• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.289-291
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• 2003

Selective logging is currently a widely adopted management practice throughout the tropics. Monitoring of spatial extent and intensity of such logging is, therefore, becoming an important issue for sustainable management of forest. This study explores the possibility of using vegetation indices and Landsat 7 ETM+ image for this purpose. Two dataset acquired on 2002 and 2000 of Labanan concession area East Kalimantan, Indonesia were used. Three different vegetation indices (MSAVI, SAVI and NDVI) slicing and differentiating methods were tested. The results showed that the MSAVI is superior with overall accuracy of 77% and kappa 0.64.

• The Korean Journal of Ecology
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• v.20 no.5
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• pp.303-313
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• 1997

A new evaluation technique, i.e. multicriterion matrix technique, on the vegetation assessment was proposed and compared with several techniques having been previously used in the environmental impact assessment. Four criterias and 10 subcriterias were selected for two evaluation indices such as vegetation naturalness value and vegetation class. These criterias were characterized by syntaxonomical informations of hemeroby concept and potential vegetation, hierarchical system between criterias, and ordinal scale of vegetation naturalness valuse. Vegetation naturalness values were classified into 11 ordinal levels and condensed to five vegetation classes for facilitating practical use. In the example study two sites were compared by using two indices. This technique could have useful applications for ssessment of regional vegetation. A vegetation map of naturalness described by combination of two indices was proposed in order to illustrate regional vegetation naturalness.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.405-407
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• 2003

An attempt has been in this study to delineate the characteristics of spectral signatures of the vegetation in terms of various VIs, particularly made the Normalized Difference Vegetation Index(NDVI), Modified Soil Adjusted Vegetation Index2(MSAVI2) and Enhanced Vegetation Index(EVI). Multitemporal SPOT-4 VEGETATION data from 1998 to 2002 have been used for the analysis. They have been compared with each other for their similarities and differences. The correlations between the vegetation indices observed at various degree of vegetation coverage during their different stages of growth were examined. All of the VIs have shown qualitative relationships to variations in vegetation. Apparently, the NDVI and MSAVI2 are highly correlated for all of the temporal changes, representing the different stages of phenology.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.103-108
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• 2023

Due to the recent impact of global warming, heavy rainfall and droughts have been occurring regardless of the season, affecting the growth of Italian ryegrass (IRG), a winter forage crop. Particularly, delayed sowing due to frequent heavy rainfall or autumn droughts leads to poor growth and reduced winter survival rates. Therefore, techniques to improve yield through additional sowing in spring have been implemented. In this study, the growth of IRG sown in Spring and Autumn was compared and analyzed using vegetation indices during the months of April and May. Spectral data was collected using an Unmanned Aerial Vehicle (UAV) equipped with a hyperspectral sensor, and the following vegetation indices were utilized: Normalized Difference Vegetation Index; NDVI, Normalized Difference Red Edge Index; NDRE (I), Chlorophyll Index, Red Green Ratio Index; RGRI, Enhanced Vegetation Index; EVI and Carotenoid Reflectance Index 1; CRI1. Indices related to chlorophyll concentration exhibited similar trends. RGRI of IRG sown in autumn increased during the experimental period, while IRG sown in spring showed a decreasing trend. The results of RGRI in IRG indicated differences in optical characteristics by sowing seasons compared to the other vegetation indices. Our findings showed that the timing of sowing influences the optical growth characteristics of crops by the results of various vegetation indices presented in this study. Further research, including the development of optimal vegetation indices related to IRG growth, is necessary in the future.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.165-167
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• 2003

To evaluate the thermal and water stress of vegetation canopy in Southern Qu$\'{e}$bec, leaf water status was evaluated from vegetation indices derived from SPOT VEGETATION images and surface temperature from NOAA AVHRR images. This study was conducted by investigating vegetation conditions for two different periods, from June to August, 1999 and 2000. The vegetation indices were integrated for the evaluating vegetation conditions as a new index, normalized moisture index (NMI). A trapezoid was defined by the NMI and surface temperature, and the thermal and water status of the vegetation canopy was determined according to separate small sections within the trapezoid.

• Journal of Forest and Environmental Science
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• v.37 no.4
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• pp.292-303
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• 2021

Studies that relate the structure of tropical regrowth vegetation to soil properties are generally lacking in the literature. This study proposes three indices for assessing the structural regeneration of secondary forests. They are: (1) the tree diameter class, (2) the plant life form and (3) the woody/herbaceous plants ratio indices. They were applied to assess the regeneration status of forest regrowth vegetation (aged 1-10 years), derived savanna regrowth vegetation in south western Nigeria, and to secondary forests in different stages of succession in Columbia and Venezuela, Bolivia, Mexico in South and Central America and semi-arid savanna in Ethiopia and seasonal deciduous forest successional stages in India. In all the cases, the indices increased with increasing age of regrowth vegetation and hence, with increasing structural complexity of regenerating vegetation. The tree diameter class index increased from 32.1% in a 9-year secondary forest to 69.0% in an 80-year-old secondary forest in Columbia and Venezuela and from 0.4% in a 1-year fallow to 20.9% in 10-year regrowth vegetation in southwestern Nigeria. In semi-arid savanna in northern Ethiopia, the woody/herbaceous plants ratio index increased from 18.1% in a 5-year protected grazing enclosure to 75.1% in 15-year protected enclosure, relative to the status of 20-year enclosure. The indices generally had correlations of 0.6-0.90 with species richness and Simpson's/Margalef's species diversity, implying that they are appropriate measures of ecosystem development over time. The proposed indices also had strong and positive correlations with soil organic carbon and nutrients. They are therefore, significant indicators of fertility status.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.80-83
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• 2008

Diverse researches using vegetation index have been carried out to monitor spring droughts that have frequently occurred since 2000. The strength of the drought monitoring using vegetation index lies in that it can reflect characteristics of satellite images: large area coverage, cyclicity, and promptness. However, vegetation index involve uncertainly caused by diverse factors that affect vegetation stress. In this study, multi-temporal vegetation index is compared with the most representative meteorological drought indices like PSDI, SPI. Based on the results from analyses, usability of vegetation index as a tool of drought analysis is proposed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• The Korean Journal of Ecology
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• v.27 no.2
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• pp.99-106
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• 2004

We developed the estimation model for the vegetation developmental processes on the severely burned slope areas after forest fire in the east coastal region, Korea. And we calculated the vegetation indices as a useful parameter for the development of land management technique in the burned area and suggested the changes of the vegetation indices after forest fire. In order to estimate the woody standing biomass in the burned area, allometric equations of the 17 woody species regenerated by sprouter were investigated. According to the our results, twenty year after forest fire need for the development to the normal forest formed by 4 stratum structure, tree, sub-tree, shrub and herb layer. The height of top vegetation layer, basal area and standing biomass of woody species show a tendency to increase linearly, and the ground vegetation coverage and litter layer show a tendency to increase logarithmically after forest fire. Among vegetation indices, Ive and Ivcd show a tendency to increase logarithmically, and Hcl and Hcdl show a tendency to increase linearly after forest fire. The spatial variation of the most vegetation factors was observed in the developmental stages less than the first 5 years which were estimated secondary disaster by soil erosion after forest fire. Among vegetation indices, Ivc and Ivcd were the good indices for the representation of the spatial heterogeneity in the earlier developmental stages, and Hcl and Hcdl were the useful indices for the long-term estimation of the vegetation development after forest fire.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.