本文選題：MODIS + 時(shí)譜��； 參考：《光譜學(xué)與光譜分析》2016年05期

【摘要】：藻類水華爆發(fā)已成為影響內(nèi)陸水體生態(tài)環(huán)境的重要因素。遙感能夠提供實(shí)時(shí)的大范圍觀測(cè),在水華監(jiān)測(cè)中起到越來(lái)越重要的作用。遙感植被指數(shù)已廣泛應(yīng)用于藻類水華監(jiān)測(cè)中,通過對(duì)研究區(qū)植被指數(shù)圖像進(jìn)行閾值分割,能夠反映不同子區(qū)域內(nèi)的藻類爆發(fā)程度;然而閾值分割法的結(jié)果只能反映某一時(shí)間點(diǎn)(圖像獲取時(shí))的藻類爆發(fā)狀況,無(wú)法表征長(zhǎng)時(shí)間內(nèi)藻類的變化。相比于單個(gè)時(shí)間點(diǎn)的植被指數(shù),植被指數(shù)時(shí)間譜(時(shí)譜)包含藻類的物候信息,能夠更加全面準(zhǔn)確地反映藻類的長(zhǎng)時(shí)間變化。目前,植被指數(shù)時(shí)間譜還尚未應(yīng)用到水華相關(guān)研究中。選取2001年—2013年太湖區(qū)域的MODIS NDVI數(shù)據(jù),構(gòu)建年度NDVI時(shí)譜數(shù)據(jù),利用(support vector machine,SVM)方法對(duì)每年的太湖藍(lán)藻水華爆發(fā)強(qiáng)度進(jìn)行分類,將太湖重度、中度和輕度藍(lán)藻水華爆發(fā)的區(qū)域以及水生植物的區(qū)域提取出來(lái),得到其空間分布和面積;并從2007年的時(shí)譜數(shù)據(jù)中抽取了8個(gè)時(shí)間點(diǎn)的NDVI圖像,利用傳統(tǒng)閾值分割法提取太湖重度、中度和輕度藍(lán)藻水華爆發(fā)的區(qū)域,將結(jié)果與2007年時(shí)譜數(shù)據(jù)分類的結(jié)果進(jìn)行對(duì)比。結(jié)果表明:所提出的方法能夠更加全面準(zhǔn)確地對(duì)太湖藍(lán)藻爆發(fā)強(qiáng)度進(jìn)行分類,通過NDVI時(shí)譜曲線提供的豐富物候信息可準(zhǔn)確區(qū)分藍(lán)藻與水生植被區(qū)域。本研究有望為準(zhǔn)確掌握和預(yù)測(cè)藻類水華的爆發(fā)趨勢(shì)及強(qiáng)度提供有效手段。
[Abstract]:The eruption of algae Shui Hua has become an important factor affecting the ecological environment of inland water bodies. Remote sensing can provide real-time wide-range observation and play a more and more important role in Shui Hua monitoring. Remote sensing vegetation index has been widely used in the monitoring of algae Shui Hua. Through threshold segmentation of vegetation index image in the study area, it can reflect the degree of algae eruption in different sub-regions. However, the results of threshold segmentation method can only reflect the algae explosion at a certain time point (image acquisition), and can not represent the changes of algae over a long period of time. Compared with the vegetation index of a single time point, the time spectrum of vegetation index (time spectrum) contains phenological information of algae, which can reflect the long-term variation of algae more comprehensively and accurately. At present, the time spectrum of vegetation index has not been applied to Shui Hua. The MODIS NDVI data of Taihu Lake region from 2001 to 2013 were selected to construct the annual NDVI time spectrum data, and the annual Shui Hua burst intensity of cyanobacteria was classified by using the support vector machine (SVM) method. The area of medium and mild cyanobacteria Shui Hua outbreak and the area of aquatic plants were extracted, and their spatial distribution and area were obtained. The NDVI images of eight time points were extracted from the time spectrum data of 2007. The regions of severe, moderate and mild cyanobacteria outbreaks in Taihu Lake were extracted by traditional threshold segmentation method, and the results were compared with the results of spectral data classification in 2007. The results show that the proposed method can more comprehensively and accurately classify the burst intensity of cyanobacteria in Taihu Lake, and the abundant phenological information provided by the NDVI time-spectrum curve can accurately distinguish the cyanobacteria from the aquatic vegetation area. This study is expected to provide an effective means to accurately grasp and predict the trend and intensity of algae Shui Hua outbreak.
【作者單位】： 中國(guó)科學(xué)院遙感與數(shù)字地球研究所遙感科學(xué)國(guó)家重點(diǎn)實(shí)驗(yàn)室;中國(guó)科學(xué)院大學(xué);
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41201348,41371359) 高分水利遙感應(yīng)用示范系統(tǒng)項(xiàng)目(08-Y30B07-9001-13/15-01)資助
【分類號(hào)】：X524;X87

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