• Atmosphere
• /
• v.33 no.1
• /
• pp.61-72
• /
• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.

• Journal of Forest and Environmental Science
• /
• v.37 no.2
• /
• pp.128-140
• /
• 2021

Tropical dry forests are one of the most threatened, widely distributed ecosystems in tropics and estimation of forest biomass is a crucial component of global carbon emission estimation. Therefore, the present study was aimed to quantify the biomass and carbon storage in trees on large scale (10, 1 ha plots) in the dry mixed evergreen forest of Javadhu forest of Eastern Ghats. Biomass of adult (≥10 cm DBH) trees was estimated by non-harvest methods. The total biomass of trees in this tropical dry mixed evergreen forest was ranged from 160.02 to 250.8 Mg/ha, with a mean of 202.04±24.64 Mg/ha. Among the 62 tree species enumerated, Memecylon umbellatum accumulated greater biomass and carbon stocks (24.29%) more than the other species in the 10 ha study plots. ANOVA revealed that there existed a significant variation in the total biomass and carbon stock among the three plant types (Evergreen, brevi-deciduous and deciduous (F (2, 17)=15.343, p<0.001). Basal area and density was significant positively correlated with aboveground biomass (R² 0.980; 0.680) while species richness exhibited negative correlation with above ground biomass (R² 0.167). Finding of present study may be interpreted as most of the trees in this forest are yet to be matured and there is a net addition to standing biomass leading to carbon storage.

• Journal of the Korean association of regional geographers
• /
• v.20 no.2
• /
• pp.206-216
• /
• 2014

In this study, forest types in Chungheongnam-do were analyzed using Landsat satellite images and digital forest type map as a means to estimate forest carbon stocks. NDVI and Tasseled Cap, ISODATA, and supervised classification among others were used to analyze the forest types. The forest carbon stocks of Chungcheongnam-do were estimated utilizing forest statistical data derived from the classified results. The results indicate that the analysis of forest types through supervised classification yielded the highest overall accuracy in analyzing forest types using satellite images. Coniferous forests(49.3%) accounted for the highest proportion in all the forest types of Chungcheongnam-do, followed by deciduous forests(28.0%) and mixed forests(22.7%). The results of a comparative analysis between forest carbon stocks estimates made using the modified digital forest type map and other estimation methods showed that the method using Tasseled Cap and unsupervised classification yielded the most similar forest carbon stock estimates. The most significant difference, though, was made when only the digital forest type map was used. It is expected that if carbon stocks are estimated by integrating satellite images and digital forest type maps in the future, more accurate results can be derived in estimating forest carbon stocks at a national level.

• Journal of Ecology and Environment
• /
• v.38 no.1
• /
• pp.75-84
• /
• 2015

Tropical forests play a critical role in mitigating climate change, and therefore, an accurate and precise estimation of tropical forest carbon (C) is needed. However, there are many uncertainties associated with C stock estimation in a tropical forest, mainly due to its large variations in biomass. Hence, we quantified C stocks in an intact lowland mixed dipterocarp forest (MDF) in Brunei, and investigated variations in biomass and topography. Tree, deadwood, and soil C stocks were estimated by using the allometric equation method, the line intersect method, and the sampling method, respectively. Understory vegetation and litter were also sampled. We then analyzed spatial variations in tree and deadwood biomass in relation to topography. The total C stock was 321.4 Mg C $ha^{-1}$, and living biomass, dead organic matter, and soil C stocks accounted for 67%, 11%, and 23%, respectively, of the total. The results reveal that there was a relatively high C stock, even compared to other tropical forests, and that there was no significant relationship between biomass and topography. Our results provide useful reference data and a greater understanding of biomass variations in lowland MDFs, which could be used for greenhouse gas emission-reduction projects.

• Journal of Climate Change Research
• /
• v.6 no.4
• /
• pp.357-366
• /
• 2015

This study was conducted to estimate carbon stocks of Quercus serrata with drawing volume of trees in each tree height and DBH applying the suitable stem taper equation and tree specific carbon emission factors, using collected growth data from all over the country. Information on distribution area, tree number per hectare, tree volume and volume stocks were obtained from the $5^{th}$ National Forest Inventory (2006~2010), and method provided in IPCC GPG was applied to estimate carbon storage and removals. Performance in predicting stem diameter at a specific point along a stem in Quercus serrata by applying Kozak's model,$d=a_1DBH^{a_2}a_3^{DBH}X^{b_1Z^2+b_2ln(Z+0.001)+b_3{\sqrt{Z}}+b_4e^Z+b_5({\frac{DBH}{H}})}$, which is well known equation in stem taper estimation, was evaluated with validations statistics, Fitness Index, Bias and Standard Error of Bias. Consequently, Kozak's model turned out to be suitable in all validations statistics. Stem volume tables of Quercus serrata were derived by applying Kozak's model and carbon stock tables in each tree height and DBH were developed with country-specific carbon emission factors ($WD=0.65t/m^3$, BEF=1.55, R=0.43) of Quercus serrata. As a result of carbon stock analysis by age class in Quercus serrata, carbon stocks of IV age class (11,358 ha, 36.5%) and V age class (10,432; 33.5%) which take up the largest area in distribution of age class were 957,000 tC and 1,312,000 tC. Total carbon stocks of Quercus serrata were 3,191,000 tC which is 3% compared with total percentage of broad-leaved forest and carbon sequestration per hectare(ha) was 3.8 tC/ha/yr, $13.9tCO_2/ha/yr$, respectively.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.4
• /
• pp.69-85
• /
• 2014

Carbon stocks of NFI plots can be accurately estimated using field survey information. However, an accurate estimation of carbon stocks in other unsurveyed sites is very difficult. In order to fill this gap, various spatial information can be used as an ancillary data. In South Korea, there is the 1:5,000 forest type map that was produced by digital air-photo interpretation and field survey. Because this map contains very detailed forest information, it can be used as the high-quality spatial data for estimating carbon stocks. In this study, we compared three upscaling methods based on the 1:5,000 forest type map and 5th national forest inventory data. Map algebra(method 1), RK(Regression Kriging)(method 2), and GWR(Geographically Weighted Regression)(method 3) were applied to estimate forest carbon stock in Chungcheong-nam Do and Daejeon metropolitan city. The range of carbon stocks from method 2(1.39~138.80 tonC/ha) and method 3(1.28~149.98 tonC/ha) were more similar to that of previous method(1.56~156.40 tonC/ha) than that of method 1(0.00~93.37 tonC/ha). This result shows that RK and GWR considering spatial autocorrelation can show spatial heterogeneity of carbon stocks. We carried out paired t-test for carbon stock data using 186 sample points to assess estimation accuracy. As a result, the average carbon stocks of method 2 and field survey method were not significantly different at p=0.05 using paired t-test. And the result of method 2 showed the lowest RMSE. Therefore regression kriging method is useful to consider spatial variations of carbon stocks distribution in rugged terrain and complex forest stand.

• Journal of Korean Society of Forest Science
• /
• v.103 no.2
• /
• pp.264-269
• /
• 2014

The aim of this study was to determine the current distribution area of Robinia pseudoacacia habitat and to estimate its stand yield as well as its carbon stocks. In order to do so, the area of R. pseudoacacia distribution is obtained based on the large-scaled forest type map (1:5,000). Also, Weibull diameter distribution model is used to predict the yield of R. pseudoacacia stands. In addition, carbon emission factor is applied to calculate carbon stocks and removals. To obtain the stand yield of R. pseudoacacia, we developed estimation equation considering growth factors of the stand, e.g. mean diameter, the basal area, maximum and minimun diameter and etc. and tested it to ensure accuracy. Consequently, estimation equation derived from all growth factors have shown significance that could also be used for analysis. Site index was also established to determine the productivity of the forestland that later turned out to be ranging from 16 to 22. Based on these results, stand yield tables were drawn up. R. pseudoacacia is widely distributed in inland areas of Gyeongsang, Chungcheong and Gyeonggi provinces which covers total area of 26,770 ha. And when it is converted into carbon stocks, it amounts to 2,517,598tC with annual carbon uptake of 3.76tC/ha which is comparable to Querqus species that is known to storer large amounts of carbon. Therefore, R. pseudoacacia is also expected to serve as a viable carbon pool that would contribute to the mitigation of climate change. Furthermore, stand yield tables, an outcome of this survey would assist not only in proper management but also in sustainable management policy of R. pseudoacacia.

• Journal of Climate Change Research
• /
• v.7 no.4
• /
• pp.499-506
• /
• 2016

This study was conducted to estimate carbon stock and uptake for Larix kaempferi Lamb., the single species, which is the most widely distributed one following Pinus densiflora, using data from 6th national forest inventory and forest type map of 1:5,000. Overall distribution area of Larix kaempferi in South Korea was shown as 272,800ha, in detail, Gangwon-do was the most widely distributed region with 39.6% (108,141 ha) of the whole forest area, and Gyeongsangbuk-do was 18.6%(50,839 ha), Chungcheongbuk-do was 15.1%(41,205ha) in order. As the results of analysis in carbon stock and uptake for each province, the values were high with Gyeonggi-do 109.0 tC/ha, $10.3tCO_2/ha/yr$, Gangwon-do 349.1 tC/ha, $9.7tCO_2/ha/yr$ in order, and Jeollabuk-do was the lowest with 78.3 tC/ha, $7.6tCO_2/ha/yr$. Also, the results of estimation in total carbon stocks and uptakes by year (1989~2015) were turned out that total carbon stocks and uptakes were 24,891 thousand tC, $2,428thousand\;tCO_2$ in 2015, increasing about 4.8 times and 3.8 times each compared with 5,238 thousand C/ha, $640thousand\;CO_2$ in 1989. Although forest area was decreased 26.6% with 371,884 ha in 1989 to 272,800 ha in 2015, carbon stocks and uptakes were increased in 2015 in that forest stock was increased 126% compared to 1989.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.18 no.4
• /
• pp.81-99
• /
• 2015

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.14 no.3
• /
• pp.236-256
• /
• 2011

Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.