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Conserving tropical forests and peatlands is considered a key strategy for international forest project cooperation and the promotion of carbon neutrality. Focusing on peatlands in the Sumatra Selatan and Jambi provinces in Indonesia, this study aimed to assess land-cover changes from 1992 to 2020 and associated ecosystem services to assist in the development of effective forest and peatland-related projects. This study first analyzed the historical land-cover changes as a baseline for establishing potential project scenarios. Carbon storage, habitat suitability, water yield, and crop production were selected as the key ecosystem services in the peatlands, and these were assessed for the current land cover and two possible project scenarios in 2050 – business-as-usual (BAU) and forest and wetland restoration (FOR) – using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tool. In terms of land-cover changes, the area of forests and wetlands decreased, but that of agriculture, grassland, and built-up areas increased in the peatlands. Based on the land-cover scenarios, the carbon storage, habitat suitability, water yield, and crop production varied on 2.32–2,74 billion tC, 92.02–93.46 %, 16.45–16,91 billion m3, and 2.34–2.73 million ton, respectively. The total economic value of the ecosystem services for the current land cover was $75.41 billion, while that predicted for BAU and FOR was estimated to be $70.59 billion and $77.79 billion, respectively. Restoration plans have the environmental and economic benefit of provincial-level assessment, so the development of appropriate projects for tropical forest and peatland conservation and restoration is necessary for international cooperation projects.

The frequency of forest fires worldwide has increased recently due to climate change, leading to severe and widespread damage. In this study, we investigate potential changes in the fire susceptibility of areas in South Korea arising from climate change. We constructed a dataset of large-scale forest fires from the past decade and employed it in machine learning models that integrate climatic, socioeconomic, and environmental variables to assess the risk of forest fires. According to the results of these models, the eastern region is identified as highly vulnerable to forest fires during the baseline period, while the western region is classified as relatively safe. However, in the future, certain areas along the western coast are predicted to become more susceptible to forest fires. Consequently, as climate change continues, the risk of domestic forest fires is expected to increase, leading to the need for proactive prevention measures and careful management. This study contributes to the understanding of forest fire occurrences under diverse climate scenarios.

This study conducted a systematic literature review focusing on peatlands studies in Southeast Asia, specifically in Jambi, South Sumatra, and the Riau province of Indonesia, covering the period from 2001 to 2023. To ensure the quality and rigor of the analyzed articles, a critical process and systematic review were employed. Journal articles were extracted using reputable resources, including Google Scholar and Scopus, to enhance the validity and reliability of the research results. We identified significant research topics based on region, province, and sector. Additionally, we synthesized the existing classification of ecosystem services, drawing on previous studies conducted in Indonesia. These services were categorized as provisioning, regulating, cultural, and supporting services. We also reviewed the classification of ecosystem service types based on peatland degradation and restoration. This study identified evidence of peatland intervention to evaluate ecosystem services in Indonesia. We found that large-scale cultivation and production of palm oil, local policies, and forest fires were the main intervening factors in Indonesian peatlands. Furthermore, Indonesian peatlands have undergone conversion to oil palm, timber, and crop plantations. It is imperative to substantiate the effectiveness of future peatland restoration plans and further refine the quantification of services provided by peatland ecosystems through cooperative projects.

Abstract Background Forests are atmospheric carbon sinks, whose natural growth can contribute to climate change mitigation. However, they are also affected by climate change and various other phenomena, for example, the low growth of coniferous forests currently reported globally, including in the Republic of Korea. In response to the implementation of the Paris Agreement, the Korean government has proposed 2030 greenhouse gas roadmap to achieve a Nationally Determined Contribution (NDC), and the forest sector set a sequestration target of 26 million tons by 2030. In this study, the Korean forest growth model (KO-G-Dynamic model) was used to analyze various climate change and forest management scenarios and their capacity to address the NDC targets. A 2050 climate change adaptation strategy is suggested based on forest growth and CO2 sequestration. Results Forest growth was predicted to gradually decline, and CO2 sequestration was predicted to reach 23 million tons per year in 2050 if current climate and conditions are maintained. According to the model, sequestrations of 33 million tCO2 year−1 in 2030 and 27 million tCO2 year−1 in 2050 can be achieved if ideal forest management is implemented. It was also estimated that the current forest management budget of 317 billion KRW (264 million USD) should be twice as large at 722 billion KRW (602 million USD) in the 2030s and 618 billion KRW (516 million USD) in the 2050s to achieve NDC targets. Conclusions The growth trend in Korea's forests transitions from young-matured stands to over-mature forests. The presented model-based forest management plans are an appropriate response and can increase the capacity of Korea to achieve its NDC targets. Such a modeling can help the forestry sector develop plans and policies for climate change adaptation.

Forests are essential for sustaining ecosystems, regulating the climate, and providing economic benefits to human society. However, activities such as commercial practices, fuelwood collection, and land use changes have resulted in severe forest degradation and deforestation. Timor-Leste, a small island nation, faces environmental sustainability challenges due to land use changes, limited infrastructure, and agricultural practices. This study proposes a simplified and highly accessible approach to assess deforestation (SHAD) nationally using limited human and non-human resources such as experts, software, and hardware facilities. To assess deforestation in developing countries, we utilize open-source software (Dryad), employ the U-Net deep learning algorithm, and utilize open-source data generated from the Google Earth Engine platform to construct a time-series land cover classification model for Timor-Leste. In addition, we utilize the open-source land cover map as label data and satellite imagery as model training inputs, and our model demonstrates satisfactory performance in classifying time-series land cover. Next, we classify the land cover in Timor-Leste for 2016 and 2021, and verified that the forest classification achieved high accuracy ranging from 0.79 to 0.89. Thereafter, we produced a deforestation map by comparing the two land cover maps. The estimated deforestation rate was 1.9% annually with a primary concentration in the northwestern municipalities of Timor-Leste with dense population and human activities. This study demonstrates the potential of the SHAD approach to assess deforestation nationwide, particularly in countries with limited scientific experts and infrastructure. We anticipate that our study will support the development of management strategies for ecosystem sustainability, climate adaptation, and the conservation of economic benefits in various fields.

This review paper presents the extent and magnitude of forest fires and adaptation responses to deepen our understanding of the dynamics of forest fires in the eastern Himalayan region. We used a narrative scooping review approach to narrate the present state of forest fires and resilient adaptation responses to guide the development of climate resilient adaptation pathways in the future. Our review concludes that forest fires (total mean fire incidence = 3,158.5) continue to destroy overwhelming areas of forests every year (mean area destroyed = 658,778.4 hectares), presenting significant threat to ecological integrity, human wellbeing, and global effort to fight climate change. Fire incidences are highest during and post dry winter months which quickly declines at the onset of monsoon. Adaptation and responding to ever growing size and frequency of forest fires are limited by lack of awareness, training on fire management, firefighting infrastructure, technology, and adequate policy and financial support. In addition, there is acute lack of scientific studies to understand forest fire dynamics in the eastern Himalayan region. Based on the forest fire literature we argue that policy makers and communities must quickly adopt policy strategies that support smart responses to forest fire management including allocating adequate funds to support capacity building and research. Such decision must be based on research into potentials for climate resilient pathways for adaptation response.

The rates of land degradation and urbanization has increased worldwide during the past century. Herein, we evaluate the spatio-temporal changes in global land cover via categorical intensity analysis of the European Space Agency’s climate change initiative (ESA-CCI) data for the period 1992 to 2018. Specifically, we evaluated intensity analysis at the category level for five time intervals, namely 1992–1997, 1997–2002, 2002–2007 and 2007–2012, 2012–2018. We also, evaluate the decrease and increase in the land cover at continental and climate zone. The study evaluates the following land cover categories: Cropland, Forest, SGO (Shrubland, Grassland, and Other), Urban, Bare areas, and WIS (Water, ice, and snow). After accuracy assessment, the global land-cover map for 2009 from the GlobCover data is selected, and a reclassified version of this map is used as a verification tool for comparison with the reclassified study data. The analysis of changes over the last 26 years shows that the loss for Cropland are dormant during the first and second time intervals, but active during the third, fourth, and fifth time intervals. By contrast, Forest experienced loss during all time intervals, and SGO experienced active loss only during the second time interval. Urban is the only category that experienced active gain during all time intervals. The present study also indicates that urbanization has and converted land in temperate regions during the past 26 years. Additionally, in South America and the tropical regions, the expansion of Cropland is the largest contributor to the decline in Forests and SGO.

Desertification is a global phenomenon caused by various processes, including climate change, vegetation processes, and human activities. The need to combat desertification is increasing in many countries. A reasonable assessment of the vulnerability or sensitivity of land cover to desertification at national scales is crucial to formulate appropriate strategies or policies for combating it. The main purpose of this work was to quantitatively assess the sensitivity of land cover to desertification in Mongolia using the MEDALUS approach. The MEDALUS method is a widely known technique for assessing desertification in the Mediterranean area. In this study, the method was adjusted to be applied to Mongolia, while the numerical methods of the MEDALUS remained the same. The modified MEDALUS method used nine factors from 2003 and 2008 to quantify the sensitivity of land to desertification. As a result, our study resulted in the calculation and spatial distribution of the Environmental Sensitive Area Index (ESAI), produced throughout Mongolia. In 2003, the middle region of the southern Mongolia had the highest sensitivity to desertification, while sensitivity in 2008 increased in the western area. Mongolia’s area with the highest ESAI range increased approximately five times, indicating rapid desertification occurring throughout Mongolia from 2003 to 2008.

North Korea is a representative country that should restore its forest ecosystem, which is vital for responding to climate change. In this study, we assessed the extent to which afforestation can reduce the climate change impact on water resources by adding an afforestation scenario to the variables used to estimate the forest water yield. We applied the InVEST seasonal water yield model and constructed a data ensemble of the SSP5–8.5 scenario for this simulation. In the projection of future forest water supply according to the SSP5–8.5 scenario, baseflow and local recharge decreased by approximately 25%, and quickflow increased by approximately 47%, compared to the baseline period. Under the three reforestation scenarios, the future water supply from the forests showed significant positive changes. The baseflow increased by approximately 4%, 15%, and 28% in the reforestation scenario of Level 1, Level 2, and Level 3, respectively. In a Level 3 scenario, most of the baseflow and local recharge, which had decreased owing to the impact of climate change, was recovered. The baseflow in Level 3 was 26,882 million m3 y−1, which was approximately 98% of that in the baseline period. The Taedong River and Chongchon River, which are the major granary areas in North Korea, were directly affected by the climate change. However, it was confirmed that the water supply in these areas can be increased through forest restoration. These results indicate the deterministic role of forest restoration in increasing the water supply.

In response to widespread deforestation, North Korea has restored forests through national policy over the past 10 years. Here, the entire process of forest degradation and restoration was evaluated through satellite-based vegetation monitoring, and its effects were also investigated. The vegetation dynamics of North Korea were characterized from 1986 to 2021 using the Landsat satellite 5–7, after which we evaluated the effect of vegetation shifts through changes in surface temperature since the 2000s. Vegetation greenness decreased significantly from the 1980s to the 2000s but increased in recent decades due to forest restoration. During the deforestation period, vegetation in all areas of North Korea tended to decrease, which was particularly noticeable in the provinces of Pyongannam-do and Hamgyongnam-do. During the forest restoration period, increases in vegetation greenness were evident in most regions except for some high-mountainous and developing regions, and the most prominent increase was seen in Pyongyang and Pyongannam-do. According to satellite-based analyses, the land surface temperature exhibited a clear upward trend (average slope = 0.13). However, large regional differences were identified when the analysis was shortened to encompass only the last 10 years. Particularly, the correlation between the area where vegetation improved and the area where the surface temperature decreased was high (−0.32). Moreover, the observed atmospheric temperature increased due to global warming, but only the surface temperature exhibited a decreasing trend, which could be understood by the effect of vegetation restoration. Our results suggest that forest restoration can affect various sectors beyond the thermal environment due to its role as an enhancer of ecosystem services.

To assess which data type is more effective for spatial modeling in the Republic of Korea, we conducted geostatistical analysis based on frequency, intensity, and spatial autocorrelation using two types of forest fire occurrence data: that collected through field survey of the Korea Forest Service (KFS) and satellite active fire data of Moderate Resolution Imaging Spectroradiometer (MODIS). The maximum entropy (MaxEnt) model was used with environmental factors in the spatial modeling of fire probability to compare the accuracy of the two data types based on 10 years of historical data. The results showed a clear difference in fire frequency and similar fire intensity patterns. The spatial autocorrelation between the fire frequency and intensity of the two data types was analyzed using a semi-variogram. Fire intensity was significantly correlated, with the MODIS data having a higher correlation than the KFS data. Examination of the spatial autocorrelation and related factors by fire source also indicated that MODIS data had higher spatial autocorrelation, with remarkable distinction found in climate factors. In spatial the modeling, MODIS data showed a similar outcome to that of hotspot analysis, with higher accuracy and better model performance attributable to high spatial autocorrelation. Even though the KFS data were collected from post-fire surveys, they resulted in low spatial autocorrelation and reduced model accuracy owing to the wide distribution of data. MODIS had many detection errors. With spatial filtering, however, the model accuracy can be improved with relatively high spatial autocorrelation.

The Earth Trends Modeler (ETM) is an earth observation software tool that allows for modeling environmental changes and trend analyses of earth observation data. We used Global Inventory Modeling and Mapping Studies (GIMMS)-Normalized Difference Vegetation Index-3rd generation (NDVI3g) and Climatic Research Unit Time Series (CRU-TS) for climate data. We applied Mann-Kendall Monotonic Trend (MKMT) test using the ETM for changing trend analyses, correlation and multiple regression for analyzing relationship between vegetation greenness and climate factors. These methods are effective approaches for conducting long-term monitoring and correlation analyses in broad area using satellite data. These methods were used to analyze the long term data, but mostly focused on national scale study. Our study expanded the methodological applicability over the whole Asia during the last 33 years. In addition, we used spatio-temporal data such as vegetation greenness, rainfall, temperature, and potential evapotranspiration in order to estimate changing trends and relationship analysis of vegetation greenness and climate factors. • MKMT test was an applicable method for broad area and analyzed the increasing or decreasing trends using time series dataset with a predetermined level of significance. • The correlation and regression analysis were suitable and useful methods to estimate spatial relationships between vegetation greenness and climate factors in the long term period. Method name: Application of Earth Trend Modeler (ETM) for long term spatio-temporal data analysis, Keywords: Mann-Kendall monotonic trend test, Spatio-temporal analysis, Earth trend modeler, Changing trend analysis, Relationship analysis

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.

The threat of fine particulate matter (PM2.5) is increasing globally. Tackling this issue requires an accurate understanding of its trends and drivers. In this study, global risk regions of PM2.5 concentrations during 1998–2016 were spatiotemporally derived. Time series analysis was conducted in the spatial relationship between PM2.5 and three socio-environmental drivers: population, urban ratio, and vegetation greenness that can cause changes in the concentration of PM2.5. “High Risk” areas were widely distributed in India and China. In India and sub-Saharan Africa, the increased overall population was strongly correlated with PM2.5 concentrations. Urban ratio increased in both developed and developing countries. A “decoupling” phenomenon occurred in developed countries, where urban expansion continued while PM2.5 concentrations decreased. Vegetation greenness and PM2.5 were strongly correlated in High Risk zones. Although urban expansion and population growth generally reduce vegetation greenness, developed countries reduced PM2.5 while maintaining greenness, whereas developing countries increased PM2.5 with decreasing greenness significantly in High Risk regions. Ultimately, economic and national growth should occur without increasing PM2.5 concentrations. Recent cases from Europe and the eastern United States demonstrate that this is possible, depending on the development pathway.

Recent cases of climate disasters such as the European floods in 2021 and Korea’s longest rainy season in 2020 strongly imply the importance of adaptation to climate change. In this study, we performed a numerical prediction on how much climate change adaptation factors related to forest policy can reduce climate disasters such as landslides. We focused on the landslide in Korea and applied a machine learning model reflecting adaptive indicators in the representative concentration pathway 8.5 climate scenario. The changes in the landslide probability were estimated using the Random Forest model, which estimated the landslide probability in the baseline period (2011) with excellent performance, and the spatial adaptation indicators used in this study contributed approximately 20%. The future landslide risk predicting indicated a significant increase in the Very High and High risk areas, especially in 2092. The application of the forest-related adaptation indices based on the policy scenario showed that in 2050, the effect was not pronounced, but in 2092, when the risk of landslides was much higher, the effect increased significantly. In particular, the effect was remarkable in the Seoul metropolitan and southern coastal regions. Even with the same adaptive capacity, it exerted a larger effect on the enhanced disasters. Our results suggest that the enhancement of adaptive capacity can reduce landslide risk up to 70% in a Very High risk region. In conclusion, it implies an importance to respond to the intensifying climate disasters, and abundant follow-up studies are expected to appear in the future.

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.

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.

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.

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.

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.

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.

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.

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.

As climate change is ongoing, many studies have recently focused on adaptation to climate change from a spatial perspective. However little is known about how changing the spatial composition of landuse could improve climate change resilience. Consideration of climate change impacts when spatially allocating landuse could be a useful and fundamental long term adaptation strategy, particularly for regional planning. Here, we identify climate adaptation scenarios based on existing extents of three landuse classes using multi-objective genetic algorithms for a 9982 km ^2 region with 3.5 million inhabitants in South Korea. We selected five objectives for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damage and existing landuse conversion, maximization of rice yield, protection of high species richness areas, and economic value. We generated 17 Pareto landuse scenarios by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial landuse composition for all adaptation objectives, suggesting that some alteration of current landuse patterns could increase overall climate resilience. Given the flexible structure of the optimization model, we expect that regional stakeholders could efficiently generate other scenarios by adjusting model parameters (weighting combinations) or replacing input data (impact maps), and selecting a scenario depending on preference or a number of problem-related factors.

Biodiversity is rapidly declining globally and targeted efforts are needed to mitigate the loss of species. Conventional conservation efforts have focused on establishing protected areas and restoring degraded lands in order to maintain current conditions or restore ecosystems to a pre-damaged state. However, as the climate changes, the current bioclimatic zones will be re-distributed globally. Historical distribution patterns may no longer serve as an effective guide for supporting biodiversity under climate change. In response to these challenges, this study proposes a spatially explicit strategy for biodiversity conservation that takes climate change into account using bioclimatic classification. The bioclimatic classification maps of Northeast Asia (NEA) were constructed for three historical time periods (the 1980s, 1990s, and 2000s) and two future time periods (the 2050s and 2070s) using five general circulation models (GCMs) in representative concentration pathway (RCP 8.5) scenarios. It was predicted that, in general, zones are shifting north, and some zones are expanding or shrinking rapidly. Based on an analysis of latitudinal and areal change for each zone, the bioclimate vulnerability index (BVI) and naturality index (NI) were developed to quantify the impact of environmental change. As a result of the BVI analysis, the distribution of vulnerable zones is expected to shift northward and expand. As is evident with the increased vulnerability of the subarctic region caused by the expansion of the temperate climate, the extent of vulnerable zones will increase. Also, the southern regions of NEA are becoming vulnerable due to the transformation of the temperate zone to a more subtropical zone. Quadrant graphs based on the BVI and NI were created to present appropriate strategies for each zone. Our proposed framework shows that conservation strategies should be modified based on the changes in the relative position of each zone over time.

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.

Habitat changes in temperate forests are more vulnerable to climate change than tropical or boreal forests. This study assessed forest habitat suitability and diversity to determine the impact of climate change on the Korean Peninsula. We used the MaxEnt (Maximum Entropy) species distribution model, three key climate indices, and two representative climate change scenarios, using short and long-term data. Two of the three key climate indices related to temperature were more capricious than the precipitation-related index in the future. In the baseline prediction, both statistical and qualitative validation using the actual vegetation map showed excellent results. Regarding forest habitat suitability, northward migration and substantial increase were definitely distinctive in warm temperate evergreen forest. On the other hand, subalpine forest areas decreased significantly due to climate change; the suitable area for Representative Concentration Pathways (RCP) 8.5 2070s decreased by more than half. With regard to forest habitat diversity, regions with high diversity declined due to climate change. In the RCP 8.5 scenario, areas where all three forest types are suitable no longer appeared; however, in the case of RCP 4.5 2050s, suitable areas for two forest types increased, which implies climate change is not only negative in terms of diversity. As this negative prediction of future change is discouraging, active mitigation and adaptation are required to prevent these changes. The sustainability of future ecosystems is still dependent on our efforts.

Bursaphelenchus xylophilus, the pine wood nematode (PWN) which causes pine wilt disease, is currently a serious problem in East Asia, including in Japan, Korea, and China. This paper investigates the hyperspectral analysis of pine wilt disease to determine the optimal detection indices for measuring changes in the spectral reflectance characteristics and leaf reflectance in the Pinus thunbergii (black pine) forest on Geoje Island, South Korea. In the present study, we collected the leaf reflectance spectra of pine trees infected with pine wilt disease using a hyperspectrometer. We used 10 existing vegetation indices (based on hyperspectral data) and introduced the green-red spectral area index (GRSAI). We made comparisons between non-infected and infected trees over time. A t-test was then performed to find the most appropriate index for detecting pine wilt disease-infected pine trees. Our main result is that, in most of the infected trees, the reflectance changed in the red and mid-infrared wavelengths within two months after pine wilt infection. The vegetation atmospherically resistant index (VARI), vegetation index green (VIgreen), normalized wilt index (NWI), and GRSAI indices detected pine wilt disease infection faster than the other indices used. Importantly, the GRSAI results showed less variability than the results of the other indices. This optimal index for detecting pine wilt disease is generated by combining red and green wavelength bands. These results are expected to be useful in the early detection of pine wilt disease-infected trees.