• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.467-480
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• 2011

Spatial and temporal variabilities of NPP(Net Primary Production) retrieved from two satellite instruments, AVHRR(Advanced Very High Resolution Radiometer, 1981-2000) and MODIS(MODerate-resolution Imaging Spectroradiometer, 2000-2006), were investigated. The range of mean NPP from A VHRR and MODIS were estimated to be 894-1068 $g{\cdot}C/m^2$/yr and 610-694.90 $g{\cdot}C/m^2$/yr, respectively. The discrepancy of NPP between the two instruments is about 325 $g{\cdot}C/m^2$/yr, and MODIS product is generally closer to the ground measurement than AVHRR despite the limitation in direct comparison such as spatial resolution and vegetation classification. The higher NPP values over South Korea are related to the regions with higher biomass (e.g., mountains) and higher annual temperature. The interannual NPP trends from the two satellite products were computed, and both mean annual trends show continuous NPP increase; 2.14 $g{\cdot}C/m^2$/yr from AVHRR(1981-2000) and 6.08 $g{\cdot}C/m^2$/yr from MODIS (2000-2006) over South Korea. Specifically, the higher increasing trends over the Southwestern region are likely due to the increasing productivity of crop fields from sufficient irrigation and fertilizer use. The retrieved NPP shows a closer relationship between monthly temperature and precipitation, which results in maximum correlation during summer monsoons. The difference in the detection wavelength and model schemes during the retrieval can make a significant difference in the satellite products, and a better accuracy in the meterological and land use data and modeling applications will be necessary to improve the satellite-based NPP data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.215-231
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• 2020

Forest landscape models (FLMs) can be used to investigate the complex interactions of various ecological processes and patterns, which makes them useful tools to evaluate how environmental and anthropogenic variables can influence forest ecosystems. However, due to the large spatio-temporal scales in FLMs studies, parameterization and validation can be extremely challenging when applying to new study areas. To address this issue, we focused on the parameterization and application of a spatially explicit forest landscape model, LANDIS-II, to Mt. Gyebang, South Korea, with the use of the National Forest Inventory (NFI) and long-term ecological research (LTER) site data. In this study, we present the followings for the biomass succession extension of LANDIS-II: 1) species-specific and spatial parameters estimation for the biomass succession extension of LANDIS-II, 2) calibration, and 3) application and validation for Mt. Gyebang. For the biomass succession extension, we selected 14 tree species, and parameterized ecoregion map, initial community map, species growth characteristics. We produced ecoregion map using elevation, aspect, and topographic wetness index based on digital elevation model. Initial community map was produced based on NFI and sub-alpine survey data. Tree species growth parameters, such as aboveground net primary production and maximum aboveground biomass, were estimated from PnET-II model based on species physiological factors and environmental variables. Literature data were used to estimate species physiological factors, such as FolN, SLWmax, HalfSat, growing temperature, and shade tolerance. For calibration and validation purposes, we compared species-specific aboveground biomass of model outputs and NFI and sub-alpine survey data and calculated coefficient of determination (R²) and root mean square error (RMSE). The final model performed very well, with 0. 98 R² and 8. 9 RMSE. This study can serve as a foundation for the use of FLMs to other applications such as comparing alternative forest management scenarios and natural disturbance effects.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.573-597
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• 2016

Recent fMRI and EEG research for neural representations of action concepts insist that processing of action concepts evoke the simulation of sensory-motor information. Moreover, there are several behavioral studies showing that understanding of action verbs or sentences describing actions interfere or facilitate current action performance. However, it is unclear that online interaction between processing of action concepts and current action is based on the simulation of sensory-motor information, or other neural mechanisms. The present research aims to explore the underlying neural mechanism that how the perception of action language influence the performance of current action using high-spacial temporal resolution EEG and multiple source analysis techniques. For this, participants were asked to perform a cued-motor reaction task in which button-pressing hand action and pedal-stepping foot action were required according to the color of the cue, and we presented auditorily action verbs describing the responding actions (i.e., /press/, /step/, /stop/) just before the color cue and examined the interaction effect from the semantic congruency between the action verbs and the current action. Behavioral results revealed consistently a facilitatory effect when action verbs and responding actions were semantically congruent in both button-pressing and pedal-stepping actions, and an inhibitory effect when semantically incongruent in the button-pressing action condition. In the results of EEG source waveform analysis, the semantic congruency effects between action verbs and the responding actions were observed in the Wernicke's area during the perception of action verbs, in the anterior cingulate gyrus and the supplementary motor area (SMA) at the time when the motor-cue was presented, and in the SMA and primary motor cortex (M1) during action execution stage. Based on the current findings, we argue that perceived action verbs evoke the facilitation/inhibition effect by influencing the expectation and preparation stage of following actions rather than the directly activating the particular motor cortex. Finally we discussed the implication on the neural representation of action concepts and methodological limitations of the current research.