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題名 | 藉由相隔15年的兩組數據探索蘇鐵蕨物種分布模型之時間可轉移能力=Exploring the Temporal Transferability in the Species Distribution Model of Brainea insignis Using Two Sets of Data Separated by 15 Years |
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作者姓名(中文) | 郭慶津; 邵寶嬅; 羅南璋; 黃凱易; | 書刊名 | 航測及遙測學刊 |
卷期 | 29:1 2024.03[民113.03] |
頁次 | 頁35-49 |
分類號 | 312.831 |
關鍵詞 | 時間可轉移性; 深度學習; 物種分布模型; 蘇鐵蕨; Temporal transferability; Deep learning; Species distribution model; Brainea insignis; |
語文 | 中文(Chinese) |
DOI引用網址 | 10.6574/JPRS.202403_29(1).0003 |
中文摘要 | 本研究旨在探討不同演算法對物種分布模型(species distribution model, SDM)時間可轉移能力(transferability)的影響,以及使用深度學習法建立SDM的可能性。研究以蘇鐵蕨為目標物種,獲取該物種兩組調查時間相隔15年之數據,以最大熵值法(maximum entropy, MAXENT)、隨機森林(random forest, RF)、支持向量機(support vector machine, SVM)和深度學習法U-net進行試驗。結果顯示,MAXENT和SVM有最佳的時間轉移能力,而U-net也有機會獲得甚佳的成果。顯示深度學習具研究潛力,後續研究有必要採納更多種類的深度學習法,並持續試驗。惟在環境變數方面,單獨使用地形因子可能限制了模型的時間轉移性,需尋找與物種更具直接因果關係的生態因子提高可轉移性。 |
英文摘要 | This study aims to investigate the impact of different algorithms on the temporal transferability of species distribution model (SDM) and the feasibility of using deep learning techniques to build SDM. The study focuses on Brainea insignis as the target species and utilizes two sets of samples collected with a 15-year interval. Experiments were conducted using the maximum entropy (MAXENT), random forest (RF), support vector machine (SVM), and U-net-a deep learning approach. The results indicate that MAXENT and SVM exhibit the best temporal transferability, while U-net also shows promising results. This highlights the research potential of deep learning, and future studies should consider incorporating a wider range of deep learning methods and continue experimentation. However, concerning environmental variables, relying solely on topographic factors may constrain the model's transferability, necessitating the identification of ecological factors with more direct causal relationships with the species to enhance transferability. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。