CSPRS

系統管理

期刊內容

28卷 / 1期 [ 點閱率 : 1056 ]
華藝線上圖書館全期下載(Full Issue Download) : 28(1).pdf
請記下華藝線上圖書館帳號(JPRS000002),密碼(8YqFKj),以便下載文章時使用,感謝您的支持 !

Pages : 1-18    氣候變遷下美洲含羞草的空間防治優先性
論文名稱 氣候變遷下美洲含羞草的空間防治優先性
Title Spatial Prioritization for Invasion Prevention of an Invasive Plant, Mimosa Diplotricha under Climate Change
作者 黃靜宜、王素芬、呂明倫
Author Jing-Yi Huang, Su-Fen Wang, Ming-Lun Lu
中文摘要 氣候變遷可能驅使入侵生物的分布擴張,掌握入侵擴散的空間資訊,有助遏阻或減緩其蔓延。本研究以美洲含羞草 (Mimosa diplotricha) 為例,透過MaxEnt與MigClim模式,模擬其入侵分布動態,並整合結構與功能連接度,建構氣候變遷下的空間防治策略。研究結果顯示,美洲含羞草偏好日夜溫度波動明顯的溫暖環境,暖化可能有利其分布擴張,預估未來將自當前棲地範圍,持續往北延伸擴張,惟促進擴散的關鍵途徑,將因暖化程度不同,而有所差異。整體而言,無論何種暖化情境,現有棲地範圍的北緣,皆應優先獲得控制,此外,隨暖化情勢的加劇,則更應側重未來促進擴散風險區位的防禦。
Abstract Climate change may drive the range expansion of invasive species. Integrations of spatially explicit information are useful for preventing and managing. In this study, we used MaxEnt and MigClim modelling approaches to map current and future distribution dynamic of the invasive plant, Mimosa diplotricha. Then, structural and functional connectivity was integrated to develop the prevention strategies under climate change scenarios. The results showed that the suitable niche of M. diplotricha was a warm environment with the large variation in day-night temperature. The range of this species may increase due to a warming climate, with future expansion to the north of current suitable habitats. However, the critical routes of dispersal is likely to vary with the degree of warming. In conclusion, regardless of the warming scenario, the northern edge of the present habitat should be prioritized for control. In addition, as the warming increases, more attention should be paid to the defense of invasion risk areas in the future.
關鍵字 生物入侵、生態棲位模式、地景連接度、防治策略、電路理論
Keywords Biological Invasion, Maximum Entropy, Landscape Connectivity, Prevention Strategies, Circuit Theory
Pages : 19-34    以高解析衛星影像輔以深度學習建置三維房屋模型
論文名稱 以高解析衛星影像輔以深度學習建置三維房屋模型
Title 3D Building Model Reconstruction Using High Resolution Satellite Images with Deep Learning Analysis
作者 呂振永、蔡富安
Author Chen-Yung Lu, Fuan Tsai
中文摘要 利用衛星影像建置三維房屋模型逐漸受到討論,衛星影像的涵蓋範圍廣、時間解析度高,對於建置三維房屋模型,有一定的優勢。本研究主要針對以高解析光學衛星影像進行影像分析,並建立符合OGC CityGML LOD1等級之三維房屋模型。研究中應用深度學習,自動萃取出房屋平面圖 (Building Footprints),並去除非房屋多餘區域,之後利用最小包絡矩形 (Minimum Bounding Rectangle, MBR) 技術、正規化(Regularization)及約化 (Generalization)處理後,塑形出較規律房屋多邊形。最後,萃取房屋上層附屬結構物,並利用高程資料與RANSAC (RANdom SAmple Consensus)演算法擬合各多邊形高度,建立積木式三維房屋模型。經過校正與去除異常值,三維房屋模型平面及高程誤差皆可符合OGC CityGML LOD1規範。
Abstract The use of satellite imagery to reconstruct 3D building models has gradually been discussed. Satellite imagery has a wide coverage and high temporal resolution, so there are certain advantages for reconstructing 3D building models. This research focuses on image analysis based on high resolution optical satellite imagery and 3D building models reconstruction with an accuracy of the OGC CityGML LOD1 level. Deep learning technique is applied in this study to automatically extract building footprints from satellite images and remove excess areas that are not buildings. Next, Minimum Bounding Rectangle (MBR), regularization and generalization processing are utilized to shape the more regular and square building polygons. Finally, sub-structures on the upper floor of each building are extracted, and the elevation data is used to fit the height of each polygon with the RANSAC (RANdom SAmple Consensus) algorithm to reconstruct block-based 3D building models. After corrections and removing outliers, the accuracy of the reconstructed 3D building models conforms to the OGC CityGML LOD1 specification.
關鍵字 高解析光學衛星影像、房屋平面圖、積木式三維房屋模型
Keywords High Resolution Optical Satellite Imagery, Building Footprints, Block-based 3D Building Models
12
Page size:
select
Page: of 2
Items 1 to 2 of 4