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  • Theses

以無人機獲取之多光譜影像建立土壤中鎘與水稻吸收量關聯性

Correlating the Soil Cadmium and its uptake of in Rice with Unmanned Aerial Vehicle-derived Multispectral Images

Advisor : 黃文達

Abstracts


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。

Parallel abstracts


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.

References


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