利用無人機影像之植物生長指數辨識潮間帶紅藻

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：48

、訪客IP：3.143.254.28

姓名

林靖恆(Ching-Heng Lin) 查詢紙本館藏

畢業系所

水文與海洋科學研究所

論文名稱

利用無人機影像之植物生長指數辨識潮間帶紅藻
(Calculation of Vegetation Index of UAV Image to Detect Rhodophyta in Intertidal Zone)

相關論文

★ 藻礁區的波浪頻譜消散特性	★ 應用聲學及光學儀器在均勻及現場懸浮質濃度之量測率定及比較
★ 碎波帶紊流及剪應力之大尺度實驗觀測研究	★ 不均勻珊瑚礁分佈對珊瑚礁冠層附近流場結構之影響
★ 藻礁區之波浪消散特性	★ 利用影像處理技術辨識藻礁範圍
★ 桃園海岸近岸流之數值模擬	★ 桃園海岸近岸流四季變化之研究
★ 無人機光達系統應用於沙岸與藻礁地區之波浪能量消散之研究	★ 桃園海岸海漂垃圾現場調查分析之研究
★ 桃園新屋海岸波流受海工結構物設置之數值模擬研究	★ 運用監督式分類技術辨識桃園藻礁露出範圍之研究初探
★ 以非結構性網格模式探討三接港對桃園海岸波流場之影響	★ 利用ADCP估算地區藻礁潮間帶紊流特性
★ 潮間帶礁體懸浮漂沙濃度之現場研究	★ 應用無人機及物件偵測於大園海灘的瓶裝海洋垃圾

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-8-14以後開放)

摘要(中)

本研究旨在測試不同植物生長指數並建立紅藻之影像辨識方法。在桃園藻礁分佈著許多紅藻門(Rhodophyta)的藻類，其中主要的造礁藻類為無節殼狀珊瑚藻(crustose coralline algae, CCA)。目前紅藻的調查方式，受限於取樣方式以及現場環境因素，較難達成效率以及標準化調查，因此本研究希望利用可見光無人機空拍影像來輔助傳統紅藻之調查。本研究於大潭藻礁G2區及觀新藻礁保護區保生樣站兩個樣站進行空拍作業取得影像，並於實驗室利用地理資訊系統ArcGIS以人工目視法的曲線繪製功能標示出包括紅藻、礁體上之空洞等目標物。本研究利用MATLAB讀取原始影像的紅色(R)、綠色(G)、藍色(B)三者可見光波段之DN數值，正規化後轉換為色度座標，利用不同顏色指標組合、植物生長指數等因素統整歸納，嘗試找出適合做為自動辨識之閾值，以達到紅藻的自動辨識，並進行紅藻的覆蓋率計算。本研究測試了11種包含了顏色指標、顏色指標組合以及植物生長指數，結果顯示以過剩綠指數(Excess Green)可以最有效辨識出紅藻之位置；此指數可以有效排除非紅藻與影像過曝之高亮度區域；經測試後，過剩綠指數<-0.032為較適當的自動化辨識之閾值。本研究利用混淆矩陣計算其可信度，所得的平均Kappa 值為0.5244，平均整體正確度(Overall Accuracy, OA)為87.79%。一般認為Kappa 值介於0.4～0.8 之間具有中等的信賴度，因此本研究的自動辨識結果應具有一定的信賴度，可見光無人機空拍影像可以做為傳統紅藻調查之輔助，提高調查作業效率，並計算其覆蓋率。

摘要(英)

This study aims to test different vegetation indices and establish an image recognition method for red algae. In the Taoyuan algae reef, many algae of the Rhodophyta phylum are present, with the primary reef-building algae being crustose coralline algae (CCA). Current red algae investigation methods are limited by sampling techniques and field environmental factors, making efficient and standardized surveys challenging. This study uses visible light drone aerial imagery to assist traditional red algae surveys. Aerial photography was conducted at two sites: G2 area of Datan algae reef and Baosheng sampling station in Guanyin algae reef protection area. Images were manually analyzed in ArcGIS to delineate targets such as red algae and holes on the reef. MATLAB was used to read the red (R), green (G), and blue (B) visible light band values of the images, identify their spectral reflection characteristics, and integrate different band combinations, vegetation indices, and habitat information to determine suitable thresholds for automatic recognition. The study tested 11 vegetation indices, finding that the Excess Green Index most effectively identified red algae, excluding non-red algae and high-brightness areas from overexposure. The optimal threshold for automated recognition was Excess Green Index <-0.032. Reliability was calculated using a confusion matrix, resulting in an average Kappa coefficient of 0.5244 and an average overall accuracy of 87.79%. A Kappa value between 0.4 and 0.8 indicates moderate reliability. Thus, visible light drone aerial imagery can effectively assist traditional red algae surveys, improving efficiency and coverage rate calculations.

關鍵字(中)

★ 紅藻
★ 影像辨識
★ 植物生長指數
★ 無人機

關鍵字(英)

★ Rhodophyta
★ Image Detection
★ Vegetation Index
★ UAV

論文目次

摘要 i
Abstract ii
謝誌 iii
目次 iv
圖次 vi
表次 viii
第一章緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.2.1 桃園海岸藻礁監測之研究 2
1.2.2 無人機棲地與海岸監測方法之研究 3
1.2.3 遙測影像分析方法 4
1.2.4 傳統人工樣框五分等級法 6
1.3 研究目的 8
1.4 文本架構 9
第二章研究方法 10
2.1 研究位址 11
2.2 現場調查 12
2.3 影像分析 15
2.3.1 影像辨識架構 15
2.3.2 影像前處理 17
2.3.2.1 影像轉置 17
2.3.2.2 無人機鏡頭測試 18
2.3.3 監督式分類 22
2.3.3.1 訓練樣本 22
2.3.3.2 閾值設定 35
2.4 ImageJ影像處理法 42
2.5 自動辨識結果驗證方法 43
第三章結果與討論 45
3.1 自動辨識結果 45
3.2 自動辨識結果驗證 48
3.3 紅藻覆蓋率計算結果比較 50
第四章結論與建議 52
4.1 結論 52
4.2 建議 53
4.3 研究限制 54
附錄一、訓練樣框經緯度 55
附錄二、自動影像辨識結果與人工辨識結果 57
附錄三、口試評審委員意見回覆表 62
參考文獻 64

參考文獻

Adey, W. and J. M. Vassar (1975). "Colonization, succession and growth rates of tropical crustose coralline algae (Rhodophyta, Cryptonemiales)." Phycologia 14(2): 55-69.
Bendig, J., K. Yu, H. Aasen, A. Bolten, S. Bennertz, J. Broscheit, M. L. Gnyp and G. Bareth (2015). "Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley." International Journal of Applied Earth Observation and Geoinformation 39: 79-87.
Brunier, G., S. Oiry, Y. Gruet, S. F. Dubois and L. Barillé (2022). "Topographic analysis of intertidal polychaete reefs (sabellaria alveolata) at a very high spatial resolution." Remote Sensing 14(2): 307.
Campbell, C., B. McConkey, R. P. Zentner, F. Selles and D. Curtin (1996). "Long-term effects of tillage and crop rotations on soil organic C and total N in a clay soil in southwestern Saskatchewan." Canadian Journal of Soil Science 76(3): 395-401.
Carbajal-Martínez, D., L. Peiffer, A. Hinojosa-Corona, A. Trasviña-Castro, S. M. Arregui-Ojeda, F. J. Carranza-Chávez, C. Flores-Luna, R. Méndez-Alonzo, C. Inguaggiato and K. L. Casallas-Moreno (2021). "UAV-based thermal imaging and heat output estimation of a coastal geothermal resource: La Jolla beach, Baja California, Mexico." Renewable Energy 168: 1364-1376.
Chen, K.-Y. (2017). 利用影像處理技術辨識藻礁範圍, National Central University.
Fritz, A., L. Li, I. Storch and B. Koch (2018). "UAV‐derived habitat predictors contribute strongly to understanding avian species–habitat relationships on the Eastern Qinghai‐Tibetan Plateau." Remote Sensing in Ecology and Conservation 4(1): 53-65.
Guenther, R., E. M. Porcher, E. Carrington and P. T. Martone (2022). "Effects of temperature and pH on the growth, calcification, and biomechanics of two species of articulated coralline algae." Marine Ecology Progress Series 700: 79-93.
Guo, Q., Y. Su, T. Hu, X. Zhao, F. Wu, Y. Li, J. Liu, L. Chen, G. Xu and G. Lin (2017). "An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China." International journal of remote sensing 38(8-10): 2954-2972.
Hassanien, A. E., A. Darwish and S. Abdelghafar (2020). "Machine learning in telemetry data mining of space mission: basics, challenging and future directions." Artificial Intelligence Review 53(5): 3201-3230.
Jang, G., J. Kim, J.-K. Yu, H.-J. Kim, Y. Kim, D.-W. Kim, K.-H. Kim, C. W. Lee and Y. S. Chung (2020). "Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application." Remote Sensing 12(6): 998.
Janot, K. G. and P. T. Martone (2018). "Bending strategies of convergently evolved, articulated coralline algae." Journal of phycology 54(3): 305-316.
Kentsch, S., S. Karatsiolis, A. Kamilaris, L. Tomhave and M. L. L. Caceres (2021). Identification of tree species in Japanese forests based on aerial photography and deep learning. Advances and New Trends in Environmental Informatics: Digital Twins for Sustainability, Springer.
Kumar, S., G. Stecher, M. Suleski and S. B. Hedges (2017). "TimeTree: a resource for timelines, timetrees, and divergence times." Molecular biology and evolution 34(7): 1812-1819.
Landis, J. R. and G. G. Koch (1977). "An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers." Biometrics: 363-374.
Larrinaga, A. R. and L. Brotons (2019). "Greenness indices from a low-cost UAV imagery as tools for monitoring post-fire forest recovery." Drones 3(1): 6.
Lin, Y.-C. (2022). 運用監督式分類技術辨識桃園藻礁露出範圍之研究初探, National Central University.
Lizcano-Sandoval, L. D., E. Londoño-Cruz and F. A. Zapata (2018). "Growth and survival of Pocillopora damicornis (Scleractinia: Pocilloporidae) coral fragments and their potential for coral reef restoration in the Tropical Eastern Pacific." Marine biology research 14(8): 887-897.
Mason, D., G. Anderson, R. Bradbury, D. Cobby, I. Davenport, M. Vandepoll and J. Wilson (2003). "Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation." International Journal of Remote Sensing 24(12): 2515-2532.
Meyer, G. E. and J. C. Neto (2008). "Verification of color vegetation indices for automated crop imaging applications." Computers and electronics in agriculture 63(2): 282-293.
Motohka, T., K. N. Nasahara, H. Oguma and S. Tsuchida (2010). "Applicability of green-red vegetation index for remote sensing of vegetation phenology." Remote Sensing 2(10): 2369-2387.
Neto, J. C. (2004). A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems, The University of Nebraska-Lincoln.
R. Morgan, G., M. E. Hodgson, C. Wang and S. R. Schill (2022). "Unmanned aerial remote sensing of coastal vegetation: A review." Annals of GIS 28(3): 385-399.
Retallack, A., G. Finlayson, B. Ostendorf and M. Lewis (2022). "Using deep learning to detect an indicator arid shrub in ultra-high-resolution UAV imagery." Ecological Indicators 145: 109698.
Retnowati, Y., G. A. Prayoga, J. D. Tamba, F. I. Fauzandi, E. M. Husni, R. Yusuf and B. N. Yahya (2021). Statistical Analysis of Urban Heat Island: A Case Study in Bandung, Indonesia. 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET), IEEE.
Román, A., A. Tovar-Sánchez, I. Olivé and G. Navarro (2021). "Using a UAV-Mounted multispectral camera for the monitoring of marine macrophytes." Frontiers in Marine Science 8: 722698.
Rouse Jr, J. W., R. H. Haas, D. Deering, J. Schell and J. C. Harlan (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.
SAITO, Y. and S. ATOBE (1970). "Phytosociological Study of Intertidal Marine Algae: Ⅰ. Usujiri Benten-Jima, Hokkaido." 北海道大學水産學部研究彙報 21(2): 37-69.
Sonnentag, O., K. Hufkens, C. Teshera-Sterne, A. M. Young, M. Friedl, B. H. Braswell, T. Milliman, J. O’Keefe and A. D. Richardson (2012). "Digital repeat photography for phenological research in forest ecosystems." Agricultural and Forest Meteorology 152: 159-177.
Stolarski, J., I. Coronado, J. G. Murphy, M. V. Kitahara, K. Janiszewska, M. Mazur, A. M. Gothmann, A.-S. Bouvier, J. Marin-Carbonne and M. L. Taylor (2021). "A modern scleractinian coral with a two-component calcite–aragonite skeleton." Proceedings of the National Academy of Sciences 118(3): e2013316117.
Sun, Y., J. Huang, Z. Ao, D. Lao and Q. Xin (2019). "Deep learning approaches for the mapping of tree species diversity in a tropical wetland using airborne LiDAR and high-spatial-resolution remote sensing images." Forests 10(11): 1047.
Suo, C., E. McGovern and A. Gilmer (2019). "Coastal dune vegetation mapping using a multispectral sensor mounted on an UAS." Remote Sensing 11(15): 1814.
Tamminga, A., C. Hugenholtz, B. Eaton and M. Lapointe (2015). "Hyperspatial remote sensing of channel reach morphology and hydraulic fish habitat using an unmanned aerial vehicle (UAV): A first assessment in the context of river research and management." River research and applications 31(3): 379-391.
Teichert, S. (2024). "Attached and free-living crustose coralline algae and their functional traits in the geological record and today." Facies 70(2): 8.
Tsouros, D. C., S. Bibi and P. G. Sarigiannidis (2019). "A review on UAV-based applications for precision agriculture." Information 10(11): 349.
Ventura, D., A. Bonifazi, M. F. Gravina, A. Belluscio and G. Ardizzone (2018). "Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)." Remote Sensing 10(9): 1331.
Villoslada, M., T. F. Bergamo, R. Ward, N. Burnside, C. Joyce, R. Bunce and K. Sepp (2020). "Fine scale plant community assessment in coastal meadows using UAV based multispectral data." Ecological Indicators 111: 105979.
Vásquez-Elizondo, R. M. and S. Enríquez (2017). "Light absorption in coralline algae (Rhodophyta): a morphological and functional approach to understanding species distribution in a coral reef lagoon." Frontiers in Marine Science 4: 297.
Wang, W., R. Arora, K. Livescu and J. Bilmes (2015). On deep multi-view representation learning. International conference on machine learning, PMLR.
Zheng, J.-Y., Y.-Y. Hao, Y.-C. Wang, S.-Q. Zhou, W.-B. Wu, Q. Yuan, Y. Gao, H.-Q. Guo, X.-X. Cai and B. Zhao (2022). "Coastal wetland vegetation classification using pixel-based, object-based and deep learning methods based on RGB-UAV." Land 11(11): 2039.
台灣中油股份有限公司111年多光譜空拍技術於殼狀珊瑚藻覆蓋率監測之標準作業計畫
林幸助, 徐顯富, 廖偉勝, 李承錄, 劉弼仁 and 林綉美 (2013). "桃園藻礁的生物多樣性." 濕地學刊 2(2): 1-24.
海洋委員會海洋保育署. (n.d.). Retrieved from https://www.oca.gov.tw/ch/home.jsp?id=177&parentpath=0,5,173
劉靜榆 (2017). "臺灣西北部藻礁海岸重金屬污染分析." 台灣生物多樣性研究 19(1): 49-95.

指導教授

黃志誠(Zhi-Cheng Huang)

審核日期

2024-8-14

推文