• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2078-2082
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• 2009

Riparian vegetation distribution patterns and diversity relative to various fluvial geomorphic channel patterns, stream bank stabilization methods, and stream flow processes are described and interpreted for selected stream of Goesan, Central Korea. Idong Stream Pilot Project, which began in May 2003 and finished in December 2003, was selected to develop effective methods. The project aim to maintain or increase stream bank stabilization ecosystem goods and services while protecting downstream and stream bank ecosystem. A number of protecting methods which are a Flight of fieldstone, Vegetation block, Green river block, Stone net, Green environment block, Eco friendly cobble, Vegetation mat and Geo green cell and Firefly block were applied on the bank of Idong stream. The stream sites have been monitored about flora conditions each method in 2007. We selected 12 points for summer seasons to separately investigate in left bank, right bank and river bed. The main purpose of this study was to find out suitable methods and to improve stream restoration techniques for ecosystem. On the stream bank, Eco friendly cobble method(9.57) was the highest average of vegetation cover and Firefly block method(3.87) was the lowest average in applied methods.

• The Korean Journal of Ecology
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• v.28 no.6
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• pp.365-373
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• 2005

Understanding the relation of water environmental factors and vegetation is critical to restoration and management of wetlands. To reveal relationships between representative plant groups and water environments, we measured cover and abundance of plant species, water depth, temperature, pH, conductivity, dissolved oxygen, $NH_4$-N, $NO_3$-N, and $PO_4-P$ concentration in water in Wangsuk stream (WS) and Gwarim reservoir (GR). This study was conducted monthly from May to October, 2004. Six vegetation groups $(W1{\sim}W6)$ in WS and five vegetation groups $(G1{\sim}G5)$ in GR were identified using TWINSPAN. WS was characterized by Phragmites japonica, Digitaria sanguinalis, Phalaris arundinacea, Beckmannia xyzigachne and Persicara hydropiper, Persicaria thunbergii, Typha angustifolia. GR was characterized by T. angustifolia, Scirpus tabernaemontani, P. thunbergii, Humulus japonicus and Scirpus fluviatilis, Typha orientalis, Zizania latifolia. The vegetation in WS experienced greater seasonal changes than in GR. A correspondence analysis suggests that water depth was the major environmental factor influencing the distribution of most plants communities in both wetlands.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Journal of Biosystems Engineering
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• v.41 no.2
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• pp.126-137
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• 2016

Purpose: The overall objective of this study was to evaluate the vegetation fraction of soybeans, grown under different cropping conditions using an unmanned aerial vehicle (UAV) equipped with a red, green, and blue (RGB) camera. Methods: Test plots were prepared based on different cropping treatments, i.e., soybean single-cropping, with and without herbicide application and soybean and barley-cover cropping, with and without herbicide application. The UAV flights were manually controlled using a remote flight controller on the ground, with 2.4 GHz radio frequency communication. For image pre-processing, the acquired images were pre-treated and georeferenced using a fisheye distortion removal function, and ground control points were collected using Google Maps. Tarpaulin panels of different colors were used to calibrate the multi-temporal images by converting the RGB digital number values into the RGB reflectance spectrum, utilizing a linear regression method. Excess Green (ExG) vegetation indices for each of the test plots were compared with the M-statistic method in order to quantitatively evaluate the greenness of soybean fields under different cropping systems. Results: The reflectance calibration methods used in the study showed high coefficients of determination, ranging from 0.8 to 0.9, indicating the feasibility of a linear regression fitting method for monitoring multi-temporal RGB images of soybean fields. As expected, the ExG vegetation indices changed according to different soybean growth stages, showing clear differences among the test plots with different cropping treatments in the early season of < 60 days after sowing (DAS). With the M-statistic method, the test plots under different treatments could be discriminated in the early seasons of <41 DAS, showing a value of M > 1. Conclusion: Therefore, multi-temporal images obtained with an UAV and a RGB camera could be applied for quantifying overall vegetation fractions and crop growth status, and this information could contribute to determine proper treatments for the vegetation fraction.

• Journal of The Korean Society of Grassland and Forage Science
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• v.21 no.3
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• pp.129-136
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• 2001

The alpine grassland vegetation at the northwest slope of Mt. Paektu were investigated by Penound- Howard's cover-degree method. The floristic composition of the alpine grassland from 2,100m altitude to the top of mountain were 35 species, 1 subspecies, and 6 varieties, and most of the plants were short p e r e ~ i a l herbs and shrubs. The dominant species of the vegetation distributed along to altitude were Rhododendron aureum (2,100-2,20Om), Dryas octopetala var. asiatica(2,200-2,30Om), Dryas octopetala var. asiatica(2,300-2,40Om), Rhododendron aureum- Rhododendron redowskianum(2,400-2,50Om), and Rhododendron redowskianum(2,500- 2,58Om), respectively. Characteristics of the shrub plants which settled in alpine grassland showed uniform low height(3-15cm), creeping stem and evergreen leaf. Life form of the plants were 29 species of Hemicryptophyte, 8 species of Chamaephyte, 1 species of Geophyte, and 5 species of Phanerophyte. (Key words : Alpine grassland, Mt. Paektu, Altitude, Dominant, Life form)

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.18-20
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• 2003

Land cover change detection is one of the most important trends in which remote sensing data could be used to assist strategists and the planners to decide the best land use policy. Two images of NOAA-AVHRR and SPOT vegetation acquired in November 1992 and 2002 were used to assess the changes of Agricultural lands in Egypt. A supervised classification together with two change images derived from classification result and NDVI were used to evaluate the trend and form of the change. It was found that agricultural areas increased by about 14.3 % during the study period in particular around the River Nile Delta and near the Northern Lakes of Egypt. The new cultivated lands were extracted mainly from the desert and from the salt marches areas. At the same time, parts of the agricultural lands were turned into non-cultivated land because of the urban expansion and soil degradation.

• Proceedings of the Zoological Society Korea Conference
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• 2003.08a
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• pp.132.1-132
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• 2003

No Abstract, See Full Text

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.

• Atmosphere
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• v.20 no.4
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• Atmosphere
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• v.19 no.2
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• pp.169-182
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• 2009

To improve the performance of climate and numerical models, concerns on the land-atmosphere schemes are steadily increased in recent years. For the realistic calculation of land-atmosphere interaction, a land surface information of high quality is strongly required. In this study, a new land cover map over the Korean peninsula was developed using MODIS (MODerate resolution Imaging Spectroradiometer) data. The seven phenological data set (maximum, minimum, amplitude, average, growing period, growing and shedding rate) derived from 15-day normalized difference vegetation index (NDVI) were used as a basic input data. The ISOData (Iterative Self-Organizing Data Analysis), a kind of unsupervised non-hierarchical clustering method, was applied to the seven phenological data set. After the clustering, assignment of land cover type to the each cluster was performed according to the phenological characteristics of each land cover defined by USGS (US. Geological Survey). Most of the Korean peninsula are occupied by deciduous broadleaf forest (46.5%), mixed forest (15.6%), and dryland crop (13%). Whereas, the dominant land cover types are very diverse in South-Korea: evergreen needleleaf forest (29.9%), mixed forest (26.6%), deciduous broadleaf forest (16.2%), irrigated crop (12.6%), and dryland crop (10.7%). The 38 in-situ observation data-base over South-Korea, Environment Geographic Information System and Google-earth are used in the validation of the new land cover map. In general, the new land cover map over the Korean peninsula seems to be better classified compared to the USGS land cover map, especially for the Savanna in the USGS land cover map.