傳統遙測均依賴載台經過目標地上方取像，衛星的再訪週期短但是解析力 差;航測解析力高但是再訪週期長，且限制於法規與經費無法涵蓋大部分範圍。 若是因應特定時間與區域的突發事件，機動性不足。池塘或是湖泊的藻華事件 通常是局部且無預警的發生，攸關生態與水質;養殖業生計等，一般以點狀取樣 後送進實驗室量測分析為主要偵測法。此法耗時且昂貴，另外點狀數據缺乏面 狀分佈，對於藻華發生與整治對策研擬助益不大。
無人機(UAV)的技術發展提供獲取遙測資料更加便利的手段，飛航技術的提升除了讓飛行過程更加穩定、負重增加，蓄電量的提升也讓覆蓋飛航範圍更加廣大，配合搭載多光譜儀器(Multi-spectral Imager)可以監測更多的波段訊號，本研究使用無人機除傳統可見光(Visible Spectrum)外，也能夠拍攝紅邊(Red Edge)及近紅外光(Near Infrared)，並配合手持式地面光譜儀以及現場採樣分析，結合地面與空拍的資料作進一步分析。
本研究選擇位於國立屏東科技大學的生態池靜思湖、周邊魚塭及牛角灣溪埤塘作為研究對象，透過3月時的現場勘查發現研究區域水色偏綠，並取水樣分析，確立其藻類濃度高於地面光譜檢測最低濃度24000cells/ml，並配合無人機拍攝時程採取水樣與現地光譜資料。
透過相關性分析、波段比分析、植生指數進行資料轉換，接著使用多變數線性迴歸型分析。並搭配觀察不同區域的藻類光譜特徵和濃度來探討結果後以觀察到藍綠菌藻(Cyanobacteria)反射光譜受藻藍蛋白(Phycocyanin)影響最為明顯，波段區間落在610~650nm。迴歸結果中室內固定光源的顯著值與誤差皆有較理想的結果，而運用R、RE及NIR三波段進行預測比起使用波段比、植生指數預測梗為準確，在多數情況下，使用藻菌數量為濃度評斷標準也符合趨勢。
利用遙測與現地資料可以結合出更為優良的結果，遙測應用可滿足大範圍連續資料的蒐集，地面的現地資料則是能提供更為詳細狀態資料，在兩者配合下最佳結果可以達到誤差7%的結果，儘管受限於範圍與藻種但依舊可以做為濃度預測上的參考。

Conventional remote sensing relies on platform of sensors to pass above the target region for data acquiring; the revisit time of satellite is short, but the low resolution is the iv drawback. On the other hand, photogrammetry offers better resolution, but the revisit time could be long; by the limitation of the air-field management regulation and finance concern, it is not possible to cover most of the ground via such means. In respond to sudden occurrence event at location, this type of technology lacking the mobile ability then could not offer required information in time. Occurrence of algal bloom in ponds or lakes is an local event without precursor，it will affect the water quality so is the ecological environments thus harming the living hood of aquaculture. In general, pointwise sampling then measuring the concentration in the laboratory to offer in-situ condition but this kind of information is not only costly but also could not fit into the requirement of remediation.
The development of UAV technology provides a more convenient means of acquiring remote sensing data. Advancements in flight technology have improved stability, payload capacity, and battery life, enabling wider coverage. Equipped with a Multi-spectral Imager, UAVs can capture signals across multiple spectral bands. In this study, the UAV captured visible spectrum, red edge, and near-infrared light, combined with handheld ground-based spectrometry and on-site sampling for further analysis.
The study focused on the Eco-Pond Jing-Shi Lake, surrounding fish ponds, and Niujiaowan river near NPUT. By conducting correlation analysis, band ratio analysis, and NDVI transformations, followed by multivariate linear regression analysis, the study aimed to investigate the relationship between spectral characteristics and algal concentrations in different areas. Cyanobacteria's reflectance spectra, influenced most notably by Phycocyanin, were observed in the wavelength range of 610-650nm. Regression results using indoor fixed light sources demonstrated significant values and satisfactory errors. Concentration-based predictions outperformed those using band ratios and vegetation indices.
Combining remote sensing with on-site data yielded improved results. Remote sensing facilitated the collection of continuous data over a large area, while ground-based data provided detailed information. The optimal outcome, with an error rate of 7%, was achieved through their combination. Despite limitations in range and algal species, the results serve as a reference for concentration predictions.

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