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
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.
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.
Li X Y, Zhang Z Q, Sun A Z. Study on the spatial-temporal evolution and influence factors of vegetation coverage in the Yellow River basin during 1982—2021[J]. Journal of Earth Environment, 2022, 13(4):428-436.
Jin C M, Yang X W, Jing H T. A RS-based study on changes in fractional vegetation cover in North Shaanxi and their driving factors[J]. Remote Sensing for Natural Resources, 2021, 33(4):258-264.doi:10.6046 /zrzyyg.2021019.
Wang L, Zhao S Y, Chen Y P, et al. Vegetation change and attribution in ecological restoration area of Loess Plateau based on GEE cloud platform[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(3):210-223.
[4]
Han Z, Song W. Interannual trends of vegetation and responses to climate change and human activities in the Great Mekong Subregion[J]. Global Ecology and Conservation, 2022,38:e02215.
Chen W Y, Xia L H, Xu G L, et al. Dynamic variation of NDVI and its influencing factors in the Pearl River basin from 2000 to 2020[J]. Ecology and Environmental Sciences, 2022, 31(7):1306-1316.
[6]
Zhang X, Cao Q, Chen H, et al. Effect of vegetation carryover and climate variability on the seasonal growth of vegetation in the upper and middle reaches of the Yellow River basin[J]. Remote Sensing, 2022, 14(19):5011.
Feng L. The study of remote sensing dynamic monitoring and its driving mechanism of vegetation cover in the Jinsha River basin[D]. Kunming: Yunnan Normal University, 2021.
Sun J L, Huang R Q. Effectively perform the responsibility of ecological environment protection and constantly create a new situation of building a beautiful China in a new era[N]. People’s Daily,2022-11-21(11).
Lin Y M, Li W H, Nan X X, et al. Spatial-temporal differentiation and driving factors of vegetation coverage in Ningxia Helan Mountain based on geodetector[J]. Chinese Journal of Applied Ecology, 2022, 33(12):3321-3327.
Sun M R, Sun P S. Climate-driving effects and sustainability of vegetation activity change in alpine and subalpine areas of southwest China[J]. Research of Soil and Water Conservation, 2023, 30(3):240-250.
[11]
Gao X, Zhao D. Impacts of climate change on vegetation phenology over the Great Lakes region of Central Asia from 1982 to 2014[J]. Science of the Total Environment, 2022,845:157227.
Zhang X H, Zhang B P, Wang J, et al. Study on the relationship between terrain and distribution of the vegetation in Shennongjia forestry district[J]. Journal of Geo-Information Science, 2020, 22(3):482-493.
[13]
Mendes M P, Rodriguez-Galiano V, Aragones D. Evaluating the BFAST method to detect and characterise changing trends in water time series:A case study on the impact of droughts on the Mediterranean climate[J]. The Science of the Total Environment, 2022,846:157428.
Xin Y, Sun M X, Zhang Y, et al. Spatiotemporal characteristics of vegetation cover and climate driving factors in Sichuan Province from 2000 to 2020[J]. Bulletin of Soil and Water Conservation, 2022, 42(4):312-319.
[15]
He C, Yan F, Wang Y, et al. Spatiotemporal variation in vegetation growth status and its response to climate in the Three-River Headwaters region,China[J]. Remote Sensing, 2022, 14(19):5041.
Pang X, Liu J. Effects of climate changes on the NDVI of vegetation in Asia[J]. Remote Sensing for Natural Resources, 2023, 35(2):295-305.doi:10.6064/zrzyyg.2022151.
Wang Y L, Yang X, Hao L N. Spatio-temporal changes in the normalized difference vegetation index of vegetation in the western Sichuan Plateau during 2001—2021 and their driving factors[J]. Remote Sensing for Natural Resources, 2023, 35(3):212-220.doi:10.6064/zrzyyg.2022240.
[18]
Geng S, Zhang H, Xie F, et al. Vegetation dynamics under rapid urbanization in the Guangdong-Hong Kong-Macao Greater Bay area urban agglomeration during the past two decades[J]. Remote Sensing, 2022, 14(16):3993.
[19]
Zhong X, Li J, Wang J, et al. Linear and nonlinear characteristics of long-term NDVI using trend analysis:A case study of lancang-mekong river basin[J]. Remote Sensing, 2022, 14(24):6271.
Luo S, Liu H Y, Gong H B. Nonlinear trends and spatial pattern analysis of vegetation cover change in China from 1982 to 2018[J]. Acta Ecologica Sinica, 2022, 42(20):8331-8342.
[21]
Schultz M, Clevers J G P W, Carter S, et al. Performance of vegetation indices from Landsat time series in deforestation monitoring[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,52:318-327.
[22]
Li L, Zhang Y, Liu Q, et al. Regional differences in shifts of temperature trends across China between 1980 and 2017[J]. International Journal of Climatology, 2019, 39(3):1157-1165.
[23]
Brakhasi F, Hajeb M, Mielonen T, et al. Investigating aerosol vertical distribution using CALIPSO time series over the Middle East and North Africa (MENA),Europe,and India:A BFAST-based gradual and abrupt change detection[J]. Remote Sensing of Environment, 2021,264:112619.
[24]
Dupas R, Minaudo C, Gruau G, et al. Multidecadal trajectory of riverine nitrogen and phosphorus dynamics in rural catchments[J]. Water Resources Research, 2018, 54(8):5327-5340.
[25]
Horion S, Ivits E, De Keersmaecker W, et al. Mapping European ecosystem change types in response to land-use change,extreme climate events,and land degradation[J]. Land Degradation & Development, 2019, 30(8):951-963.
Qin G X, Wu J, Li C B, et al. Grassland vegetation phenology change and its response to climate changes in North China[J]. Chinese Journal of Applied Ecology, 2019, 30(12):4099-4107.
Fang H, Zhang Y H, He Y, et al. Spatio-temporal variations of vegetation ecological quality in Zhejiang Province and their driving factors[J]. Remote Sensing for Natural Resources, 2023, 35(2):245-254.doi:10.6064/zrzyyg.2022070.
Wang J F, Xu C D. Geodetector:Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1):116-134.
[29]
Song Y, Wang J, Ge Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis:Cases with different types of spatial data[J]. GIScience & Remote Sensing, 2020, 57(5):593-610.
Wang J. Methods for detecting vegetation changes and quantifying the driving factors using NDVI timeseries by taking Hexi as a case area[D]. Lanzhou: Lanzhou University, 2020.
[31]
Fang X, Zhu Q, Ren L, et al. Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST:A case study in Quebec,Canada[J]. Remote Sensing of Environment, 2018,206:391-402.
[32]
Verbesselt J, Hyndman R, Newnham G, et al. Detecting trend and seasonal changes in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(1):106-115.
[33]
Berveglieri A, Imai N N, Christovam L E, et al. Analysis of trends and changes in the successional trajectories of tropical forest using the Landsat NDVI time series[J]. Remote Sensing Applications:Society and Environment, 2021,24:100622.
[34]
Kovács G M, Horion S, Fensholt R. Characterizing ecosystem change in wetlands using dense earth observation time series[J]. Remote Sensing of Environment, 2022,281:113267.
[35]
R Core Team. 2018. R:A Language and Environment for Statistical Computing,Vienna[EB/OL].https://www.R-project.org.
[36]
Higginbottom T P, Symeonakis E. Identifying ecosystem function shifts in Africa using breakpoint analysis of long-term NDVI and RUE data[J]. Remote Sensing, 2020, 12(11):1894.
[37]
Smith V, Portillo-Quintero C, Sanchez-Azofeifa A, et al. Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica[J]. Remote Sensing of Environment, 2019,221:707-721.
Zhong X Z, Zhang S, Wu R J, et al. Analysis of dynamic changes and driving forces of soil erosion in Tuojiang River basin[J]. Research of Soil and Water Conservation, 2022, 29(2):43-49,56.
Zhu S J, Feng H H, Zou B, et al. Spatial-temporal characteristics of 2000—2019 vegetation NPP of the Dongting Lake basin and their driving factors[J]. Remote Sensing for Natural Resources, 2022, 34(3):196-206.doi:10.6064/zrzyyg.2021283.
Ma B X, He C X, Jing J L, et al. Attribution of vegetation dynamics in Southwest China from 1982 to 2019[J]. Acta Geographica Sinica, 2023, 78(3):714-728.
doi: 10.11821/dlxb202303013