• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.431-441
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• 2016

Since the use of UAV (Unmanned Aerial Vehicle) is convenient for the acquisition of data on broad or inaccessible regions, it is nowadays used to establish spatial information for various fields, such as the environment, ecosystem, forest, or for military purposes. In this study, the process of estimating FVC (Fractional Vegetation Cover), based on multi-spectral UAV, to overcome the limitations of conventional methods is suggested. Hence, we propose that the FVC map is generated by using multi-spectral imaging. First, two types of result classifications were obtained based on RF (Random Forest) using RGB images and NDVI (Normalized Difference Vegetation Index) with RGB images. Then, the result map was reclassified into vegetation and non-vegetation. Finally, an FVC map-based RF were generated by using pixel calculation and FVC map-based GI (Gutman and Ignatov) model were indirectly made by fixed parameters. The method of adding NDVI shows a relatively higher accuracy compared to that of adding only RGB, and in particular, the GI model shows a lower RMSE (Root Mean Square Error) with 0.182 than RF. In this regard, the availability of the GI model which uses only the values of NDVI is higher than that of RF whose accuracy varies according to the results of classification. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.90-103
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• 2017

This study analyzed the surface temperature characteristics of urban green spaces under high summer temperatures to clarify the functions of green spaces in reducing urban temperatures. We obtained accurate surface temperature data using highresolution unmanned aerial vehicle images of the survey site, which was an isolated green space in the city. We analyzed differences in the surface temperature by land cover type, vegetation type, species type, and the relationship between surface temperature and vegetation volume. Based on the results, among the land cover types, wetlands and forests had low temperatures and paving areas had very high temperatures. Regarding vegetation type, broad-leaved trees had lower temperatures than coniferous trees in forests. However, in planted areas, coniferous trees had lower temperatures than broad-leaved trees. The temperature of long grass was higher than that of short grass, which suggested that the volume of grass affected the temperature. Regarding forest species type, the temperature of broad-leaved Robinia pseudoacacia forest and mixed broad-leaved forest was lower than coniferous Pinus densiflora forest. There was a slight difference in temperature between R. pseudoacacia forest and mixed broad-leaved forest. The analysis of the relationship between vegetation volume and temperature by forest species type indicated a negative correlation, where the temperature decreased with increasing vegetation volume, similar to the results of previous studies. However, we found a weak positive correlation in R. pseudoacacia forest; therefore, an increase in volume may not reduce the surface temperature depending on the dominant species.

• Geomechanics and Engineering
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• v.17 no.5
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• pp.475-483
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• 2019

Vegetation cover plays a vital role in stabilizing the soil structure, thereby contributing to surface erosion control. Surface vegetation acts as a shelterbelt that controls the flow velocity and reduces the kinetic energy of the water near the soil surface, whereas vegetation roots reinforce the soil via the formation of root-particle interactions that reduce particle detachment. In this study, two vegetation-testing trials were conducted. The first trial was held on cool-season turfgrasses seeded in a biopolymer-treated site soil in an open greenhouse. At the end of the test, the most suitable grass type was suggested for the second vegetation test, which was conducted in an environmental control chamber. In the second test, biopolymers, namely, starch and xanthan gum hydrogels (pure starch, pure xanthan gum, and xanthan gum-starch mixtures), were tested as soil conditioners for improving the water-holding capacity and vegetation growth in sandy soils. The results support the possibility that biopolymer treatments may enhance the survival rate of vegetation under severe drought environments, which could be applicable for soil stabilization in arid and semiarid regions.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.3
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• pp.260-267
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• 2010

As sloped farmland is subject to runoff and soil erosion and consequently require appropriate vegetative coverage to conserve soil and water, a field study was carried out to evaluate the impact of crop canopy coverage on soil loss and runoff from the experimental plot with three different textural types (clay loam, loam, and sandy loam). The runoff and soil loss were examined at lysimeters with 15% slope, 5 m in length, and 2 m in width for five months from May to September 2009 in Suwon ($37^{\circ}$ 16' 42.67" N, $126^{\circ}$ 59' 0.11" E). Red pepper (Capsicum annum L. cv. Daechon) seedlings were transplanted on three different dates, May 4 (RP1), 15 (RP2), and 25 (RP3) to check vegetation coverage. During the experimental period, the vegetation coverage and plant height were measured at 7 day-intervals and then the 'canopy cover subfactor' (an inverse of vegetation cover) was subsequently calculated. After each rainfall ceased, the amounts of soil loss and runoff were measured from each plot. Under rainfall events >100 mm, both soil loss and runoff ratio increased with increasing canopy cover subfactor ($R^2$=0.35, p<0.01, $R^2$=0.09, p<0.1), indicating that as vegetation cover increases, the amount of soil loss and runoff reduces. However, the soil loss and runoff were depending on the soil texture and rainfall intensity (i. e., $EI_{30}$). The red pepper canopy cover subfactor was more highly correlated with soil loss in clay loam ($R^2$=0.83, p<0.001) than in sandy loam ($R^2$=0.48, p<0.05) and loam ($R^2$=0.43, p<0.1) plots. However, the runoff ratio was effectively mitigated by the canopy coverage under the rainfall only with $EI_{30}$<1000 MJ mm $ha^{-1}hr^{-1}$ ($R^2$=0.34, p<0.05). Therefore, this result suggested that soil loss from the red pepper field could be reduced by adjusting seedling transplanting dates, but it was also affected by the various soil textures and $EI_{30}$.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.116-121
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• 1998

Global land related datasets such as land use, land cover, vegetation cover percentage, forest cover percentage, are part of important global geospatial environmental datasets for global change studies. Since land cover varies place by place, continental production of dataset is a usual approach. Western academically developed countries have some projects to describe land cover related information in digital form using remote sensing technology in African, American continent and Oceania. In this paper, the author introduce his initiative to coordinate Asian scientists in order to develop land related dataset of Asia for our better understanding of the environment of Asia and for contribution to the development of global dataset. This paper explains activities by Land Cover Working Group (LCWG) of the Asian Association on Remote Sensing(AARS), Data and Information System(DIS) sub-committee of Japan national committee for the International Geosphere and Biosphere Program(IGBP), and the International Society for Photogrammetry and Remote Sensing(ISPRS) Working Group IV/6 on Global databases supporting environmental monitoring.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• Journal of Ecology and Environment
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• v.36 no.1
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• pp.49-56
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• 2013

The entire land of Southern Iran faces problems arising out of various types of land degradation of which vegetation degradation forms one of the major types. The present work introduces a model developed for assessing the current status of hazard of vegetation degradation using Geographic Information System (GIS). This kind of assessment differs from those assessments based on vulnerability or potential hazard assessments. The Sadra watershed which covers the upper reaches of Marharlu basin, Fars Province, has been chosen for a hazard assessment of this type of degradation. The different kinds of data for indicators of current status of vegetation degradation were gathered from collecting of field data and also records of the governmental offices of Iran. Taking into consideration three indicators of current status of vegetation degradation the model identifies areas with different hazard classes. By fixing the thresholds of severity classes of the three indicators including per cent of vegetation cover, biomass production and ratio of actual biomass to potential biomass production, a hazard map for each indicator was first prepared in GIS. The final hazard map of current status of vegetation degradation was prepared by intersecting three hazards in the GIS. Results show areas under severe hazard class have been found to be widespread (89 %) while areas under moderate and very severe hazard classes have been found less extensive in the Sadra watershed. The preparation of hazard maps based on the GIS analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.175-182
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• 2006

Because of the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions of agricultural croplands, grasslands, and disturbed forests by multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been a primary problem to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement (GER-2600) and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Red-edge near 700nm and shortwave IR near 2,200nm are more sensitive to the fraction of dry vegetation. The use of hyperspectral data would allow us for better separation between bare soils and other surfaces covered by dry vegetation during the leaf-off season.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.