• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.21-29
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• 2010

Nakdong Estuary Delta plays various roles of worldwide habitat for migratory birds and a sand supplier to Haewoondae Beach and Gwanganri, which are tourist attractions of Busan. In this study, long-term topographical changes of Nakdong Estuary (Jinwoo Islet, Sinja Islet, Doyodeung, Dadae Beach) coast were detected and interpreted. Through the analysis of 34 years' satellite images, it was found out that a part in between front side and back side of Jinwoo Islet increased, Sinja Islet was divided into two belts in 1970, and has formed an islet since the 1980s and extended westward. Due to the rapid development of small islets in front of Baekhabdeung since 1990s, Doyodeung formed in the late 1990s and is still growing. To make coastal map of Nakdong Estuary area, 13 images, of which the tide level was $99{\pm}13cm$, from the 112 Landsat images of the period from 1975 to 2009 were selected to section into water zone and land zone using NDV. And the rates of coastal line change such as MATLAB EPR(End Point Rate) and LRR(Linear Regression Rate) were calculated using DSAS 4.0(Digital Shoreline Analysis System). Through detecting topographical changes, EPR showed that the front(south) and back side(north) of Jinwoo Islet moved southward at -0.93~2.56m/yr, and changes in costal line and area of Jinwoo Islet were low and stable. The front and backside of Sinja Islet moved northward at 1~4m/yr, whereas the west side of Sinja Islet was stable at 2~3m/yr and east side of Sinja Islet moved northward at 10m/yr or faster. The front and back side of Doyodeung moved northward at 18~27m/yr, causing the increase of area, while the coastal line of Dadae Beach moved westward at 7m/yr, causing the expansion of the beach. LRR also demonstrated a similar trend to EPR. Although analysis of satellite images and GIS could enabled detection of topographical changes and quantitative analysis of natural phenomena, we found that continuous observation of natural phenomena and various analytical methods are required.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.855-866
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• 2017

Several research cases using remote sensing methods to analyze changes of storage and dynamics of groundwater aquifer were reviewed in this paper. The status of groundwater storage, in an area with regional scale, could be qualitatively inferred from geological feature, surface water altimetry and topography, distribution of vegetation, and difference between precipitation and evapotranspiration. These qualitative indicators could be measured by geological lineament analysis, airborne magnetic survey, DEM analysis, LAI and NDVI calculation, and surface energy balance modeling. It is certain that GRACE and InSAR have received remarkable attentions as direct utilization from satellite data for quantification of groundwater storage and dynamics. GRACE, composed of twin satellites having acceleration sensors, could detect global or regional microgravity changes and transform them into mass changes of water on surface and inside of the Earth. Numerous studies in terms of groundwater storage using GRACE sensor data were performed with several merits such that (1) there is no requirement of sensor data, (2) auxiliary data for quantification of groundwater can be entirely obtained from another satellite sensors, and (3) algorithms for processing measured data have continuously progressed from designated data management center. The limitations of GRACE for groundwater storage measurement could be defined as follows: (1) In an area with small scale, mass change quantification of groundwater might be inaccurate due to detection limit of the acceleration sensor, and (2) the results would be overestimated in case of combination between sensor and field survey data. InSAR can quantify the dynamic characteristics of aquifer by measuring vertical micro displacement, using linear proportional relation between groundwater head and vertical surface movement. However, InSAR data might now constrain their application to arid or semi-arid area whose land cover appear to be simple, and are hard to apply to the area with the anticipation of loss of coherence with surface. Development of GRACE and InSAR sensor data preprocessing algorithms optimized to topography, geology, and natural conditions of Korea should be prioritized to regionally quantify the mass change and dynamics of the groundwater resources of Korea.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.954-961
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• 2010

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for quantifying yield, biomass, and nitrogen (N) stress during growing season. This study was conducted to assess biomass and nitrogen (N) status of paddy rice (Oryza sativa L.) plants under N stress using passive and active ground-based remote sensors. Nitrogen application rates were 0, 70, 100, and 130 kg N $ha^{-1}$. At each growth stage, reflectance indices measured with active sensor showed higher correlation with DW, N uptake and N concentration than those with the passive sensor. NIR/Red and NIR/Amber indices measured with Crop Circle active sensors generally had a better correlation with dry weight (DW), N uptake and N content than vegetation indices from Crop Circle passive sensor and NDVIs from active sensors. Especially NIR/Red and NIR/amber ratios at the panicle initiation stage were most closely correlated with DW, N content, and N uptake. Rice grain yield, DW, N content and N uptake at harvest were highly positively correlated with canopy reflectance indices measured with active sensors at all sampling dates. N application rate explains about 91~92% of the variability in the SI calculated from NIR/Red or NIR/Amber indices measured with Crop Circle active sensors on 12 July. Therefore, the in-season sufficiency index (SI) by NIR/Red or NIR/Amber index from Crop Circle active sensors can be used for determination of N application rate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.251-267
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• 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 CROP SCIENCE
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• v.66 no.4
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• pp.307-317
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• 2021

In recent years, global warming has led to frequent climate change-related problems, and elevated temperatures, among adverse climatic factors, represent a critical problem negatively affecting crop growth and yield. In this context, the present study examined the physiological traits of wheat plants grown under high temperatures. Specifically, the effects of elevated temperatures on seed development after heading were evaluated, and the vegetation indices of different organs were assessed using hyperspectral analysis. Among physiological traits, leaf greenness and OJIP parameters were higher in the high-temperature treatment than in the control treatment. Similarly, the leaf photosynthetic rate during seed development was higher in the high-temperature treatment than in the control treatment. Moreover, temperature by organ was higher in the high-temperature treatment than in the control treatment; consequently, the leaf transpiration rate and stomatal conductance were higher in the control treatment than in the high-temperature treatment. On all measuring dates, the weight of spikes and seeds corresponding to the sink organs was greater in the high-temperature treatment than in the control treatment. Additionally, the seed growth rate was higher in the high-temperature treatment than in the control treatment 14 days after heading, which may be attributed to the higher redistribution of photosynthates at the early stage of seed development in the former. In hyperspectral analysis, the vegetation indices related to leaf chlorophyll content and nitrogen state were higher in the high-temperature treatment than in the control treatment after heading. Our results suggest that elevated temperatures after the booting stage positively affect wheat growth and yield.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.