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時系列Sentinel-2およびLandsat 8画像に基づく侵入植物オオフサモ管理方法の識別と動的監視

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：2230902139@cnu.edu.cn
  • Introduction：戚炜淳，研究方向为湿地变化检测，生态水文遥感应用。E-mail： 2230902139@cnu.edu.cn
QI Weichun123,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：yke@cnu.edu.cn
  • Introduction：柯樱海，研究方向为多源遥感数据融合、人工智能及数据挖掘方法，生态水文遥感应用。E-mail： yke@cnu.edu.cn
KE Yinghai123*,  
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

LI Jinyuan123,  
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

MIN Yukui123,  
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

ZHUO Zhaojun123,  
  • Affiliation：

    State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing 100048, China

    Beijing Laboratory of Water Resources Security, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

WU Xin123,  
  • Affiliation：

    College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China

LI Peng4

サマリー

侵入植物であるオオフサモの管理は、沿岸湿地の生態系保護と修復において重要な内容です。現在、沿岸の複数の省ではオオフサモ管理プロジェクトを段階的に実施しており、管理方法には物理的除去と化学的除去が含まれています。前者は効果が早いものの再発しやすく、後者はコストが低いものの環境への潜在的な負の影響があります。オオフサモの除去状況を適時に監視し、除去方法を識別することは、管理プロジェクトの実施効果評価および環境影響評価に非常に重要です。本研究では浙江省と上海市の沿岸湿地に分布するオオフサモ域を研究対象とし、時系列の光学衛星画像（Sentinel-2およびLandsat 8）を基に、オオフサモの除去状態と管理方法（未除去／物理的除去／化学的除去）を識別し、物理的除去の日付を抽出する方法を提案しました。まず、Sentinel-2 MSI画像とLandsat 8 OLI画像を融合し、時系列のスペクトル指標データセットを構築しました。スライディングウィンドウとルールベースの識別方法を用いて管理の影響期間を判別し、除去状態を特定しました。続いて、ランダムフォレスト法により管理方法を分類し、未除去・物理的除去・化学的除去の三つのカテゴリーを得るとともに、物理的除去の日付も識別しました。結果は、除去状態および除去方法の分類精度が高く、全体精度は98.8％、カッパ係数0.979でした。物理的除去日付検出の平均絶対誤差は3.91日、精度は93.67％でした。2023年における上海市および浙江省のオオフサモ除去率は、それぞれ4.2％および62.7％でした。本研究で提案した管理方法識別および動的監視手法は、沿岸湿地管理の意思決定に重要な参考資料となります。

キーワード

光学リモートセンシング;外来種侵入;時系列解析;湿地修復;植物群動態

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