• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.385-394
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• 2015

Pine wilt disease has greatly damaged pine forests not only in East Asia including South Korea and China, but also in European region. The damage caused by pine wood nematode (Bursaphelenchus xylophilus) is expressed in bundles within stands and rapidly spreading, however, present field survey methods have limitations to detecting damaged trees at regional level. This study extracted the damaged trees by pine wilt disease using time series hyperspectral aerial photographs, and analyzed their distribution characteristics. Hyperspectral aerial photographs of 1 meter spatial resolution were obtained in June, September, and October. Damaged trees by pine wilt disease were extracted using Normalized Difference Vegetation Index (NDVI) and Vegetation Index green (VIgreen) of the September photograph. Among extracted damaged trees, dead trees with leaves and without leaves were classified, and the spectral reflectance values from the photographs obtained in June, September, and October were compared to extract new outbreaks in September and October. Based on the time series dispersion of extracted damaged trees, nearest neighbor analysis was conducted to analyze distribution characteristics of the damaged trees within the region where hyperspectral aerial photographs were acquired. As a result, 2,262 damaged trees were extracted in the study area, and 604 dead trees (dead trees in last year) with leaves in relation to the damaged time and 300 and 101 newly damaged trees in September and October were classified. The result of nearest neighbor analysis using the data shows that aggregated distribution was the dominant pattern both previous and current year in the study area. Also, 80% of the damaged trees in current year were found within 60 m of dead trees in previous year.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.34-39
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• 2018

This research was conducted to estimate moisture content in cucurbitaceae seedlings, such as cucumber and watermelon, using hyperspectral imagery. Using a hyperspectral image acquisition system, the reflectance of leaf area of cucumber and watermelon seedlings was calculated after providing water stress. Then, moisture content in each seedling was measured by using a dry oven. Finally, using reflectance and moisture content, the moisture content estimation models were developed by PLSR analysis. After developing the estimation models, performance of the cucumber showed 0.73 of $R^2$, 1.45% of RMSE, and 1.58% of RE. Performance of the watermelon showed 0.66 of $R^2$, 1.06% of RMSE, and 1.14% of RE. The model performed slightly better after removing one sample from cucumber seedlings as outlier and unnecessary. Hence, the performance of new model for cucumber seedlings showed 0.79 of $R^2$, 1.10% of RMSE, and 1.20% of RE. The model performance combined with all samples showed 0.67 of $R^2$, 1.26% of RMSE, and 1.36% of RE. The model of cucumber showed better performance than the model of watermelon. This is because variables of cucumber are consisted of widely distributed variation, and it affected the performance. Further, accuracy and precision of the cucumber model were increased when an insignificant sample was eliminated from the dataset. Finally, it is considered that both models can be significantly used to estimate moisture content, as gradients of trend line are almost same and intersected. It is considered that the accuracy and precision of the estimating models possibly can be improved, if the models are constructed by using variables with widely distributed variation. The improved models will be utilized as the basis for developing low-priced sensors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.317-326
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• 2019

Salinity which is often detected on reclaimed land is a major detrimental factor to crop growth. It would be advantageous to develop an approach for assessment of salinity and drought damages using a non-destructive method in a large landfills area. The objective of this study was to examine applicability of the decision tree classifier using imagery for classifying for spring potatoes (Solanum tuberosum) damaged by salinity or drought at vegetation growth stages. We focused on comparing the accuracies of OA (Overall accuracy) and KC (Kappa coefficient) between the simple reflectance and the band ratios minimizing the effect on the light unevenness. Spectral merging based on the commercial band width with full width at half maximum (FWHM) such as 10 nm, 25 nm, and 50 nm was also considered to invent the multispectral image sensor. In the case of the classification based on original simple reflectance with 5 nm of FWHM, the selected bands ranged from 3-13 bands with the accuracy of less than 66.7% of OA and 40.8% of KC in all FWHMs. The maximum values of OA and KC values were 78.7% and 57.7%, respectively, with 10 nm of FWHM to classify salinity and drought damages of spring potato. When the classifier was built based on the band ratios, the accuracy was more than 95% of OA and KC regardless of growth stages and FWHMs. If the multispectral image sensor is made with the six bands (the ratios of three bands) with 10 nm of FWHM, it is possible to classify the damaged spring potato by salinity or drought using the reflectance of images with 91.3% of OA and 85.0% of KC.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.306-315
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• 2021

This study analyzed the effects of light-shielding curtains and halogens on spectrum when acquiring hyperspectral images in a greenhouse. The image data of tarp (1.4*1.4 m, 12%) with 30 degrees of angles was achieved three times with four conditions depending on 14 heights using the automatic image acquisition system installed in the greenhouse at the department of Southern Area of National Institute of Crop Science. When the image was acquired without both a light-shielding curtain and halogen lamp, there was a difference in spectral tendencies between direct light and shadow parts on the base of 550 nm. The average coefficient of variation (CV) for direct light and shadow parts was 1.8% and 4.2%, respective. The average CV value was increased to 12.5% regardless of shadows. When the image was acquired only used a halogen lamp, the average CV of the direct light and shadow parts were 2 .6% and 10.6%, and the width of change on the spectrum was increased because the amount of halogen light was changed depending on the height. In the case of shading curtains only used, the average CV was 1.6%, and the distinction between direct light and shadows disappeared. When the image was acquired using a shading curtain and halogen lamp, the average CV was increased to 10.2% because the amount of halogen light differed depending on the height. When the average CV depending on the height was calculated using halogen and light-shielding curtains, it was 1.4% at 0.1m and 1.9% at 0.2 m, 2 .6% at 0.3m, and 3.3% at 0.4m of height, respectively. When hyperspectral imagery is acquired, it is necessary to use a shading curtain to minimize the effect of shadows. Moreover, in case of supplementary lighting by using a halogen lamp, it is judged to be effective when the size of the object is less than 0.2 m and the distance between the object and the housing is kept constant.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.40-40
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• 2017

본 연구는 빛의 파장대가 넓어 보다 다양한 접근과 검출이 가능한 초분광 카메라 (VNIR spectral camera PS, SPECIN Filand)를 이용하여 정식시기가 다른 배추를 생육단계별로 영상을 취득한 후 배추 캐노피의 전 파장 (400~1000nm)으로 생육 추정모델을 개발하기 위해 수행하였다. 정식시기가 다른 배추를 생육단계별로 초분광 카메라로 영상을 취득한 후 취득된 영상 ($348{\times}1040$)을 ENVI (ver. 5.2, Exelis Visual Information Solutions, USA) 프로그램을 이용하여 식생지수 NDVI로 작물과 배경을 구분하였다. 배추 캐노피 영역에 전 파장을 산출한 후 반사판 영역의 전 파장을 이용하여 광 보정된 반사율을 산출하였다. 통계 프로그램인 R Project (ver.3.3.3, Development Core Team, Vienna, Austria)를 이용하여 배추의 반사율과 계측한 생육 정보를 PLSR (Partial least squares regression) 분석하여 정확도($R^2$) 및 정밀도 (RMSE [g,cm,count], RE [%])로 나타내었고 그 모델은 full-cross validation (FV) 하여 타당성을 검증하였다. 정식시기가 다른 배추의 모든 생육단계의 생육정보를 이용하여 PLSR (Partial least squares regression) 결과 엽장을 추정한 모델의 $R^2$는 84% 이상의 정확도와 RMSE 3.2cm 이하의 좋은 정밀도를 보였다. 엽폭을 추정한 모델의 $R^2$는 73% 이상의 정확도와 RMSE 3.5cm 이하의 정밀도를 보였고 엽수를 추정한 모델의 $R^2$는 93% 이상의 정확도와 RMSE 6.3Count 이하의 정밀도로 보여 캐노피의 전 파장을 이용해 생육을 추정하는 것이 가능하다고 판단되었으며 이 모델들의 타당성 검증에서도 좋은 정확도와 정밀도를 보였다. 그러나 배추의 중요한 생육인자 중 생체중을 추정한 모델의 $R^2$는 89% 이상으로 정확도가 높았으나 RMSE 571.1g 이하로 낮은 정밀도를 보여 생체중을 정확히 추정하기 어려웠다. 따라서 다른 통계분석방법으로 전 파장과 생육정보를 분석하거나 특정 밴드를 선택하여 산출한 식생지수를 이용한 추정 모델의 개발을 통하여 오차를 개선할 필요가 있다고 사료된다. 추후 반복 실험하여 분석한 추정 모델과 비교 분석하여 다양한 환경 및 생물 조건에 범용성을 가진 모델을 개발할 필요가 있다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1335-1342
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• 2014

Shallow water depths were estimated using Compact Airborne Spectrographic Imager (CASI)-1500 and mapped to analyze the bottom bathymetry changes due to the rip currents in Haeundae beach, South Korea for the first time. The depths were estimated empirically using the maximum reflectances from 420nm to 597nm wavelength of CASI and 47 in situ water depth measurements, which were compared with ground-truth bathymetry measurements. The comparisons showed that the RMSE was 1.1m with a correlation coefficient of 0.76. In addition, CASI imagery showed remarkably detailed bottom features, especially those resulting from the rip currents within the beach. Two different channels carved by the rip current were analyzed and characterized with respect to the width and slope compared to surrounding regions. While the west side of the channel showed a wide and gentle slope, the east side of the channel showed a narrow and steep slope. The estimated bathymetry map revealed that the uneven offshore bottom features were related to the transport and accumulation of sediments by the rip current, which reaches hundreds of meters offshore. Accordingly, the accumulated sediments were estimated by adding topography changes compared to the depths of the non-rip current regions. The sediments were accumulated in off channels as much as almost twice the amount of annual sand supplements along the Haeundae beach.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.45-58
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• 2014

The purpose of this study is to find out the influence on Organizational Performance of Autonomy in Cadastral Employees. The survey was conducted around the branch Korea Cadastral Survey Corporation. Using SPSS 20.0 and Amos 19.0, Structural Equating model was verified. The results of the analysis and the proposal is as follows. First, autonomy not directly but indirectly affected job satisfaction and organizational commitment. This had a positive impact on job satisfaction and organizational commitment, autonomy existing research. But it was not the organization's performance by increasing the autonomy to higher study. Perhaps it is analyzed the results of the public sector. Second, the relationship between autonomy and organizational performance in leadership had a mediating effect. And increase organizational performance factors, leadership has a very important meaning. Public good is not guaranteed autonomy. Therefore, it is possible to compensate through leadership. Third, cadastral field manager leadership competencies, it is necessary to develop a training program to enhance the organizational commitment and job satisfaction.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.54-54
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• 2023

하천유역조사는 관련 법률의 규정에 의해 물관리정책의 수립에 필요한 기초정보를 제공하는 것을 목적으로 기본현황, 이수, 치수 환경생태 등 유역관리에 필요한 주요 조사항목을 대상으로 수행되고 있다. 조사방법 중 원격탐사자료 활용한 조사는 드론 모니터링 영상 및 위성영상자료를 이용해 댐·제방과 같은 치수 시설물의 안전관리, 수질 모니터링, 하천지형조사, 하상변동조사 등에 활용되고 있다. 최근에는 일반 RGB 영상뿐만 아니라 수백개의 분광밴드를 포함한 초분광영상을 이용한 하천조사 연구가 이루어지고 있다. 초분광영상은 분광해상도가 높아 다항목 조사에 활용할 수 있다는 장점이 있지만, 많은 양의 분광정보를 포함하고 있기 때문에 초기 수집 자료의 용량이 너무 크고, 분석을 위한 전처리 과정이 까다롭다는 단점이 있다. 반면, 10개 이하 밴드의 분광정보를 수집하는 다분광영상은 2개 밴드를 이용해 정규식생지수(NDVI)를 즉각적으로 모니터링할 수 있고, 작물의 생육현황 등을 분석할 수 있어 농업 및 산림분야에서 널리 활용되고 있다. 초분광영상을 이용한 수심산정 연구는 최적 밴드비 탐색 기법(OBRA)을 활용해 측정수심과 상관관계가 높은 밴드비를 이용해 수심맵을 구축하는 방식이 활용되어왔다. 본 연구에서는 기존의 초분광영상을 활용한 수심산정기법을 다분광영상에 적용하여 분광밴드수가 축소된(경량화된) 자료를 활용한 수심산정 가능성을 확인하기 위해 동일한 현장에서 초분광과 다분광 두가지 영상을 촬영하였으며, 각각 수심맵을 구축해 하천분야에서 다분광영상의 활용도를 평가하였다. 또한, 기존의 OBRA의 한계를 개선하기 위해 가우시안 혼합 모델(GMM; Gaussian Mixture Model)을 활용해 영상을 군집화하여 수심산정 정확도를 개선하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.89-89
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• 2022

하천 합류부에 지천이 유입되는 경우 복잡한 3차원적 흐름 구조를 발생시키고 이로 인해 유사혼합 및 지형 변화가 활발히 발생하게 된다. 특히, 하천 합류부에서 부유사 거동은 하천의 세굴과퇴적, 하천 지형 변화, 하천 생태계, 하천구조물 안정성 등에 직접적으로 영향을 미치기 때문에 이에 대한 정확한 분석이 하천 관리 및 재해 예방에 필수적인 요소이다. 기존의 하천 합류부 부유사 계측 자료들은 재래식 채취 방식으로 수행되어 시공간적 해상도가 매우 낮아서 실측 자료만으로 합류부에서 부유사 혼합을 분석하기에는 한계가 존재하기에 대하천의 부유사 혼합 거동 해석에 수치모형이 주로 활용되어 왔다. 본 연구에서는 하천 합류부에서 부유사 거동을 공간적으로 정밀하게 분석하기 위해 드론 기반초분광 영상을 활용하여 하천 합류부에 최적화된 부유사 계측 방법론을 제시하였다. 현장에서 계측한 초분광 자료와 부유사 농도간의 관계를 구축하기 위하여 기계학습모형인 랜덤포레스트(Random Forest) 회귀 모형과 합류부에서 분광 특성이 다른 두 하천의 특성을 정확하게 반영하기 위한 가우시안 혼합 모형 (Gaussian Mixture Model) 기반 초분광 군집화 기법을 결합하였다. 본 연구에서 구축한 방법론을 낙동강과 황강의 합류부에 적용한 결과, 초분광 군집을 통해 두하천 흐름의 경계층을 명확히 구별하였으며, 이를 바탕으로 지류와 본류에 대해 각각 분리된 회귀 모형을 구축하여 복잡한 합류부 근역 경계층에서의 부유사 거동을 보다 정확하게 재현하였다. 또한 나아가서 재현된 고해상도의 부유사 공간분포를 바탕으로 경계층에서 강한 두 흐름이 혼합되어 발생한 와류(Wake)가 부유사 혼합에 미치는 영향을 규명하였고, 하천 합류부에서 발생하는 전단층의 수평방향 대규모 와류가 부유사 혼합 양상에 지배적 영향을 미치는 것으로 확인하였다.