Data-Driven Artificial Intelligence Model of Meteorological Elements Influence on Vegetation Coverage in North China
Data-Driven Artificial Intelligence Model of Meteorological Elements Influence on Vegetation Coverage in North China
复制标题
数据驱动的华北气象要素对植被覆盖影响的人工智能模型
DOI：
10.3390/rs14061307
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发表时间：
2022-03
期刊：
影响因子：
5
通讯作者：
Li Li
中科院分区：
文献类型：
--
作者：
Huimin Bai;Zhiqiang Gong;Guiquan Sun;Li Li
基于植被覆盖度遥感数据、基本气象要素观测数据和支持向量机（SVM）方法，建立了气象要素对植被覆盖度的影响分析模型。利用SVM模型中的5个气象要素（温度、降水、相对湿度、日照时数和地温）识别植被覆盖度变化。通过与统计模型多元线性回归（MLR）和偏最小二乘（PLS）模型的比较，评价了SVM模型对植被覆盖度异常变化的模拟效果。MLR、PLS和SVM模型模拟的符号一致性率（SAR）分别为55%、57%和66%。SVM模型在模拟与气象要素相关的华北植被覆盖度年际变化方面表现出明显优于PLS和MLR模型的效果。因此，在模型开发中引入支持向量机的智能分析方法，对于研究气象要素对区域植被覆盖度的内在影响具有一定的优势。还可以进一步应用于预测未来植被异常变化。
Based on remote sensing data of vegetation coverage, observation data of basic meteorological elements, and support vector machine (SVM) method, this study develops an analysis model of meteorological elements influence on vegetation coverage (MEVC). The variations for the vegetation coverage changes are identified utilizing five meteorological elements (temperature, precipitation, relative humidity, sunshine hour, and ground temperature) in the SVM model. The performance of the SVM model is also evaluated on simulating vegetation coverage anomaly change by comparing with statistical model multiple linear regression (MLR) and partial least squares (PLS)-based models. The symbol agreement rates (SAR) of simulations produced by MLR, PLS, and SVM models are 55%, 57%, and 66%, respectively. The SVM model shows obviously better performance than PLS and MLR models in simulating meteorological elements-related interannual variation of vegetation coverage in North China. Therefore, the introduction of the intelligent analysis method in term of SVM in model development has certain advantages in studying the internal impact of meteorological elements on regional vegetation coverage. It can also be further applied to predict the future vegetation anomaly change.
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影响因子：
13.5
作者：
Yulong Zhang;C. Song;L. Band;G. Sun;Junxiang Li
通讯作者：
Yulong Zhang;C. Song;L. Band;G. Sun;Junxiang Li
影响因子：
4.6
作者：
Manali Pal;R. Maity;J. V. Ratnam;M. Nonaka;S. Behera
通讯作者：
Manali Pal;R. Maity;J. V. Ratnam;M. Nonaka;S. Behera
气候变化和人类活动对中国南方丘陵丘陵植被覆盖的影响
影响因子：
3.8
作者：
Wang Jing;Wang Kelin;Zhang Mingyang;Zhang Chunhua
通讯作者：
Zhang Chunhua
全球植被对气候变化响应的时滞效应
影响因子：
11.6
作者：
Wu Donghai;Zhao Xiang;Liang Shunlin;Tang Bijian;Zhao Wenqian;Wu Donghai;Tang Bijian;Zhao Wenqian;Liang Shunlin;Zhou Tao;Huang Kaicheng;Zhou Tao;Huang Kaicheng;Zhou Tao;Huang Kaicheng;Zhao X
通讯作者：
Zhao X
DOI：
10.4236/ajcc.2018.72018
发表时间：
2018-06
期刊：
American Journal of Climate Change
影响因子：
--
作者：
A. Chaitra;S. Upgupta;L. Bhatta;J. Mathangi;D. Anitha;K. Sindhu;Vidya P Kumar;N. Agrawal;M. Murthy;F. Qamar;I. Murthy;J. Sharma;R. Chaturvedi;G. Bala;N. Ravindranath
通讯作者：
A. Chaitra;S. Upgupta;L. Bhatta;J. Mathangi;D. Anitha;K. Sindhu;Vidya P Kumar;N. Agrawal;M. Murthy;F. Qamar;I. Murthy;J. Sharma;R. Chaturvedi;G. Bala;N. Ravindranath