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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (1) : 131-141     DOI: 10.6046/zrzyyg.2023216
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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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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.
Keywords NDVI      nonlinear trend      BFAST improved model      OPGD      Tuojiang River basin     
ZTFLH:  TP79  
  X87  
Issue Date: 17 February 2025
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Xuzhen ZHONG
Ruijuan WU
Cite this article:   
Xuzhen ZHONG,Ruijuan WU. Analysis of changing trends in NDVI and their driving forces in the Tuojiang River basin based on an improved BFAST model[J]. Remote Sensing for Natural Resources, 2025, 37(1): 131-141.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023216     OR     https://www.gtzyyg.com/EN/Y2025/V37/I1/131
Fig.1  Geographical location of the Tuojiang River basin
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  Types of NDVI change trends detected by BFAST01
Fig.2  Schematic diagram of BFAST01 trend mutation types
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  Discretization methods and categories of geographical detector factors
Fig.3  Spatial distribution and linear change trend of NDVI in the Tuojiang River basin from 2000 to 2021
Fig.4-1  BFAST01 mutation types and significance
Fig.4-2  BFAST01 mutation types and significance
Fig.5  BFAST01 mutation time
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  Distribution of years of changes in different mutation types (%)
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  Linear and nonlinear trend statistics (%)
Fig.6  Histogram of impact factor q values for various years in the Tuojiang River basin
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