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| 題 名 | 應用四物候日無人機影像空間外推入侵外來種--小花蔓澤蘭之空間型態=Extrapolating the Spatial Patterns of Invasive Alien Species--Mikania micrantha Based on Four Phenological-Date Drone Images |
|---|---|
| 作 者 | 洪浩源; 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51-64 |
| 分類號 | 440.98 |
| 關鍵詞 | 空間外推; 小花蔓澤蘭; 物候; 無人機; Spatial extrapolation; Mikania micrantha; Phenology; Unmanned aerial vehicle; |
| 語 文 | 中文(Chinese) |
| DOI | 10.6574/JPRS.202403_29(1).0004 |
| 中文摘要 | 本研究以無人機獲取外來入侵種小花蔓澤蘭(Mikania micrantha;bitter vine, BV)兩試區A與B的四物候日多光譜影像,續以最大概似法、隨機森林與U-net空間外推BV的空間型態。建、驗模採兩組取樣策略:(1)兩試區以各自訓練樣本推測本身所在地、(2)雙向外推至另一無訓練樣本之試區。結果顯示,第一組三模型之kappa值皆高於0.75。第二組由試區A正向外推至B較試區B反向外推至A成效差,乃兩區植被型態及BV花況不同而致,尤以U-net更能掌握BV的空間型態,外推之kappa值最高達0.73。為改善此情況並更完整探討模型之效能及穩定性,後續將結合兩區樣本執行空間外推,期能改善目前外推之結果。 |
| 英文摘要 | This study utilized unmanned aerial vehicle to obtain multispectral imagery of Mikania micrantha (bitter vine, BV) in two study areas, Plot A and Plot B. Employing maximum likelihood classification, random forest, and U-net, the study aimed to assess the model's performance in spatial extrapolation of spatial patterns of BV, with the goal of discovering new populations of previously unidentified species. Two sampling designs were employed for model training and testing, Set 1, conducting individual plot classification using training data from it's owned; Set 2, performing spatial extrapolation from one plot with training data to another without training data, whose validation data there represent previously unidentified new populations. The results indicate that the three models performed well in Set 1. The kappa values for all models exceeded 0.75. However, the performance in plot B was slightly lower compared to plot A, possibly due to the more complex vegetation patterns in plot B. In Set 2, extrapolation from plot A to B showed fewer effective results compared to extrapolation from plot B to A. This discrepancy can be attributed to the differences in vegetation patterns and the flowering conditions of BV between the two areas. In terms of model performance, U-net demonstrated a better ability to capture the spatial patterns of BV, achieving the highest kappa value of up to 0.73 among the three models in Set 2. To comprehensively examine the model's performance in spatial extrapolation, future work will involve combining training data from both plots for extrapolation, and testing with other deep learning models such as convolution neural network (CNN) and environmental variables. |
本系統中英文摘要資訊取自各篇刊載內容。