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基于可解释机器学习的青藏高原草地物候变化多因素影响分析
摘要点击 512  全文点击 117  投稿时间:2023-06-23  修订日期:2023-08-31
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中文关键词  青藏高原  草地物候  时空变化  极限梯度提升(XGBoost)  SHAP  交互影响
英文关键词  Qinghai-Xizang Plateau  grassland phenology  spatial and temporal variation  extreme gradient boosting (XGBoost)  shapley additive explanations (SHAP)  interactive effects
作者单位E-mail
刘慧文 中国海洋大学环境科学与工程学院, 青岛 266100 Sacajawea0125@163.com 
刘欢 中国水利水电科学研究院流域水循环模拟与调控国家重点实验室, 北京 100038  
胡鹏 中国水利水电科学研究院流域水循环模拟与调控国家重点实验室, 北京 100038  
彭辉 中国海洋大学环境科学与工程学院, 青岛 266100
中国海洋大学海洋环境与生态教育部重点实验室, 青岛 266100
中国海洋大学海洋环境地质工程山东省重点实验室, 青岛 266100 
pengh@ouc.edu.cn 
王硕 中国海洋大学环境科学与工程学院, 青岛 266100  
中文摘要
      气候变化背景下,青藏高原植被物候发生显著改变.然而,影响物候的水热因素众多,目前较少有研究关注多因素对青藏高原物候的影响效应,导致对青藏高原物候变化机制认识不足.为此,研究通过遥感数据解译,在对2002~2021年青藏高原草地物候时空变化特征分析的基础上,聚焦降水、气温、海拔和土壤等多方面,利用可解释机器学习方法(SHAP)揭示物候变化的主导因素,并量化分析多因素对物候的交互影响.结果表明:①青藏高原分别有56.32 %、67.65 %和65.50 %的草地表现出生长季开始时间(SOS)提前、生长季结束时间(EOS)延迟和生长季长度(LOS)延长趋势;②青藏高原草地SOS和LOS主要受水分条件影响,3月0~10 cm土壤水分对SOS提前和LOS延长起促进作用的范围分别在10~25 kg·m-2和15~25 kg·m-2之间,峰值分别在20 kg·m-2和18 kg·m-2左右;EOS则主要受温度影响,9月和10月温度越高对EOS延迟促进作用越强,并分别在高于8 ℃和-0.5 ℃时达到峰值;③水热等因素对物候的影响存在非线性交互效应,3月0~10 cm土壤水分达到20 kg·m-2后,更有利于低降水和低海拔地区SOS提前;10月温度高于0 ℃后较好的水分条件更有利于EOS延迟;3月0~10 cm土壤水分在12~22 kg·m-2之间时,高降水地区LOS更长.研究表明,可解释机器学习方法可为物候变化的多因素影响定量分析提供一种新的方法.
英文摘要
      The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors on the phenology of the Qinghai-Xizang Plateau, resulting in a lack of understanding of the mechanisms underlying phenological changes on the Qinghai-Xizang Plateau. In this study, we used remote sensing data interpretation to analyze the spatial and temporal variability of grassland phenology on the Qinghai-Xizang Plateau from 2002 to 2021, focusing on precipitation, temperature, altitude, soil, and other aspects to reveal the dominant factors of phenological variability using an interpretable machine learning method (SHAP) and to quantify the interactive effects of multiple factors on phenology. The results showed that:① The growing season start (SOS) of grasslands on the Qinghai-Xizang Plateau mostly ranged from 110 to 150 d, with 56.32 % of grasslands showing an early SOS trend; the growing season end (EOS) mostly ranged from 290-320 d, with 67.65 % of grasslands showing a delayed EOS trend; and the growing season length (LOS) mostly ranged from 120 to 210 d, with 65.50 % of the grasslands showing a trend towards longer growing season lengths. ② SOS in grasslands on the Qinghai-Xizang Plateau was mainly influenced by moisture conditions, in which soil moisture between 10 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the advancement of SOS and peaked at approximately 20 kg·m-2. EOS was mainly influenced by temperature, with higher temperatures in September and October having a stronger effect on EOS latency promotion and peaking at over 8 ℃ and -0.5 ℃, respectively. The main influencing factors of LOS were more consistent with SOS, in which soil moisture between 15 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the prolongation of LOS and peaked at approximately 18 kg·m-2. ③ There was an obvious interactive effect of water and heat and other factors on phenology; after soil moisture reached 20 kg·m-2 in the 0-10 cm soil layer in March, SOS was more advanced in low-precipitation and low-altitude areas. Better moisture conditions were more conducive to EOS delay at temperatures above 0 ℃ in October, and soil moisture in high precipitation areas promoted LOS prolongation more when soil moisture was between 12 and 22 kg·m-2 in 0-10 cm in March. The results also demonstrated that interpretable machine learning methods could provide a new approach to the analysis of the multifactorial effects of phenological change.

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