• The Journal of Engineering Geology
• /
• v.31 no.3
• /
• pp.395-432
• /
• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Journal of the Korean Geographical Society
• /
• v.35 no.1
• /
• pp.39-51
• /
• 2000

본 연구는 원격탐사 기법을 이용하여 북한 지역을 포함한 한반도 전지역을 대상으로 식생활력도(vegetation activity)의 시계열적 변화를 모니터링하고, 식생지수의 연중변화 특성을 이용하여 한반도의 식생 분포도를 작성하는데 그 목적이 있다. 1997년 4월부터 11월까지 8개월 동안 NOAA-14 위성에서 수신된 AVHRR 자료를 수집하여 정규 식생지수(Nomalized Difference Vegetation Index)를 구하고 이들을 MVC(Maximum Value Composite) 방법으로 조합하여 월별 NDVI 합성도를 작성하여 식생활력도의 시계열 변화를 고찰하였다. 또한 식물의 생장시기인 5월부터 10월까지의 NDVI를 무감독 분류하여 한반도의 식생분포 유형을 도시.나대지가 4.49%, 초지 4.49%, 경작지 27.54%, 활엽수림 25.61%, 침엽수림 38.22%로 나타났다.

• Journal of the Korean Geographical Society
• /
• v.9
• /
• pp.67-75
• /
• 1974

The vegetation activity of the Korean peninsula has been monitored temporal variations through a satellite remote sensing and the vegetation index was used to set up the vegetation data map of Korea. The AVHRR data sent by the NOAA-14 satellite was collected for 8 months between April and November, 1997 to calculate the normalized difference vegetation index(NDVI) which was combined the MVC(Maximum Value Composite). Then this NDVI composite map was prepared to review the temporal variations in the vegetation activity. The NDVI has been subject to the unsupervised classification for the growing season between May and October. And the vegetation type is divided into five classes ; urban, bare soil, grass, farming land, deciduous forest and coniferous forest. The unsupervised classificaion of vegetation distribution in the Korean Peninsula shows that the urban and bare soil take 4.14% of total national area, grass 4.49%, farming land 27.54%, deciduous forest 25.61% and coniferous forest 38.22%.

• Ecology and Resilient Infrastructure
• /
• v.3 no.4
• /
• pp.315-321
• /
• 2016

To identify the relationship between land use and thermal environment in an urban area, the air temperature was measured at different places of land use, and the changes of land use and air temperature were traced for 40 years in Incheon City. The relationship between land use and temperature was also investigated using satellite image data. The results of temperature measurements on a forest, a cropland (rice paddy), a bareland (school ground), and an urban area (asphalt road) from 19 to 21 August 2014 showed that air temperature was the highest on a pavement road. The temperature increased by about $1.4^{\circ}C$ ($0.035^{\circ}C/year$) for 40 years from 1975 to 2014 in Incheon. The changes in land use patterns of Incheon for the past 40 years showed that urban dry land, bareland and grassland have increased and cultivated land, wetland and forest land have decreased gradually. The land surface temperature (LST) was correlated with the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) extracted from Landsat satellite image. The land surface temperature was lower at higher NDVI, and higher at higher NDBI. Therefore, it is important to conserve and restore the land use of greenery, wetlands, and agricultural land in order to mitigate the heat island effect and improve the thermal environment in an urban area.