• 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.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1083-1096
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• 2020

Global attention to the greenhouse gas emissions from deforestation and forest degradation is increasing. There is a growing recognition of reducing emission from deforestation and forest degradation plus (REDD+) as an effective way to reduce greenhouse gas emissions in the forestry sector. The Republic of Korea is implementing REDD+ pilot projects in four Southeast Asian countries as part of its efforts to reduce greenhouse gas emissions. This study evaluates countries with the potential to become priority partner countries for Korea's REDD+ programs, using the following five criteria: The first criterion is that a country should include the forest sector and REDD+ in its national plan for reducing greenhouse gas (GHG) emissions. The second and third criteria refer to an average forest coverage rate of over 44% and a forest change rate of over - 0.1%, among the countries with forest cover of more than 10 million ha. The fourth criterion is that the country should meet the Forest Reference Emission Level requirements, one of the four elements of the Warsaw REDD+ Framework. The fifth criterion is that the country should have bilateral relations with the Republic of Korea in forestry while at the same time be a partner country for cooperation on climate change as well as a REDD+ pilot country. Based on our evaluation, we conclude that the first priority countries are Indonesia, Cambodia, and Myanmar. The second priority countries include Brazil, Ecuador, and Peru. Finally, the third priority countries are Columbia, Congo, and Mozambique. This study suggests that for the selection of priority partner countries, Korean REDD+ programs should center on existing REDD+ pilot countries.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.277-287
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• 2015

This paper addresses limitations of land-change modeling application in the context of REDD (Reducing Emissions from Deforestation and forest Degradation). REDD is an international conservation policy that aims to protect forests via carbon credit generation and trading. In REDD, carbon credits are generated only if there is measurable quantied carbon sequestration activities that are additional to business-as-usual (BAU). A "reference level" is defined as simulated baseline carbon emissions for the future under a BAU scenario, and predictive land-change modeling plays an important role in constructing reference levels. It is tested in this research how predictive accuracies of two land-change models, namely Geographic Emission Benchmark (GEB) and GEOMOD, vary with respect to different spatial scales: Xishuangbanna prefecture and Yunnan province. The accuracies are measured by Figure of Merit. In this Chinese case study, it turns out that GEB's better performance is mainly due to quantity (e.g., how many hectares of forest will be converted to agricultural land?) rather than spatial allocation (e.g., where will the conversion happen?). As both quantity and allocation are crucial in REDD reference level setting it appears to be fundamental to systematically analyze accuracies of quantity and allocation independently in pursuit of accurate reference levels.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.238-246
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• 2016

The study aims to examine Japan's National REDD+ Strategies prepared for Post-2020 and the status of its implementation by organizations in Japan, and then to suggest the potential REDD+ countermeasures against Joint Credit Mechanism (JCM) for Republic of Korea and their implications. As for the technical limitations of the guidelines of REDD+ under the JCM, it is pointed out that forests located at the place with less potential safeguard intervention tend to be selected as the target area for a project and that, as reference emission trend changes depending on the basic year of the baseline, differences could occur among the amounts of greenhouse gas emission. In addition, it is pointed out that the result of the calculation of the displacement of emissions, or leakeage, in REDD+, can have an uncertainty, since the calculation is done by just multiplying leakage area by certain coefficients, without considering the size of the leakage area. Furthermore, the lack of implementation guideline or methodologies for a project level is also pointed out as a limitation, considering that there are only some national and sub-national monitoring guidelines at present. Finally, internationally accepted guidelines for safeguard and its sub-items needed to be prepared, as current safeguard policy only includes lists without detailed items. Such things mentioned above are all related to, and can lead to the problem of double counting of items in Nested Approach etc., as well as of the distribution of credits. Therefore, Republic of Korea should take these into consideration when implementing its REDD+ projects.