• Title/Summary/Keyword: Vegetation data

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Development of a Methodology to Estimate the Degree of Green Naturality in Forest Area using Remote Sensor Data (임상도와 위성영상자료를 이용한 산림지역의 녹지자연도 추정기법 개발)

  • Lee, Kyu-Sung;Yoon, Jong-Suk
    • Journal of Environmental Impact Assessment
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    • v.8 no.3
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    • pp.77-90
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    • 1999
  • The degree of green naturality (DGN) has played a key role for maintaining the environmental quality from inappropriate developments, although the quality and effectiveness of the mapping of DGN has been under debate. In this study, spatial distribution of degree of green naturality was initially estimated from forest stand maps that were produced from the aerial photo interpretation and extensive field survey. Once the boundary of initial classes of DGN were defined, it were overlaid with normalized difference vegetation index (NDVI) data that were derived from the recently obtained Landsat Thematic Mapper data. NDVI was calculated for each pixel from the radiometrically corrected satellite image. There were no significant differences in mean values of vegetation index among the initial DGN classes. However, the satellite derived vegetation index was very effective to delineate the developed and damaged forest lands and to adjust the initial value of DGN according to the distribution of NDVI within each class.

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Assessment of Climate and Vegetation Canopy Change Impacts on Water Resources using SWAT Model (SWAT 모형을 이용한 기후와 식생 활력도 변화가 수자원에 미치는 영향 평가)

  • Park, Min-Ji;Shin, Hyung-Jin;Park, Jong-Yoon;Kang, Boo-Sik;Kim, Seong-Joon
    • Journal of The Korean Society of Agricultural Engineers
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    • v.51 no.5
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    • pp.25-34
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    • 2009
  • The objective of this study is to evaluate the future potential climate and vegetation canopy change impact on a dam watershed hydrology. A $6,661.5\;km^2$ dam watershed, the part of Han-river basin which has the watershed outlet at Chungju dam was selected. The SWAT model was calibrated and verified using 9 year and another 7 year daily dam inflow data. The Nash-Sutcliffe model efficiency ranged from 0.43 to 0.91. The Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model3 (CGCM3) data based on Intergovernmental Panel on Climate Change (IPCC) SRES (Special Report Emission Scenarios) B1 scenario was adopted for future climate condition and the data were downscaled by artificial neural network method. The future vegetation canopy condition was predicted by using nonlinear regression between monthly LAI (Leaf Area Index) of each land cover from MODIS satellite image and monthly mean temperature was accomplished. The future watershed mean temperatures of 2100 increased by $2.0^{\circ}C$, and the precipitation increased by 20.4 % based on 2001 data. The vegetation canopy prediction results showed that the 2100 year LAI of deciduous, evergreen and mixed on April increased 57.1 %, 15.5 %, and 62.5% respectively. The 2100 evapotranspiration, dam inflow, soil moisture content and groundwater recharge increased 10.2 %, 38.1 %, 16.6 %, and 118.9 % respectively. The consideration of future vegetation canopy affected up to 3.0%, 1.3%, 4.2%, and 3.6% respectively for each component.

Vegetation Classification in Natural Swamp Area Using LANDSAT MSS (LANDSAT MSS 영상에 의한 자연 소택지의 식생분류)

  • 지광훈;강필종;조명희
    • Korean Journal of Remote Sensing
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    • v.2 no.1
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    • pp.13-21
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    • 1986
  • The study was emphasized on the applicability of Landsat data for vegetation classification of touch as small natural swamp areas Yujeon natural swamp in Haman-gun through image processing system. The image processing technique which was applied is maximum likelihood method. The classified types on the Landsat image are water, nelumbo, grass, agricultural field and conifer. The computer processed classification was compared the existed data for evaluating the result, but there are some difficulties on the exact comparison between them because of discordance of the temporal resolution. The result, anyhow, is quite remarkable that Landsat MSS data can be used for the quantitative estimates of vegetation type classification in such small area.

A Study of Extracting Method of Soil and Vegetation Information for Hydrological Analysis - Mainly using the Satellite Remote Sensing. - (수문해석을 위한 광역유역내의 토양 및 식생정보 추출방법에 관한 연구 -주로 위성리모트센싱을 이용하여)

  • 박종화
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.38 no.2
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    • pp.123-132
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    • 1996
  • The purpose of this study was to clarify the reflectance of soil and plant in order to extract the soil and vegetation informations using the satellite remote sensing data. The influence of soil moisture content and vegetation cover rate on the soil reflectance, and the relationship between vegetation reflectence and its vitality were discussed. The results obtained in this study were summarized as follows; I) The soil reflectance($R_{0.65}$) of wavelength ($0.65{\mu}m$) was closely related to the reflectance((($IR_{0.85}$) of wavelength $0.85{\mu}m$. This relationship could be expressed by soil line. 2) A point on the soil line was affected by the soil moisture ratio and the organic matter content. 3) The vegetation cover rate was formulated in terms of soil and vegetation reflectance at wavelengths pf $0.65{\mu}m$($R_{0.65}$) and ($0.85{\mu}m$) (($IR_{0.85}$). 4) The vegetation reflectence was strongly influenced by its vitality, which was related to the moisture content and the chlorophy [[-a concentration.

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A Study on Vegetation Index for Zoning of Natural Ecosystem on Baekdudaegan (백두대간 자연생태계의 지역구분을 위한 식생지수에 관한 연구)

  • 김갑태
    • Korean Journal of Environment and Ecology
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    • v.16 no.3
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    • pp.223-232
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    • 2002
  • For the zoning of natural ecosystem, Vegetation Index is calculated from the vegetation data surveyed on Baekdudaegan (Pijae-Doraegijae). Five factors -biodiversity, conservation value of the stand, environmental quality, longevity of the stand, site productivity- are considered in the calculation of Vegetation Index. Vegetation Index might be a useful zoning tool for management of Baekdudaegan. For Vegetation Index I, 59 sample plots 52.2% of total 113 sample plots are belong to core area, 34 sample plots 30.l% and 20 sample plots 17.7% are belong to buffer zone and transition area, respectively. For Vegetation Index II, 49 sample plots 43.4% of total 113 sample plots are belong to core area, 38 sample plots 33.6% and 26 sample plots 23.0% are belong to buffer zone and transition area, respectively.

Wetness or Warmth, Which is the Dominant Factor for Vegetation?

  • Suzuki, Rikie;Xu, Jianqing;Motoya, Ken
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.147-149
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    • 2003
  • The wetness, a function of precipitation and temperature etc, and the warmth, a function of temperature, are the dominant factor for global vegetation distribution. This paper employs the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index (WEI), and focuses on an essential climate-vegetation relationship at global scale. The NDVI was acquired from ‘Twenty-year global 4-minute AVHRR NDVI dataset.’ The WEI is defined as the fraction of the precipitation to the potential evaporation. The WAI was calculated by accumulating the monthly mean temperature of the portion exceeded 5$^{\circ}C$ throughout the year. Meteorological data for the WEI and WAI calculation were obtained from the ISLSCP CD-ROM. All analyses were conducted for 1 ${\times}$ 1 degree grid box on the terrestrial area of the Earth, and on annual value basis averaged in 1987 and 1988. The result of analyses demonstrated that there are two regimes in their relations, that is, a regime in which NDVIs vary depending on the WEI, and a regime in which NDVIs vary depending on the WAI. These two regimes appeared to correspond to the wetness dominant and warmth dominant vegetation, respectively. The geographical distributions of two regimes were mapped. Most of the world vegetation is categorized into wetness dominant, while warmth dominant vegetation is seen in the high-latitude area mainly to the north of 60$^{\circ}$N in the Northern Hemisphere and high-altitude areas.

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Analysis of factors affecting vegetation cover for stabilization of granite weathered soil forest road cut slopes

  • Seong-Man Kim;Sung-Min Choi;Ye Jun Choe;Yun-Jin Shim;Joon-Woo Lee
    • Korean Journal of Agricultural Science
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    • v.49 no.4
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    • pp.809-819
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    • 2022
  • This study was conducted to improve the stability of cut slopes of forest roads in granitic weathered soil areas. The study area is a national forest road (road length 28.48 km) in Pyeongchang-gun, Gangwon-do. After data collection, a statistical analysis was performed using IBM SPSS (Ver. 26.0). First, the correlation analysis showed that structure, slope position, soil erosion, slope, and aspect (N, S) were correlated with vegetation coverage (p < 0.05). Elapsed years, slope distance, and aspect (E, W) were found to have no correlation with vegetation coverage. (p > 0.05) Second, one-way ANOVA and Kruskal-Wallis test results showed that vegetation coverage was worse when the slope was located at the top or the middle of the slope than at the bottom of the slope. In addition, the site with sheathing and gabions showed good vegetation coverage when compared with the site without structures. In the case of soil erosion, areas with severe damage and moderate damage showed worse vegetation coverage. Therefore, it is necessary to strengthen the slope angle of the cut soil of the granitic weathered soil area from 1 : 0.5 - 1.2 to 1 : 0.8 - 1.5. In addition, structures such as sheathing and gabions should be installed on granitic weathered land.

Application of Geo-Statistic and Data-Mining for Determining Sampling Number and Interval for Monitoring Microbial Diversity in Tidal Mudflat (갯벌 미생물 다양성 모니터링 시료 채취 개수 및 간격 선정을 위한 지구통계학적 기법과 데이터 마이닝 적용 연구)

  • Yang, Ji-Hoon;Lee, Jae-Jin;Yoo, Keun-Je;Park, Joon-Hong
    • Journal of Korean Society of Environmental Engineers
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    • v.32 no.12
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    • pp.1102-1110
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    • 2010
  • Tidal mudflat is a reservoir for diverse microbial resources. Microbial diversity in tidal mudflat sediment can be easily influenced by various human activities. It is necessary to take representative samples to monitor microbial diversity in tidal mudflat sediments. In this study, we analyzed the microbial diversity and chemical characteristics of vegetation and non-vegetation tidal mudflat regions in the Kangwha tidal mudflat using geo-statistics and data-mining. According to the geo-statistical analysis, most correlation range values for the vegetation region were smaller than those for the non-vegetation region, which suggested that the shorter number and interval of sampling are required for the vegetation tidal mudflat environment due to its higher degree of chemical and biological complexity and heterogeneity. The data-mining analysis suggested that the organic content and nitrate were the major environmental factors influencing microbial diversity in the vegetation region while pH and sulfate were the major influencing factors in the non-vegetation region. Using the geo-statistical and data-mining integration approach, we proposed a guideline for determining the sampling interval and number to monitor microbial diversity in tidal mudflat.

Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Classification Upland Crop in Small Scale Agricultural Land (무인항공기와 딥러닝(UNet)을 이용한 소규모 농지의 밭작물 분류)

  • Choi, Seokkeun;Lee, Soungki;Kang, Yeonbin;Choi, Do Yeon;Choi, Juweon
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.6
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    • pp.671-679
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    • 2020
  • In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

The Vegetation of Seogno Island (序毛島의 植生)

  • Kil, Bong-Seop;Jeong-Un Kim
    • The Korean Journal of Ecology
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    • v.7 no.4
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    • pp.208-232
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    • 1984
  • A survey was conducted on the vegetation of Seogmo island, which is small island (ca. 41 okm2) located about 1km west of Kanghwa island in the central Korea, from Aug. 1983 to May 1984. Based on the field data, actual vegetation map and potential natural vegetation map were made. The island is covered with broad-leaved trees dominated with Quercus variabilis. The dominance diversity curves obtaiend in different associations are grouped in two types, lognormal distribution forms at the undisturbed vegetation and geometric series at the disturbed or rocky sites. It seems that the curves show to us the nature of their ecocline by the hypothesis of some investigators, i.e. Random niche boundary hypothesis, niche preemption hypothesis, lognormal distribution and logarithmic series. Total numbers of vascular plant species of the island were recorded 108 families, 348 genera, 475 species, 73 variaties and 7 forma. And Pte.-Q Index, 0.93, and Fisher's Index, 589.8, were higher than those in neighboring islands.

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