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NTIS 바로가기한국산림과학회지 = Journal of korean society of forest science, v.111 no.4, 2022년, pp.603 - 612
이용규 (강원대학교 산림경영학과) , 이정수 (강원대학교 산림경영학과)
The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information...
2050 Carbon Neutrality and Green Growth Commission. 2021.?2030 Nationally Determined Contribution (NDC) Upgrade?Plan. 2050 Carbon Neutrality and Green Growth Commission. Sejong, Korea pp. 23.
Goussanou, C.A., Guendehou, S., Assogbadjo, A. E. and?Sinsin, B. 2017. Application of site-specific biomass models to quantify spatial distribution of stocks and historical?emissions from deforestation in a tropical forest ecosystem.?Journal of Forestry Research volume 29(1): 205-213.
Jung, J.H., Heo, J., Yoo, S.H., Kim, K.M. and Lee, J.B. 2010.?Estimation of Aboveground Biomass Carbon Stock in?Danyang Area using kNN Algorithm and Landsat TM?Seasonal Satellite Images. journal of Korean Society for?Geospatial Information Science 18(4): 119-129.
Korea Forest Service. 2018. Forest Master Plans 6th. Korea?Forest Service. Daejeon, Korea. pp. 55.
Kuyper, J., Schroeder, H. and Linner, B. 2018. The Evolution?of the UNFCCC. Annual Review of Environment and?Resources 43: 343-368.
Lee, W.K., Son, Y.H., Yang, S.R., Kim, O.S., Lim, S.T., Yoo,?S.M., Kim, J.Y., Kwak, H.B. and Kim, M.I. 2013a. REDD+ Implementation Guidelines. Korea Forest Service. Daejeon, Korea. pp. 1.
Lee, W.K., Son, Y.H., Yang, S.R., Kim, O.S., Lim, S.T., Yoo,?S.M., Kim, J.Y., Kwak, H.B. and Kim, M.I. 2013b.?REDD+ Introduction. Korea Forest Service. Daejeon,?Korea. pp. 33.
Li, Y., Li, M., Li, C. and Liu. Z. 2020. Forest aboveground?biomass estimation using Landsat 8 and Sentinel-1A data?with machine learning algorithms. Scientific Reports 10:?1-12.
Ma, J., Ding, Y., Cheng, J.C., Tan, Y., Gan, V.J. and Zhang,?J. 2019. Analyzing the leading causes of traffic fatalities?using XGBoost and grid-based analysis: a city management perspective. IEEE 7: 148059-148072.
Meng, S., Pang, Y., Zhang, Z., Jia, W. and Li, Z. 2016. Mapping aboveground biomass using texture indices from?aerial photos in a temperate forest of Northeastern China.?Remote Sensing 8(3): 230-243.
Ministry of Environment and Sustainable Development. 2015.?Nivel de Referencia de las Emisiones Forestales por?Deforestacion en la Republica del Paraguay para pago por?resultados de REDD+ bajo la CMNUCC. Ministry of?Environment and Sustainable Development. Asuncion,?Paraguay. pp. 10.
Mutanga, O., Adam, E. and Cho, M.A. 2012. High density?biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm.?International Journal of Applied Earth Observation and?Geoinformation 18: 399-406.
Nyamari, N. and Cabral, P. 2021. Impact of land cover?changes on carbon stock trends in Kenya for spatial implementation of REDD+ policy. Applied Geography 133: 102479.
Pan, Y., Chen, S., Qiao, F., Ukkusuri, S.V. and Tang, K. 2019.?Estimation of real-driving emissions for buses fueled with?liquefied natural gas based on gradient boosted regression?trees. Science of The Total Environment 660: 741-750.
Seo, H.S., Park, D.H., Yim, J.S. and LEE, J.S. 2012. Assessment of Forest Biomass using k-Neighbor Techniques-A Case Study in the Research Forest at Kangwon National?University- . Journal of Korean Society of Forest Science?101(4): 547-557.
Vahedi, A.A. 2016. Artificial neural network application in?comparison with modeling allometric equations for?predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran. Biomass and Bioenergy 88: 66-76.
VCS. 2012. VCS MODULE VMD0007 Redd Methodological?Module: Estimation of Baseline Carbon Stock Changes?and Greenhouse Gas Emissions from Unplanned Deforestation (BL-UP). VERRA. Washington DC, U.S.A. pp. 16.
Wu, C., Shen, H., Shen A., Deng, J., Gan, M., Zhu, J., Xu,?H. and Wang, K. 2016. Comparison of machine-learning?methods for above-ground biomass estimation based on?Landsat imagery. Journal of Applied Remote Sensing?10(3): 035010.
Yoo, S.M., Song, C.H., Hong, M.A., Kim, W.J., Kim, J.W.,?KO, Y.J. and LEE, W.K. 2021. Analysis and evaluation?of A/R CDM p rojects in India for abroad afforestation?project. Journal of Climate Change Research 12(5): 443-460.
Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y. and Yu, S. 2016. Examining spectral reflectance saturation in Landsat?imagery and corresponding solutions to improve forest?aboveground biomass estimation. Remote Sensing 8(6):?469-495.
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