• Korean journal of applied entomology
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• v.21 no.4 s.53
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• pp.200-206
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• 1982

A computer simulation model was constructed to explore the phonology of the Striped Rice Borer, Chilo suppressalis (Walker), in Korea. The phenological system model based on the concept of distributed time delay was written in the computer program 'INSECT' and simulated with the estimated parameters of the effective day-degrees (DEL) and the order of time delay (K) for determining the validity of the system model. The accumulated emergence curves obtained from the phenological model were slightly different from the observed light trap data at the early and late stage of the moth emergence in 1978. The differences between observed and simulated $50\%$ emergence date were five to six days in the locations of Suweon and Chuncheon, while it was only two to three days in Iri, Daegu, Boseong, and Milyang. The phenological model should be further improved for simulation of field population changes by adding the information of the time delay process in each developmental stage, the age distribution of overwintered population, and the limiting factors of the borer mortality.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.E
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• pp.33-46
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• 1990

Abstract Over 700/0 of the rural land area in Korea is mountainous and small watersheds provide most of the water resources for agricutural use. To provide an appropriate tool for the agricultural water resource development project, SNUA2, a mathematical model for simulating the physical processes governing the precipitation-runoff relationships and predicting the storm and long-term runoff quantities from the small mountainous watersheds was developed. The hydrological characteristics of small mountainous watersheds were reviewed to select appropriate theories for the simulation of the runoff processes, and a deterministic and distributed model was developed. In this, subsurface flows are routed by solving Richard's two dimensional equation, the dynamics of soil moisture contents are simulated by the consideration of phenological factors of canopy plants and surface flows are routed by solving the kinematic wave theory by numerical analysis. As a result of an application test of the model to the Sanglim watershed, peak flow rates of storm runoff were over-estimated by up to 184.2%. The occurence time of peak flow and total runoff volume of storm runoffs simulated were consistent with observed values and the annual runoff volumes were simulated in the error range of less than 5.8%.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.202-208
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• 2001

Under the constant daylength of 13 hours and growth temperatures of 15$^{\circ}C$ to 27$^{\circ}C$, the final number of loaves (FNL) on the main culm was constant as 15 regardless of temperature in rice variety 'Kwanganbyeo'. Leaf appearance rate (LAR) increased with rising temperature and decreased with phenological development. Threshold temperature (T$_{o}$) was not constant across growth stages, but increased with phenological development. Effective accumulated temperature (EAT), which is calculated by the summation of values subtracting T0 from daily mean temperature, is closely related with number of leaves appeared (LA). LA was fitted to bilinear, quadratic, power and logistic function of EAT. Among the functions, logistic function had the best fitness of which coefficient of determination was $R^2$=0.995. Therefore, LAR prediction model was established by differentiating this function in terms of time: (equation omitted). where dL/dt is LAR, T$_1$ is daily mean temperature, L is the number of leaves appeared, and a, b, and c are constants that were estimated as 41.8, 1098.38, and -0.9273, respectively. When predictions of LA were made by LAR prediction model using data independent of model establishment, the observed and predicted LA showed good agreement of $R^2$$\geq$0.99.

• Asian Journal of Turfgrass Science
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• v.15 no.3
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• pp.127-136
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• 2001

A field trial was initiated to examine the range of inter- and intraspecific variations in morphological and phenological traits with five different accessions of smooth and large crabgrass. In addition, a controlled environment study was conducted to determine the phenotypic plasticity among the accessions of both species in response to 4 daily tempera-ture differentials. In the field experiment, significant inter- and intraspecific variations of smooth and large crabgrass were observed in morphological traits such as leaf length and width. However, most phenological traits were not substantially different between the species and among the accessions of each species. The first seedling emerged at the same time, requiring 9~ 10 days, regardless of the accessions and species. In a controlled environment study, all accessions of each species responded similarly to the 4 temperature differentials in seedling emergence, indicating seedling emergence was not a plastic trait. These results suggest that predicting crabgrass seedling emergence could be independent of geographical regions in the US.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.11-18
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• 2012

Daily temperature data produced by harmonic analysis of monthly climate summary have been used as an input to plant phenology model. This study was carried out to evaluate the performance of the harmonic based daily temperature data in prediction of major phenological developments and to apply the results in improving decision support for agricultural production in relation to the climate change scenarios. Daily maximum and minimum temperature data for a climatological normal year (Jan. 1 to Dec. 31, 1971-2000) were produced by harmonic analysis of the monthly climate means for Seoul weather station. The data were used as inputs to a thermal time - based phenology model to predict dormancy, budburst, and flowering of Japanese cherry in Seoul. Daily temperature measurements at Seoul station from 1971 to 2000 were used to run the same model and the results were compared with the harmonic data case. Leaving no information on annual variation aside, the harmonic based simulation showed 25 days earlier release from endodormancy, 57 days longer period for maximum cold tolerance, delayed budburst and flowering by 14 and 13 days, respectively, compared with the simulation based on the observed data. As an alternative to the harmonic data, 30 years daily temperature data were generated by a stochastic process (SIMMETEO + WGEN) using climatic summary of Seoul station for 1971-2000. When these data were used to simulate major phenology of Japanese cherry for 30 years, deviations from the results using observed data were much less than the harmonic data case: 6 days earlier dormancy release, 10 days reduction in maximum cold tolerance period, only 3 and 2 days delay in budburst and flowering, respectively. Inter-annual variation in phenological developments was also in accordance with the observed data. If stochastically generated temperature data could be used in agroclimatic mapping and zoning, more reliable and practical aids will be available to climate change adaptation policy or decision makers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.3
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• pp.185-191
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• 2005

An accurate prediction of dormancy release and bud burst in temperate zone fruit trees is indispensable for farmers to plan heating time under partially controlled environments as well as to reduce the risk of frost damage in open fields. A thermal time-based two-step phenological model that originated in Italy was applied to two important grapevine cultivars in Korea for predicting bud-burst dates. The model consists of two sequential periods: a rest period described by chilling requirement and a forcing period described by heating requirement. It requires daily maximum and minimum temperature as an input and calculates daily chill units (chill days in negative sign) until a pre-determined chilling requirement for rest release is met. After the projected rest release date, it adds daily heat units (anti-chill days in positive sign) to the chilling requirement. The date when the sum reaches zero isregarded as the bud-burst in the model. Controlled environment experiments using field sampled twigs of 'Campbell Early' and 'Kyoho' cultivars were carried out in the vineyard at the National Horticultural Research Institute (NHRI) in Suwon during 2004-2005 to derive the model parameters: threshold temperature for chilling and chilling requirement for breaking dormancy. The model adjusted with the selected parameters was applied to the 1994-2004 daily temperature data obtained from the automated weather station in the NHRI vineyard to estimate bud burst dates of two cultivars and the results were compared with the observed data. The model showed a consistently good performance in predicting the bud burst of 'Campbell Early' and 'Kyoho' cultivars with 2.6 and 2.5 days of root mean squared error, respectively.

• Journal of Environmental Science International
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• v.26 no.11
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• pp.1247-1253
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• 2017

The Fuji variety of apple, introduced in Japan, has excellent storage quality and good taste, such that it is the most commonly cultivated apple variety in Gunwi County, North Gyeongsang Province, Korean Peninsula. Accurate prediction of harvest maturity allows farmers to more efficiently manage their farm in important aspects such as working time, fruit storage, market shipment, and labor distribution. Temperature is one of the most important factors that determine plant growth, development, and yield. This paper reports on the beta distribution (function) model that can be used to simulate the the phenological response of plants to temperature. The beta function, commonly used as a skewed probability density in statistics, was introduced to estimate apple harvest maturity as a function of temperature in this study. The model parameters were daily maximum temperature, daily optimum temperature, and maximum growth rate. They were estimated from the input data of daily maximum and minimum temperature and apple harvest maturity. The difference in observed and predicted maturity day from 2009 to 2012, with optimal parameters, was from two days earlier to one day later.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.268-278
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• 2020

Flowering time has been put forward due to the recent abnormally warm winter, which often caused damages of flower buds by late frosts persistently. In the present study, cumulative chill unit and cumulative heat unit of Niitaka pear, which are required for releasing the endogenous dormancy and for flowering after breaking dormancy, respectively, were compared between flowering time prediction models used in South K orea. Observation weather data were collected at eight locations for the recent three years from 2018-2020. The dates of full bloom were also collected to determine the confidence level of models including DVR, mDVR and CD models. It was found that mDVR model tended to have smaller values (8.4%) of the coefficient of variation (cv) of chill units than any other models. The CD model tended to have a low value of cv (17.5%) for calculation of heat unit required to reach flowering after breaking dormancy. The mDVR model had the most accurate prediction of full bloom during the study period compared with the other models. The DVR model usually had poor skills in prediction of full bloom dates. In particular, the error of the DVR model was large especially in southern coastal areas (e.g., Ulju and Sacheon) where the temperature was warm. Our results indicated that the mDVR model had relatively consistent accuracy in prediction of full bloom dates over region and years of interest. When observation data for full bloom date are compiled for an extended period, the full bloom date can be predicted with greater accuracy improving the mDVR model further.