|本期目錄/Table of Contents|

[1]曹鵬,梁其椿,李淑敏.基于Otsu算法的太湖藍藻水華與水生植被遙感同步監測方法[J].江蘇農業科學,2019,47(14):288-294.
 Cao Peng,et al.A novel remote sensing simultaneous monitoring method for cyanobacteria blooms and aquatic vegetation in Taihu Lake based on Otsu algorithm[J].,2019,47(14):288-294.
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基于Otsu算法的太湖藍藻水華與水生植被
遙感同步監測方法
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《江蘇農業科學》[ISSN:1002-1302/CN:32-1214/S]

卷:
第47卷
期數:
2019年第14期
頁碼:
288-294
欄目:
資源與環境
出版日期:
2019-08-10

文章信息/Info

Title:
A novel remote sensing simultaneous monitoring method for cyanobacteria blooms and aquatic vegetation in Taihu Lake based on Otsu algorithm
作者:
曹鵬1 梁其椿2 李淑敏2
1.北京大學遙感與地理信息系統研究所,北京 100871; 2.中國電子科技集團海洋信息技術研究院,海南陵水 572427
Author(s):
Cao Penget al
關鍵詞:
藍藻水華水生植被太湖OtsuMODIS
Keywords:
-
分類號:
Q178.5
DOI:
-
文獻標志碼:
A
摘要:
藍藻水華與水生植被在光學遙感影像上容易混淆,傳統方法將太湖劃分爲藻型湖區和草型湖區進行分區監測,近年來太湖梅梁湖等藍藻水華易發區域出現了大量的水生植物,分區的方法已無法滿足藍藻水華和水生植被遙感監測要求。基于光譜特征分析,采用藍藻水華與水生植被指數(cyanobacteria and macrophytes index,簡稱CMI)判別藍藻水華與水生植被水域,采用浮遊藻類指數(floating algae index,簡稱FAI)識別藍藻水華、浮葉/挺水植被與沉水植被,構建同步監測決策樹,基于Otsu算法自動獲取阈值,將中分辨率成像光增儀(MODIS)衛星影像分成湖水、藍藻水華、沉水植被和浮葉/挺水植被幾種類型。結果表明,分類結果較好,符合太湖不同地物類型實際分布情況;與相關研究HJ衛星影像東部湖區水生植被監測結果進行交叉檢驗,水生植被的空間分布基本一致,一致性檢驗結果顯示,2種分類結果一致的像元比例爲70.11%。實現藍藻水華及水生植物的同步遙感監測,有助于精確評估藍藻水華的實際強度和水生植被區範圍,爲富營養化湖泊的水環境管理和決策提供重要的科技支撐。
Abstract:
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相似文獻/References:

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[2]蔣晨韻,唐曉先,王璨,等.氣象因子對巢湖水源地藍藻水華暴發的影響[J].江蘇農業科學,2019,47(10):281.
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備注/Memo

備注/Memo:
收稿日期:2018-04-23
基金項目:國家自然科學基金(編號:41625003);中電科海洋信息技術研究院創新基金(編號:xyxt)。
作者簡介:曹鵬(1992—),男,江蘇南通人,碩士研究生,主要從事遙感技術應用、地理空間信息研究。E-mail:caopeng@pku.edu.cn。
通信作者:梁其椿,碩士,工程師,主要從事環境遙感研究。E-mail:liangqc@cetcocean.com。
更新日期/Last Update: 2019-07-20