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基于BFAST改进模型的沱江流域NDVI变化趋势及驱动力分析

Analysis of changing trends in NDVI and their driving forces in the Tuojiang River basin based on an improved BFAST model

  • 摘要: 植被是陆地生态系统的主体, 对区域生态系统环境变化有着重要指示。沱江流域是四川经济、工业较为发达的地区, 对该流域植被进行动态监测并分析影响其变化的因素, 对生态环境变化评估与保护具有重要意义。以沱江流域为研究区, 基于2000—2021年MODIS NDVI数据, 利用Slope线性回归趋势和BFAST改进模型BFAST01对其线性特征、突变类型和突变年份等非线性特征进行检测、分析和比对, 并利用基于最优参数的地理探测器模型(optimal parameters-based geographical detector, OPGD)对植被NDVI的影响因素进行探讨。结果表明: 沱江流域95%以上的区域NDVI值都大于0.6, 线性回归趋势表明, 植被覆盖呈显著改善趋势的像元面积占比为18.07%, 呈显著退化的区域像元面积占比为10.60%; BFAST01非线性突变检验可知, 沱江流域22 a间植被NDVI趋势可分为8种突变类型, 总体为改善的区域占比(58.62%)大于总体为退化的区域(41.38%), 检测结果与线性回归趋势相似, 说明近年来研究区植被得到较好保护; 发生突变的年份集中分布在2002—2018年, “中断-+”、“反转+-”是发生突变最多的类型, 主要集中在2008—2013年, 分别占14.83%和13.19%, 其他突变类型在各阶段发生突变的比例分布较为均匀; OPGD结果表明, 不同年份NDVI的影响因素略有差异, 总体上影响较大的因子为土地利用、海拔、地形地貌, 其次是气温、降水等气象因子, 其他因子影响力相差不大, 总的来说, 人口、国内生产总值(gross domestic product, GDP)等人为因子对沱江流域植被的影响程度比自然因子低, 但也有一定影响, 因此, 植被保护与恢复应综合考虑不同自然和人类活动条件的影响。

     

    Abstract: Vegetation, the main body of a terrestrial ecosystem, serves as an important indicator of environmental changes in a regional ecosystem. The Tuojiang River basin is an economically and industrially developed area in Sichuan. Dynamic vegetation monitoring and the analysis of factors driving its changes hold great significance for ecological change assessment and ecological protection. This study investigated the Tuojiang River basin. Based on MODIS data of normalized difference vegetation index (NDVI) from 2000 to 2021, this study detected, analyzed, and compared linear and nonlinear characteristics of the data, including mutation types and years, using linear regression Slope and an improved BFAST01 model. Additionally, this study explored the factors influencing the NDVI using the Optimal Parameters-based Geographic Detector (OPGD) model. The results indicate that more than 95% of the Tuojiang River basin exhibited NDVI values exceeding 0.6. The linear regression analysis for NDVI trends revealed that regions with significantly improved and significantly degraded vegetation coverage accounted for 18.07% and 10.60%, respectively, of the total area of the river basin, as indicated by image pixels. The BFAST01 nonlinear mutation analysis showed that the NDVI trends in the Tuojiang River basin over the 22 years can be categorized into eight mutation types, with the proportion of regions exhibiting improved vegetation coverage (58.62%) exceeding that of regions with degraded vegetation coverage (41.38%). These findings were consistent with the linear regression analysis, suggesting that the vegetation in the river basin was well protected in the 22 years. Mutations were concentrated between 2002 and 2018, with “interruption-+” and “reversal+-” representing the most common mutation types, accounting for 14.83% and 13.19%, respectively. In contrast, other mutation types exhibited a relatively even distribution across different stages. The results of the OPGD model revealed slight variations in the factors influencing NDVI across different years. Generally, the most influential factors included land use/land cover (LULC), elevation, and terrain and landforms, followed by meteorological factors such as temperature and precipitation. In contrast, other factors produced relatively minor impacts. Overall, despite some impacts, human factors like population and GDP exerted less influence on vegetation than natural factors in the Tuojiang River basin. Therefore, vegetation protection and restoration should consider the combined effects of both natural factors and anthropogenic activities.

     

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