://doi.org/10.1186/s13717-021-00285-6.
[26] T. T. P. Nguyen, M. H. Duy, T. N. Duong,
T. D. Nghiem, Forecast of Hourly Tropospheric Ozone Concentration in Quang Ninh using MLP and SVM, VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3, 2020,
pp. 46-54,
https://doi.org/10.25073/2588-1094/vnuees.4604.
[27] G. Chen, G. J. Hay, A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level using Airborne Lidar Transects and Quickbird Data, Photogrammetric Engineering & Remote Sensing, Vol. 77, No. 7, 2011, pp 733-741, https://doi.org/10.14358/PERS.77.7.733.
[28] L. H. An, S. H. Dang, Determination of the Mineral Volumes for the Pre-Cenozoic Magmatic Basement Rocks of Cuu Long Basin from Well Log Data via Using the Artificial Neural Networks, VNU Journal of Science: Earth and Environmental Sciences, Vol. 30, No. 1, 2014, pp. 1-11.
[29] V. D. Hai, T. V. Do, D. T. Trieu, T. Sato, O. Kozan, Carbon Stocks in Tropical Evergreen Broadleaf Forests in Central Highland, Vietnam, International Forestry Review, Vol. 17, No. 1, 2015, pp. 20-29, https://doi.org/10.1505/146554815814725086.
[30] A. Safari, H. Sohrabi, S. Powell, S. Shataee, A Comparative Assessment of Multi-temporal Landsat 8 and Machine Learning Algorithms for Estimating Aboveground Carbon Stock in Coppice Oak Forests, International Journal of Remote Sensing, Vol. 38, No. 22, 2017, pp. 6407-6432, https://doi.org/10.1080/01431161.2017.1356488.
[31] T. Dube, O. Mutanga, Evaluating the Utility of the Medium-Spatial Resolution Landsat 8 Multispectral Sensor in Quantifying Aboveground Biomass in uMgeni Catchment, South Africa, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 101, 2015, pp. 36-46, https://doi.org/10.1016/j.isprsjprs.2014.11.001.
[32] K. Liu, J. Wang, W. Zeng, J. Song, Comparison and Evaluation of three Methods for Estimating Forest Above Ground Biomass using TM and GLAS Data, Remote Sensing, Vol. 9, No. 4, 2017, pp. 341-353, https://doi.org/10.3390/rs9040341.
[33] A. T. Dang, S. Nandy, R. Srinet, N. V. Luong,
S. Ghosh, A. S. Kumar, Forest Aboveground Biomass Estimation using Machine Learning Regression Algorithm in Yok Don National Park, Vietnam. Ecological Informatics, Vol. 50, 2019, pp. 24-32, https://doi.org/10.1016/j.ecoinf.2018.12.010.
[34] N. V. Hop, N. V. Quy, B. H. Quoc, N. T. Luong, Relationship between Plant Biodiversity and Carbon Stocks in Evergreen Broad-leaved Forests in the Central Highlands, Journal of Forestry Science and Technology, No. 11, 2021, pp. 59-69.
[35] T. D. Nguyen, M. Kappas, Estimating the Above-ground Biomass of an Evergreen Broadleaf forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, using SPOT-6 data and the Random forest Algorithm, International Journal of Forestry Research, Vol. 2020, 2020, pp. 1-13, https://doi.org/10.1155/2020/4216160.
[36] V. Avitabile, M. Schultz, N. Herold, S. D. Bruin, A. K. Pratihast, C. P. Manh, M. Herold, Carbon Emissions from Land Cover Change in Central Vietnam, Carbon Management, Vol. 7, No. 6, 2016, pp. 333-346, https://doi.org/10.1080/17583004.2016.1254009.