Assessment of Vegetation Dynamics in Xinjiang Using NDVI Data and Machine Learning Models from 2000 to 2023

被引:0
|
作者
Ma, Nan [1 ,2 ,3 ,4 ]
Cao, Shanshan [2 ]
Bai, Tao [1 ,3 ,4 ]
Yang, Zhihao [5 ]
Cai, Zhaozhao [1 ,3 ,4 ]
Sun, Wei [2 ]
机构
[1] Xinjiang Agr Univ, Coll Comp & Informat Engn, Urumqi 830052, Peoples R China
[2] Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Agr, Urumqi 830052, Peoples R China
[4] Xinjiang Agr Informatizat Engn Technol Res Ctr, Urumqi 830052, Peoples R China
[5] Xinjiang Agr Univ, Coll Hydraul & Civil Engn, Urumqi 830052, Peoples R China
基金
中国国家自然科学基金;
关键词
NDVI; machine learning; soil moisture; runoff; potential evaporation; spatio-temporal change; RANDOM FOREST; REGRESSION; CLIMATE; GROWTH; TREND;
D O I
10.3390/su17010306
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study utilizes NASA's Normalized Difference Vegetation Index (NDVI) data from the Google Earth Engine (GEE) platform and employs methods such as mean analysis, trend analysis, and the Hurst index to assess NDVI dynamics in Xinjiang, with a particular focus on desert, meadow, and grassland vegetation. Furthermore, multiple linear regression, random forest, support vector machines, and XGBoost models are applied to construct and evaluate the NDVI prediction models. The key driving forces are identified and ranked based on the results of the optimal model. Changes in the vegetation cover in response to these driving forces are analyzed using the Mann-Kendall test and partial correlation analysis. The results indicate the following: (1) From 2000 to 2023, the annual variation in NDVI in Xinjian fluctuates at a rate of 0.0012 per year. The intra-annual trend follows an inverted U shape, with meadow vegetation exhibiting the highest monthly NDVI fluctuations. (2) During this period, the annual average NDVI in Xinjiang ranges from 0 to 0.3, covering 74.74% of the region. Spatially, higher NDVI values are observed in the north and northwest, while lower values are concentrated in the south and southeast. (3) The overall slope of the variation in NDVI in Xinjiang between 2000 and 2023 ranges between -0.034 and 0.047, indicating no significant upward trend. According to the Hurst index, future projections suggest a shift from vegetation improvement to potential degradation. (4) Machine learning models are developed to predict NDVI, with random forest and XGBoost showing the highest precision. Soil moisture, runoff, and potential evaporation are identified as key drivers. In the last 24 years, the temperatures in Xinjiang have generally increased, while precipitation, soil moisture, and runoff have declined. There is a significant negative correlation between NDVI and both temperature and potential evaporation, while the correlation between NDVI and precipitation, soil moisture, and runoff is positive and significant, with distinct spatial variations throughout the region. The overall trend of vegetation cover in Xinjiang has been increasing, but the future outlook is less promising. Enhanced environmental monitoring and protective measures are essential moving forward.
引用
收藏
页数:24
相关论文
共 50 条
  • [31] Assessing vegetation dynamics in the Three-North Shelter Forest region of China using AVHRR NDVI data
    Duan, Hanchen
    Yan, Changzhen
    Tsunekawa, Atsushi
    Song, Xiang
    Li, Sen
    Xie, Jiali
    ENVIRONMENTAL EARTH SCIENCES, 2011, 64 (04) : 1011 - 1020
  • [32] Assessing vegetation dynamics in the Three-North Shelter Forest region of China using AVHRR NDVI data
    Hanchen Duan
    Changzhen Yan
    Atsushi Tsunekawa
    Xiang Song
    Sen Li
    Jiali Xie
    Environmental Earth Sciences, 2011, 64 : 1011 - 1020
  • [33] Learning EPON delay models from data: a machine learning approach
    Alberto Hernandez, Jose
    Ebrahimzadeh, Amin
    Maier, Martin
    Larrabeiti, David
    JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING, 2021, 13 (12) : 322 - 330
  • [34] Estimation of soil temperature from meteorological data using different machine learning models
    Feng, Yu
    Cui, Ningbo
    Hao, Weiping
    Gao, Lili
    Gong, Daozhi
    GEODERMA, 2019, 338 : 67 - 77
  • [35] ANALYSIS OF VEGETATION DYNAMICS IN BAICHENG DISTRICT, CHINA FROM SPOT-VEGETATION NDVI TIME SERIES USING WAVELET TRANSFORM
    Huang, Fang
    Wang, Ping
    Wu, Wenli
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 6499 - 6502
  • [36] Using Visualization to Illustrate Machine Learning Models for Genomic Data
    Qu, Zhonglin
    Zhou, Yi
    Quang Vinh Nguyen
    Catchpoole, Daniel R.
    PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2019), 2019,
  • [37] Classification of a-thalassemia data using machine learning models
    Christensen, Frederik
    Kilic, Deniz Kenan
    Nielsen, Izabela Ewa
    El-Galaly, Tarec Christoffer
    Glenthoj, Andreas
    Helby, Jens
    Frederiksen, Henrik
    Moller, Soren
    Fuglkjaer, Alexander Djupnes
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2025, 260
  • [38] Explicable Machine Learning Models Using Rich Geospatial Data
    Bramson, Aaron
    Mita, Masayoshi
    2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024, 2024, : 2381 - 2386
  • [39] A study of learning likely data structure properties using machine learning models
    Usman, Muhammad
    Wang, Wenxi
    Wang, Kaiyuan
    Yelen, Cagdas
    Dini, Nima
    Khurshid, Sarfraz
    INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER, 2020, 22 (05) : 601 - 615
  • [40] A study of learning likely data structure properties using machine learning models
    Muhammad Usman
    Wenxi Wang
    Kaiyuan Wang
    Cagdas Yelen
    Nima Dini
    Sarfraz Khurshid
    International Journal on Software Tools for Technology Transfer, 2020, 22 : 601 - 615