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Climate change has inherent multidisciplinary characteristics, and predicting the future of a single field of work has a limit. Therefore, this study proposes a water-centric nexus approach for the agriculture and forest sectors for improving the response to climate change in the Korean Peninsula. Two spatial models, i.e., Environmental Policy Integrated Climate and Integrated Valuation of Ecosystem Services and Tradeoffs, were used to assess the extent of changes in agricultural water demand, forest water supply, and their balance at the watershed level in the current and future climatic conditions. Climate changed has increased the agricultural water demand and forest water supply significantly in all future scenarios and periods. Comparing the results with RCP8.5 2070s and the baseline, the agricultural water demand and forest water supply increased by 35% and 28%, respectively. Water balance assessment at the main watershed level in the Korean Peninsula revealed that although most scenarios of the future water supply increases offset the demand growth, a risk to water balance exists in case of a low forest ratio or smaller watershed. For instance, the western plains, which are the granary regions of South and North Korea, indicate a higher risk than other areas. These results show that the land-use balance can be an essential factor in a water-centric adaptation to climate change. Ultimately, the water-centric nexus approach can make synergies by overcoming increasing water demands attributable to climate change.
Chul-Hee Lim. Water-Centric Nexus Approach for the Agriculture and Forest Sectors in Response to Climate Change in the Korean Peninsula. Agronomy 2021, 11, 1657 .
AMA StyleChul-Hee Lim. Water-Centric Nexus Approach for the Agriculture and Forest Sectors in Response to Climate Change in the Korean Peninsula. Agronomy. 2021; 11 (8):1657.
Chicago/Turabian StyleChul-Hee Lim. 2021. "Water-Centric Nexus Approach for the Agriculture and Forest Sectors in Response to Climate Change in the Korean Peninsula." Agronomy 11, no. 8: 1657.
It is essential to maintain the health of forests so that they are protected against a diverse range of stressors and show improved resilience. An area-based forest health map is required for efficient forest management on a national scale however, most national forest inventories are based on in-situ observations. This study examined methodologies to establish an area-based map on tree vitality grade using field survey data, particularly that containing information on several trees at one point. The forest health monitoring dataset of the Republic of Korea was used in combination with 37 satellite-based environmental predictors. Four methods were considered: Multinomial logistic regression (MLR), random forest classification (RF), indicator kriging (IK), and multi-model ensemble (MME) approaches using species distribution models. The MLR and RF produced biased results, whereby almost all regions were classified as first grade; the spatialization results of these methods were considered inappropriate for forest management. The maps produced using the IK and MME methods improved the distinctions between the distributions of five grades compared to the previous two methodologies however, the MME method produced better results, reliably reflecting topographical and climatic characteristics. Comparisons with the vegetation condition index and bioclimate vulnerability index also emphasized the usefulness of the MME. This study is particularly relevant to the national forest managers who struggle to find the most effective forest monitoring and management strategies. Suggestions to improve spatialization of field survey data are further discussed.
Yuyoung Choi; Hye Chung; Chul-Hee Lim; Jun-Hee Lee; Won Choi; Seong Jeon. Multi-Model Approaches to the Spatialization of Tree Vitality Surveys: Constructing a National Tree Vitality Map. Forests 2021, 12, 1009 .
AMA StyleYuyoung Choi, Hye Chung, Chul-Hee Lim, Jun-Hee Lee, Won Choi, Seong Jeon. Multi-Model Approaches to the Spatialization of Tree Vitality Surveys: Constructing a National Tree Vitality Map. Forests. 2021; 12 (8):1009.
Chicago/Turabian StyleYuyoung Choi; Hye Chung; Chul-Hee Lim; Jun-Hee Lee; Won Choi; Seong Jeon. 2021. "Multi-Model Approaches to the Spatialization of Tree Vitality Surveys: Constructing a National Tree Vitality Map." Forests 12, no. 8: 1009.
Climate change is one of the greatest challenges in Kyrgyzstan. There have been negative spillover effects in agriculture. This study aims to assess the climate change impacts on cropland suitability in Kyrgyzstan. We used the random forest algorithm to develop a model that captures the effects of multiple climate and environment factors at a spatial resolution of 1 km2. The model was then applied in the scenario analysis for an understanding of how climate change affects cropland distribution. The potential high-quality cropland was found to be included in existing croplands, while the remaining were distributed around the Chu-Talas valley, the Issyk-kul area, and the Fergana valley. These potential high-quality croplands comprise grasslands (47.1%) and croplands (43.7%). In the future, the potential high-quality cropland exhibited inland trends at the periphery of original cropland category, with grassland and cropland as the primary land components. Due to climate change, potential high-quality cropland is expected to gradually reduce from the 2050s to the 2070s, exhibiting the largest reduction in potential high-quality areas for the Representative Concentration Pathway 8.5 scenario. Therefore, the short- and long-term adaptation strategies are needed for prioritizing the croplands to ensure food security and agricultural resilience.
Sugyeong Park; Chul-Hee Lim; Sea Kim; Erkin Isaev; Sol-E Choi; Sung-Dae Lee; Woo-Kyun Lee. Assessing Climate Change Impact on Cropland Suitability in Kyrgyzstan: Where Are Potential High-Quality Cropland and the Way to the Future. Agronomy 2021, 11, 1490 .
AMA StyleSugyeong Park, Chul-Hee Lim, Sea Kim, Erkin Isaev, Sol-E Choi, Sung-Dae Lee, Woo-Kyun Lee. Assessing Climate Change Impact on Cropland Suitability in Kyrgyzstan: Where Are Potential High-Quality Cropland and the Way to the Future. Agronomy. 2021; 11 (8):1490.
Chicago/Turabian StyleSugyeong Park; Chul-Hee Lim; Sea Kim; Erkin Isaev; Sol-E Choi; Sung-Dae Lee; Woo-Kyun Lee. 2021. "Assessing Climate Change Impact on Cropland Suitability in Kyrgyzstan: Where Are Potential High-Quality Cropland and the Way to the Future." Agronomy 11, no. 8: 1490.
The role of forests to sequester carbon is considered an important strategy for mitigating climate change and achieving net zero emissions. However, forests in North Korea have continued to be cleared since the 1990s due to the lack of food and energy resources. Deforestation in this country has not been accurately classified nor consistently reported because of the characteristics of small patches. This study precisely determined the area of deforested land in North Korea through the vegetation phenological classification using high-resolution satellite imagery and deep learning algorithms. Effective afforestation target sites in North Korea were identified with priority grade. The U-Net deep learning algorithm and time-series Sentinel-2 satellite images were applied to phenological classification; the results reflected the small patch-like characteristics of deforestation in North Korea. Based on the phenological classification, the land cover of the country was classified with an accuracy of 84.6%; this included 2.6 million ha of unstocked forest and reclaimed forest. Sites for afforestation were prioritized into five grades based on deforested characteristics, altitude and slope. Forest area is expanded and the forest ecosystem is restored through successful afforestation, this may improve the overall ecosystem services in North Korea. In the long term, it will be possible to contribute to carbon neutrality and greenhouse gas reduction on the Korean Peninsula level through optimal afforestation by using these outcomes.
Joon Kim; Chul-Hee Lim; Hyun-Woo Jo; Woo-Kyun Lee. Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea. Remote Sensing 2021, 13, 2946 .
AMA StyleJoon Kim, Chul-Hee Lim, Hyun-Woo Jo, Woo-Kyun Lee. Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea. Remote Sensing. 2021; 13 (15):2946.
Chicago/Turabian StyleJoon Kim; Chul-Hee Lim; Hyun-Woo Jo; Woo-Kyun Lee. 2021. "Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea." Remote Sensing 13, no. 15: 2946.
Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality.
Girma Adane; Birtukan Hirpa; Chul-Hee Lim; Woo-Kyun Lee. Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia. Remote Sensing 2021, 13, 1275 .
AMA StyleGirma Adane, Birtukan Hirpa, Chul-Hee Lim, Woo-Kyun Lee. Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia. Remote Sensing. 2021; 13 (7):1275.
Chicago/Turabian StyleGirma Adane; Birtukan Hirpa; Chul-Hee Lim; Woo-Kyun Lee. 2021. "Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia." Remote Sensing 13, no. 7: 1275.
In this study, a recent climate change phenomenon and impact were identified by predicting a time-series suitable region of indicator species of warm-temperate and subalpine forests, in which climate change effects are prominent, using machine learning models and recent climate information. In the recent bioclimatic indices, temperature and seasonality of precipitation have increased. The suitable habitat regions of the past warm-temperate and subalpine forests predicted through the machine learning-based random forest model and the bioclimatic indices were similar to the actual natural forest distribution. In the last 18 years, in the warm-temperate forest, though the annual deviation was high, a clear increasing trend has been observed. On average, the potentially suitable habitat areas increased more than three times. In subalpine forests, the suitable habitat area decreased significantly and is very limited in the southern sub-alpine area. After 2013, little suitable area was present, with an average of only 23% compared to the past period. These results are related to the group death of subalpine forest in the 2010s and indicate that the accumulated climatic non-suitability has caused death of the subalpine forest. Climate change impacts on indicator species are both a risk and an opportunity, depending on the species, and we expect wise adaptation and measures to create more opportunities.
Chul-Hee Lim; Hyun-Jun Kim. Machine Learning Application for Identifying Habitat Suitability Changes of Indicator Tree Species against Recent Climate Change. Journal of Climate Change Research 2020, 11, 793 -805.
AMA StyleChul-Hee Lim, Hyun-Jun Kim. Machine Learning Application for Identifying Habitat Suitability Changes of Indicator Tree Species against Recent Climate Change. Journal of Climate Change Research. 2020; 11 (6-2):793-805.
Chicago/Turabian StyleChul-Hee Lim; Hyun-Jun Kim. 2020. "Machine Learning Application for Identifying Habitat Suitability Changes of Indicator Tree Species against Recent Climate Change." Journal of Climate Change Research 11, no. 6-2: 793-805.
This study aimed to analyze the probability of the occurrence of dry/wet spell rainfall using the Markov chain model in the Upper Awash River Basin, Ethiopia. The rainfall analysis was conducted in the short rainy (Belg) and long rainy (Kiremt) seasons on a dekadal (10–day) scale over a 30-year period. In the Belg season, continuous, three-dekad dry spells were prevalent at all stations. Persistent dry spells might result in meteorological, hydrological, and socio-economic drought (in that order) and merge with the Kiremt season. The consecutive wet dekads of the Kiremt season indicate a higher probability of wet dekads at all stations, except Metehara. This station experienced a short duration (dekads 20–23) of wet spells, in which precipitation is more than 50% likely. Nevertheless, surplus rainwater may be recorded at Debrezeit and Wonji only in the Kiremt season because of a higher probability of wet spells in most dekads (dekads 19–24). At these stations, rainfall can be harvested for better water management practices to supply irrigation during the dry season, to conserve moisture, and to reduce erosion. This reduces the vulnerability of the farmers around the river basin, particularly in areas where dry spell dekads are dominant.
Girma Adane; Birtukan Hirpa; Chul-Hee Lim; Woo-Kyun Lee. Spatial and Temporal Analysis of Dry and Wet Spells in Upper Awash River Basin, Ethiopia. Water 2020, 12, 3051 .
AMA StyleGirma Adane, Birtukan Hirpa, Chul-Hee Lim, Woo-Kyun Lee. Spatial and Temporal Analysis of Dry and Wet Spells in Upper Awash River Basin, Ethiopia. Water. 2020; 12 (11):3051.
Chicago/Turabian StyleGirma Adane; Birtukan Hirpa; Chul-Hee Lim; Woo-Kyun Lee. 2020. "Spatial and Temporal Analysis of Dry and Wet Spells in Upper Awash River Basin, Ethiopia." Water 12, no. 11: 3051.
Analysis of the correlation between vegetation greenness and climate variable trends is important in the study of vegetation greenness. Our study used Normalized Difference Vegetation Index-3rd generation data from the Advanced Very High-Resolution Radiometer - Global Inventory Modeling and Mapping Studies (AVHRR-GIMMS NDVI3g), land cover data from the Climate Change Initiative (CCI-LC), and climate data from the Climatic Research Unit global time series (CRU TS) of climate variables (temperature and precipitation, solar radiation) over the past 33 years. First, we estimated the overall trends for vegetation greenness and climate variables over five time periods. Second, we subjected the data to correlation, regression, and residual analyses to detect correlations between vegetation greenness and different climate variables. Third, we extracted trends and correlation results by primary land cover types for each climate zone. Our study was focused at the global scale, and findings indicate that the largest decreasing trend of vegetation greenness and grasslands occurred in the mid-latitude regions of the Northern Hemisphere and in parts of South America, Africa, Saudi Arabia, and south and northeast Asia. In particular, the cold climatic zones of forest (36.6%), cropland (36.6%), and grassland (14.1%) suffered significant decline in vegetation greenness. Anthropogenic activities are mainly responsible for declining vegetation greenness particularly in northern Africa, central and western Asia. However, residual analysis shows an increase in vegetation greenness in some parts of western Europe, southern Australia, and the northern part of South America. The study also identified temperature and precipitation as the main factors responsible for controlling vegetation growth. Hot-spot areas with the largest temperature increases were found in the Amazon, Central America, southern Greenland, east Africa, south-east Asia, and other areas. However, temperatures decreased in the western part of South America, Angola, the Philippines, Indonesia, and Papua New Guinea. Precipitation decreased the most from March to May over most parts of the world with high correlation (r = 0.88) in Russia Canada, northeast Asia, and central Africa. In general, climate factors were the principal drivers of the variation in vegetation greenness globally in recent years.
Munkhnasan Lamchin; Sonam Wangyel Wang; Chul-Hee Lim; Altansukh Ochir; Ukrainskiy Pavel; Belay Manju Gebru; Yuyoung Choi; Seong Woo Jeon; Woo-Kyun Lee. Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014. Global Ecology and Conservation 2020, 24, e01299 .
AMA StyleMunkhnasan Lamchin, Sonam Wangyel Wang, Chul-Hee Lim, Altansukh Ochir, Ukrainskiy Pavel, Belay Manju Gebru, Yuyoung Choi, Seong Woo Jeon, Woo-Kyun Lee. Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014. Global Ecology and Conservation. 2020; 24 ():e01299.
Chicago/Turabian StyleMunkhnasan Lamchin; Sonam Wangyel Wang; Chul-Hee Lim; Altansukh Ochir; Ukrainskiy Pavel; Belay Manju Gebru; Yuyoung Choi; Seong Woo Jeon; Woo-Kyun Lee. 2020. "Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014." Global Ecology and Conservation 24, no. : e01299.
Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective.
Eunbeen Park; Jiwon Kim; Cholho Song; Hyun-Woo Jo; Sujong Lee; Sea Kim; Sugyeong Park; Chul-Hee Lim; Woo-Kyun Lee. Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region. Sustainability 2020, 12, 7256 .
AMA StyleEunbeen Park, Jiwon Kim, Cholho Song, Hyun-Woo Jo, Sujong Lee, Sea Kim, Sugyeong Park, Chul-Hee Lim, Woo-Kyun Lee. Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region. Sustainability. 2020; 12 (18):7256.
Chicago/Turabian StyleEunbeen Park; Jiwon Kim; Cholho Song; Hyun-Woo Jo; Sujong Lee; Sea Kim; Sugyeong Park; Chul-Hee Lim; Woo-Kyun Lee. 2020. "Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region." Sustainability 12, no. 18: 7256.
The objective of this study was to predict the potential distribution change for vulnerable and endangered tree species (Picea jezoensis, Abies koreana, Abies nephrolepis, and Pinus pumila) under climate change. To this end, HyTAG (Hydrological and Thermal Analogy Groups), the Korea-specific forest cover distribution model based on hydrological and thermal indices, was used to predict the potential spatio-temporal distribution changes in tree species. As a result, the optimal habitat of vulnerable and endangered tree species (Picea jezoensis, Abies koreana, Abies nephrolepis, and Pinus pumila) under the potential impact of climate change is expected to shrink rapidly and is going to appear only in Gangwon Province in the future. Also, comparing the distribution range of tree species, Picea jezoensis has the largest distribution range compared to the other tree species. On the other hand, the optimal habitat of Pinus pumila is the smallest. This means that physical environment for Pinus pumila tends to be rapidly reduced. These results show that these tree species are facing a great risk due to climate change, and this is expected to be an important input or tool for decision-making with respect to establishing management and conservation measures to reduce the negative impact of climate change.
Somin Yoo; Chul-Hee Lim; Moonil Kim; Cholho Song; Sea Jin Kim; Woo-Kyun Lee. Potential Distribution of Endangered Coniferous Tree Species under Climate Change. Journal of Climate Change Research 2020, 11, 215 -226.
AMA StyleSomin Yoo, Chul-Hee Lim, Moonil Kim, Cholho Song, Sea Jin Kim, Woo-Kyun Lee. Potential Distribution of Endangered Coniferous Tree Species under Climate Change. Journal of Climate Change Research. 2020; 11 (4):215-226.
Chicago/Turabian StyleSomin Yoo; Chul-Hee Lim; Moonil Kim; Cholho Song; Sea Jin Kim; Woo-Kyun Lee. 2020. "Potential Distribution of Endangered Coniferous Tree Species under Climate Change." Journal of Climate Change Research 11, no. 4: 215-226.
Despite being Asia’s fastest-growing economy, as of 2015, the Asian Development Bank (ADB) ranked the Philippines 33rd out of 48 countries in terms of water security. This verifies that economic development does not always lead to better provisions of basic needs. This study attempts to discover the fundamental issues that decrease water security in Metro Manila, the capital region of the Philippines. With El Niño disrupting the optimal weather conditions, Metro Manila is facing the lasting impacts of a water shortage crisis, which is the worst in the past decade. This research inspects the role of climate change in exacerbating El Niño, and its threat to the water security of the developing city. Furthermore, other factors that influence Metro Manila’s water security are discussed. Upon establishing a correlation between climate change and El Niño, Metro Manila’s general water management strategy is evaluated to better assess the multiple factors that have led to the current water shortage crisis. This paper is intended to recommend necessary and feasible proactive measures that are geared towards water security in Metro Manila, and possibly other cities with similar circumstances.
Halim Lee; Jaewon Son; Dayoon Joo; Jinhyeok Ha; SeongReal Yun; Chul-Hee Lim; Woo-Kyun Lee. Sustainable Water Security Based on the SDG Framework: A Case Study of the 2019 Metro Manila Water Crisis. Sustainability 2020, 12, 6860 .
AMA StyleHalim Lee, Jaewon Son, Dayoon Joo, Jinhyeok Ha, SeongReal Yun, Chul-Hee Lim, Woo-Kyun Lee. Sustainable Water Security Based on the SDG Framework: A Case Study of the 2019 Metro Manila Water Crisis. Sustainability. 2020; 12 (17):6860.
Chicago/Turabian StyleHalim Lee; Jaewon Son; Dayoon Joo; Jinhyeok Ha; SeongReal Yun; Chul-Hee Lim; Woo-Kyun Lee. 2020. "Sustainable Water Security Based on the SDG Framework: A Case Study of the 2019 Metro Manila Water Crisis." Sustainability 12, no. 17: 6860.
The applicability of deep learning to remote sensing is rapidly increasing in accordance with the improvement in spatiotemporal resolution of satellite images. However, unlike satellite images acquired in near-real-time over wide areas, there are limited amount of labeled data used for model training. In this article, three kinds of deep learning applications--data augmentation, semisupervised classification, and domain-adapted architecture--were tested in an effort to overcome the limitation of insufficient labeled data. Among the diverse tasks that can be used for classification, rice paddy detection in South Korea was performed for its ability to fully utilize the advantages of deep learning and high spatiotemporal image resolution. In the process of designing each application, the domain knowledge of remote sensing and rice phenology was integrated. Then, all possible combinations of the three applications were examined and evaluated with pixel-based comparisons in various environments and city-level comparisons using national statistics. The results of this article indicated that all combinations of the applications can contribute to increase classification performance, even though the uncertainty involved in imitating or utilizing unlabeled data remains. As the effectiveness of the proposed applications was experimentally confirmed, enhancement in the applicability of deep learning was expected in various remote sensing areas. In particular, the proposed applications would be significant when they are applied to a wide range of study areas and high-resolution images, as they tend to require a large amount of learning data from diverse environments, owing to high intra-class heterogeneity.
Hyun-Woo Jo; Sujong Lee; Eunbeen Park; Chul-Hee Lim; Cholho Song; Halim Lee; Youngjin Ko; Sungeun Cha; Hoonjoo Yoon; Woo-Kyun Lee. Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 7589 -7601.
AMA StyleHyun-Woo Jo, Sujong Lee, Eunbeen Park, Chul-Hee Lim, Cholho Song, Halim Lee, Youngjin Ko, Sungeun Cha, Hoonjoo Yoon, Woo-Kyun Lee. Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (11):7589-7601.
Chicago/Turabian StyleHyun-Woo Jo; Sujong Lee; Eunbeen Park; Chul-Hee Lim; Cholho Song; Halim Lee; Youngjin Ko; Sungeun Cha; Hoonjoo Yoon; Woo-Kyun Lee. 2020. "Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11: 7589-7601.
Air pollution is one of the most significant environmental hazards. The elderly, young, and poor are more vulnerable to air pollution. The risk of air pollution was assessed based on the risk framework published by the Intergovernmental Panel on Climate Change (IPCC) in terms of three aspects: hazard, exposure, and vulnerability. This study determined the concentrations of hazardous pollutants using satellite images from 2015 at 1 km2 spatial resolution. In addition, the study identified vulnerable groups who are exposed to hazardous air pollutants. The study highlighted the degree of vulnerability based on environmental sensitivity and institutional abilities, such as mitigation and social adaption policies, using statistical data. Based on the results, Seoul City and Gyeonggi Province have low air pollution risk owing to good institutional abilities, while the western coastal area has the highest air pollution risk. Three adaption pathway scenarios were assessed in terms of the effect of increases in the budget for social adaptation policies on the level of risk. The study found that the risk can be reduced when the social adaptation budget of 2015 base level is increased by 20% in Gyeonggi Province and by 30% in the western coastal area. In conclusion, this risk assessment can support policy-making to target more vulnerable groups based on scientific evidence and to ensure environmental justice at the national level.
Sugyeong Park; Sea Jin Kim; Hangnan Yu; Chul-Hee Lim; Eunbeen Park; Jiwon Kim; Woo-Kyun Lee. Developing an Adaptive Pathway to Mitigate Air Pollution Risk for Vulnerable Groups in South Korea. Sustainability 2020, 12, 1790 .
AMA StyleSugyeong Park, Sea Jin Kim, Hangnan Yu, Chul-Hee Lim, Eunbeen Park, Jiwon Kim, Woo-Kyun Lee. Developing an Adaptive Pathway to Mitigate Air Pollution Risk for Vulnerable Groups in South Korea. Sustainability. 2020; 12 (5):1790.
Chicago/Turabian StyleSugyeong Park; Sea Jin Kim; Hangnan Yu; Chul-Hee Lim; Eunbeen Park; Jiwon Kim; Woo-Kyun Lee. 2020. "Developing an Adaptive Pathway to Mitigate Air Pollution Risk for Vulnerable Groups in South Korea." Sustainability 12, no. 5: 1790.
Forests play an important role in regulating the carbon (C) cycle. The main objective of this study was to quantify the effects of South Korean national reforestation programs on carbon budgets. We estimated the changes in C stocks and annual C sequestration in the years 1961–2014 using Korea-specific models, a forest cover map (FCM), national forest inventory (NFI) data, and climate data. Furthermore, we examined the differences in C budgets between Cool forests (forests at elevations above 700 m) and forests in lower-altitude areas. Simulations including the effects of climate conditions on forest dynamics showed that the C stocks of the total forest area increased from 6.65 Tg C in 1961 to 476.21 Tg C in 2014. The model developed here showed a high degree of spatiotemporal reliability. The mean C stocks of the Cool forests and other forests increased from 4.03 and 0.43 Mg C ha−1, respectively, to 102.43 and 73.76 Mg C ha−1 at a rate of 1.82 and 1.36 Mg C ha−1 yr−1 during the same period. These results imply that, although the total Cool forest area of South Korea occupied only about 12.3% (772,788 ha) of the total forest area, the Cool forests play important roles in C balances and forest ecosystems in South Korea. Annual C sequestration totals are projected to decrease at a low rate in the near future because the overall growth rate of a mature forest decreases as the stand ages. Our results quantified forest C dynamics in South Korean forests before and after national reforestation programs. Furthermore, our results can help in development of regional and national forest management strategies to allow for sustainable development of society and to cope with climate change in South Korea.
Moonil Kim; Florian Kraxner; Yowhan Son; Seong Woo Jeon; Anatoly Shvidenko; Dmitry Schepaschenko; Bo-Young Ham; Chul-Hee Lim; Cholho Song; Mina Hong; Woo-Kyun Lee. Quantifying Impacts of National-Scale Afforestation on Carbon Budgets in South Korea from 1961 to 2014. Forests 2019, 10, 579 .
AMA StyleMoonil Kim, Florian Kraxner, Yowhan Son, Seong Woo Jeon, Anatoly Shvidenko, Dmitry Schepaschenko, Bo-Young Ham, Chul-Hee Lim, Cholho Song, Mina Hong, Woo-Kyun Lee. Quantifying Impacts of National-Scale Afforestation on Carbon Budgets in South Korea from 1961 to 2014. Forests. 2019; 10 (7):579.
Chicago/Turabian StyleMoonil Kim; Florian Kraxner; Yowhan Son; Seong Woo Jeon; Anatoly Shvidenko; Dmitry Schepaschenko; Bo-Young Ham; Chul-Hee Lim; Cholho Song; Mina Hong; Woo-Kyun Lee. 2019. "Quantifying Impacts of National-Scale Afforestation on Carbon Budgets in South Korea from 1961 to 2014." Forests 10, no. 7: 579.
Hydrological changes attributable to global warming increase the severity and frequency of droughts, which in turn affect agriculture. Hence, we proposed the Standardized Agricultural Drought Index (SADI), which is a new drought index specialized for agriculture and crops, and evaluated current and expected droughts in the Korean Peninsula. The SADI applies crop phenology to the hydrological cycle, which is a basic element that assesses drought. The SADI of rice and maize was calculated using representative hydrological variables (precipitation, evapotranspiration, and runoff) of the crop growing season. In order to evaluate the effectiveness of SADI, the three-month Standardized Precipitation Index, which is a representative drought index, and rainfed crop yield were estimated together. The performance evaluation of SADI showed that the correlation between rainfed crop yield and SADI was very high compared with that of existing drought index. The results of the assessment of drought over the past three decades provided a good indication of a major drought period and differentiated the results for crops and regions. The results of two future scenarios showed common drought risks in the western plains of North Korea. Successfully validated SADIs could be effectively applied to agricultural drought assessments in light of future climate change, and would be a good example of the water-food nexus approach.
Chul-Hee Lim; Seung Hee Kim; Jong Ahn Chun; Menas C. Kafatos; Woo-Kyun Lee. Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula. Water 2019, 11, 1105 .
AMA StyleChul-Hee Lim, Seung Hee Kim, Jong Ahn Chun, Menas C. Kafatos, Woo-Kyun Lee. Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula. Water. 2019; 11 (5):1105.
Chicago/Turabian StyleChul-Hee Lim; Seung Hee Kim; Jong Ahn Chun; Menas C. Kafatos; Woo-Kyun Lee. 2019. "Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula." Water 11, no. 5: 1105.
As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.
Sea Jin Kim; Chul-Hee Lim; Gang Sun Kim; Jongyeol Lee; Tobias Geiger; Omid Rahmati; Yowhan Son; Woo-Kyun Lee. Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables. Remote Sensing 2019, 11, 86 .
AMA StyleSea Jin Kim, Chul-Hee Lim, Gang Sun Kim, Jongyeol Lee, Tobias Geiger, Omid Rahmati, Yowhan Son, Woo-Kyun Lee. Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables. Remote Sensing. 2019; 11 (1):86.
Chicago/Turabian StyleSea Jin Kim; Chul-Hee Lim; Gang Sun Kim; Jongyeol Lee; Tobias Geiger; Omid Rahmati; Yowhan Son; Woo-Kyun Lee. 2019. "Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables." Remote Sensing 11, no. 1: 86.
Chul-Hee Lim; You Seung Kim; Myungsoo Won; Sea Jin Kim; Woo-Kyun Lee. Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea. Geomatics, Natural Hazards and Risk 2019, 10, 719 -739.
AMA StyleChul-Hee Lim, You Seung Kim, Myungsoo Won, Sea Jin Kim, Woo-Kyun Lee. Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea. Geomatics, Natural Hazards and Risk. 2019; 10 (1):719-739.
Chicago/Turabian StyleChul-Hee Lim; You Seung Kim; Myungsoo Won; Sea Jin Kim; Woo-Kyun Lee. 2019. "Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea." Geomatics, Natural Hazards and Risk 10, no. 1: 719-739.
Su Gyeong Park; Soon Chul Park; Cholho Song; Chul-Hee Lim; Soo Jeong Lee; Woo-Kyun Lee; Park; Su Gyeong; Soon Chul; Song; Cholho; Lim; Chul- Hee; Lee; Soo Jeong; Woo- Kyun. Analysis of Design Elements and Barriers to Link the Emission Trading Systems between the Republic of Korea and China. Journal of Climate Change Research 2018, 9, 471 -485.
AMA StyleSu Gyeong Park, Soon Chul Park, Cholho Song, Chul-Hee Lim, Soo Jeong Lee, Woo-Kyun Lee, Park, Su Gyeong, Soon Chul, Song, Cholho, Lim, Chul- Hee, Lee, Soo Jeong, Woo- Kyun. Analysis of Design Elements and Barriers to Link the Emission Trading Systems between the Republic of Korea and China. Journal of Climate Change Research. 2018; 9 (4):471-485.
Chicago/Turabian StyleSu Gyeong Park; Soon Chul Park; Cholho Song; Chul-Hee Lim; Soo Jeong Lee; Woo-Kyun Lee; Park; Su Gyeong; Soon Chul; Song; Cholho; Lim; Chul- Hee; Lee; Soo Jeong; Woo- Kyun. 2018. "Analysis of Design Elements and Barriers to Link the Emission Trading Systems between the Republic of Korea and China." Journal of Climate Change Research 9, no. 4: 471-485.
Cholho Song; Somin Yoo; Moonil Kim; Chul-Hee Lim; Jiwon Kim; Sea Jin Kim; Gang Sun Kim; Woo-Kyun Lee; Song; Cholho; Yoo; Somin; Kim; MoonIl; Lim; Chul- Hee; Jiwon; Sea Jin; Gang Sun; Lee; Woo- Kyun. Estimation of Future Land Cover Considering Shared Socioeconomic Pathways using Scenario Generators. Journal of Climate Change Research 2018, 9, 223 -234.
AMA StyleCholho Song, Somin Yoo, Moonil Kim, Chul-Hee Lim, Jiwon Kim, Sea Jin Kim, Gang Sun Kim, Woo-Kyun Lee, Song, Cholho, Yoo, Somin, Kim, MoonIl, Lim, Chul- Hee, Jiwon, Sea Jin, Gang Sun, Lee, Woo- Kyun. Estimation of Future Land Cover Considering Shared Socioeconomic Pathways using Scenario Generators. Journal of Climate Change Research. 2018; 9 (3):223-234.
Chicago/Turabian StyleCholho Song; Somin Yoo; Moonil Kim; Chul-Hee Lim; Jiwon Kim; Sea Jin Kim; Gang Sun Kim; Woo-Kyun Lee; Song; Cholho; Yoo; Somin; Kim; MoonIl; Lim; Chul- Hee; Jiwon; Sea Jin; Gang Sun; Lee; Woo- Kyun. 2018. "Estimation of Future Land Cover Considering Shared Socioeconomic Pathways using Scenario Generators." Journal of Climate Change Research 9, no. 3: 223-234.
본 연구는 산사태 위험지도의 정확도를 향상시킬 수 있는 방안을 모색하기 위해, 2011년 7월 26일부터 28일까지 서울지역에서 집중호우 시 발생한 산사태를 바탕으로 지형공간 및 기상인자를 이용해 산사태 위험지도를 작성하였다. 그 결과, 서울지역의 지형공간 및 기상인자를 모두 고려한 통합된 산사태 위험지도에서는 총 19회의 산사태중 18회(약 95%)가 높은 산사태 위험 지역(high landslide risk area; HLRA)에서, 나머지 1회(약 5%)는 중간 산사태 위험 지역(medium landslide risk area; MLRA)에서 발생하였다. 이를 통해 본 연구에서 지형공간인자를 기반으로 도출한 산사태 위험지역은 실제 집중호우 발생 시 산사태 발생 위험이 상당히 높아짐을 확인하였다.
Sung Eun Cha; Chul Hee Lim; Ji Won Kim; Moon Il Kim; Chol Ho Song; Woo Kyun Lee; Cha; Sung Eun; Lim; Chul Hee; Kim; Ji Won; Moon Il; Song; Chol Ho; Lee; Woo Kyun. Analysis of Landslide Hazard Area due to Heavy Rainfall in the Seoul Metropolitan Area. Journal of Korean Society for Geospatial Information Science 2018, 26, 3 -11.
AMA StyleSung Eun Cha, Chul Hee Lim, Ji Won Kim, Moon Il Kim, Chol Ho Song, Woo Kyun Lee, Cha, Sung Eun, Lim, Chul Hee, Kim, Ji Won, Moon Il, Song, Chol Ho, Lee, Woo Kyun. Analysis of Landslide Hazard Area due to Heavy Rainfall in the Seoul Metropolitan Area. Journal of Korean Society for Geospatial Information Science. 2018; 26 (3):3-11.
Chicago/Turabian StyleSung Eun Cha; Chul Hee Lim; Ji Won Kim; Moon Il Kim; Chol Ho Song; Woo Kyun Lee; Cha; Sung Eun; Lim; Chul Hee; Kim; Ji Won; Moon Il; Song; Chol Ho; Lee; Woo Kyun. 2018. "Analysis of Landslide Hazard Area due to Heavy Rainfall in the Seoul Metropolitan Area." Journal of Korean Society for Geospatial Information Science 26, no. 3: 3-11.