• Journal of Korea Water Resources Association
• /
• v.49 no.4
• /
• pp.305-314
• /
• 2016

In this study, a quantitative assessment was carried out in order to identify the agricultural drought in time and space using the Terra MODIS remote sensing data for the agricultural drought. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were selected by MOD13A3 image which shows the changes in vegetation conditions. The land cover classification was made to show only vegetation excluding water and urbanized areas in order to collect the land information efficiently by Type1 of MCD12Q1 images. NDVI and EVI index calculated using land cover classification indicates the strong seasonal tendency. Therefore, standardized Vegetation Stress Index Anomaly (VSIA) of EVI were used to estimated the medium-scale regions in Korea during the extreme drought year 2001. In addition, the agricultural drought damages were investigated in the country's past, and it was calculated based on the Standardized Precipitation Index (SPI) using the data of the ground stations. The VSIA were compared with SPI based on historical drought in Korea and application for drought assessment was made by temporal and spatial correlation analysis to diagnose the properties of agricultural droughts in Korea.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.251-267
• /
• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.198-198
• /
• 2022

최근 도시화에 따른 토지 피복 변화와 열섬현상 등의 원인으로 상승하는 도시의 기온이 식물 계절에 미치는 영향에 관한 연구들이 다수 진행되고 있다. 본 연구는 수도권인 서울과 경기도 지역을 대상으로 도시 내 열섬현상으로 인한 기온 상승과 도시 지역 내 식생 생장기간 변화의 관계성을 분석하였다. 식물계절 모니터링에 사용한 개량식생지수(Enhanced Vegetation Index, EVI)는 Google Earth Engine (GEE)에서 제공하는 30 m 해상도의 2000-2021년 NASA-USGS Landsat 위성(TM5, ETM+7, OLI8)의 지표면 반사율(surface reflectance, SR) 자료에서 도출하여 생장기간 산정에 사용하였다. 또한 PRISM (Parameter-elevation Regressions on Independent Slopes Model)을 각 기상관측지점의 일별 지상 기온 자료에 적용하여 30 m 해상도로 생성한 격자형 지표면 온도의 공간적 패턴을 분석하였다. 연구 지역 내 도시화 정도(magnitude)를 도심으로부터의 거리와 환경부 토지피복도 및 인구 밀도를 종합하여 특정하였고, 최종적으로 기후변화 및 도시화 정도와 생장기간 변화의 특징을 분석하였다. 비선형 로지스틱 회귀를 사용하여 EVI 데이터를 종합하여 분석한 결과, 수도권 지역에서 전반적으로 식물계절 개엽일(Start of Season)은 앞당겨지며 낙엽일(End of Season, EOS)은 늦춰져 생장기간(Length of Growing Season, LOS)이 길어짐을 발견하였다.

• The Korean Journal of Ecology
• /
• v.27 no.4
• /
• pp.245-256
• /
• 2004

The simple biosphere model 2 (SiB2), which is one of the land surface models, simulates the exchange of momentum, energy and mass such as water vapor and carbon dioxide between atmosphere and biosphere, and includes the biochemical sub-model for representation of stomatal conductance and photosynthetical activities. Throughout the SiB2 simulation, the significant information not only to understand of water and carbon budget but also to make an analysis of interaction such as feed-back and-forward between environment and vegetation is given. Using revised SiB2-Paddy, one sample study which is the evaluation of the runoff in Chaophraya river basin according to land use/cover change is presented in this review. Hence, SiB2 is available in order to ecological studied, if revised SiB2 for realistic simulation about soil respiration, computing leaf area index, vegetation competition and soil moisture is improved.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.9 no.1
• /
• pp.7-14
• /
• 1989

This experiment was carried out to determine the seasonal herbage production and utilization during the growing season of pasture on the forest (shading 30%). Plant height, leaf area index(LAl), dry matter(DM) production and distribution, chemical composition, in vitro dry matter digestibility(IVDMD), herbage utilization percentage and chewing efficiency were investigated using the Corridale sheep. Experimental field was treated by one plot design(3 rep.) and performed from 1987 to 1988 at Chungnam National University, Daejon. The results obtained are summarized as follows: 1. The highest plant height and LA1 were observed in May(35.0 cm, 4.89), followed by April(28.0 cm, 4.23), while the plant height and LA1 in October (13.0 cm, 0.49) showed very low. 2. During the growing season, about 58.3 % of annual DM production (7240 kg/ha) was produced during the spring (April, May and June) and the highest DM production was obtained in May (2040 kg/ha), which was more than 28.2 % of total DM production. However, DM production in July and August was about 24.2 % and those in September and October (17.5 %) was very low, but the difference of DM production from June to September was small. 3. The maximum DM production per day (65.8 kg/ha) was observed in May, followed by June (28.7 kglha), while DM production per day in October (16.5 kg/ha) showed very low (p <0.01). 4. Crude protein content and IVDMD of herbage samples during the spring (April, May and June) were higher, while crude fiber, ADF, and NDF content were lower in an summer growth herbage samples (July and August), but autumn growth herbage samples was intermediate. Crude ash content and IVDMD of collected herbage samples were slightly more, while crude fiber, ADF and NDF content were slightly less than offered and residued herbage samples during the growing season. 5. The maximum DM intake per metabolic body size was observed in May(68.9 g), followed by October (66.7 g), while very low in August (52.5 g). Significant positive correlation (p <0.05) was found between DM intake and IVDMD. 6. Herbage utilization percentage was very high in April (83.4 %), while very low in August (64.0 %). The percentage of annual herbage utilization was about 75.5 %. 7. The maximum ruminating and chewing efficiency of herbage samples were observed in May, followed by October, while very low in August.