测绘通报 ›› 2025, Vol. 0 ›› Issue (7): 52-57.doi: 10.13474/j.cnki.11-2246.2025.0709

• 学术研究 • 上一篇    下一篇

基于Landsat影像的半荒漠化草地地上生物量反演——以达茂旗为例

王力琪1,2, 程博2, 张晓平2, 李可冬2, 宋梦龙3, 颜涛3   

  1. 1. 兰州交通大学, 甘肃 兰州 730070;
    2. 中国科学院空天信息创新研究院, 北京 100094;
    3. 内蒙古遥感中心有限责任公司, 内蒙古 呼和浩特 010010
  • 收稿日期:2024-12-09 发布日期:2025-08-02
  • 通讯作者: 程博。E-mail:chengbo@aircas.ac.cn
  • 作者简介:王力琪(2000—),男,硕士生,主要研究方向为遥感影像处理与解译。E-mail:wlq6508@163.com
  • 基金资助:
    内蒙古半荒漠化草原承载力变化与驱动因素挖掘研究(2022YFSJ0008)

Aboveground biomass inversion of semi desertified grassland based on Landsat image: a case study of Damao banner

WANG Liqi1,2, CHENG Bo2, ZHANG Xiaoping2, LI Kedong2, SONG Menglong3, YAN Tao3   

  1. 1. Lanzhou Jiaotong University, Lanzhou 730070, China;
    2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
    3. Inner Mongolia Remote Sensing Center Co., Ltd., Hohhot 010010, China
  • Received:2024-12-09 Published:2025-08-02

摘要: 准确监测半荒漠化草地地上生物量是草地生态状况评价和相应草地可持续管理的必要条件。达茂旗草地资源丰富,植被群落结构简单,属半荒漠化草地的典型性代表。为了提升半荒漠化草地资源信息的质量,本文以内蒙古达茂旗为研究区,基于Landsat遥感影像,借助地面实测样地数据,结合光谱、植被指数、气象数据和数字地形数据构建了23个原始特征,分别采用随机森林(RF)、支持向量机(SVM)、梯度提升回归树(GBRT)和决策树(CART)回归算法进行草地地上生物量反演,并使用特征重要性得分和递归特征消除(RFE)进行特征优化,最终完成达茂旗2021年半荒漠化草地AGB反演制图。结果表明,RF模型在半荒漠化草地AGB反演结果中精度最高,利用递归特征消除法筛选最优特征至12个,其中气象和地形特征对草地AGB反演贡献最大,最终的反演模型精度决定系数(R2)为0.83,均方根误差(RMSE)为20.31。本文对半荒漠化草地进行生物量的估算,为易受影响的草地生态系统管理和保护提供了科学依据,为生物量反演研究提供了有效的方法论。

关键词: 半荒漠化草地, 草地地上生物量, Landsat影像, 机器学习, 递归特征消除法

Abstract: Accurately monitoring the aboveground biomass of semi desertified grasslands is a necessary condition for evaluating the ecological status of grasslands and corresponding sustainable management of grasslands. Damao banner has abundant grassland resources and a simple vegetation community structure, which is a typical representative of semi desertification grassland. In order to improve the quality of information on semi desertification grassland resources, taking Damao banner as the research area, based on Landsat remote sensing images, 23 original features are constructed using ground measured sample data, combined with spectral, vegetation index, meteorological data, and digital terrain data. Random forest (RF), support vector machine (SVM), gradient boosting regression tree (GBRT) and decision tree(CART) regression algorithms are used for grassland aboveground biomass inversion, and feature importance score and recursive feature elimination (RFE) are used for feature optimization. Finally, the 2021 semi desertification grassland AGB inversion mapping in Damao banner was completed. The results show that the RF model had the highest accuracy in the AGB inversion of semi desertified grasslands. After recursive feature elimination, the optimal number of features is selected to 12, among which meteorological and topographic features contributed the most to the AGB inversion of grasslands. The final accuracy determination coefficient (R2) of the inversion model is 0.83, and the root mean square error (RMSE) is 20.31. This study estimates the biomass of semi desertified grasslands, providing a scientific basis for the management and protection of vulnerable grassland ecosystems and an effective methodology for biomass inversion research.

Key words: semi desertified grassland, aboveground biomass of grassland, Landsat imagery, machine learning, recursive feature elimination method

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