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Estudio de extracción de terrazas de series temporales largas basado en imágenes Landsat y el algoritmo LandTrendr — caso del cuenca del río Sandu en la provincia de Gansu

  • role： First author第一作者
  • Affiliation：

    School of Computer Science, China University of Geosciences, Wuhan 430078, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：liyifan202401@163.com
  • Introduction：黎一帆，研究方向为遥感信息智能提取。E-mail： liyifan202401@163.com
LI Yifan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computer Science, China University of Geosciences, Wuhan 430078, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：wubf@aircas.ac.cn
  • Introduction：吴炳方，研究方向为农业与生态遥感。E-mail： wubf@aircas.ac.cn
WU Bingfang12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

TIAN Fuyou2,  
  • Affiliation：

    Soil and Water Conservation Monitoring Center of the Ministry of Water Resources, Beijing 100053

CHANG Shengwei4,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Dan23,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Miao23,  
  • Affiliation：

    School of Computer Science, China University of Geosciences, Wuhan 430078, China

FAN Runyu1,  
  • Affiliation：

    Qilu Aerospace Information Research Institute, Ji'nan 250132

HU Shuo5

resumen

Los campos en terrazas, como un paisaje agrícola importante, juegan un papel fundamental en el aumento del rendimiento y la conservación del suelo y el agua. Los datos espacio-temporales de las terrazas son una base importante para evaluar los beneficios productivos y los efectos hidroecológicos de las terrazas, pero son difíciles de obtener. Este artículo propone un método de extracción de terrazas de series temporales largas basado en un algoritmo de detección de cambios. Utilizando imágenes satelitales de series temporales largas de la serie Landsat que fusionan la banda pancromática, se realiza una normalización de los datos de Landsat 7 ETM+ y Landsat 8 OLI para eliminar las interferencias y distorsiones entre diferentes sensores; basándose en el mapa de distribución de terrazas de 2020, se utiliza el algoritmo de detección de cambios LandTrendr basado en datos para extraer la distribución espacial y temporal de las terrazas en la cuenca del río Sandu desde 2001 hasta 2020, y analizar la relación entre el área de terrazas y la producción de alimentos. Los resultados muestran que la precisión general de monitoreo de las terrazas en la cuenca del río Sandu durante el período de series temporales es del 82,50%, con un F1-score de 0,84. En 2020, el área total de terrazas en la cuenca del río Sandu aumentó 397 km² en comparación con 2001, con una tasa de crecimiento del 45,27%. Antes de 2015, la tasa de crecimiento de terrazas alcanzó su punto máximo, luego la velocidad de aumento se desaceleró, pero la tendencia general sigue siendo ascendente. Existe una relación muy estrecha entre el área de terrazas y la producción de alimentos (R²=0,94). Mediante la fusión de datos satelitales de series temporales largas de Landsat con el algoritmo de detección de cambios LandTrendr se obtuvo información sobre la distribución espacio-temporal de las terrazas, revelando efectivamente la trayectoria dinámica de la evolución de la construcción de terrazas en las dimensiones temporal y espacial, así como los beneficios productivos.

palabra clave

Cartografía de terrazas; extracción secuencial; Landsat; detección de cambios; LandTrendr

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