Potential range shift of a long-distance migratory rice pest, Nilaparvata lugens, under climate change.

The biogeographical range shift of insect pests is primarily governed by temperature. However, the range shift of seasonal long-distance migratory insects may be very different from that of sedentary insects. Nilaparvata lugens (BPH), a serious rice pest, can only overwinter in tropical-to-subtropical regions, and some populations migrate seasonally to temperate zones with the aid of low-level jet stream air currents. This study utilized the CLIMEX model to project the overwintering area under the climate change scenarios of RCP2.6 and RCP8.5, both in 2030s and 2080s. The overwintering boundary is predicted to expand poleward and new overwintering areas are predicted in the mid-latitude regions of central-to-eastern China and mid-to-southern Australia. With climate change, the habitable areas remained similar, but suitability decreased substantially, especially in the near-equatorial regions, owing to increasing heat stress. The range shift is similar between RCP2.6-2030s, RCP2.6-2080s, and RCP8.5-2030s, but extreme changes are projected under RCP8.5-2080s with marginal areas increasing from 27.2 to 38.8% and very favorable areas dropping from 27.5 to 3.6% compared to the current climate. These findings indicate that climate change will drive range shifts in BPH and alter regional risks differently. Therefore, international monitoring programs are needed to effectively manage these emerging challenges.

Construction of land-use change matrix and estimation of greenhouse gas inventory focusing on settlements in South Korea.

BACKGROUND: Five ministries are involved in estimating the greenhouse gas (GHG) inventory in the South Korean land use, land-use change, and forestry (LULUCF) sectors. However, these ministries have not established a consistent land classification standard between land-use categories. Therefore, the GHG inventory is estimated at the approach 1 level with no spatial clarity between land-use categories. Moreover, the settlements category is not estimated because activity data and the spatial scope are lacking. This study proposed a methodology for constructing a land-use change (LUC) matrix in the LULUCF sector for improving approach level and estimating the GHG inventory in the settlements. RESULT: We examined 10 sets of spatiotemporal data in South Korea to construct a LUC matrix. To maintain consistency in the spatial land classification, we constructed a LUC matrix using cadastral maps, which provide useful data for consistent land-use classification in South Korea. The LUC matrix was divided into remaining and land-converted settlements between 2005 and 2019 with estimated areas of 878,393.17 and 203,260.42 ha, respectively. CO2 emissions, according to Intergovernmental Panel Climate Change's Guideline Tier 1, were estimated at 18.94 MtCO2 for 15 years, with an annual CO2 emission of 1.26 MtCO2 yr-1. CO2 emission by land conversion type was found to be the largest at 16.93 MtCO2 in the case of forest converted to settlements. In addition, the area with the largest CO2 emission density was Sejong-si at 7.59 tCO2/ha. CONCLUSION: Based on reviewing available spatial data in South Korea, it is possible to improve Approach 3, which is more advanced than previous Approach 1 in the settlement category. In addition, the national GHG inventory also can be estimated by our constructed LUC matrix and activity data in this study. Under the many discussions about developing the Approach system, this study can provide in-detail information on developing LUC in South Korea in the settlement category as well as suggesting a methodology for constructing the LUC matrix for countries with similar problems to South Korea.

Understanding the Impact of the COVID-19 Pandemic on the Perception and Use of Urban Green Spaces in Korea.

Faced with the prospect that the impact of the COVID-19 pandemic and climate change will be far-reaching and long-term, the international community is showing interest in urban green space (UGS) and urban green infrastructure utilization as a solution. In this study, we investigated how citizens' perceptions and use of UGS have changed during COVID-19. We also collected their ideas on how UGS can raise its usability. As a result, more people became to realize the importance of UGS. In particular, the urban environmental purification function from UGS was recognized as giving great benefits to respondents. On the other hand, the patterns of UGS use were mixed with decreasing UGS use to maintain social distancing or increasing UGS use to maintain health or substitute other restricted facilities. More than half of respondents had their UGS visit patterns impacted by COVID-19. In particular, the increase rate of UGS use was rather high in the group that seldom used UGS before COVID-19. In addition, they increased the use of UGS to replace other limited facilities, and thus tended to demand an increase in rest facilities. Based on these results, this paper suggested securing social support and sustainability for the policy by reflecting users' demand in landscape planning related to the increase of UGS in the city. This study can contribute to improving the resilience of UGS and the sustainability of urban space planning.

Application of integrated Korean forest growth dynamics model to meet NDC target by considering forest management scenarios and budget.

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.

Forest management can mitigate negative impacts of climate and land-use change on plant biodiversity: Insights from the Republic of Korea.

Over the past century, the decline in biodiversity due to climate change and habitat loss has become unprecedentedly serious. Multiple drivers, including climate change, land-use/cover change, and qualitative change in habitat need to be considered in an integrated approach, which has rarely been taken, to create an effective conservation strategy. The purpose of this study is to quantitatively evaluate and map the combined impacts of those multiple drivers on biodiversity in the Republic of Korea (ROK). To this end, biodiversity persistence (BP) was simulated by employing generalized dissimilarity modeling with estimates of habitat conditions. Habitat Condition Index was newly developed based on national survey datasets to represent the changes in habitat quality according to the land cover changes and forest management, especially after the ROK's National Reforestation Programme. The changes in habitat conditions were simulated for a period ranging from the 1960s to the 2010s; additionally, future (2050s) spatial scenarios were constructed. By focusing on the changes in forest habitat quality along with climate and land use, this study quantitatively and spatially analyzed the changes in BP over time and presented the effects of reforestation and forest management. The results revealed that continuous forest management had a positive impact on BP by offsetting the negative effects of past urbanization. Improvements in forest habitat quality also can effectively reduce the negative impacts of climate change. This quantitative analysis of successful forest restoration in Korea proved that economic development and urbanization could be in parallel with biodiversity enhancement. Nevertheless, current forest management practices were found to be insufficient in fully offsetting the decline in future BP caused by climate change. This indicates that there is a need for additional measures along with mitigation of climate change to maintain the current biodiversity level.

Understanding global PM2.5 concentrations and their drivers in recent decades (1998-2016).

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.

Corrigendum to "Mann-Kendall Monotonic Trend Test and Correlation Analysis using Spatio-temporal Dataset: the case of Asia using vegetation greenness and climate factors" [MethodsX 5 (2018) 803-807].

[This corrects the article DOI: 10.1016/j.mex.2018.07.006.][This corrects the article DOI: 10.1016/j.scitotenv.2017.09.145.].

Decoupling of forest water supply and agricultural water demand attributable to deforestation in North Korea.

This study focused on changes in water balance attributable to severe deforestation in North Korea. The forest water supply and agricultural water demand of North Korea were quantified to identify their decoupling over the past three decades. Forest water supply and agricultural water demand before and after deforestation were estimated using the InVEST-WY (Integrated Valuation of Ecosystem Services and Trade-offs - Water Yield) and EPIC (Environmental Policy Integrated Climate) models, respectively. Analysis of land cover change before and after deforestation showed that area under forests decreased by 25%, whereas that under cropland increased by 63%, and that the conversion from forest to cropland was the largest for the study period. As a result, agricultural water demand increased and forest water supply decreased, significantly. Analysis of the net impact of deforestation on water budgets using recent climate and two land covers showed that forest water supply decreased by 43% and agricultural water demand increased by 62%. An assessment of the water balance at the watershed level showed that the Taedong, Ryesong, and Tumen Rivers suffered the largest negative change in terms of the large gross impact of deforestation on water resources. The water balance of the entire North Korea has declined by 51% and this is attributable to deforestation. In contrast, South Korea has experienced success in national-scale afforestation in recent decades, and North Korea can emulate this. The restoration of forests in North Korea promises more than environmental benefits; it will provide a new growth engine for the prosperity of the Korean Peninsula as a whole.

Development of a Screening Method for Health Hazard Ranking and Scoring of Chemicals Using the Mahalanobis⁻Taguchi System.

For efficient management of chemicals, it is necessary to preferentially select hazardous chemicals as being high-priority through a screening method. Over the past 20 years, chemical ranking and scoring (CRS) methods have been applied in many countries; however, these CRS methods have a few limitations. Most of the existing methods only use some of the variables to calculate the hazard of chemicals or use the most conservative score without consideration of the correlation between chemical toxicities. This evaluation could underestimate or overestimate the real health hazard of the chemicals. To overcome the limitations of these methods, we developed a new CRS method using the Mahalanobis⁻Taguchi System (MTS). The MTS, which conducts multivariate analysis, produced chemical rankings that took into accounts the correlation between variables related to chemical health hazards. Also, the proportion of chemicals managed by the Korea Chemicals Control Act that were given a high rating appeared to be higher when the MTS was used, compared to the existing methods. These results indicated that the new method evaluated the health hazards of chemicals more accurately, and we expect that the MTS method could be applied to a greater range of chemicals than the existing CRS methods.

Long-term trend of and correlation between vegetation greenness and climate variables in Asia based on satellite data.

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982-2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer-Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness and climate variables and analyzed trends during summer (April-October), winter (November-March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05-0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to -0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. Increases in both the mean annual signal and annual cycle occurred in the forest, herbaceous, and cropland areas of India, Northwest China, and eastern Kazakhstan. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R = 0.84-0.96). •Temperature is the main climatic variable affecting vegetation greenness.•A downward trend in vegetation greenness was observed during summer (April-October).•Temperature showed an upward trend across many areas of Asia during the study period.•In winter, rainfall showed downward and upward trends in different parts of Asia.

Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data.

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982-2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer - Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness, climate variables and analyzed trends during summer (April to October), winter (November to March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05 to 0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to -0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R=0.84-0.96).

Spatio-temporal change in forest cover and carbon storage considering actual and potential forest cover in South Korea.

This study analyzes change in carbon storage by applying forest growth models and final cutting age to actual and potential forest cover for six major tree species in South Korea. Using National Forest Inventory data, the growth models were developed to estimate mean diameter at breast height, tree height, and number of trees for Pinus densiflora, Pinus koraiensis, Pinus rigida, Larix kaempferi, Castanea crenata and Quercus spp. stands. We assumed that actual forest cover in a forest type map will change into potential forest covers according to the Hydrological and Thermal Analogy Groups model. When actual forest cover reaches the final cutting age, forest volume and carbon storage are estimated by changed forest cover and its growth model. Forest volume between 2010 and 2110 would increase from 126.73 to 157.33 m(3) hm(-2). Our results also show that forest cover, volume, and carbon storage could abruptly change by 2060. This is attributed to the fact that most forests are presumed to reach final cutting age. To avoid such dramatic change, a regeneration and yield control scheme should be prepared and implemented in a way that ensures balance in forest practice and yield.

Forest cover classification by optimal segmentation of high resolution satellite imagery.

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens(®) Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the "salt-and-pepper effect" and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

Changes in the distribution of South Korean forest vegetation simulated using thermal gradient indices.

To predict changes in South Korean vegetation distribution, the Warmth Index (WI) and the Minimum Temperature of the Coldest Month Index (MTCI) were used. Historical climate data of the past 30 years, from 1971 to 2000, was obtained from the Korea Meteorological Administration. The Fifth-Generation National Center for Atmospheric Research (NCAR) /Penn State Mesoscale Model (MM5) was used as a source for future climatic data under the A1B scenario from the Special Report on Emission Scenario (SRES) of the Intergovernmental Panel on Climate Change (IPCC). To simulate future vegetation distribution due to climate change, the optimal habitat ranges of Korean tree species were delimited by the thermal gradient indices, such as WI and MTCI. To categorize the Thermal Analogy Groups (TAGs) for the tree species, the WI and MTCI were orthogonally plotted on a two-dimensional grid map. The TAGs were then designated by the analogue composition of tree species belonging to the optimal WI and MTCI ranges. As a result of the clustering process, 22 TAGs were generated to explain the forest vegetation distribution in Korea. The primary change in distribution for these TAGs will likely be in the shrinkage of areas for the TAGs related to Pinus densiflora and P. koraiensis, and in the expansion of the other TAG areas, mainly occupied by evergreen broad-leaved trees, such as Camellia japonica, Cyclobalanopsis glauca, and Schima superba. Using the TAGs to explain the effects of climate change on vegetation distribution on a more regional scale resulted in greater detail than previously used global or continental scale vegetation models.

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data.

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

Estimation of effective plant area index for South Korean forests using LiDAR system.

Light Detection and Ranging (LiDAR) systems can be used to estimate both vertical and horizontal forest structure. Woody components, the leaves of trees and the understory can be described with high precision, using geo-registered 3D-points. Based on this concept, the Effective Plant Area Indices (PAI(e)) for areas of Korean Pine (Pinus koraiensis), Japanese Larch (Larix leptolepis) and Oak (Quercus spp.) were estimated by calculating the ratio of intercepted and incident LIDAR laser rays for the canopies of the three forest types. Initially, the canopy gap fraction (G ( LiDAR )) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area, using k-means statistics. The LiDAR-derived PAI(e) was then estimated by using G ( LIDAR ) with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAI(e) revealed the coefficients of determination for Korean Pine, Japanese Larch and Oak to be 0.82, 0.64 and 0.59, respectively. These differences between field-based and LIDAR-based PAI(e) for the different forest types were attributed to the amount of leaves and branches in the forest stands. The absence of leaves, in the case of both Larch and Oak, meant that the LiDAR pulses were only reflected from branches. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density. This is because the size of the branch is smaller than the resolution across and along the 1 meter LIDAR laser track. Therefore, a better predictive accuracy would be expected for the model if the study would be repeated in late spring when the shoots and leaves of the deciduous trees begin to appear.

Influence of stand density on soil CO2 efflux for a Pinus densiflora forest in Korea.

We investigated the influence of stand density [938 tree ha(-1) for high stand density (HD), 600 tree ha(-1) for medium stand density (MD), and 375 tree ha(-1) for low stand density (LD)] on soil CO(2) efflux (R (S)) in a 70-year-old natural Pinus densiflora S. et Z. forest in central Korea. Concurrent with R (S) measurements, we measured litterfall, total belowground carbon allocation (TBCA), leaf area index (LAI), soil temperature (ST), soil water content (SWC), and soil nitrogen (N) concentration over a 2-year period. The R (S) (t C ha(-1) year(-1)) and leaf litterfall (t C ha(-1) year(-1)) values varied with stand density: 6.21 and 2.03 for HD, 7.45 and 2.37 for MD, and 6.96 and 2.23 for LD, respectively. In addition, R (S) was correlated with ST (R (2) = 0.77-0.80, P < 0.001) and SWC (R (2) = 0.31-0.35, P < 0.001). It appeared that stand density influenced R (S) via changes in leaf litterfall, LAI and SWC. Leaf litterfall (R (2) = 0.71), TBCA (R (2) = 0.64-0.87), and total soil N contents in 2007 (R (2) = 0.94) explained a significant amount of the variance in R (S) (P < 0.01). The current study showed that stand density is one of the key factors influencing R (S) due to the changing biophysical and environmental factors in P. densiflora.

Estimating stem volume and biomass of Pinus koraiensis using LiDAR data.

The objective of this study was to estimate the stem volume and biomass of individual trees using the crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data. Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate individual trees, extended maxima transformation and watershed segmentation of image processing methods were applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDAR-derived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R (2) = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R (2) = 0.48, 0.36, and 0.44, respectively). In the case of medium tree-density plots accuracy was R (2) = 0.51, 0.52, and 0.62, respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm(3)), and the LiDAR-derived above-ground biomass can then be estimated from the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI).