草地学报

草地学报 ›› 2021, Vol. 29 ›› Issue (8): 1757-1768.DOI: 10.11733/j.issn.1007-0435.2021.08.019

• 研究论文 • 上一篇    下一篇

基于随机森林的高寒草地地上生物量高光谱估算

高宏元1, 侯蒙京1, 葛静1, 包旭莹1, 李元春1, 刘洁1, 冯琦胜1, 梁天刚1, 贺金生1,2, 钱大文3   

  1. 1. 兰州大学草地农业科技学院, 兰州大学草地农业生态系统国家重点实验室, 兰州大学农业农村部草牧业创新重点实验室, 兰州大学草地农业教育部工程研究中心, 甘肃 兰州 730020;
    2. 北京大学城市与环境学院, 北京 100871;
    3. 中国科学院西北高原生物研究所, 青海 西宁 810008
  • 收稿日期:2021-04-13 修回日期:2021-05-19 出版日期:2021-08-15 发布日期:2021-09-06
  • 通讯作者: 梁天刚,E-mail:tgliang@lzu.edu.cn
  • 作者简介:高宏元(1996-),男,汉族,甘肃静宁人,硕士研究生,主要从事草地遥感与地理信息系统,E-mail:gaohy2015@lzu.edu.cn
  • 基金资助:
    国家重点研发计划（2019YFC0507701）；国家自然科学基金（31672484，41805086，41801191）；中国工程院咨询研究项目（2021-HZ-5，2020-XZ-29）；兰州大学中央高校基本科研业务费专项资金（lzujbky-2021-kb13）；财政部和农业农村部：国家现代农业产业技术体系资助

Hyperspectral Estimation of Aboveground Biomass of Alpine Grassland based on Random Forest Algorithm

GAO Hong-yuan1, HOU Meng-jing1, GE Jing1, BAO Xu-ying1, LI Yuan-chun1, LIU Jie1, FENG Qi-sheng1, LIANG Tian-gang1, HE Jin-sheng1,2, QIAN Da-wen3   

  1. 1. College of Pastoral Agriculture Science and Technology, Lanzhou University; State Key Laboratory of Grassland Agro-ecosystem; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou, Gansu Province 730020, China;
    2. College of Urban and Environmental Science, Peking University, Beijing 100871, China;
    3. Northest Institute of Plateau Biology, Chinese Academy of Science, Xining, Qinghai Province 810008, China
  • Received:2021-04-13 Revised:2021-05-19 Online:2021-08-15 Published:2021-09-06

PDF

摘要： 草地地上生物量（Aboveground biomass，AGB）是衡量草地生产力的关键因素，准确测定草地AGB具有重要意义。高光谱因具有时效性强、不破坏草地等特点被广泛用于草地生理生态指标的测定。本研究提取和计算了海北试验站高寒草地冠层的原始光谱（Original spectrum，OR）反射率、一阶微分光谱（First derivative spectrum，FD）反射率、光谱位置面积参数（Spectral parameters of spectral position and area，PA）和植被指数（Vegetation indices，VI）4种不同类型的特征变量，使用连续投影算法（Successive projections algorithm，SPA）和递归特征消除算法（Recursive feature elimination，RFE）进行特征选择，采用随机森林算法（Random forest，RF）构建草地AGB估测模型。结果表明：在由4种特征变量分别构建的草地AGB估测模型中，基于VI的RF模型精度最高（测试集R2=0.70，RMSE=557.87 kg·ha-1），实测AGB与估测AGB的线性R2达到0.72；不同类型特征变量组合构建的草地AGB估测模型中，PA+VI组合的RF模型精度最高（R2=0.71，RMSE=548.97 kg·ha-1），实测AGB和估测AGB的线性R2达到0.73。

关键词: 高寒草地, 地上生物量, 高光谱, 随机森林, 连续投影算法, 递归特征消除

Abstract: Aboveground biomass (AGB) is a key indicator of grassland productivity and it is important to measure grassland AGB accurately in grassland resource survey. Hyperspectrum is an effective method to measure the physiological and ecological indexes of grassland without physical damage to the grassland. In this study, the original spectrum (OR), the first derivative spectrum (FD), spectral parameters of spectral position and area (PA) and the vegetation index (VI) of alpine grassland canopy were calculated near Haibei National Field Research Station for Alpine Grassland Ecosystem (Haibei Station). Based on the above variables, feature selection was performed with successive projections algorithm (SPA) and recursive feature elimination algorithm (RFE), and the model was constructed with random forest algorithm (RF). The results showed that among the grassland AGB estimation models constructed by different variables, the accuracy of RF model based on VI was the highest (R2=0.70, RMSE=557.87 kg·ha-1), and R2 of measured AGB and predicted AGB was 0.72. Among the grassland AGB estimation models constructed by different combination of variables, the accuracy of RF model of PA + VI combination was the highest (R2=0.71, RMSE=548.97 kg·ha-1), and R2 of measured AGB and predicted AGB was 0.73.

Key words: Alpine grassland, Aboveground biomass, Hyperspectral, Random forest, Successive projections algorithm, Recursive feature elimination

中图分类号: