[논문]드론 광학센서 기반의 식생지수 정확도 평가

이근상; 조기성; 황지욱; 김평곤

doi:10.7848/ksgpc.2022.40.2.135

드론 광학센서 기반의 식생지수 정확도 평가
Evaluation of vegetation index accuracy based on drone optical sensor 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.40 no.2, 2022년, pp.135 - 144

이근상 (Dept. of Cadastre & Civil Engineering, Vision College of Jeonju) , 조기성 (Dept. of Civil Engineering, Jeonbuk National University) , 황지욱 (Dept. of Urban Engineering, Dept. of Urban Engineering, Jeonbuk National University) , 김평곤 (Dept. of Civil Engineering, Jeonbuk National University)

초록
AI-Helper

식생은 인간에게 다양한 생태공간을 제공하고 수자원 및 기후환경 측면에서도 매우 중요하기 때문에 근적외선 센서 기반의 식생지수를 활용한 식생 모니터링 연구가 많이 수행되어 왔다. 따라서 근적외선 센서를 구비하지 못할 경우 식생 모니터링 연구가 현실적으로 어려운 문제가 있었다. 본 연구에서는 이러한 문제를 개선하고자 NDVI 식생지수를 기준자료로 하여 광학센서 기반의 식생지수 정확도를 평가하였다. 먼저 현장에서 조사한 식생조사 지점과 NDVI 식생지수와의 중첩을 통해 Kappa 계수를 계산하였으며, 그 결과 Kappa 계수가 0.930으로 가장 높게 나타난 0.6 이상의 임계값을 갖는 식생영역을 광학센서 기반의 식생지수 정확도 평가의 기준자료로 선정할 수 있었다. NDVI 식생지수를 기준자료로 선정하여 광학센서 기반의 식생지수와 비교한 결과, 0.04, 0.08, 0.30 이상의 임계값 구간에서 Kappa 계수가 각각 0.713, 0.713, 0.828로 가장 높게 분석되었다. 특히 RGBVI 식생지수의 경우 Kappa 계수가 0.828로 높게 나타났으며, 따라서 근적외선 센서를 활용하지 못하는 환경에서도 광학센서를 활용한 식생 모니터링 연구가 가능함을 알 수 있었다.

Abstract ▼ AI-Helper

Since vegetation provides humans with various ecological spaces and is also very important in terms of water resources and climatic environment, many vegetation monitoring studies using vegetation indexes based on near infrared sensors have been conducted. Therefore, if the near infrared sensor is not provided, the vegetation monitoring study has a practical problem. In this study, to improve this problem, the NDVI (Normalized Difference Vegetation Index) was used as a reference to evaluate the accuracy of the vegetation index based on the optical sensor. First, the Kappa coefficient was calculated by overlapping the vegetation survey point surveyed in the field with the NDVI. As a result, the vegetation area with a threshold value of 0.6 or higher, which has the highest Kappa coefficient of 0.930, was evaluated based on optical sensor based vegetation index accuracy. It could be selected as standard data. As a result of selecting NDVI as reference data and comparing with vegetation index based on optical sensor, the Kappa coefficients at the threshold values of 0.04, 0.08, and 0.30 or higher were the highest, 0.713, 0.713, and 0.828, respectively. In particular, in the case of the RGBVI (Red Green Red Vegetation Index), the Kappa coefficient was high at 0.828. Therefore, it was found that the vegetation monitoring study using the optical sensor is possible even in environments where the near infrared sensor is not available.

주제어

표/그림 (15)

그림 Fig. 1. Study site
그림 Fig. 3. Drone ﬂight planning and drone photography
그림 Fig. 4. Drone image processing using Pix4D Mapper software
그림 Fig. 2. GCP survey
표 Table 1. Equation of vegetation index
표 Table 2. GCP survey value
그림 Fig. 5. NDVI map
그림 Fig. 6. Vegetation area detection according to threshold value based on NDVI
그림 Fig. 7. Vegetation survey points
표 Table 3. Statistical characteristics of NDVI
표 Table 4. Kappa coefﬁcient of NDVI according to threshold value
그림 Fig. 9. Vegetation area detection according to the threshold section based on GRVI, MGRVI, RGBVI
그림 Fig. 8. Vegetation index map based on optical sensor
표 Table 5. Statistical characteristics of vegetation index based on optical sensor
표 Table 6. Kappa coefﬁcient of GRVI, MGRVI, and RGBVI according to threshold value

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