[논문]드론 영상기반 잔디밭 내 클로버의 퇴치 범위에 대한 시계열 분석

이용창; 강준오; 오성종

doi:10.7848/ksgpc.2021.39.4.211

드론 영상기반 잔디밭 내 클로버의 퇴치 범위에 대한 시계열 분석
Drone Image based Time Series Analysis for the Range of Eradication of Clover in Lawn 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.39 no.4, 2021년, pp.211 - 221

이용창 (Dept. of Urban Construction Engineering, Incheon National University) , 강준오 (Dept. of Urban Construction Engineering, Incheon National University) , 오성종 (Dept. of Urban Construction Engineering, Incheon National University)

초록
AI-Helper

클로버는 잔디의 대표적 유해 식물로 양지식물인 잔디보다 일찍 생육활동을 시작하여 잔디의 상부에 수관을 형성하고 잔디의 광합성과 성장을 방해한다. 이로 인해 두 식생종 간 경쟁에서 대부분, 클로버 영역은 확산되고 잔디의 경우는 훼손과 고사가 진행되게 된다. 훼손된 부분은 장마 및 생장 휴면 기간 중, 토양표출 확산으로 전개되어 잔디 복구에 심리적 스트레스 및 많은 경제적 부담을 초래하고 있다. 본 연구의 목적은 잔디의 대표적 유해식물인 클로버를 구분하고 클로버의 확산에 따른 훼손지역 분포, 퇴치 전·후의 식생변화 추이를 고찰하는 것이다. 이를 위해 RGB, BG-NIR 센서를 탑재한 융·복합 드론기반 영상을 활용, 3가지 식생지수의 시계열 분석을 통해 선별적 퇴치를 위한 식생구분, 복구전략 수립을 위한 잔디 훼손 분포 등을 고찰하였다. 특히, 인력 및 기기에 의한 선별적 제초 및 예초 전·후, 클로버의 생태변화 추이를 시계열로 분석하였다. 또한, 잔디와 클로버의 성장 중반기 기간 중, 식생 종간 구분 방안도 모색하였다. 연구결과, 잔디와 클로버 생육 특성에 따른 RGB 및 BGNIR 드론영상의 MGRVI 및 NDVI, MSAVI 지수의 시계열 분석을 통해 잔디 훼손과 클로버 퇴치 후 변화 추이 분석의 활용성을 확인하여 잔디 유해 잡초에 대한 효율적 관리의 활용 가능성을 입증할 수 있었다.

Abstract ▼ AI-Helper

The Rabbit grass(Trifolium Repens, call it 'Clover') is a representative harmful plant of lawn, and it starts growing earlier than lawn, forming a water pipe on top of the lawn and hindering the photosynthesis and growth of the lawn. As a result, in competition between lawn and clover, clover territory spreads, but lawn is damaged and dried up. Damage to the affected lawn area will accelerate during the rainy season as well as during the plant's rear stage, spreading the area where soil is exposed. Therefore, the restoration of damaged lawn is causing psychological stress and a lot of economic burden. The purpose of this study is to distinguish clover which is a representative harmful plant on lawn, to identify the distribution of damaged areas due to the spread of clover, and to review of changes in vegetation before and after the eradication of clover. For this purpose, a time series analysis of three vegetation indices calculated based on images of convergence Drone with RGB(Red Green Blue) and BG-NIR(Near Infra Red)sensors was reviewed to identify the separation between lawn and clover for selective eradication, and the distribution of damaged lawn for recovery plan. In particular, examined timeseries changes in the ecology of clover before and after the weed-whacking by manual and brush cutter. And also, the method of distinguishing lawn from clover was explored during the mid-year period of growth of the two plants. This study shows that the time series analysis of the MGRVI(Modified Green-Red Vegetation Index), NDVI(Normalized Difference Vegetation Index), and MSAVI(Modified Soil Adjusted Vegetation Index) indices of drone-based RGB and BG-NIR images according to the growth characteristics between lawn and clover can confirm the availability of change trends after lawn damage and clover eradication.

주제어

표/그림 (14)

그림 Fig. 1. (a) Whole view of target area, (b) region of interest, and (c) MGRVI index map reproduced in RGB (acquired in March 26, 2021)
표 Table 1. The considered vegetation indices related to the vegetation cover and chlorophyll content (N = NIR, R=Red, G=Green)
표 Table 2. Specification of the converged Drone and Sensors Source : www.dji.com
그림 Fig. 2. RGB image-based MGRVI (distribution range = -0.7~0.5, class=5, dr=0.24) of 'round lawn' test field between April 9, 2018 and June 6, 2018, The images (a'), (d'), and (e') are color images of (a), (d), and (e), respectively
그림 Fig. 3. RGB image-based MGRVI (distribution range = -0.7∼0.5, class=5, dr=0.24) of 'round lawn' test field between March 14, 2019 and May 8, 2019. The images (a'), (b'), (c') and (d') are color images of (a), (b), (c), and (d) respectively. Expecially, (d) and (d') are images after the weed-whacking by manual on April 29
그림 Fig. 4. RGB image-based MGRVI (distribution range = -0.7∼0.5, class=5, dr=0.24) of 'round lawn' test field between March 3, 2020 and May 7, 2020, The images (a'), (b'), (c') and (d') are color images of (a), (b), (c), and (d) respectively. Expecially, (c) and (c') are images after the weed-whacking on the beginning of April by manual.
그림 Fig. 5. BG-NIR image-based NDVI and MSAVI (distribution range = 0.0∼0.8, class=5, dr=0.24) of 'round lawn' test field between April 17, 2018 and June 6, 2018, (a), (b), (c), and (d) are maps of NDVI by date, (a'), (b'), (c') and (d') are maps of MSAVI respectively.
그림 Fig. 6. BG-NIR image-based NDVI and MSAVI (distribution range = 0.0∼0.8, class=5, dr=0.16) of 'round lawn' test field between March 14, 2019 and May 8, 2019, (a), (b), (c), and (d) are maps of NDVI by date, (a'), (b'), (c') and (d') are maps of MSAVI respectively.
그림 Fig. 7. BG-NIR image-based NDVI and MSAVI (distribution range = 0.0∼0.8, class=5, dr=0.16) of 'round lawn' test field between March 3, 2020 and May 7, 2020, (a), (b), (c), and (d) are maps of NDVI by date, (a'), (b'), (c') and (d') are maps of MSAVI respectively.
그림 Fig. 8. BG-NIR image-based NDVI and MSAVI (distribution range = 0.0∼0.8, class=5, dr=0.16) of 'round lawn' test field on January 2, 2020, February 22, 2021 and March 26, 2021. (a), (b), and (c), are maps of NDVI by date, (a'), (b'), and (c') are maps of MSAVI respectively.
그림 Fig. 9. Status of clover appearance and damaged grass area by precise visual observation of 'Round lawn' test site on March 26, 2021.
그림 Fig. 10. Time series index distribution of 'Round lawn' test site by class of NDVI and MSAVI vegetation indices during grass dormancy (late March to early April) ((a) NDVI, and (b) MSAVI))
그림 Fig. 12. Vegetation according to the photographing direction ((1)∼(4)) in Figure 11(d)
그림 Fig. 11. (a) BG-NIR image-based NDVI, (b) MSAVI, (c) RGB image-based MGRVI(class=5), and (d) RGB image of 'round lawn' test field on August 14, 2018 respectively. (dr = distribution range)

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