以衛星資訊建立預測玉米產量之模型

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姓名

施又升(You-Sheng Shih) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

以衛星資訊建立預測玉米產量之模型
(Predicting maize yields with satellite information)

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摘要(中)

美國的玉米是全球產量最大的作物，故美國的玉米產量足以牽動整個穀物市場的價格。大多數預測美國玉米產量的研究聚焦於植被指數對產量的影響，而本研究根據植被覆蓋率越高且種植面積越大則作物產量越大的假設，將針對植被指數和遙測面積建立迴歸模型預測美國玉米產量。使用的迴歸模型有多元線性迴歸、偏最小二乘迴歸、逐步迴歸以及利用高斯核的支持向量迴歸，最後實驗結果以高斯核(Radial basis function kernel)的支持向量迴歸表現最佳，?2值為 0.94。

摘要(英)

Unite States of America harvests the largest crop of maize in the world. The volume it grows, therefore, critically affects many countries and industries. Predicting the yields thus have discussed by prior studies. Recently, with the conveniently available of Satellite images, several research has attempted to make prediction vegetation index on yield. However, this research argues that besides vegetation index, the data of telemetry area are also needed, as higher vegetation coverage and larger planting area lead to greater crop yields. This research therefore, strives to derive 9 years of data for 4 most important states to train various regression models, which include multivariable linear regression, partial least squares regression, stepwise regression, and support vector regression with Gaussian kernel. The result shows that the support vector regression with Gaussian kernel (Radial basis function kernel) performed the best, with R^2 value reaches 0.94.

關鍵字(中)

★ 衛星遙測
★ 遙測面積
★ 植被指數
★ 迴歸分析

關鍵字(英)

★ Satellite telemetry
★ Telemetry area
★ Vegetation Index
★ Regression Analysis

論文目次

摘要 viii
Abstract ix
目錄 x
圖目錄 xii
表目錄 xiv
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究架構 2
第二章文獻探討 4
2-1 光學衛星 4
2-2 植被指數 6
2-2-1 NDVI 標準化植被指數 6
2-2-2 EVI 增強植被指數 7
2-2-3 ARVI 大氣阻抗植被指數 8
2-3 作物估計 9
2-4 與其他作物遙測研究之差異 10
第三章研究方法 11
3-1 研究流程 11
3-2 迴歸模型 12
3-2-1 多元線性迴歸(Multivariable linear regression) 12
3-2-2 偏最小二乘迴歸(Partial least squares regression) 13
3-2-3 逐步迴歸(Stepwise regression) 14
3-2-4 支持向量迴歸(Support vector regression) 15
第四章研究實驗 17
4-1 資料蒐集 17
4-2 資料處理流程 19
4-2-1　植被指數取得流程 19
4-2-2　遙測面積取得流程 22
4-3 實驗結果與分析 23
4-3-1　多元線性迴歸 23
4-3-2　偏最小二乘迴歸 27
4-3-3　逐步迴歸 29
4-3-4 支持向量迴歸 31
第五章結論與未來研究之建議 38
5-1 研究結論 38
5-2 研究限制與未來建議 39
第六章參考文獻 40

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指導教授

許秉瑜陳以錚(Ping-Yu Hsu Yi-Cheng Chen)

審核日期

2022-6-30

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