• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.159-164
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• 2019

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.915-922
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• 2008

Impervious surface is an important index for the estimation of urbanization and the assessment of environmental change. In addition, impervious surface influences on short-term rainfall-runoff model during rainy season in hydrology. Recently, the necessity of impervious surface estimation is increased because the effect of impervious surface is increased by rapid urbanization. In this study, impervious surface estimation is performed by using remote sensing image such as Landsat-7 ETM+image with $30m{\times}30m$ spatial resolution and satellite image with $1m{\times}1m$ spatial resolution based on Jungnangcheon basin. A tasseled cap transformation and NDVI(normalized difference vegetation index) transformation are applied to Landsat-7 ETM+ image to collect various predict variables. Moreover, the training data sets are collected by overlaying between Landsat-7 ETM+ image and satellite image, and CART(classification and regression tree) is applied to the training data sets. As a result, impervious surface prediction model is consisted and the impervious surface map is generated for Jungnangcheon basin.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.595-600
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• 2013

In order to spatially interpolate the near-surface temperature (Ta) values, satellite and reanalysis methods were used from previous studies. Accuracy of reanalysis Ta was generally better than that of satellite-based Ta, but spatial resolution of reanalysis Ta was large to use at local scale studies. Our purpose is to evaluate accuracy of reanalysis Ta and satellite-based Ta according to elevation from April 2011 to March 2012 in Northeast Asia that includes various topographic features. In this study, we used reanalysis data that is ERA-Interim produced by European Centre for Medium-Range Weather Forecasts (ECMWF), and estimated satellite-based Ta using Digital Elevation Meter (DEM), Normalized Difference Vegetation Index (NDVI), difference between brightness temperature of $11{\mu}m$ and $12{\mu}m$, and Land Surface Temperature (LST) data. The DEM data was used as auxiliary data, and observed Ta at 470 meteorological stations was used in order to evaluate accuracy. We confirmed that the accuracy of satellite-based Ta was less accurate than that of ERA-Interim Ta for total data. Results of analyzing according to elevation that was divided nine cases, ERA-Interim Ta showed higher accurate than satellite-based Ta at the low elevation (less than 500 m). However, satellite-based Ta was more accurate than ERA-Interim Ta at the higher elevation from 500 to 3500 m. Also, the width of the upper and lower quartile appeared largely from 2500 to 3500 m. It is clear from these results that ERA-Interim Ta do not consider elevation because of large spatial resolution. Therefore, satellite-based Ta was more effective than ERA-Interim Ta in the regions that is range from 500 m to 3500 m, and satellite-based Ta was recommended at a region of above 2500 m.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.55-61
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• 2012

The primary factors of the LOS(Line-of-Sight) analysis process are terrain height, camera capacity, and canopy cover. The canopy cover rate differs depending on the changing season, and its value is influenced by the tree density, tree height, and etc. This study generated the canopy cover value based on relationship between NDVI(Normalized Difference Vegetation Index) and DMT(Density Measure % of Tree/Canopy Cover), which is a digital map attribute, and then performed the LOS analysis on six station of test sites. As results, It was found that NDVI and DMT are correlated with each other through the experiments. Based on this finding, new DMT map can be generated using NDVI. Also, There is a difference between the result of visibility analysis using the present DMT and one using a new DMT. Especially, the spatial distributions of the detected visible areas are significantly different between the two visibility analysis results.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.13-20
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• 2009

Techniques for change detection using satellite images enable efficient detection of natural and artificial changes in use of land through multi-phase images. As for change detection, different results are made based on methods of calibration of satellite images, types of input data, and techniques in change analysis. Thus, an analytic technique that is appropriate to objectives of a study shall be applied as results are different based on diverse conditions even when an identical satellite and an identical image are used for change detection. In this study, Normalized Difference Vegetation Index (NDVI) and Principal Component Analysis (PCA) were conducted after geometric calibration of satellite images which went through absolute and relative radiometric calibrations and change detection analysis was conducted using Image Difference (ID) and Image Rationing (IR). As a result, ID-NDVI showed excellent accuracy in change detection related to vegetation. ID-PCA showed 90% of accuracy in all areas. IR-NDVI had 90% of accuracy while it was 70% and below as for paddies and dry fields${\rightarrow}$grassland. IR-PCA had excellent change detection over all areas.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.598-607
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• 2016

This study was conducted to evaluate the amount of nitrogen (N) top dressing based on the normalized difference vegetation indices (NDVI) by ground based sensors for leaf perilla under the polyethylene house. Experimental design was the randomized complete block design for five N fertilization levels and conventional fertilization with 3 and 4 replications in Gumsan-gun and Milyang-si field, respectively. Dry weight (DW), concentration of N, and amount of N uptake by leaf perilla as well as NDVIs from sensors were measured monthly. Difference of growth characteristics among treatments in Gumsan field was wider than Milyang. SPAD-502 chlorophyll meter reading explained 43.4% of the variability in N content of leaves in Gumsan field at $150^{th}$ day after seedling (DAS) and 45.9% in Milyang at $239^{th}$ DAS. Indexes of red sensor (RNDVI) and amber sensor (ANDVI) at $172^{th}$ day after seedling (DAS) in Gumsan explained 50% and 57% of the variability in N content of leaves. RNDVI and ANDVI at $31^{th}$ DAS in Milyang explained 60% and 65% of the variability in DW of leaves. Based on the relationship between ANDVI and N application rate, ANDVI at $172^{th}$ DAS in Gumsan explained 57% of the variability in N application rate but non significant relationship in Milyang field. Average sufficiency index (SI) calculated from ratio of each measurement index per maximum index of ANDVI at $172^{th}$ DAS in Gumsan explained 73% of the variability in N application rate. Although the relationship between NDVIs and growth characteristics was various upon growing season, SI by NDVIs of ground based remote sensors at top dressing season was thought to be useful index for recommendation of N top dressing rate of leaf perilla.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.506-514
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• 2019

Unlike natural ecosystems, the urban ecosystem proVides an interdependent enVironment in which wild organisms and urban people co-exist. Hornets (genus Vespa) appearing in urban green and parks haVe a positiVe effect on urban ecosystems, but they also cause ecosystem disserVices that cause physical and psychological discomforts to the urban people. Children's parks, for example, are Very popular among children and residents for easy accessibility, and hornets also use them as bases and habitats. HoweVer, there is still a lack of spatial analysis of habitats and appearance characteristics of hornets in children's parks. This study installed hornet traps in 27 children's parks in Cheonan from April to NoVember 2018 in consideration of the life cycle of hornets. We captured a total of fiVe Vespa species (Vespa crabro, V. analis, V. mandarinia, V. ducalis, and V. Velutina) for 32 weeks and analyzed the emergence of hornets in relation to the composition of seasonal characteristics, species characteristics, and enVironmental spatial information. We captured a total of 818 hornets during the study period. They included 290 V. analis (35.4%), 260 V. crabro (31.8%), 100 V. ducalis (12.1%), 87 V. mandaninia (10.6%), and 81 V. Velutina(9.9%). Most of the hornets showed a common feature that queen hornets were largely captured in May through June after they awake from hibernation, and the number of caught hornets decreased sharply beginning in mid-June, which was the cooperatiVe period. HoweVer, V. Velutina showed a seasonal specificity that more than 80% were captured beginning in the third week of October when other hornet species had already entered a decline phase. The analysis of the number of hornets caught in each spot in children's parks showed significant difference among the spots as 363 hornets (44.3%) were captured in top children's parks, and 35 hornets (4%) were captured in bottom children's parks. In particular, the mean NDVI (Normalized difference Vegetation index) of the top six children's parks was 0.79, and that of the bottom six children's parks was 0.38 (t=2.67*, *=p<0.05), indicating a significant difference. The frequency of capturing hornets was high when the ground around the children's parks was grass or bare land. This study is meaningful as a reference study that confirms the ecological characteristics of hornets appearing in green and parks in the city. We expect it to be a foundation for effectiVe urban green area management in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.69-81
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• 2009

This study was intended to evaluate the objective landscape of rural region using an additive integration index method in the Seondong region of Gochang-gun, Jeollabuk-do, Korea. This study consisted of the following three steps. First, this study developed an additive integration index calculation model for landscape assessment based on indicators and weight to each space type in accordance with three landscape fields which were developed by the expert Delphi method. Second, this study used NDVI (Normalized Difference Vegetation Index) and permeable area rate, which were available from high resolution satellite image, to calculate the green naturality degree, area rate, and building coverage respectively. Third, this study has calculated the landscape assessment index of rural regions using an additive integration index method made of assessment data and weight for each indicator. This study has found the following results: 1) landscape level was very poor in all 6 types of space, marking grade five; 2) while the highest level of natural landscape and mixed landscape was grade two, that of artificial landscape was grade five; 3) based on objective landscape, grade five showed the highest frequency, and grade one, two, three, and four followed in that order.

• 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 Remote Sensing
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• v.30 no.4
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• pp.493-503
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• 2014

A hierarchical classification scheme, which can reduce the spectral ambiguity and also reflect crop cultivation patterns from past land-cover maps, is presented for the purpose of the early production of crop classification maps in large-scale crop areas. Specifically, the effects of mixed pixels are minimized not only by applying a hierarchical classification approach based on different spectral characteristics from crop growth cycles, but also by considering temporal contextual information derived from past crop cultivation patterns. The applicability of the presented classification scheme was evaluated by a case study of Iowa State in USA with time-series MODIS 250 m Normalized Difference Vegetation Index(NDVI) data sets and past Cropland Data Layers(CDLs). Corn and soybean, which are major crop types in the study area and also display spectral similarity, could be properly classified by applying different classification stages and accounting for past crop cultivation patterns. The classification result by the presented scheme showed increases of minimum 7.68%p and maximum 20.96%p in overall accuracy, compared with one based on purely spectral information. In addition, the combination of temporal contextual information during classification was less affected by the number of NDVI data sets and the best overall accuracy of 86.63% was achieved. Thus, it is expected that this classification scheme can be effectively used for the early production of large-area crop classification maps in major feed-grain importing countries.