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Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery

  • 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：hy1602311920@163.com
  • Introduction：韩月，研究方向为多源遥感数据融合、生态水文遥感应用。E-mail： hy1602311920@163.com
HAN Yue123,  
  • 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

WANG Zhanpeng123,  
  • Affiliation：

    Remote Sensing Satellite General Division, China Academy of Space Technology, Beijing 100086, China

LIANG Deyin4,  
  • 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

ZHOU Demin123

résumé

The ZY-1 02D satellite was successfully launched in 2019 and officially used in October 2020. It is the first civil hyperspectral satellite independently developed and operated by China and has a wide application prospect. Taking the Yellow River Delta wetland as study area, this work investigated the performance of ZY-1 02D Advanced Hyperspectral Instrument (AHSI) images in wetland landscape classification. First, the ZY-1 02D AHSI spectral curves of typical land cover types in the study area were evaluated. Second, two levels of wetland landscape classification systems, including seven basic classes and nine refined classes considering the difference of vegetation coverage, were developed with the assistance of field surveys and unmanned aerial vehicle images. Then, the random forest algorithm was used for classification, and the tree SHAP method was introduced to sort and select important bands. The most important ZY-1 02D AHSI bands overlapping with Landsat 8 OLI bands were selected for classification, and the classification results were compared. Our results showed the following: (1) ZY-1 02D AHSI data can represent the differences of spectral features of different landscape types. (2) For the two classification systems, the first 40 important bands achieved the highest overall classification accuracy. The classification accuracy of seven basic classes and nine refined classes were 92.18% and 90.76%, respectively. Most of the important bands were visible and near-infrared bands. (3) For the two classification systems, a total of 24 and 29 bands overlapping with Landsat 8 OLI multispectral bands were selected for classification. The classification accuracy reached 90.01% and 89.76%, which were remarkably higher than that of Landsat 8 OLI. (4) Blue and green bands are important for the identification of high- and low-density Spartina alterniflora and Phragmites australis; blue, green, and short-wave infrared bands are important for Phragmites australis identification, and the red band is important for the identification of high- and low-density Suaeda salsa. Results showed that ZY-1 02D AHSI data have advantages in distinguishing different landscape features and differences in vegetation coverage. Our study is expected to provide scientific basis for the application of ZY-1 02D hyperspectral data in wetland ecological resources.

mots-clés

ZY-1 02D;AHSI satellite image;hyperspectral data;vegetation coverage;Random Forest;Tree SHAP

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