• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

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 were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.4
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• pp.393-402
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• 2017

In this study, greenhouse gas (GHG) reductions from bioenergy (biogas, biomass) have been estimated in Korea, 2015. This study for construction of reduction inventories as direct and indirect reduction sources was derived from IPCC 2006 guidelines for national greenhouse gas inventories, guidelines for local government greenhouse inventories published in 2016, also purchased electricity and steam indirect emission factors obtained from KPX, GIR respectively. As a result, the annual GHG reductions were estimated as $1,860,000tonCO_{2eq}$ accounting for 76.8% of direct reduction (scope 1) and 23.2% of indirect reduction (scope 2). Estimation of individual greenhouse gases (GHGs) from biogas appeared that $CO_2$, $CH_4$, $N_2O$ were $90,000tonCO_2$ (5.5%), $55,000tonCH_4$ (94.5%), $0.3tonN_2O$ (0.004%), respectively. In addition, biomass was $250,000tonCO_2$ (107%), $-300tonCH_4$ (-3.2%), $-33tonN_2O$ (-3.9%). For understanding the values of estimation method levels, field data (this study) appeared to be approximately 85.47% compared to installed capacity. In details, biogas and biomass resulting from field data showed to be 76%, 74% compared to installed capacity, respectively. In the comparison of this study and CDM project with GHG reduction unit per year installed capacity, this study showed as 42% level versus CDM project. Scenario analysis of GHG reductions potential from bioenergy was analyzed that generation efficiency, availability and cumulative distribution were significantly effective on reducing GHG.

• Journal of Forest and Environmental Science
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• v.30 no.2
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• pp.233-242
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• 2014

Estimating above-ground biomass is important in establishing an applicable methodology of Measurement, Reporting and Verification (MRV) System for Reducing Emissions from Deforestation and Forest Degradation-Plus (REDD+). We developed an estimation model of diameter at breast height (DBH) from IKONOS-2 image that led to above-ground biomass estimation (AGB). The IKONOS image was preprocessed with dark object subtraction and topographic effect correction prior to watershed segmentation for tree crown delineation. Compared to the field observation, the overall segmentation accuracy was 64%. Crown detection percent had a strong negative correlation to tree density. In addition, satellite-based crown area had the highest correlation with the field measured DBH. We then developed the DBH allometric model that explained 74% of the data variance. In average, the estimated DBH was very similar to the measured DBH as well as for AGB. Overall, this method can potentially be applied to estimate AGB over a relatively large and remote tropical forest in Northern Borneo.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.965-967
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• 2003

Information on forest volume, forest coverage and biomass are important for developing global perspectives about CO$_{2}$ concentration changes. Forest biomass cannot be directly measured from space yet, but remotely sensed greenness can be used to estimate biomass on decadal and longer time scales in regions of distinct seasonality, as in the north. Hence, in this research, numerical methods were used to estimate forest biomass in higher northern regions. A regression model linking Normalized Difference Vegetation Index(NDVI), to forest biomass extracted from SPOT/4 VEGETATION data and PAL 8km data in regional and continental area (N40-N70) respectively. Statistical tests indicated that the regression model can be used to represent the changes of forest biomass carbon pools and sinks at high latitude regions over years 1982-2000. This study suggests that the implementation of estimation of biomass based on 8-km resolution NOAA/AVHRR PAL and SPOT-4/VEGETATION data could be detected over a range of land cover change processes of interest for global biomass change studies.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.133-136
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• 2008

In this paper, ALOS-AVNIR, PRISM, and JERS-1 images are used in a multilayer perceptron neural network (MLPNN) that relates them to forest variable measurements on the ground. The structure of this MLPNN is a three layers neural network that contains eight input neurons, 10 hidden neurons and five output neurons. It is shown that the biomass estimation accuracy is significantly improved when the MLPNN is used in comparison with Maximum Likelihood algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.133-139
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• 2011

Estimation of biomass carbon stock is an important research for estimation of public benefit of forest. Previous studies about biomass carbon stock estimation have limitations, which come from the used deterministic models. The most serious problem of deterministic models is that deterministic models do not provide any explanation about the relevant effects of errors. In this study, the effects of location errors were analyzed in order to estimation of biomass carbon stock of Danyang area using Monte Carlo simulation method. More specifically, the k-Nearest Neighbor(kNN) algorithm was used for basic estimation. In this procedure, random and systematic errors were added on the location of Sample plot, and effects on estimation error were analyzed by checking the changes of RMSE. As a result of random error simulation, mean RMSE of estimation was increased from 24.8 tonC/ha to 26 tonC/ha when 0.5~1 pixel location errors were added. However, mean RMSE was converged after the location errors were added 0.8 pixel, because of characteristic of study site. In case of the systematic error simulation, any significant trends of RMSE were not detected in the test data.

• Journal of Forest and Environmental Science
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• v.28 no.2
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• pp.68-74
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• 2012

A total of 45 Pinus sylvestnis var. mongolica trees from 9 plots in northeast China were destructively sampled to develop aboveground prediction equations for inventory application. Sampling plots covered a range of stand ages (12-47-years-old) and densities (450-3,840/ha). The distribution of aboveground biomass of whole-trees and tree component (stems, branches and leaves) of individual trees were studied and 4 equations were developed as functions of diameter at breast height (DBH), total height (HT). All the equations have good estimation effect with high prediction precision over 90%. Forest biomass was estimated based on the individual biomass prediction equations. It was found forest biomass of all organs increased with the increasing of stand age and density. And the period of 45-50 years was the suitable harvest time for Pinus sylvesris plantation.

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

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.4
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• pp.445-450
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• 2002

A series of 29 sampling with a 330 ${\mu}$m and a 64 ${\mu}$m mesh size of nets was conducted at a fixed station in Ilkwang Bay, southeast cost of Korea, from Oct, 2, 1991 to Oct. 10, 1992, to investigate the effects of mesh size of nets on biomass estimation of copepod Acartia steueri. The catch of copepodite and nauplius stages of A. steueli taken by two nets with different mesh size was different, showing that all developmental stages of A. steueri were retained on the 64 ${\mu}$m mesh net, but only $\geq$stage 4 copepodite were caught by the 330 ${\mu}$m mesh net. Abundance and biomass in each developmental stage estimated with the 64 ${\mu}$m mesh net were significantly higher than those of the 330 ${\mu}$m mesh net, except for adult female and stage 5 copepodite in female. The body length as well as the body width is likely to affect the catch of the nets. The mean biomass of A. steueli estimated with the traditional 330 ${\mu}$m net was 2.8 times lower than the value obtained with the 64 ${\mu}$m mesh net. However, the seasonal patterns of the biomass were comparable. These results suggest that accurate sampling strategr of the entire copepods assemblage including nauplii and copepodites are essential when estimating the abundance and biomass of copepods for the better understanding of the role of copepods in marine ecosystem.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.332-336
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• 2008

The resource potentials biomass resources of South Korea are estimated as Preliminary stage using relevant National statistics. Biomass resources possibly be collected, used and converted to bioenergy in Korea are forest biomass, agricultural residue, livestock manure and municipal solid wastes. The potential biomass resources are classifying into total potential, available potential and technically feasible biomass resources, Total potential biomass resources in Korea are estimated to be around 140million tons of oil equivalent (toe). Available potentials are estimated to be around 11million annually. The technically feasible biomass resources with current technologies are estimated to be 2.3million toe annually. These estimated values are the minimum of all potentials since they are all estimated from explicit statistics. Although actually there exist huge amount of biomass on the land as well as in the sea, potential resources for bioenergy are believed to be limited. The potentials are to be inclosed with the improvement of bioenergy technologies.