• 한국산림과학회지
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• 제111권4호
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• pp.603-612
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• 2022

본 연구는 다중시기 위성영상과 머신러닝 알고리즘을 이용하여 준국가수준의 시계열 산림바이오매스량을 추정하였으며, 이를 바탕으로 산림배출기준선 설정하여 비교·분석하였다. 머신러닝기반의 산림바이오매스 추정 모델을 구축하기 위하여 Landsat TM 위성영상과 유럽항공우주국에서 제공하는 Biomass Climate Change Initiative 정보를 이용하였으며, 머신러닝 알고리즘은 비모수 학습모델인 k-Nearest Neighbor(kNN)과 의사결정나무 기반의 Random Forest(RF)를 적용하였다. 또한, 추정된 산림바이오매스량은 Forest reference emission levels(FREL) 자료와 비교하였다. 머신러닝 알고리즘 별 산림바이오매스 추정 모델을 비교해보면, 최적의 kNN 모델과 RF 모델의 Root Mean Square Error (RMSE)는 각각 35.9와 34.41였으며, RF모델이 kNN모델보다 상대적으로 우수하였다. 또한, FREL, kNN, RF 모델 별 산림배출기준선의 기울기는 각각 약 -33천ton, -253천ton, -92천ton으로 설정되었다.

• 한국대기환경학회지
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• 제33권4호
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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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• 제30권2호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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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.

• 한국측량학회지
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• 제29권2호
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• pp.133-139
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• 2011

산림의 바이오매스 탄소저장량을 추정하는 것은 산림의 공익적인 가치를 평가하기 위해 선행되어야 하는 연구이다. 하지만 기존의 바이오매스 탄소저장량 추정에 관한 연구는 대부분 결정론적 모델이 사용되어 오차에 의한 영향을 알 수 없다는 한계를 가진다. 본 연구에서는 단양군의 지상부 바이오매스 탄소저장량 추정의 경우를 대상으로 몬테카를로 시뮬레이션을 통해 위치 오차에 의한 추정오차의 영향을 분석하고자 하였다. 기본적인 추정 방법으로는 kNN 알고리즘이 사용되었으며, 표본점의 위치에 우연오차 및 계통오차를 추가하여 RMSE의 변화를 통해 추정오차에 미치는 영향을 분석하였다. 분석결과 일반적인 위성영상에서 발생 할 수 있는 0.5~1 영상소의 위치오차에 의해 추정의 평균 RMSE가 24.8 tonC/ha에서 26 tonC/ha로 증가하는 것으로 확인되었으며, 추정 오차의 범위는 23.8 tonC/ha에서 28.1 tonC/ha로 나타났다. 하지만， 대상지역의 특성에 의해 0.8 영상소 이상의 우연오차에 대해서는 더 이상의 RMSE 증가가 없이 수렴하는 것으로 확인되었다. 방향을 고려한 계통오차에 대한분석의 경우 실험자료에서 특정한 경향은 발견되지 않았다.

• Journal of Forest and Environmental Science
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• 제28권2호
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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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• 제38권1호
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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.

• 한국수산과학회지
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• 제35권4호
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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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• 한국태양에너지학회 2008년도 추계학술발표대회 논문집
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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.