• Title/Summary/Keyword: Vegetation filtering

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Sun-induced Fluorescence Data: Case of the Rice Paddy Field in Naju (논벼에서 관측된 태양 유도 엽록소 형광 자료: 나주에서 2020년 6월 10일부터 10월 5일까지)

  • Ryu, Jae-Hyun;Jang, Seon Woong;Kim, Hyunki;Moon, Hyun-Dong;Sin, Seo-Ho;Lee, Yang-Won;Cho, Jaeil
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.23 no.1
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    • pp.82-88
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    • 2021
  • Sun-induced fluorescence (SIF) retrieval using remote sensing technique has been used in an effort to understand the photosynthetic efficiency and stress condition of vegetation. Although optical devices and SIF retrieval methodologies were established in order to retrieve SIF, the SIF measurements are domestically sparse. SIF data of paddy rice w as measured in Naju, South Korea from June 10, 2020 to October 5, 2020. The SIFs based red (O2A) and far-red (O2B) w ere retrieved using a spectral fitting method and an improved Fraunhofer line depth, and photosynthetically active radiation was also produced. In addition, the SIF data was filtered considering solar zenith angle, saturation conditions, the rapid and sudden change of solar irradiance, and sun glint. The provided SIF data can help to understand a SIF product and the filtering method of SIF data can contribute to producing high-quality SIF data.

Major Factors Influencing Landslide Occurrence along a Forest Road Determined Using Structural Equation Model Analysis and Logistic Regression Analysis (구조방정식과 로지스틱 회귀분석을 이용한 임도비탈면 산사태의 주요 영향인자 선정)

  • Kim, Hyeong-Sin;Moon, Seong-Woo;Seo, Yong-Seok
    • The Journal of Engineering Geology
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    • v.32 no.4
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    • pp.585-596
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    • 2022
  • This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.