植物生态学报 ›› 2021, Vol. 45 ›› Issue (5): 487-495.DOI: 10.17521/cjpe.2020.0076

• 研究论文 • 上一篇    下一篇

基于物候相机归一化植被指数估算高寒草地植物地上生物量的季节动态

陈哲1, 汪浩2,*(), 王金洲1, 石慧瑾1, 刘慧颖3, 贺金生1,2   

  1. 1北京大学城市与环境学院, 地表过程分析与模拟教育部重点实验室, 北京大学生态中心, 北京 100871
    2兰州大学草地农业生态系统国家重点实验室, 兰州 730000
    3华东师范大学生态与环境科学学院, 上海 200241
  • 收稿日期:2020-03-19 接受日期:2020-05-28 出版日期:2021-05-20 发布日期:2020-06-12
  • 通讯作者: 汪浩
  • 作者简介:*(wanghao@lzu.edu.cn)
  • 基金资助:
    国家自然科学基金(31630009);国家自然科学基金(31901168);国家自然科学基金(31901145)

Estimation on seasonal dynamics of alpine grassland aboveground biomass using phenology camera-derived NDVI

CHEN Zhe1, WANG Hao2,*(), WANG Jin-Zhou1, SHI Hui-Jin1, LIU Hui-Ying3, HE Jin-Sheng1,2   

  1. 1Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
    2State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou 730000, China
    3School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
  • Received:2020-03-19 Accepted:2020-05-28 Online:2021-05-20 Published:2020-06-12
  • Contact: WANG Hao
  • Supported by:
    the National Natural Science Foundation of China(31630009);the National Natural Science Foundation of China(31901168);the National Natural Science Foundation of China(31901145)

摘要:

准确评估地上生物量对优化草地资源管理和理解草地碳、水和能量平衡具有重要意义。该文通过近地遥感归一化植被指数(NDVI)构建最优经验模型, 对青藏高原高寒草地地上生物量进行估算。该文利用2018-2019年5-9月野外实测的地上生物量和植物冠层光谱仪(RapidSCAN)测定的NDVIRS数据, 构建了生长季不同时期地上生物量的估算模型; 并结合2018年NetCam物候相机测定的NDVICam时间序列数据, 实现地上生物量季节动态的模拟。主要结果: (1) NDVICamNDVIRS与地上生物量具有相似的单峰型季节变化格局, 但NDVI峰值出现的时间(7月)较地上生物量(8月)更早; (2)基于NDVI的生物量估算最优经验模型在5、7和9月是幂函数, 在6和8月是二次多项式, 估算精度为0.29-0.77; (3)基于NDVICam时间序列数据, 生长季不同时期建模(R2 = 0.91)较单一时期(9月)建模(R2 = 0.49)对地上生物量季节动态的估算更为准确。这些结果表明, 近地遥感是估算高寒草地植物地上生物量的有效手段, 开展季节性植物生长调查将有助于准确评估草地资源。

关键词: 物候相机, 近地遥感, 归一化植被指数, 地上生物量, 高寒草地, 青藏高原

Abstract:

Aims Accurate assessment of plant aboveground biomass is important for optimizing grassland resource management and for understanding the balance of carbon, water and energy fluxes in grassland ecosystems. This study constructed the optimal empirical models by near-surface remote sensing normalized difference vegetation index (NDVI) data, and then estimated plant aboveground biomass in an alpine grassland on the Qingzang Plateau.
Methods Using the dataset of both the field-measured aboveground biomass and the NDVIRS observed by plant canopy spectrometer (RapidSCAN), we constructed the empirical models for estimating aboveground biomass in different phases of the growing season across 2018 and 2019. Using the NDVICam time series observed by phenology camera and the estimated models, we simulated seasonal dynamics of aboveground biomass in 2018.
Important findings (1) The seasonal dynamics of NDVICam, NDVIRS and aboveground biomass exhibited a similar unimodal pattern; however, the timing of peak NDVI (August) preceded that of peak aboveground biomass (July). (2) The best model for estimating aboveground biomass is the power function in May, July and September, and the quadratic equation in June and August. The estimation accuracy ranged from 0.29 to 0.77. (3) The estimation of aboveground biomass based on the models in different phases of growing season (R2 = 0.91) showed a higher accuracy compared to that based on the model at a single time (September)(R2 = 0.49). Our results suggest that the near-surface remote sensing is an effective approach for estimating alpine grassland aboveground biomass, and further investigation on the seasonal growth of plants will help accurately evaluate grassland resources.

Key words: phenology camera, near-surface remote sensing, normalized difference vegetation index (NDVI), aboveground biomass, alpine grassland, Qingzang Plateau