• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.45-56
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• 2024

Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.754-763
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• 2004

This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.4
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• pp.29-41
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• 2015

Abandoned paddy field provides an excellent opportunity to improve the species diversity and habitat quality. Ecological characteristic on the changing of plant communities at different seral stages is a major basis data for ecological restoration. In this study, we investigated changes of the species composition and community indices on the plant community associated with abandonment of cultivated rice paddies. The ecological stability of the habitat was evaluated by using eco-floristic characters(Di; Disturbance index, AUI; Actual urbanization index). Survey sites were grouped into six stages(stageI (${\leq}3years$), stageII(3-5years), stageIII(5-7years), stageIV(7-10years), stageV(10-15years), stageVI(${\geq}20years$). Vegetation investigation was done from May 2009 to October 2012 and carried out phytosociological approach. The total flora were summarized as 176 taxa including 58 families, 127 genera, 157 species, 3 subspecies, 15 varieties and 1 forms. At each of successional stages, 64 taxa in stage I, 34 taxa in stage II, 84 taxa in stage III, 83 taxa in stage IV, 92 taxa in stage V, 23 taxa in stage VI were identified. Of the occurrence plants, the species with the highest r-NCD value were Alopecurus aequalis, Juncus effuusus var. decipiens, Persicaria thunbergii, Artemisia princeps, Salix koreensis and Alnus japonica at each stages. Herbaceous annual plants were dominated in the early stage, but its r-NCD value declined in the middle stage and the late stage. On the other hand, herbaceous perennial plants and Persicaria thunbergii, annual hydrophytes, increases in the middle stage. Woody plant and herbaceous plant which appeared in the forest edge increases in the late stage. Community indices correlate with successional stages. Richness and diversity index increase along the successional gradient. But dominance index decrease along the successional gradient. Evenness index was correlated with lower. In the ecological stability analysis of the habitat that evaluated by eco-floristic characters, stage I was the most unstable habitat. And the stability of the habitat has improved according to the successional stage.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• 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.50 no.6
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• pp.369-377
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• 2005

Precise application of topdressing nitrogen (N) fertilizer is indispensible for securing high yield and good quality of rice and minimizing N losses to the environment as well. For precise N management, growth and nitrogen nutrition status (NNS) should be diagnosed rapidly and accurately. The objective of the study was to evaluate the applicability of vegetation index (VI) calculated from hyperspectral canopy reflectance measurement and SPAD reading to nondestructive in situ diagnosis of growth and NNS of rice. Canopy reflectance, SPAD read­ing, growth parameters, and NNS characteristics were measured from various N treatments to evaluate the relationships among them for two cropping seasons from 2001 to 2002. The correlation coefficient of VIs with variables of growth and NNS increased positively as rice canopy became more closed. Regardless of growth stages, VIs had significantly high correlations with LAI, shoot dry weight (DW), shoot N content and nitrogen nutrition index (NNI). Those correlation coefficients increased steadily before heading stage as rice grew up. However, tiller number and leaf N concentration showed significantly high correlations with VIs only at and after panicle initiation stage (PIS). Among the VIs, RVIgreen had significantly higher correlation with the measured parameters than the other VIs: it showed correlation coefficients greater than 0.8 with leaf and shoot N concentration and DW, and much higher coefficients greater than 0.9 with LAI, shoot N content, and NNI. At LAI of below 2.5, VIs had non-significant or low correlations with the growth and NNS indicators due to the background effects. SPAD reading had significantly high correlation with leaf N concentration and NNI at each growth stage. In addition, it had significant correlations with variables of growth and NNS at PIS and booting stage, particularly, at booting stage. Though SPAD reading had a significantly high correlation value at a given growth stage in each year, it showed very weak relationship with variables of growth and NNS when pooled across growth stages and years. In conclusion, RVIgreen was found to be the most reliable VI to estimate the growth and NNS of rice around at PIS, but SPAD reading had much limitations.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.483-497
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• 2016

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of barley and wheat growth prediction equation by using UAV derived vegetation index. UAV imagery was taken on the test plots six times from late February to late June during the barley and wheat growing season. The field spectral reflectance during growing period for the 5 variety (Keunal-bori, Huinchalssal-bori, Saechalssal-bori, Keumkang and Jopum) were measured using ground spectroradiometer and three growth parameters, including plant height, shoot dry weight and number of tiller were investigated for each ground survey. Among the 6 Vegetation Indices (VI), the RVI, NDVI, NGRDI and GLI between measured and image derived showed high relationship with the coefficient of determination respectively. Using the field investigation data, the vegetation indices regression curves were derived, and the growth parameters were tried to compare with the VIs value.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.68-80
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• 2021

This study aimed to provide basic data such as the structure of the Carpinus turczaninowii community and characteristics of the habitat environment for ex situ conservation by analyzing the plant community structure of Carpinus turczaninowii, an island plant resource. For the community structure analysis, this study established 29 plots, sized 100㎡ each, in Seokmo, Yeongjong, Yeongheung, and Daebu islands. TWINSPAN was used for the classification of communities. The classification identified six communities. Group I was the C. turczaninowii-Quercus serrata community, group II was the C. turczaninowii-Pinus densiflora community, group III was the C. turczaninowii-Quercus mongolica community, group IV was the C. turczaninowii-Sorbus alnifolia community, group V was the C. turczaninowii typical community, and group VI was the C. turczaninowii-Quercus variabilis community. The species diversity was 0.90008~1.12868, the dominance was 0.17536~0.25665, and the similarity index was 17.1429~38.2979%. The result of correlation analysis of 7 environmental factors for 6 communities by RDA ordination showed a positive correlation between the crown density and litter layer and a negative correlation between the bare rock, soil hardness, and altitude on the 1st axis. On the 2nd axis, the bare rock and crown density showed a positive correlation, and the slope showed a negative correlation. In the C. turczaninowii-Quercus serrata community (I), the crown density and the litter layer were the environmental factors affecting the vegetation distribution. In the C. turczaninowii-Pinus densiflora (II) and C. turczaninowii-Quercus mongolica (III) communities, the slope was the factor affecting vegetation distribution. In the C. turczaninowii-Sorbus alnifolia (IV), C. turczaninowii typical (V), and C. turczaninowii-Quercus variabilis (VI) communities, the bare rock, altitude, and soil hardness were the factors affecting vegetation distribution.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.27-42
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• 2016

Forest accounts for almost 64 percents of total land cover in South Korea. For inventorying, monitoring, and managing such large area of forest, application of remote sensing and geographic information system (RS/GIS) technology is essential. On the basis of spectral characteristics of satellite imagery, forest cover and tree species can be classified, and forest cover map can be prepared. Using three dimensional data of LiDAR(Light Detection and Ranging), tree location and tree height can be measured, and biomass and carbon stocks can be also estimated. In addition, many indices can be extracted using reflection characteristics of land cover. For example, the level of vegetation vitality and forest degradation can be analyzed with VI (vegetation Index) and TGSI (Top Grain Soil Index), respectively. Also, pine wilt disease and o ak w ilt d isease c an b e e arly detected and controled through understanding of change in vegetation indices. RS and GIS take an important role in assessing carbon storage in climate change related projects such as A/R CDM, REDD+ as well. In the field of climate change adaptation, impact and vulnerability can be spatio-temporally assessed for national and local level with the help of spatio-temporal data of GIS. Forest growth, tree mortality, land slide, forest fire can be spatio-temporally estimated using the models in which spatio-temporal data of GIS are added as influence variables.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.4
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• pp.29-44
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• 2016

This study was carried out to the structure of plant community, and ecological succession sere of forest ecosystem in Soguemgang Valley, Odaesan National Park. Fifteenth plots(size is $20m{\times}20m$) were set up and the results analyzed by DCA which is one of the ordination technique showed that the plant communities were divided into seven groups which area community I(Quercus variabilis-Pinus densiflora community), II(Pinus densiflora community), III(Pinus densiflora-Quercus variabilis community), IV(Pinus densiflora-Quercus serrata community), V(Quercus serrata community), VI(Pinus densiflora-Deciduous broad-leaved plant community), VII(Cornus controversa-Carpinus laxiflora community). Shannon diversity index per $400m^2$ was to 0.7777 to 1.1440 and the age of Pinus densiflora 86 years old, Quercus variabilis was ranged from 66 to 87 years old, Quercus serrata was ranged from 51 to 62 years old, Carpinus laxiflora was 94 years old. In 2013, the succession trend was predicted Pinus densiflora${\rightarrow}$Quercus variabilis, Quercus serrata ${\rightarrow}$Cornus controversa, Carpinus laxiflora. The ecological sucession progress has declined power of the Pinus densiflora and the increased power of the deciduous broad-leaved and Quercus spp. in Soguemgang Valley, Odaesan National Park. Quercus serrata and Quercus variabilis communities that judged Pinus densiflora were progressing by direction of landform. The southern slopes vegetation were progressing for Quercus variabilis, the northern slopes vegetation were progressing for Quercus serrata. In flat Valley, mainly native species of Cornus controversa, Carpinus laxiflora are predicted ecological succession for deciduous broad-leaved tree community.