臺灣博碩士論文加值系統

農田土壤重金屬污染問題日趨嚴重，對糧食安全以及人體健康構成威脅，而傳統重金屬檢測方式僅以點代面、破壞性檢測重金屬污染。隨著遙測技術結合無人機載具平臺發展日漸成熟，以快速、高通量、低人力成本對作物及環境污染程度做即時監控。本試驗旨於建立一套標準程序可用於無人機多光譜影像輔助預測植體重金屬鎘濃度與糙米累積重金屬鎘含量管理評估系統。試驗場域位於桃園八德鎘隱患區水稻(Oryza sativa L.)試驗田，試驗期作為2018年一期作，種植台稉9號、台稉14號以及台東30號。試驗期間由無人機攜載多光譜(Red、Green、Red edge、NIR) Parrot Sequoia相機進行時序蒐集空拍影像。分析水稻營養生長期、生殖生長期以及成熟期植體鎘含量與53種植生指數之相關性。結果顯示，水稻不同品種與生育階段之植體鎘含量，各有相關性較佳之植生指數。其中，與台稉9號之收穫時期糙米鎘累積量最相關之植生指數為GNDVI (R squared為0.870；移植後第89天)；與台稉14號之收穫時期糙米鎘累積量最相關之植生指數為ARI (R squared為0.985；移植後第75天)；與台東30號之營養生長期植體鎘累積量最相關之植生指數為MCARI/MTVI2 (R squared為0.959)。以常用單波段、多光譜以及以高光譜數值計算多光譜波段，三種方式計算53種植生指數與糙米鎘濃度進行回歸分析，分別可得到最佳估算模式：台稉9號分別為Y= – 0.0002 × MCARI/MTVI– 0.0153、– 3.701 × NGRDI+ 1.497、0.0002 × TVI– 1.472；台稉14號分別為– 4.513 × GOSAVI + 3.846、– 4.386 × NGRDI + 1.866、– 2.088 × GSAVI + 2.590；台東30號分別為– 0.011 × TCI – 0.955、– 0.139 × Cl Red Edge + 0.908、– 0.003 × TCARIMSAVI + 0.299，以植生指數估算當下穀粒濃度，若超過食米限量標準0.4 mg · kg -1，進行農藝管理措施，實現預警之目的，因此以水稻葉片或植冠反射光譜計算植生指數，可以進行非破壞性地估算水稻植體累積重金屬鎘濃度之變化，可即時輔助監測水稻重金屬含量與作物生長狀況，有助於精準農耕之發展。

The problem of heavy metal pollution in farmland soil is more critical with every passing, which poses a threat to food security and human health. However, traditional methods of detecting heavy metal pollution was only selective abstraction and destructive. With the development of remote sensing technique combined with the platform for Unmanned Aerial Vehicle (UAVs), the crops and environmental pollution levels are monitored in real time with fast, high throughput and low labor costs. The aim of this study is to develop a standard protocol for field UAV times series multispectral images to assist in the prediction of above ground and brown rice cumulative cadmium (Cd) concentration management evaluation system. Rice (Oryza sativa L.) potential danger of Cd pollution field located in Taoyuan Bade. The experiment period during first crop season in 2018, planting Taikeng 9 (TK 9), Taikeng 14 (TK 14) and Taitung 30 (TT 30). UAVs carried Parrot Sequoia Multispectral camera (Red, Green, Red Edge, NIR) for collecting aerial images in time series. Fifty-three vegetative indices (VIs) derived from four original wavebands reflectance and integration of VIs were tested in this study for analysis correlation between Cd concentration in vegetative phase, reproductive phase and maturation phase. Result shows that the best VIs for different phenotypes regression varies over time and varieties. Cd content in brown rice of rice variety TY9 were highly correlated with GNDVI at 89 days after transplanting (DAT) (R squared = 0.870) , TY14 were highly correlated with ARI at 75 DAT (R squared = 0.985) and TT30 were highly correlated with MCARI/MTVI2 at 40 DAT (R squared = 0.985). VIs derived from three calculated methods about normal single band, UAV multiple spectral and simulating multiple spectral from hyperspectral were tested in this study for regression analysis. Result shows that the best prediction models to estimate Cd content in brown rice: TK 9 respectively Y= – 0.0002 × MCARI/MTVI– 0.0153、– 3.701 × NGRDI+ 1.497、0.0002 × TVI– 1.472; TK 14 respectively Y= – 4.513 × GOSAVI + 3.846、– 4.386 × NGRDI + 1.866、– 2.088 × GSAVI + 2.590; TT 30 respectively Y=– 0.011 × TCI – 0.955、– 0.139 × Cl Red Edge + 0.908、– 0.003 × TCARIMSAVI + 0.299. In summary, this system can provide estimate the current grain concentration by VIs. If it exceeds the rice limit of 0.4 mg · kg -1, agronomic management measures are taken to achieve the purpose of early warning. Therefore, the VIs can be calculated by reflectance from leaf and canopy, and non-destructive estimation of rice plants can be carried out. The accumulation of heavy metal Cd concentration in rice can immediately assist in monitoring content and crop growth of rice, and contribute to the development of precision agriculture.

致謝 i
摘要 iii
Abstract iv
目錄 vi
圖目錄 ix
表目錄 xiii
第一章、緒言 1
第二章、前人研究 4
第一節、重金屬 4
1. 重金屬簡介 4
2. 影響重金屬可利用之因素 5
3. 鎘之吸收、運移及累積特性 8
4. 鎘對植物生理代謝之影響 10
5. 鎘污染監測系統現況 12
6. 國內土壤重金屬污染現況統整 12
第二節、反射光譜在作物上生理應用 14
1. 植物光譜反射光譜特徵理論 14
2. 光譜遙感探測重金屬污染之應用與其原理 16
3. 植生指數 17
第三節、精準農業與遙感探測 19
1. 精準農業 19
2. 遙感探測 20
3. 遙感探測於農灣農業上之應用 21
第四節、論文目標 23
第三章、材料與方法 24
第一節、試驗設計 24
第二節、地面資料 24
1. 試驗材料 24
2. 反射光譜測定 25
3. 地上部植體重金屬鎘含量測定 25
4. 糙米重金屬鎘含量測定 26
5. 土壤採樣與前處理 27
6. 土壤重金屬含量測定 27
第三節、無人機及相機感測器 28
第四節、影像前處理程序 28
第五節、統計分析 32
第四章、結果與討論 33
第一節、地真資料蒐集 33
1. 水稻採樣與影像資料拍攝頻率 33
2. 水稻生育情形 33
3. 重金屬鎘含量分析 34
4. 生育時序外表型紀錄 36
第二節、無人機影像品質分析 36
第三節、植冠多光譜反射光譜分析與重金屬鎘之遙測估算 37
1. 土壤鎘濃度與植冠多光譜關係 37
2. 地上部植體鎘濃度與植冠多光譜 38
3. 糙米鎘濃度與植冠多光譜 39
第四節、水稻植冠熱影像分析與重金屬鎘之遙測估算 40
第五節、水稻葉片高光譜分析與重金屬鎘之遙測估算 41
1. 葉片反射光譜測定 42
2. 重金屬鎘與地面高光譜反射率的敏感波段篩選 44
第六節、不同鎘梯度對植冠多光譜變化之多時序分析 46
1. 不同鎘濃度水稻植冠多光譜反射值之影響 46
2. 水稻植冠多光譜反射值之多時序分析 47
3. 水稻植冠多光譜之雙光譜圖 49
第七節、建立植生指數推估重金屬鎘含量 51
1. 重金屬鎘濃度與植生指數回歸方程序 52
2. 最佳預測糙米時期與植生指標 53
第五章、結論與展望 56
第一節、光譜遙感監測土壤重金屬鎘污染存在的問題 56
第二節、由點到面以及由被動監測上升到主動監測重金屬鎘污染 56
第三節、多學科、多技術結合 57
第四節、光譜遙感最佳光譜參數或植被指數的選擇 57
第六章、參考文獻 121

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