ScholarWorks@Korea University: LEE, Woo Kyun

Detailed Information

LEE, Woo Kyun (이우균)

College of Life Sciences and Biotechnology

Research Area

Tel 3016
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Keyword

Climate Change:17||climate change:16||LiDAR:10||Pinus densiflora:10||Climate change:10||GIS:8||Forest management:6||afforestation:6||ecosystem services:6||deforestation:6||Remote sensing:6||remote sensing:6||Carbon sequestration:5||Forest:5||forest fire:5||North Korea:5||기후변화:5||resilience:4||National Forest Inventory:4||Correlation:4||national forest inventory:4||vegetation index:4||land degradation:4||spatial distribution:4||Temperature:4||Bhutan:4||spatial analysis:4||A/R CDM:4||MODIS:3||Forest Fire:3||Forest Management:3||Vulnerability Assessment:3||Precipitation:3||LULUCF:3||kmeans clustering:3||Thinning:3||Ecosystem service:3||Classification:3||carbon storage:3||Landsat:3||sustainable development goals:3||desertification:3||Ecosystem services:3||REDD+:3||forest management:3||Kyrgyzstan:3||deep learning:3||Biomass:3||Principal Component Analysis:3||net primary productivity:3||pine wilt disease:3||SemiVariogram:3||취약성 평가:3||Desertification:3||South Korea:3||Sensitivity:3||NDVI:3||disaster risk reduction:2||National forest inventory:2||spatial statistics:2||REDD:2||Land cover change:2||Vulnerability:2||원격탐사:2||Forest Ecosystem:2||Logistic Regression:2||models:2||CFFDRS:2||Awash River Basin:2||urbanization:2||Greenhouse gas inventory:2||Korean Peninsula:2||Decision Tree:2||midlatitude region:2||InVEST:2||SWAT:2||land cover:2||Heavy Metals:2||Site Suitability Analysis.:2||Exposure:2||Uireung:2||REDD mechanism:2||Fine fuel moisture code(FFMC):2||topographical factors:2||COVID19:2||Spatial analysis:2||diameter at breast height (DBH):2||GEPIC:2||Warmth index:2||vegetation:2||climate change adaptation:2||Mitigation:2||시공간분포:2||variogram:2||Soils:2||Extreme Heat:2||Epidemic Disease:2||Litterfall:2||Soopkakkugi:2||Reforestation CDM:2||Lapse Rate:2||forests:2||Yasso:2||landcover change:2||tree species classification:2||livelihood vulnerability index:2||LANDSAT:2||Forest inventory:2||soil organic carbon:2||reforestation:2||suitable tree species:2||restoration:2||Ordinary Kriging:2||Image Classification:2||DownScaling:2||nomads:2||Normalized Difference Vegetation Index (NDVI):2||SDGs:2||forest carbon stock:2||oak wilt disease:2||Carbon stock:2||biomass:2||yield table:2||Assessment Vulnerability:2||CheonjangMountain:2||Larix kaempferi:2||IDSW:2||GRR:2||TWI:2||greenhouse gas inventory:2||Forest fire:2||Mongolia:2||Carbon dynamics:2||Tunisia:2||Vegetation:2||As:2||thinning:2||Nutrient cycling:2||Adaptative Capacity:2||forest distribution:2||산불:2||Interpolation:2||precipitation:2||carbon cycle:2||Land cover:2||Settlement:2||HyTAG:2||virtual water content:2||Radial growth:2||Forest Type Map:2||Biodiversity:2||Adaptation:2||Carbon budget:2||school garden:2||Airborne LiDAR:2||Cu:2||Ozone:2||Change Detection:2||REDD/REDDplus:2||water cycle:2||Forest fire occurrence:2||sustainable development:2||Economic viability:2||LULC:2||Spatiotemporal analysis:2||Precipitation effectiveness index:2||Lineament:2||VISIT:2||sensitivity:2||Potential Forest Distribution:2||Cultural Heritage:2||Risk Estimation:2||temperature:2||Segment Based Classification:2||KOMPSAT2 Imagery:2||land use:2||3D:2||tree height:2||multitemporally integrated imageries:2||Carbon storage:2||evapotranspiration:1||Freshwater supply:1||Top Soil Grain Size Index (TGSI):1||Tourism pressure:1||forest assessments:1||wet spell:1||rainfall probability:1||Upper Awash River Basin:1||semisupervised classification:1||Potential distribution:1||Disaster Risk Reduction (DRR):1||MidLatitude Region (MLR):1||Agricultural water demand:1||development pressure:1||carbon cost:1||Hydrological modeling:1||fruit trees:1||Cool forest:1||growing stock volume (GSV):1||Standardized Agricultural Drought Index:1||hydrological cycle:1||Maxent:1||geostatistical analysis:1||Changing trend analysis:1||land sensitivity:1||Thematic map:1||forest habitat suitability:1||climate factor:1||residuals:1||Water yield:1||cropland water budget:1||Slope:1||Landslide Risk Area:1||Drone:1||기후변화 기회와 위험:1||온실가스 감축 성과:1||Forest CO2 storage:1||사하라 사막 확장:1||Emission Trading System:1||MidLatitude region:1||forest stock change:1||Geographic information system:1||assessment:1||wetness topographic index:1||Hyperspectral:1||Individual tree detection algorithm:1||DBH growth:1||RS:1||three climate zones were classified for the result. Each zone was then analyzed and validated with climatic and geological features including topography:1||there was a difference in temperature:1||elevation:1||environmental management or climate change response.:1||Value of the Relative Density (VRD):1||Population:1||인구:1||생태계 서비스:1||Crop Yield Forecasting Model:1||Unevenaged stand:1||education:1||Tropical Rain Forest:1||Above Ground Biomass:1||endangered mammals:1||Hydrometeorology:1||Recreation Forest:1||세미베리오그램:1||Water Yield:1||Scenario:1||Ecosystem carbon budget:1||Combustion efficiency:1||Emission factor:1||Outlier cleaner:1||GLMM:1||오염토양:1||Digital weather forecast data:1||Daily weather index:1||IKONOS:1||Pinus rigida:1||오존:1||Airborne hyperspectral data:1||Linear regression analysis:1||Nutrient dynamic:1||MRV system:1||공간분석:1||nonlinear growth functions:1||MSAVI:1||규모변환:1||Aboveground biomass:1||Crown geometric volume:1||Stem volume:1||warmth index:1||문화재:1||최적센서위치 선정:1||3D Spatial Information:1||climate factors:1||forest functions:1||Soil respiration:1||biofuel:1||understanding of the youth:1||Forest functions:1||spatiotemporal information:1||서울:1||웹 플랫폼:1||Web 2.0:1||Web Platform. Participate and Share:1||토지피복:1||지형기복:1||Bursaphelenchus xylophilus:1||individual trees:1||naturebased solutions:1||habitat quality:1||naturebased solution:1||vegetation phenology:1||random forest model:1||rainfall&#8211:1||Ethiopia:1||Risk Assessment:1||Moran’s I 통계 분석:1||Deep Learning:1||runoff:1||forest:1||human wildlife conflicts:1||carbon neutrality:1||Landuse change matrix:1||stand volume estimation:1||Climate technology package:1||SDM:1||Validation:1||paddy rice mapping:1||SPI:1||scoping:1||herders:1||water availability:1||environmental code of Kazakhstan Republic:1||WI:1||PEI:1||focus group interview:1||site index:1||climate variation:1||Land cover changes:1||Peatlands restoration:1||peatland:1||Domain adaptation:1||Climate resilient development pathways (CRDP):1||Industrial wood production:1||domain adaptation:1||rainfall end:1||CBD:1||lowland forests:1||increment core:1||Urban Flood:1||Green Infrastructure Planning:1||Water security:1||Aridity Index (AI):1||Forest water supply:1||Density Estimation:1||LULC change:1||net primary productivity (NPP):1||Carbon Emission:1||spatial plant distribution:1||remote sensing pine wood nematode:1||GRSAI:1||Climate Factor:1||corporate perception of climate change:1||Land Cover Change:1||linkage analysis:1||튀니지:1||regional climate models:1||Spatial distribution:1||Tree mortality:1||Carbon Dynamics:1||Hargreaves Equation:1||Limited Data:1||Environmental spatial information:1||4th industrial revolution:1||북한:1||Environmental service:1||Climatic factors:1||an analysis of land cover map showed that there were more agricultural land near Andong Reservoir. This study on the climatic classification is considered to be useful as the basis for decisionmaking or policy enforcement regarding ecosystem:1||Environmental Education:1||Odaesan Earthquake:1||상대밀도 값(VRD):1||environmental experiences:1||Factor analysis:1||Semivariogram:1||Residual:1||management information systems:1||수문기상:1||Andong Dam Basin:1||Weather Condition:1||자연휴양림:1||Inner boundary:1||Land Use Change:1||aboveground net primary productivity:1||Stratification:1||forest cover map:1||Spatial additive model:1||Western flower thrips:1||BoxCox transformation:1||주성분 분석:1||구리:1||Logistic:1||Airborne Light Detection and Ranging (LiDAR):1||potential distribution:1||Forest volume:1||crown geometric volume:1||mountainous forest:1||적지분석:1||tree detection:1||autocorrelation:1||Wind speed:1||GLM:1||Distance from roads:1||Topographic Wetness Index:1||region growing:1||beerlambert law:1||point pattern:1||3차원 정보:1||Obstacle model:1||fuzzy classification:1||suitable area for rubber tree plantation:1||coarse woody debris:1||충청북도:1||Japanese larch:1||DBH growth model:1||변이성:1||CDM:1||economic feasibility of CDM project:1||국토지리정보체계:1||표준화:1||National Geographic Information System:1||생태도시계획:1||방위:1||growth model:1||Ethiopia forest:1||cropland suitability:1||코로나바이러스19:1||핫스팟 분석:1||India:1||watershed:1||Land use:1||national level of stand volume:1||geospatial information:1||Forest Governance:1||urban sprawl:1||PDSI:1||Sustainable Development Goals:1||Green water flow/storage:1||upper Awash basin:1||Climate Changes:1||rapid assessment method:1||Policymaking:1||urban green space (UGS):1||AFOLU:1||structural equation modelling:1||SSEBop model:1||Protected area:1||Vegetation greenness trends:1||Global trend analysis:1||Global correlation analysis:1||Upper Awash:1||standlevel annual increment:1||Quantitative assessment:1||LULUCF sector:1||Endangered tree species:1||agroforestry:1||cash crops:1||home garden:1||BioGeoChemistry Management Model (BGCMAN):1||rice and maize:1||multitemporal analysis:1||Aral Sea:1||priority setting:1||forest habitat diversity:1||Vegetation greenness:1||degraded forest:1||DPRK:1||Disaster risk reduction:1||Lowland mixed dipterocarp forest:1||집중호우:1||Method of least squares:1||emission reduction performance:1||Scenario Generator:1||sustainable development goals(SDGs):1||토지피복 변화:1||distribution pattern:1||agroecosystem:1||degradation:1||agroenvironmental variables in cropland:1||Maximum stem number:1||Selfthinning:1||CBMCFS3:1||Climate mitigation service:1||sustainable forest management:1||Ecosystem function:1||Quercus mongolica:1||precipitation and tree species distribution among the zones. Also:1||LANDSAT8 Satellite Image:1||Major Vegetable Crop:1||Soil survey:1||forest growth model:1||Vegetation dynamics:1||Spatiotemporal Distribution:1||Provisioning Services:1||InVEST 모형:1||Chemical Fate Model:1||토지이용균형모델:1||적응:1||forestcover change:1||Baseline scenario:1||stratification:1||Urban forestry:1||크리깅:1||vegetation carbon budget:1||validation:1||Waveform analysis:1||high resolution:1||segmentbased classification:1||공간자료:1||민감성:1||high spatial resolution:1||early detection:1||heightDBH models:1||East Asia:1||Minimum temperature index:1||aerial photograph:1||thermal analogy group:1||Soil nitrogen:1||로지스틱 회귀분석:1||Best Index Slope Extraction (BISE):1||radial growth:1||weight overlay:1||PRISM:1||heightdiameter prediction:1||dendrochronological reconstruction:1||Watershed management:1||Clean development mechanism:1||pitch pine:1||소나무:1||aspect:1||National Forest Policy:1||elevation relationship:1||Vegetation Phenology:1||snow leopards:1||economic evaluation:1||land use management:1||Technology transfer:1||Climate technology:1||Carbon Cycle:1||Models:1||decisionmaking:1||meteorological drought index:1||waterfoodenergyecosystem nexus:1||wildlife:1||convolutional neural networks (CNNs):1||Landslide Vulnerability Assessment:1||allometric equation:1||Regional Forest Plans:1||TACCC Principles:1||Spatial Data Matching for LULUCF:1||LULUCF’s Approach Levels:1||Greenhouse Gas Inventory:1||processbased model:1||landscape planning:1||Restoration projects:1||activity data:1||ecological preservation:1||Deep learning:1||EVI2:1||agroforestry parkland:1||Forest growth:1||Management policy:1||Global:1||Markov chain model:1||Data augmentation:1||data labeling:1||Vegetation structure:1||Drone image:1||Nagoya Protocol:1||land use and land cover:1||mountain forests:1||Surface Runoff:1||Impervious Area:1||Metro Manila:1||Source and receptor:1||Geospatial Data:1||Forest Cover Detection:1||poverty:1||Ecotourism:1||Low‐carbon:1||Life Cycle:1||Jeju Island:1||spatial decision support:1||climate change impact:1||Evaporation:1||Trend:1||agricultural water demand and supply:1||산사태 위험지역:1||SSOS system:1||Shared Socioeconomic Pathways (SSP):1||Approach 3:1||Linking:1||Hotspot analysis:1||land development phase:1||cropland expansion:1||drought:1||Combined vegetation index:1||negative emission:1||hotspot analysis:1||Conservation strategies:1||Environmental issues:1||복원:1||Agriculture:1||Stepwise approach:1||forest biomass:1||Ecosystem productivity:1||Social service:1||environment:1||지리정보시스템:1||Watershed area can be submerged due to constructions and management of dams:1||수원함양기능:1||강수량:1||Meteorological Information:1||community garden:1||Vegetation change:1||benefit transfer method:1||social discount rate:1||SpatioTemporal Distribution:1||Forest carbon:1||net ecosystem production:1||standard radial growth:1||Afforestation/reforestation CDM:1||word:1||ecosystem model:1||Geoscience Laser Altimeter System (GLAS):1||dynamic growth model:1||Forest plot volume:1||digital forest cover map:1||satellite image:1||pixelbased classification:1||crown delineation:1||adaptation:1||공간자기상관성:1||LANDSAT TM:1||monitoring:1||Leafexpansion:1||Phenology:1||기온감율:1||Climatic controllers:1||Crown base height:1||Soil CO2 efflux:1||Soil water content:1||Kriging:1||forest floor:1||litter decomposition:1||캐나다 산불 위험 지수:1||산불 발생:1||Bolivian plateau area:1||Project design document:1||연륜생장:1||vulnerability assessment:1||unevenaged stand:1||Quercus variabilis:1||Seoul:1||토지이용:1||Land Cover Classification:1||Ecological Urban Plan:1||지형인자:1||topographic undulation:1||morphological image analysis:1||Agricultural drought risk index:1||quality indices:1||Vegetation condition index (VCI):1||Moran’s I Statistics:1||Sustainable Development Goal:1||Forest Carbon:1||MRV:1||runoff response:1||semantic segmentation:1||drought review:1||MidLatitude Region:1||waterbirds:1||wetlands:1||Random Forest Model:1||Risks:1||forest modeling:1||mean pairwise distance:1||West Africa:1||Carbon sink:1||Naturebased tourism:1||PM2.5:1||Sustainable forest development:1||Access and benefit sharing framework:1||adaptive pathway:1||Geographical information system:1||LeastCost Paths (LCP) Analysis:1||climate change Impact:1||Climate Change Adaptation:1||Sustainable Development:1||indigenous knowledge:1||agriculture:1||farm machinery:1||copayments:1||MidLatitude Ecotone (MLE):1||socioeconomic:1||MODIS active fire data:1||spatial autocorrelation:1||chemical ranking and scoring:1||MahalanobisTaguchi System:1||conservation:1||national assessment:1||MaxEnt:1||National forestation:1||Soil erosion:1||사막화:1||diffusion type:1||sustainability:1||waterfoodecosystem nexus:1||crop yield:1||PET methods:1||Aquatic insect:1||Hydrogen fluoride:1||Temperate forest:1||Urban forest:1||FAO56 PenmanMonteith Methodology:1||volumetric pixel:1||Deforestation and reforestation:1||Forest assessment:1||botanical garden:1||nature:1||Sand:1||Final Age:1||and forest type map. As a result of the analysis:1||climate mitigation:1||LANDSAT 8호 위성영상:1||선구조선:1||Mixed stand:1||Radial growth model:1||Soil quality indicator:1||national forest management evaluation:1||EcoNatural map:1||Land cover map:1||Quantitative valuation:1||Ecosystem Services:1||Land Use Equilibrium Model:1||total belowground carbon allocation:1||CDM methodology:1||carbon stocks:1||Variogram models:1||폭염:1||매개질환 전염병.:1||Satellite imagery:1||HyTAGs:1||terrestrial laser scanner:1||영상분류:1||노출:1||Topographic factors:1||Groundtruthing:1||minimum temperature:1||plant area index:1||gap fraction:1||Sensor detection model:1||boiethanol:1||mixedeffects:1||Litter decomposition process:1||Reforestation:1||시․공간 정보:1||fine root:1||individual tree:1||variability:1||Kyoto Mechanism:1||공유와 개방:1||라이다:1||토지피복분류:1||경사:1||slope:1||reconstruction:1||재구성:1||Linkage Analysis:1||anthropogenic pressure:1||decisionmaking support:1||scenario analysis:1||Early warning system:1||Standardized precipitation index (SPI):1||GR2M Hydrologic Model:1||Spatial Distribution Characteristics:1||Compact Advanced Satellite 500:1||SPEI:1||relationship:1||biological corridor:1||questionnaire surveys:1||Third Pole:1||habitat conservation:1||adaptive ability:1||Landuse and Landuse Change Matrix:1||aboveground biomass:1||moderate resolution imaging spectroradiometer:1||paddy methane:1||drone:1||methane calculation model:1||carbon stock:1||Forest harvest:1||Precipitation Concentration Index (PCI):1||unmanned aerial vehicles:1||CNNs:1||Socioenvironmental drivers:1||dry:1||rainfall onset:1||wet spell length:1||Mann&#8211:1||Kendall trend:1||natural resources:1||Kathmandu:1||air pollution:1||Forest management rate:1||Forest statistics:1||El Niño:1||KFS fire survey data:1||Earth trend modeler:1||Relationship analysis:1||MEDALUS method:1||allometry:1||Haloxylon aphyllum:1||national level:1||agricultural water equilibrium:1||기상인자:1||기후변화 대응:1||탄소경영활동:1||탄소정보공개(CDP):1||Future Land Cover:1||Sampling method:1||Wall‐to‐Wall method:1||forestry sector ODA project:1||Land Cover changes:1||Expansion of Sahara desert:1||Density map:1||EPT assemblage:1||correlation:1||Water provisioning service:1||Beneficiarybased ecosystem service:1||Bioclimatic classification:1||Experiential Education:1||forest carbon stocks:1||Shaded Relief Map:1||음영기복도:1||Water Yield Function:1||Ecosystem Service:1||Oak stands:1||Geostatistics:1||안동댐 유역:1||Environmental impact assessment:1||Classification accuracy:1||InVEST Model:1||생태계서비스:1||수원함량:1||Harzardous Chemicals:1||시나리오:1||Vegetation productivity:1||Laser dendrometer:1||Normalized burn ratio:1||중금속:1||비소:1||Forest cover map:1||Canopy removal:1||Carbon:1||airborne laser scanner:1||피복변화탐지:1||Forest law:1||Digital Elevation Model:1||Landcover change:1||Leaffall:1||Forest cover distribution:1||위험성평가:1||센서감지모형:1||seasonal variation:1||Soil:1||biodiesel:1||randomeffects:1||missing heights:1||Methodology:1||liming:1||mixed stand:1||Korea:1||Data Standard:1||Remote Sensing:1||3D surface model:1||portable NIR camera:1||Risk:1||Disaster:1||thinning intensity:1||ecosystembased approach and assessment:1||landcover classification:1||Diagnostic drought prediction model (DDPM):1||areal rainfall comparison:1||reference analysis:1||Multi Series:1||Greenhouse Gas:1||livestock:1||net present value:1||Land use change:1||satellite imageries:1||Neural network model:1||Data driven modeling:1||Climate Technology Centre and Network:1||CERs:1||CAS5004:1||CAMarkov:1||future trends:1||review:1||borderless:1||cooperation:1||SSP:1||Detailed Plans for Local Climate Change Adaptation Measures:1||National Risks:1||National Inventory Report:1||systematic review:1||OBIA:1||Recreation area:1||Land degradation:1||Artificial Neural Networks (ANNs):1||Convention on Biological Diversity:1||Activity data:1||Subalpine conifer species:1||Regional differences:1||NSDI:1||Deforestation:1||Decoupling of supply and demand:1||Gaussian Probability Density Function:1||Satellite Imagery:1||carbon storage capacity:1||carbon sequestration service:1||SCSCN:1||carbon sequestration:1||subsidy:1||Convolutional neural networks:1||Hyperparameter optimization:1||semivariogram:1||Chemical accident:1||Geospatial Factor:1||Heavy Rainfall:1||지형공간인자:1||corporate responses to climate change:1||ROK‐China:1||Topographic wetness index:1||Frequency ratio model:1||topsoil grain size index:1||geographical features:1||Moran’s I statistical analysis:1||Statistical classification:1||산림정책:1||적정 조림수종:1||Quantification:1||voxel:1||Ecosystem respiration:1||Assessment:1||Cutting Age:1||산림:1||오대산 지진:1||Multiple Linear Regression:1||Individual tree:1||Reference wetland:1||final cutting age:1||Rainfall:1||완화:1||Interannual analysis:1||Japanese red pine:1||stand density:1||land eligibility:1||Biomass burning:1||NonCO2 GHGs:1||Greenhouse whitefly:1||Korean peninsula:1||SpatioTemporal Information:1||Spatiotemporal:1||Urban green space:1||Urban heat distribution (UHD):1||Forest growth model:1||digital canopy model:1||hybrid classification:1||vulnerability:1||Relative humidity:1||DI:1||markercontrol watershed segmentation:1||Stand density:1||leaf area index:1||방해물 모형:1||Quercus spp.:1||agriculture area map:1||geographic information system (GIS):1||IDW:1||statistical interpolation techniques:1||Thinning intensites:1||Carbon cycling:1||soil:1||soil carbon model:1||Chungbuk province:1||기온:1||항공라이다:1||Adaptive capacity:1||Sustainable Development Goals (SDGs):1||Strategic Goals and Indicators:1||Policy Recommendation:1||MEDALUS:1||공간분포특성:1||위험성 평가:1||Hotspot Analysis:1||PDD:1||model optimization:1||random forest (RF):1||Environmental impact:1||Forestry:1||Carbon stocks:1||Biogeochemistry:1||Calibration:1||Hydrological modelling:1||SCSCN model:1||blue water flow:1||forest growth:1||Forest Sector:1||forest ecosystem:1||naturebased solutions (NbS):1||interventions:1||Landcover mapping:1||Phenological classification:1||soil phosphorus content:1||Forest policy:1||Tumen River Basin:1||Social big data:1||vegetation conservation classification:1||Afforestation plan:1||Risk assessment:1||awareness:1||biodiversity:1||Chemical risk mapping:1||Forest activity data:1||Cadastral Map:1||processbased ecosystem model:1||agricultural drought:1||crop phenology:1||probability:1||MannKendall monotonic trend test:1||ecosystem service:1||Hazard:1||Safety management:1||spectrometer:1||Growth rate:1||Litter flux:1||기업의 기후변화 인식:1||CDP:1||Land‐use change matrix:1||official development assistance(ODA):1||Barriers:1||Earthquake:1||multimodel ensemble:1||ensemble result:1||forest water yield:1||soil loss:1||aerial photographs:1||Potential Evapotranspiration:1||Thematic maps:1||forest policy:1||Food security:1||Air purification:1||curriculum:1||Topsoil Grain Size Index:1||CO2 Absorption:1||시공간분석:1||and these change can impact not only on ecosystem and environment of river basin area but also on local climate. This study is conducted to construct and classify climate zones of Andong Dam watershed where the area is submerged due to the construction of the dam. By applying Principal Components Analysis (PCA) and GetisOrd Gi* statistics:1||national forestry inventory:1||national forest management:1||habitat:1||values per unit area:1||기상조건:1||Forest types:1||공급서비스:1||Climate Change Impact:1||토지이용변화:1||Vegetation respiration:1||general additive modelclimatic factors:1||Carbon pools:1||GHG removals:1||forest volumeage relationships:1||Landsat TM:1||Forest fire danger rating:1||regression:1||model simulation:1||Maximum canopy height:1||DBH:1||Forest canopy density:1||band ratios:1||재조림 CDM:1||적응성:1||aerial imagery:1||climate change scenario:1||베리오그램:1||spatial:1||Asian dust:1||Fire danger rating index:1||Forest function:1||잠재 산림분포:1||보간:1||Soil temperature:1||천장산:1||의릉:1||Optimal Sensor Placement:1||Vegetation classification:1||global warming:1||greenhouse gases:1||Quercus species:1||연료 수분 지수:1||Soil carbon model:1||Afforestation:1||연년연륜생장:1||Annual tree ring growth:1||강수:1||웹 2.0:1||공간분포:1||aerial LIDAR(LIght Detection And Ranging):1||3차원 표면모형:1||tree top:1||national park management:1||Agricultural drought:1||satellitederived rainfall estimate:1||PERSIANNCDR:1||compressed development:1||landcover prediction:1||third pole:1||risk modeling:1||Settlements:1||compact advanced satellite 500 (CAS5004):1||convolutional neural networks:1||Technical assistance:1||UNFCCC:1||water conservation:1||Addis Ababa:1||Recurrent UNet:1||early prediction:1||metaanalysis:1||green/blue water scarcity:1||RCP:1||MTCI:1||wetland conservation:1||biogeochemistry model:1||settlements spatial extent:1||land use change:1||belowground biomass:1||

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