지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 박경훈
- 발행연도
- 2020
- 저작권
- 창원대학교 논문은 저작권에 의해 보호받습니다.
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- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
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초록· 키워드
오류제보하기
The purpose of this study is to develop a water quality prediction
model that considers the characteristics of Small streams in
agricultural areas where the inflow of Non-point sources is easy by
utilizing Unmanned aerial vehicles (UAV), spectroradiometer, and
In-situ water collection data. Data used in this study were obtained
through UAV, spectroradiometer, and water quality sampling method
during both periods, August 9 (after rainfall) and September 30
(before rainfall). The results of this study are summarized as follows.
First, the verification and correction of UAV images was carried
out using the spectroradiometer data for water sampling point and
land cover. Then, the spectral characteristics of the water quality
factor and water quality prediction model were developed through a comparative analysis of calibrated UAV images and water quality.
The analysis of the spectral characteristics of water quality factors
showed that Chl-a had a correlation with NIR (0.67, p < 0.05), NDVI
(0.89, p < 0.01), GNDVI (0.91, p < 0.01), and NGRDI (0.77, p < 0.05).
TN showed a negative (?) correlation in the visible light area (Blue,
Green, Red), indicating a high tendency for incident light to be
absorbed without reflection, and a positive (+) correlation between the
NDVI (0.94, p < 0.01) and GNDVI (0.87, p < 0.01). For BOD, the
correlation between the Red wavelength and the negative (?)
correlation of ?0.74 was shown to show that the higher the
concentration, the higher the incident light was mainly absorbed in the
red wavelength area. Finally, a water quality prediction model with the
explanatory power of BOD (R2 : 0.5482), TN (R2 : 0.886), and Chl-a
(R2 : 8237) was developed by performing regression analysis.
In conclusion, this study developed a water quality prediction model
for three factors (BOD, TN, and Chl-a) as target locations in
agricultural regions. The water quality prediction model developed in
this study is considered significant in the decision-making stage for
managing the water system by enabling the determination of water
distribution and prediction of concentration at the point where no water
collection has been performed on the stream in the agricultural region.
model that considers the characteristics of Small streams in
agricultural areas where the inflow of Non-point sources is easy by
utilizing Unmanned aerial vehicles (UAV), spectroradiometer, and
In-situ water collection data. Data used in this study were obtained
through UAV, spectroradiometer, and water quality sampling method
during both periods, August 9 (after rainfall) and September 30
(before rainfall). The results of this study are summarized as follows.
First, the verification and correction of UAV images was carried
out using the spectroradiometer data for water sampling point and
land cover. Then, the spectral characteristics of the water quality
factor and water quality prediction model were developed through a comparative analysis of calibrated UAV images and water quality.
The analysis of the spectral characteristics of water quality factors
showed that Chl-a had a correlation with NIR (0.67, p < 0.05), NDVI
(0.89, p < 0.01), GNDVI (0.91, p < 0.01), and NGRDI (0.77, p < 0.05).
TN showed a negative (?) correlation in the visible light area (Blue,
Green, Red), indicating a high tendency for incident light to be
absorbed without reflection, and a positive (+) correlation between the
NDVI (0.94, p < 0.01) and GNDVI (0.87, p < 0.01). For BOD, the
correlation between the Red wavelength and the negative (?)
correlation of ?0.74 was shown to show that the higher the
concentration, the higher the incident light was mainly absorbed in the
red wavelength area. Finally, a water quality prediction model with the
explanatory power of BOD (R2 : 0.5482), TN (R2 : 0.886), and Chl-a
(R2 : 8237) was developed by performing regression analysis.
In conclusion, this study developed a water quality prediction model
for three factors (BOD, TN, and Chl-a) as target locations in
agricultural regions. The water quality prediction model developed in
this study is considered significant in the decision-making stage for
managing the water system by enabling the determination of water
distribution and prediction of concentration at the point where no water
collection has been performed on the stream in the agricultural region.
목차
I. 서 론 ·······················································································································11. 연구배경 및 목적 ··························································································12. 이론적 배경 ·····································································································33. 국내·외 연구동향 및 고찰 ········································································7Ⅱ. 연구과정 및 방법 ·····················································································121. 연구 수행과정 ·······························································································122. 연구 대상지 ···································································································153. 연구 방법 ········································································································171) 자료수집·····························································································172) UAV 영상 정확성 검·보정 및 수질예측모형 개발 ·················· 28Ⅲ. 연구결과 및 고찰 ·····················································································301. UAV 정사영상 제작 ·················································································302. 지상분광계 측정 결과 ···············································································391) 토지피복유형별 분광특성 ·······························································392) 수질 채수 지점별 분광특성 ···························································463. 수질 분석 결과 ····························································································494. UAV 영상 검·보정 및 수질예측모형 개발 ····································561) UAV 영상 정확성 검·보정 ····························································562) UAV 영상 기반 수질예측모형 개발 ··········································· 67Ⅳ. 결론 및 향후계획 ·····················································································811. 결과 요약 및 결론 ······················································································812. 향후계획 ··········································································································84참고문헌 ··················································································································85Abstract ··················································································································92
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