환경영향평가의 훼손수목량 추정을 위한 드론영상 분석법과 방형구법의 정확성 비교
Comparison of Accuracy between Analysis Tree Detection in UAV Aerial Image Analysis and Quadrat Method for Estimating the Number of Trees to be Removed in the Environmental Impact Assessment
1
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
환경영향평가의 훼손수목량은 온실가스 배출량, 임목폐기물 산정 등 다양한 부분에 활용되는 환경 지표이다. 지금까지 훼손수목량은 식생조사표의 임목밀도에 의존하였고, 이에 따른 표본편향으로 훼손수목량 추정의 불확실성이 가중되었다. 훼손수목량 추정의 정확성을 높이려면 전수조사를 대안으로 제시할 수 있으나 불가능한 것이 현실이다. 대안으로 드론영상을 이용한 개별 수목 탐지 방법이 있으며, 이 연구는 개별 수목 탐지 방법론으로 표본조사(방형구법)와 드론영상 분석법으로 추정된 훼손수목량을 전수조사 결과와 비교하였다. 연구 결과 전수조사 기준으로 드론 영상 분석법은 25주 과대추정 하였고 방형구법(평균)은 58주 과대 추정하였다. 그러나 기존 환경영향평가에서 시행하는 방형구법은 방형구의 개수, 방형구의 위치에 따른 표본편향의 영향을 많이 받을 것으로 예상된다.
Abstract
The number of trees to be removed trees (ART) in the environmental impact assessment is an environmental indicator used in various parts such as greenhouse gas emissions and waste of forest trees calculation. Until now, the ART has depended on the forest tree density of the vegetation survey, and the uncertainty of estimating the amount of removed trees has increased due to the sampling bias. A full-scale survey can be offered as an alternative to improve the accuracy of ART, but the reality is that it is impossible. As an alternative, there is an individual tree detection using aerial image (ITD), and in this study, we compared the ARTs estimated by full-scale survey, sample survey, and ITD. According to the research results, compared to the result of full-scale survey, the result of ITD was overestimated by 25. While 58 were overestimated by the sample survey (average). However, as the sample survey is an estimate based on random samples, ART will be overestimated or underestimated depending on the number and size of quadrats.
참고문헌 (37)
- Birdal AC, "Estimating tree heights with images from an unmanned aerial vehicle", Geomatics, Natural Hazards and Risk, 8, pp.1144~1156 (2017)
- Chang H, "Determination of sample size in using central limit theorem for weibull distribution", International Journal of Information and Management Sciences, 17, pp.31~ (2006)
- 장안진, "컬러항공사진과 LiDAR 데이터를 이용한 수목 개체 및 수고 추정", 대한원격탐사학회지, 22(6), pp.543~551 (2006), 대한원격탐사학회
- 조두영, "항공라이다 자료를 이용한 가로수 정보의 추출", Spatial Information Research, 20(6), pp.45~57 (2012), 대한공간정보학회
- 최경아, "라이다데이터를 이용한 도시지역의 자동변화탐지", 한국측량학회지, 26(4), pp.341~350 (2008), 한국측량학회
- Comas C, "On tree intensity estimation for forest inventories : Some statistical issues", Biometrical Journal, 53, pp.994~1010 (2011)
- Duncanson L, "Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests", Scientific Reports, 5, pp.17153~ (2015)
- Fujimoto A, "An end to end process development for UAV-SfM based forest monitoring : Individual tree detection, species classification and carbon dynamics simulation", Forests, 10, pp.680~ (2019)
- Gray AN, "Forest inventory and analysis database of the United States of America (FIA)", Biodiversity and Ecology, 4, pp.225~231 (2012)
- 황세란, "항공 라이다데이터를 이용한 개별수목탐지 및 평균수고추정", Spatial Information Research, 20(3), pp.27~38 (2012), 대한공간정보학회[DOI]
- 황상일, "개발사업 환경영향평가시 식생의 탄소저장 및 흡수량 산정법에 대한 비교", 환경영향평가, 20(4), pp.477~487 (2011), 한국환경영향평가학회
- Itakura K, "Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based lidar", Journal of Agricultural Meteorology, 74, pp.109~113 (2018)
- Kentsch S, "Computer vision and deep learning techniques for the analysis of drone-acquired forest images, a transfer learning study", Remote Sensing, 12, pp.1287~ (2020)
- Reviewing Feasibility of Applying of Drones and BIM in Environment Impact Assessment, "Korea Environment Institute", Korea Environment Institute, ~ (2019)
- Lei XD, "Forest inventory in China : status and challenges", International Forestry Review, 11, pp.52~63 (2009)
- Li W, "A new method for segmenting individual trees from the lidar point cloud", Photogrammetric Engineering & Remote Sensing, 78, pp.75~84 (2012)
- Sampling designs for national forest assessments. Knowledge Reference for National Forest Assessments, "McRoberts RE", FAO, 23~40 (2015)
- Morales G, "Automatic segmentation of Mauritia flexuosa in unmanned aerial vehicle(UAV)imagery using deep learning", Forests, 9, pp.736~ (2018)
- Mohan M, "Individual tree detection from unmanned aerial vehicle(UAV)derived canopy height model in an open canopy mixed conifer forest", Forests, 8, pp.340~ (2017)
- Ni W, "Evaluation of UAV-based forest inventory system compared with LiDAR data", 2015 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), pp.3874~3877 (2015)
- Paul TSH, "Thinking outside the square : Evidence that plot shape and layout in forest inventories can bias estimates of stand metrics", Methods in Ecology and Evolution, 10, pp.381~388 (2019)
- Pitkänen J, "Adaptive methods for individual tree detection on airborne laser based canopy height model", International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, pp.187~191 (2004)
- "ForestTools: Analyzing Remotely Sensed Forest Data. R package version 0.2.1" (2020)[URL]
- Popescu SC, "Seeing the Trees in the Forest", Photogrammetric Engineering & Remote Sensing, 70, pp.589~604 (2004)
- Powers D, "Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation", Mach. Learn. Technol., 2~ (2008)
- Quiroz RAA, "A method for automatic identification of crop lines in drone images from a mango tree plantation using segmentation over YCrCb color space and Hough transform", 2019 XXII Symposium on Image, Signal Processing and Artificial Vision(STSIVA), pp.1~5 (2019)
- "R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria" (2020)[URL]
- Safonova A, "Detection of fir trees(Abies sibirica)damaged by the bark beetle in unmanned aerial vehicle images with deep learning", Remote Sensing, 11, pp.643~ (2019)
- Sample-based estimators used by the forest inventory and analysis national information management system, "Scott CT", US Department of Agriculture, Forest Service, Southern Research Station, 53~77, pp.SRS-80 (2005)
- Selim S, "Semi-automatic tree detection from images of unmanned aerial vehicle using object-based image analysis method", Journal of the Indian Society of Remote Sensing, 47, pp.193~200 (2019)
- Toffanin P, "OpenDroneMap: The Missing Guide", ~ (2019)
- Tomppo E, "A sampling design for a large area forest inventory : case Tanzania", Canadian Journal of Forest Research, 44, pp.931~948 (2014)
- Wallace L, "Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data", IEEE Transactions on Geoscience and Remote Sensing, 52, pp.7619~7628 (2014)
- Wang Y, "A lidar point cloud based procedure for vertical canopy structure analysis and 3D single tree modelling in forest", Sensors, 8, pp.3938~3951 (2008)
- Wu X, "Assessment of individual tree detection and canopy cover estimation using unmanned aerial vehicle based light detection and ranging(UAV-LiDAR)data in planted forests", Remote Sensing, 11, pp.908~ (2019)
- "MLmetrics: Machine Learning Evaluation Metrics. R package version 1.1.1" (2016)[URL]
- Yan WY, "Urban land cover classification using airborne LiDAR data : A review", Remote Sensing of Environment, 158, pp.295~310 (2015)