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http://dx.doi.org/10.14249/eia.2018.27.2.124

A Study on the Availability of Spatial and Statistical Data for Assessing CO2 Absorption Rate in Forests - A Case Study on Ansan-si -  

Kim, Sunghoon (National Institute of Ecology)
Kim, Ilkwon (National Institute of Ecology)
Jun, Baysok (National Institute of Ecology)
Kwon, Hyuksoo (National Institute of Ecology)
Publication Information
Journal of Environmental Impact Assessment / v.27, no.2, 2018 , pp. 124-138 More about this Journal
Abstract
This research was conducted to examine the availability of spatial data for assessing absorption rates of $CO_2$ in the forest of Ansan-si and evaluate the validity of methods that analyze $CO_2$ absorption. To statistically assess the $CO_2$ absorption rates per year, the 1:5,000 Digital Forest-Map (Lim5000) and Standard Carbon Removal of Major Forest Species (SCRMF) methods were employed. Furthermore, Land Cover Map (LCM) was also used to verify $CO_2$ absorption rate availability per year. Great variations in $CO_2$ absorption rates occurred before and after the year 2010. This was due to improvement in precision and accuracy of the Forest Basic Statistics (FBS) in 2010, which resulted in rapid increase in growing stock. Thus, calibration of data prior to 2010 is necessary, based on recent FBS standards. Previous studies that employed Lim5000 and FBS (2015, 2010) did not take into account the $CO_2$ absorption rates of different tree species, and the combination of SCRMF and Lim5000 resulted in $CO_2$ absorption of 42,369 ton. In contrast to the combination of SCRMF and Lim5000, LCM and SCRMF resulted in $CO_2$ absorption of 40,696 ton. Homoscedasticity tests for Lim5000 and LCM resulted in p-value <0.01, with a difference in $CO_2$ absorption of 1,673 ton. Given that $CO_2$ absorption in forests is an important factor that reduces greenhouse gas emissions, the findings of this study should provide fundamental information for supporting a wide range of decision-making processes for land use and management.
Keywords
Ecosystem services; Forest basic statistics; 15,000 Digital Forest Map; Land Cover Map; Annual $CO_2$ absorption;
Citations & Related Records
Times Cited By KSCI : 13  (Citation Analysis)
연도 인용수 순위
1 AGEC (Ansan Green Environment Center). 2008. Study on Emission and Source of Greenhouse Gas in Ansan area. Ansan Green Environment Center, 1-184. [Korean Literature]
2 AGEC (Ansan Green Environment Center). 2016. A roadmap for realization of Environmentally Friendly Ansan City : 2030 Ecological City Vision. Ansan Green Environment Center, 1-172. [Korean Literature]
3 Ansan-si. 2015. Basic plan for urban forest creation and management, Ansan-si, 1-234. [Korean Literature]
4 Cha SY, Pi UH, Park CH. 2013. Mapping and estimating forest carbon absorption using time-series MODIS imagery in South Korea. Korean Journal of Remote Sensing. 29(5): 517-525. [Korean Literature]   DOI
5 Elmqvist T, Setala H, Handel SN, Van Der Ploeg S, Aronson J, Blignaut JN, De Groot R. 2015. Benefits of restoring ecosystem services in urban areas. Current Opinion in Environmental Sustainability 14: 101-108.   DOI
6 GGIRCK (Greenhouse Gas Inventory & Research Center of Korea). 2016. National Greenhouse Gas Inventory Report of Korea. Greenhouse Gas Inventory & Research Center of Korea, 1-411. [Korean Literature]
7 Hong JY, Shim CS, Lee MJ, Baek GH, Song WK, Jeon SW, Park YH. 2011. Net primary production changes over Korea and climate factors. Korean Journal of Remote Sensing. 27(4): 467-480. [Korean Literature]   DOI
8 IPCC (Intergovernmental Panel on Climate Change).2003. Good practice guidance for landuse, land-use change and forestry. The Intergovernmental Panel on ClimateChange, 1-590.
9 Jeon SW, Kim JU, Jung HC. 2013. A Study on the Forest Classification for Ecosystem Services Valuation. Journal of the Korea Society of Environmental Restoration Tecnology 16(3): 31-39. [Korean Literature]
10 Jeon SW, Kim JU, Kim YH, Jung HC, Lee WK, Kim JS. 2015. Improvement of Forest Boundary in Landcover Classification Map(Level-II) for Functional Assessment of Ecosystem Services. Journal of the Korea Society of Environmental Restoration Tecnology 18(1): 127-133. [Korean Literature]   DOI
11 Jung JH, Nguyen HC, Heo J, Kim KM, Im JH. 2014. Estimation of Aboveground Forest Biomass Carbon Stock by Satellite Remote Sensing-A Comparison between k-Nearest Neighbor and Regression Tree Analysis. Korean Journal of Remote Sensing. 30(5): 651-664. [Korean Literature]   DOI
12 KFRI (Korea Forest Research Institute). 2008. Guideline for the 1:5,000 Forest Type Map. Korea Forest Research Institute, 1-99. [Korean Literature]
13 KFRI (Korea Forest Research Institute). 2012. Standard carbon removal of major forest species briefing memo. National Institute of Forest Science, 1-18. [Korean Literature]
14 Kim KM, Lee JB, Jung J. 2015. Comparison of Forest Carbon Stocks Estimation Methods Using Forest Type Map and Landsat TM Satellite Imagery. Korean Journal of Remote Sensing. 31(5): 449-459. [Korean Literature]   DOI
15 KFRI (Korea Forest Research Institute). 2012. Studies on the quantification of welfare functions of forests. Korea Forest Research Institute, 1-225. [Korean Literature]
16 KFRI (Korea Forest Research Institute). 2015. Statistical analysis of National Forest Greenhouse Gas Using Carbon Emission Factors. Korea Forest Research Institute, 1-26. [Korean Literature]
17 KFS (Korea Forest Service). 1995-2015. Forest Basic Statistics(Forest area & Growing stock). Korea Forest Service. [Korean Literature]
18 KFS (Korea Forest Service). 2013. 5th Forest Basic Plan(revised). Korea Forest Service, 1-235. 1-107.[Korean Literature]
19 KFS (Korea Forest Service). 2014. 1st Forest Carbon Sink Enhancement Plan. Korea Forest Service, [Korean Literature]
20 Kim KM, Roh YH, Kim ES. 2014. Comparison of three kinds of methods on estimation of forest carbon stocks distribution using national forest inventory DB and forest type map. Journal of the korean association of Geographic Information Studies. 17(4): 69-85. [Korean Literature]   DOI
21 Kim SH, Jang DH, Yu JJ. 2016. Value Assessment Study for Erosion Control in Regulation Services of Ecosystems Services -A case study on Seocheon-. Jorunal of Photo Geography 26(1): 15-34. [Korean Literature]
22 MOF (Ministry of Oceans and Fisheries). 2013. Statistics of Coastal Wetland Area. Ministry of Oceans and Fisheries. [Korean Literature]
23 Laurin GV, Puletti N, Chen Q, Corona P, Papale D, Valentini R. 2016. Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests. International Journal of Applied Earth Observation and Geoinformation. 52: 371-379.   DOI
24 Lee BR, Kang WM, Kim CK, Kim GE, Lee CH. 2017. Estimating carbon uptake in forest and agricultural ecosystems of Korea and other countries using eddy covariance flux data. Journal of Environmental Impact Assessment. 26(2): 127-139. [Korean Literature]   DOI
25 Lee JH, Im JH, Kim KM, Heo J. 2015. Change Analysis of aboveground forest carbon stocks according to the land cover change using multi-temporal Landsat TM images and machine learning algorithms. Journal of the Korean Association of Geographic Information Studies. 18(4): 81-99. [Korean Literature]   DOI
26 MA (Millennium Ecosystem Assessment). 2005. Ecosystems and Human Wellbeing Millennium Ecosystem Assessment. Island Press. Washington DC.
27 MOE (Ministry of Environment). 2016. Establishment of Land Cover Map(7th) and Improvement of National Environment Map System. Ministry of Environment. [Korean Literature]
28 NIFoS (National Institute of Forest Science). 2016. Main agreements of the Paris Agreement and the main contents of forest sector. National Institute of Forest Science, 1-28. [Korean Literature]
29 Park HJ, Shin HS, Roh YH, Kim KM, Park KH. 2012. Estimating forest carbon stocks in Danyang using kriging methods for aboveground biomass. Journal of the Korean Association of Geographic Information Studies. 15(1): 16-33. [Korean Literature]   DOI
30 Rana P, Gautam B, Tokola T. 2016. Optimizing the number of training areas for modeling above-ground biomass with ALS and multispectral remote sensing in subtropical Nepal. International Journal of Applied Earth Observation and Geoinformation. 49: 52-62.   DOI
31 Roh YH, Kim CK, Hong HJ, 2016. Time-Series Changes to Ecosystem Regulating Services in Jeju: Focusing on Estimating Carbon Sequestration and Evaluating Economic Feasibility. Journal of environmental policy 24(2): 29-44. [Korean Literature]
32 Schlund M, Scipal K, Davidson MW. 2017. Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation. International Journal of Applied Earth Observation and Geoinformation. 56: 65-76.   DOI
33 TEEB (The Economics of Ecosystems and Biodiversity). 2010. The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundation, Kumar P.(Ed),Earthscan, London and Washington.
34 Seo YO, Jung SC, Lee YJ. 2017. Mapping of Spatial Distribution for Carbon Storage in Pinus rigida Stands Using the National Forest Inventory and Forest Type Map: Case Study for Muju Gun. Journal of Korean Forest Society. 106(2): 258-266. [Korean Literature]   DOI
35 Son YM, Lee SJ, Kim SW, Hwang JS, Kim RH, Park H. 2014. Mapping and Assessment of Forest Biomass Resources in Korea. Journal of korean forest society 103(3): 431-438.   DOI
36 Song CH, Lee WK, Choi HA, Jeon SW, Kim JU, Kim JS, Kim JT. 2015. Application of InVEST Water Yield Model for Assessing Forest Water Provisioning Ecosystem Service. Journal of the Korean Association of Geographic Information Studies 18(1): 120-134. [Korean Literature]   DOI
37 Yoo SJ, Lee WK, Son YW, Ito A. 2012. Estimation of Vegetation Carbon Budget in South Korea using Ecosystem Model and Spatio-temporal Environmental Information. Korean Journal of Remote Sensing. 28(1): 145-157. [Korean Literature]   DOI
38 Tian X, Yan M, van der Tol C, Li Z, Su Z, Chen E, Gao L. 2017. Modeling forest above- ground biomass dynamics using multi- source data and incorporated models: A case study over the qilian mountains. Agricultural and Forest Meteorology. 246: 1-14.   DOI