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自然资源遥感  2025, Vol. 37 Issue (1): 131-141    DOI: 10.6046/zrzyyg.2023216
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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
钟旭珍1,2,3(), 吴瑞娟1,4()
1.内江师范学院地理与资源科学学院,内江 641100
2.云南师范大学地理学部,昆明 650500
3.云南师范大学西南联合研究生院,昆明 650500
4.资源与环境信息系统国家重点实验室,北京 100101
Analysis of changing trends in NDVI and their driving forces in the Tuojiang River basin based on an improved BFAST model
ZHONG Xuzhen1,2,3(), WU Ruijuan1,4()
1. School of Geography and Resource Science, Neijiang Normal University, Neijiang 641100, China
2. Faculty of Geography, Yunnan Normal University, Kunming 650500, China
3. Southwest United Graduate School, Yunnan Normal University, Kunming 650500, China
4. State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
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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。
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钟旭珍
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关键词 NDVI非线性趋势BFAST改进模型OPGD沱江流域    
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.

Key wordsNDVI    nonlinear trend    BFAST improved model    OPGD    Tuojiang River basin
收稿日期: 2023-07-20      出版日期: 2025-02-17
ZTFLH:  TP79  
  X87  
基金资助:四川省科技计划项目“基于时序遥感数据时-空-谱预测模型构建的森林扰动监测研究”(2023NSFSC0754);资源与环境信息系统国家重点实验室开放基金项目(2022-30);国家重点研发计划政府间国际科技创新合作重点专项“利用地理空间技术监测和评估土地利用/土地覆被变化对区域生态安全的影响”(2018YFE0184300);沱江流域高质量发展研究中心项目“基于RS和GIS的沱江流域生态环境质量评价预测及修复对策研究”(TJGZL2022-15);内江师范学院校级科研项目“沱江流域生态环境脆弱性评价及生态修复研究”(2022YB17);内江师范学院科研创新团队项目(2021TD01)
通讯作者: 吴瑞娟(1985-), 女, 博士, 副教授, 主要从事3S技术集成及应用研究。Email: rjwu@njtc.edu.cn
作者简介: 钟旭珍(1993-), 女, 博士研究生, 讲师, 主要从事GIS与环境遥感研究。Email: zxzxuzhen@njtc.edu.cn
引用本文:   
钟旭珍, 吴瑞娟. 基于BFAST改进模型的沱江流域NDVI变化趋势及驱动力分析[J]. 自然资源遥感, 2025, 37(1): 131-141.
ZHONG Xuzhen, WU Ruijuan. Analysis of changing trends in NDVI and their driving forces in the Tuojiang River basin based on an improved BFAST model. Remote Sensing for Natural Resources, 2025, 37(1): 131-141.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023216      或      https://www.gtzyyg.com/CN/Y2025/V37/I1/131
Fig.1  沱江流域地理位置
序号 趋势类型名称 第一段 第二段 趋势类型含义
1 单调递增 + + 未检测出明显突变,趋势整体表现为单调性增加
2 单调递减 - - 未检测出明显突变,趋势整体表现为单调性减小
3 单调递增(带正中断) + + 检测出1个明显突变,且断点处值突然增大,趋势整体表现为单调性增加,用符号“单调递增+”表示
4 单调递减(带负中断) - - 检测出1个明显突变,且断点处值突然减小,趋势整体表现为单调性减小,用符号“单调递减-”表示
5 中断(随着负中断增加) + + 检测出1个明显突变,且断点处值突然减小,趋势表现为显著增加,显著负中断,然后显著增加,用符号“中断-+”表示
6 中断(随着正中断减少) - - 检测出1个明显突变,且断点处值突然增大,趋势表现为显著减小,显著正中断,然后显著减小,用符号“中断+-”表示
7 反转(由增到减) + - 检测出1个明显突变,趋势表现为从显著增加转换为显著减小,用符号“反转+-”表示
8 反转(由减到增) - + 检测出1个明显突变,趋势表现为从显著减小转换为显著增加,用符号“反转-+”表示
Tab.1  BFAST01检测的NDVI变化趋势类型
Fig.2  BFAST01趋势突变类型示意图
变量名称 变量 2000年 2005年 2010年 2015年 2020年
离散方法 类别数 离散方法 类别数 离散方法 类别数 离散方法 类别数 离散方法 类别数
海拔 X1 自然间断点 12 自然间断点 12 自然间断点 12 自然间断点 12 自然间断点 10
坡度 X2 几何间隔 12 自然间断点 12 自然间断点 12 自然间断点 12 自然间断点 11
坡向 X3 手动 10 手动 10 手动 10 手动 10 手动 10
气温 X4 自然间断点 10 相等间隔 12 相等间隔 11 相等间隔 12 自然间断点 11
降水 X5 自然间断点 12 分位数 10 自然间断点 8 自然间断点 12 自然间断点 12
土壤类型 X6 手动 16 手动 16 手动 16 手动 16 手动 16
地形地貌 X7 手动 24 手动 24 手动 24 手动 24 手动 24
LULC X8 手动 6 手动 6 手动 6 手动 6 手动 6
人口 X9 自然间断点 11 自然间断点 12 自然间断点 10 自然间断点 11 自然间断点 12
GDP X10 分位数 12 自然间断点 11 自然间断点 12 自然间断点 12 自然间断点 12
Tab.2  地理探测器因子离散化方法与类别
Fig.3  2000—2021年沱江流域NDVI空间分布及线性变化趋势
Fig.4-1  BFAST01突变类型与显著性
Fig.4-2  BFAST01突变类型与显著性
Fig.5  BFAST01突变时间
突变时间 单调递增 单调递减 单调增加+ 单调递减- 中断-+ 中断+- 反转+- 反转-+ 合计
2002—2007年 0.31 0.10 14.08 4.45 12.59 3.58 35.11
2008—2013年 0.06 0.53 14.83 4.50 13.19 3.90 37.02
2014—2018年 0.11 0.62 10.73 4.66 6.68 5.07 27.88
合计 0.48 1.26 39.64 13.61 32.46 12.56 100
Tab.3  各突变类型发生改变的年份分布
线性
回归
BFAST01
单调递增 单调递减 单调增加+ 单调递减- 中断-+ 中断+- 反转+- 反转-+
退化 0 54.33 0 1.18 12.82 7.98 15.88 7.81
改善 64.99 0 0.32 0 17.69 3.49 10.61 2.91
Tab.4  线性与非线性趋势统计
Fig.6  沱江流域各年份影响因子q值柱状图
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