# Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}, with mountains, hills and plains, and lakes accounting for 56%, 24%, and 20% of the total area, respectively [31]. The topography of Hubei Province varies greatly in height, with the terrain showing an incomplete basin area that is elevated on three sides, flat in the center, open to the south, and with a gap in the north [32]. Located in the north–south climate transition zone and belonging to the subtropical monsoon humid climate, there is rain and heat at the same time, it is cold in the winter and hot in the summer, and precipitation is mainly concentrated from June to July, with the average annual precipitation of 800 to 1600 mm and the average annual temperature of 15 to 22 °C [33]. The vertical distribution of natural vegetation is distinctive, rich in biological resources, characterized by the transition between the north and the south, with broad-leaved forests as the main type of vegetation, and at the same time, includes diversified vegetation types, such as coniferous forests, mixed forests, wetland forests, etc. It is in the transition zone between the east and the west of China’s flora and is one of the provinces in China that are richer in botanical resources. It has 32 national-level ecological civilization demonstration zones in China, four forest ecological stations, one bamboo forest ecological station and one wetland ecological station [34].

#### 2.2. Data Sources

#### 2.3. Research Methods

#### 2.3.1. Sen+Mann–Kendall Significance Tests

_{1−α/2}at a given significance level α. Z

_{1−α/2}is the value corresponding to the distribution table of the standard normal distribution function at the confidence level α. At |Z| ≥ 2.58, |Z| ≥ 1.96 indicates that the trend passes the significance test with 99% and 95% confidence levels, which are highly significant and significant, respectively, and |Z| < 1.96 indicates that the trend is not significant.

#### 2.3.2. Stability Analysis

#### 2.3.3. Future Variation Trend

_{t}, t = 1, 2, 3 … n, defines its mean series:

#### 2.3.4. Partial Correlation Analysis

#### 2.3.5. Geographical Detector Model

#### Factor Detector

#### Interaction Detector

## 3. Results

#### 3.1. Spatiotemporal Variation Characteristics of the NDVI

#### 3.1.1. Interannual Variation Characteristics of the NDVI

#### 3.1.2. Spatial Variation Characteristics of the NDVI

#### 3.1.3. Stability Analysis of the NDVI

#### 3.1.4. Stability Analysis of the NDVI

#### 3.2. NDVI Driver Analysis

#### 3.2.1. Influence of Climatic Factors on Vegetation NDVI

#### 3.2.2. Anthropogenic Effects on Vegetation NDVI

#### 3.2.3. Impact of Land Cover on Vegetation NDVI

^{2}and 2371 km

^{2}transferred from Cropland/Natural Vegetation Mosaics and Savannas, respectively, and the greatest loss of Savanna, with a decrease of 7131 km

^{2}. The overall change in land cover from 2006 to 2011 was relatively smooth, with a more pronounced shift away from Woody Savannas and mainly towards Deciduous Broadleaf Forests. From 2011 to 2017, Woody Savannas in Hubei Province shrank significantly, decreasing by about 2945 km

^{2}, and the Savannas increased by 1730 km

^{2}. Land cover changed dramatically in 2017–2022, with a decrease of 4555 km

^{2}of Croplands, mainly draining to the Savannahs and an additional area of 325 km

^{2}of Urban and Built-up Lands.

^{2}of cultivated land and Woody Savannas in Hubei Province have been converted to forested land, which is mainly due to the implementation of the Returning Cultivated Land to Forestry Project in Hubei Province, which began in 2000, resulting in an increase in the area of vegetation. The addition of 880 km

^{2}to the Urban and Built-up Lands indicates that Hubei Province has experienced rapid urbanization during the study period, but the urbanization process also constrains the vegetation NDVI, resulting in a reduction in vegetation cover.

#### 3.2.4. Geographical Detection of Factors Influencing the NDVI

- (1)
- Factor detection

_{8}) and soil type (X

_{7}), on the spatial distribution of vegetation cover, was over 60%, and the explanatory power of the land cover type (X

_{8}) factor was over 70%. Different land cover types can significantly affect the effectiveness of soil moisture, and the soil is the growth substrate for plants, which has a significant impact on the growth of vegetation and the efficiency of rainwater reuse. The average annual temperature (X

_{1}) and population density (X

_{9}) are secondary driving factors, with an explanatory power of more than 50%, and the temperature has an impact on the dissolution of nutrients in the soil and the uptake of the plant root system, and the fluctuating increase in the explanatory power of the average annual temperature from 2000 to 2022, with the explanatory power of the average annual temperature increasing to 70.7% in 2022, which indicates that the NDVI of the vegetation in Hubei Province by the temperature. The influence of temperature on the NDVI of vegetation in Hubei Province gradually increased, and land use changes caused by human activities, such as urban expansion, agricultural expansion, and deforestation, negatively affected the distribution of vegetation. Those with 30% to 50% explanatory power include elevation (X

_{4}), nighttime light (X

_{10}), and a total of five factors, slope (X

_{5}), slope direction (X

_{6}), solar radiation (X

_{3}), and precipitation (X

_{2}), have the least explanatory power for vegetation cover characterized by NDVI among the 10 drivers.

- (2)
- Interaction detection

## 4. Discussion

#### 4.1. Spatiotemporal Variation Characteristics in the NDVI in Hubei Province

#### 4.2. Drivers of Spatial Variability in Vegetation NDVI

^{2}of cultivated land and Woody Savannas in Hubei Province have been converted to forested land, which is mainly due to the implementation of the Returning Cultivated Land to Forestry Project in Hubei Province, which started in 2000, resulting in an increase in the area of vegetation. At the same time, rapid urbanization during the study period resulted in a significant reduction in vegetation cover in the urban and peripheral areas of the study area, with an additional area of 880 km

^{2}in the Urban and Built-up Lands.

## 5. Conclusions

- (1)
- During the period over 23 years, the average value of NDVI of vegetation in Hubei Province was 0.762. The overall change in NDVI showed a fluctuating upward trend, and the growth rate was 0.01/10a (p < 0.005); the spatial distribution of NDVI showed a pattern of “low in the east and high in the west”, and the vegetation cover condition of Hubei Province was at a relatively high level, in general. The spatial change in NDVI is characterized by “large improvement in the surrounding hills and mountains and small degradation in the middle plains”; the stability of the spatial change in NDVI is characterized by “uniform distribution in general, with obvious differences between urban and rural areas and fluctuation of the river reservoirs and reservoir heights”.
- (2)
- In terms of trend prediction, 70.76% of the regions in Hubei Province are likely to maintain the same NDVI trend as that of the 2000–2022 period in the future, and the future evolution of NDVI tends to be mostly benign, accounting for 70.78% of the total; however, 27.73% of the regions may still have the potential deterioration of sustained degradation or change from improvement to degradation of vegetation cover in the future.
- (3)
- The results of partial correlation analysis showed that at the level of interannual variation, NDVI was positively correlated with precipitation and air temperature and negatively correlated with solar radiation, population density, and nighttime lighting in most of the study areas in Hubei Province, for which 23.98% of the areas had a significant negative correlation between NDVI and population density (p < 0.05) and, furthermore, among the climatic factors.
- (4)
- The results of the factor detector showed that the influencing factors of vegetation distribution and change in Hubei Province were ranked as follows: land cover factor > human activities > topographic factor > climatic factor, and land cover type and soil type were the main drivers; mean annual air temperature and population density were the secondary drivers, and the influence of air temperature on the NDVI of the vegetation in Hubei Province increased gradually, and the precipitation had the smallest explanatory power of the NDVI characterization of the vegetation cover.
- (5)
- The results of interaction detection showed that, at full scale, the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships, which were significantly enhanced compared with the single-factor effects, and most of the factor interactions showed two-factor enhancement effects, and the nonlinear enhancements were all climate and topographic factor interactions. The strongest interaction explanatory power was between land cover type and elevation.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Li, X.; Xin, Z.; Yang, J.; Liu, J. The spatiotemporal changes and influencing factors of vegetation NDVI in the Hehuang Valley of Qinghai Province from 2000 to 2020. J. Soil Water Conserv.
**2023**, 13, 79–90. [Google Scholar] - Lai, J.-L.; Qi, S.; Cui, R.-R.; Liao, R.-E.; Tang, Y.; Li, P. Analysis of Vegetation Change and Influencing Factors in Southwest Alpine Canyon Area. Huan Jing Ke Xue = Huanjing Kexue
**2023**, 44, 6833–6846. [Google Scholar] [PubMed] - Zhu, L.; Sun, S.; Li, Y.; Liu, X.; Hu, K. Effects of Climate Change and Anthropogenic Activity on the Vegetation Greening in the Liaohe River Basin of Northeastern China. Ecol. Indic.
**2023**, 148, 110105. [Google Scholar] [CrossRef] - Yang, J.; Wan, Z.; Borjigin, S.; Zhang, D.; Yan, Y.; Chen, Y.; Gu, R.; Gao, Q. Changing Trends of NDVI and Their Responses to Climatic Variation in Different Types of Grassland in Inner Mongolia from 1982 to 2011. Sustainability
**2019**, 11, 3256. [Google Scholar] [CrossRef] - Liu, D.; Pan, P.; Fu, J.; OUYANG, X. Spatiotemporal variation and driving factor of vegetation coverage in southern Jiangxi Province, China from 2000 to 2020. Chin. J. Appl. Ecol.
**2023**, 34, 2919–2928. [Google Scholar] - Wang, J.; Wang, K.; Zhang, M.; Zhang, C. Impacts of Climate Change and Human Activities on Vegetation Cover in Hilly Southern China. Ecol. Eng.
**2015**, 81, 451–461. [Google Scholar] [CrossRef] - Liu, S.; Li, W.; Qiao, W.; Wang, Q.; Hu, Y.; Wang, Z. Effect of Natural Conditions and Mining Activities on Vegetation Variations in Arid and Semiarid Mining Regions. Ecol. Indic.
**2019**, 103, 331–345. [Google Scholar] [CrossRef] - Gao, S.; Dong, G.; Jiang, X.; Nie, T.; Guo, X. Analysis of Factors Influencing Spatiotemporal Differentiation of the NDVI in the Upper and Middle Reaches of the Yellow River from 2000 to 2020. Front. Environ. Sci.
**2023**, 10, 1072430. [Google Scholar] [CrossRef] - Shi, S.; Li, W.; Lin, X.; Zhai, Y.; Ding, Y. Spatiotemporal Variations of Vegetation NDVl and Influencing Factors in Heilonjiang Province. Res. Soil Water Conserv.
**2023**, 30, 294–305. [Google Scholar] - Zhang, L.; Cong, Z.; Zhang, D.; Li, Q. Response of Vegetation Dynamics to Climatic Variables across a Precipitation Gradient in the Northeast China Transect. Hydrol. Sci. J.
**2017**, 62, 1517–1531. [Google Scholar] [CrossRef] - Jia, X.; You, G.; McKenzie, S.; Zou, C.; Gao, J.; Wang, A. Inter-Annual Variations of Vegetation Dynamics to Climate Change in Ordos, Inner Mongolia, China. PLoS ONE
**2022**, 17, e0264263. [Google Scholar] [CrossRef] - Zhang, Y.; Zhang, L.; Wang, J.; Dong, G.; Wei, Y. Quantitative Analysis of NDVI Driving Factors Based on the Geographical Detector Model in the Chengdu-Chongqing Region, China. Ecol. Indic.
**2023**, 155, 110978. [Google Scholar] [CrossRef] - Liang, Z.; Sun, R.; Duan, Q. Spatiotemporal variation of NDVI in the Yellow River water conservation zone and its driving factors. Prog. Geogr.
**2023**, 42, 1717–1732. [Google Scholar] [CrossRef] - Pan, J.; Ren, Z.; Xu, S.; Li, P.; Zhang, X.; Xu, Y.; Ren, Z. Variation Characteristics of NDVl of Different Vegetation Types in Ningxia, China and Their Responses to Climate. J. Earth Sci. Environ.
**2023**, 45, 819–832. [Google Scholar] - Cetin, M.; Ozenen Kavlak, M.; Senyel Kurkcuoglu, M.A.; Bilge Ozturk, G.; Cabuk, S.N.; Cabuk, A. Determination of land surface temperature and urban heat island effects with remote sensing capabilities: The case of Kayseri, Türkiye. Nat. Hazards
**2024**, 120, 5509–5536. [Google Scholar] [CrossRef] - Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.-M.; Tucker, C.; Stenseth, N.C. Using the Satellite-Derived Normalized Difference Vegetation Index (NDVI) to Assess Ecological Responses to Environmental Change. Trends Ecol. Evol.
**2005**, 20, 503–510. [Google Scholar] [CrossRef] - Sun, H.; Wang, C.; Niu, Z. Analysis of the Vegetation Cover Change and the Relationship between NDVI and Environmental Factors by Using NOAA Time Series Data. J. Remote Sens. Beijing
**1998**, 2, 210–216. [Google Scholar] - Ma, M.; Wang, J.; Wang, X. Advance in the Inter-Annual Variability of Vegetation and Its Relation to Climate Based on Remote Sensing. J. Remote Sens. Beijing
**2006**, 10, 421. [Google Scholar] - Yuan, M.; Zhou, L.; Lin, A.; Zhu, H. Analyzing dynamic vegetation change and response to climatic factors in Hubei Province, China. Acta Ecol. Sin.
**2016**, 36, 5315–5323. [Google Scholar] - Zhao, W.; Li, J.; Chu, L.; Wang, T.; Li, Z.; Cai, C. Analysis of spatial and temporal variations in vegetation index and its driving force in Hubei Province in the last 10 years. Acta Ecol. Sin.
**2019**, 39, 7722–7736. [Google Scholar] - Dastigerdi, M.; Nadi, M.; Sarjaz, M.R.; Kiapasha, K. Trend analysis of MODIS NDVI time series and its relationship to temperature and precipitation in Northeastern of Iran. Env. Monit Assess
**2024**, 196, 346. [Google Scholar] [CrossRef] - Degerli, B.; Çetin, M. Evaluation from rural to urban scale for the effect of NDVI-NDBI indices on land surface temperature, in Samsun, Türkiye. Turk. J. Agric. Food Sci. Technol.
**2022**, 10, 2446–2452. [Google Scholar] [CrossRef] - Chen, T.; Xia, J.; Zou, L.; Hong, S. Quantifying the Influences of Natural Factors and Human Activities on NDVI Changes in the Hanjiang River Basin, China. Remote Sens.
**2020**, 12, 3780. [Google Scholar] [CrossRef] - Zhong, H.; Wang, H. Temporal and spatial variation of normalized vegetation index in Hubei Province from 2007 to 2016. J. Cent. China Norm. Univ. (Nat. Sci.)
**2018**, 52, 582–588. [Google Scholar] - Whetton, R.; Zhao, Y.; Shaddad, S.; Mouazen, A.M. Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI. Comput. Electron. Agric.
**2017**, 138, 127–136. [Google Scholar] [CrossRef] - Cetin, M. The effect of urban planning on urban formations determining bioclimatic comfort area’s effect using satellitia imagines on air quality: A case study of Bursa city. Air Qual. Atmos. Health
**2019**, 12, 1237–1249. [Google Scholar] [CrossRef] - Degerli, B.; Çetin, M. Using the remote sensing method to simulate the land change in the year 2030. Turk. J. Agric. Food Sci. Technol.
**2022**, 10, 2453–2466. [Google Scholar] [CrossRef] - Guerrero FJ, D.T.; Hinojosa-Corona, A.; Kretzschmar, T.G. A comparative study of NDVI values between north-and south-facing slopes in a semiarid mountainous region. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2016**, 9, 5350–5356. [Google Scholar] [CrossRef] - Fan, J.; Fan, Y.; Cheng, J.; Wu, H.; Yan, Y.; Zheng, K.; Shi, M.; Yang, Q. The Spatio-Temporal Evolution Characteristics of the Vegetation NDVI in the Northern Slope of the Tianshan Mountains at Different Spatial Scales. Sustainability
**2023**, 15, 6642. [Google Scholar] [CrossRef] - Chen, S.; Zhu, Z.; Liu, X.; Yang, L. Variation in Vegetation and Its Driving Force in the Pearl River Delta Region of China. Int. J. Environ. Res. Public Health
**2022**, 19, 10343. [Google Scholar] [CrossRef] - Li, Q.; Zhou, Y.; Wang, L.; Zuo, Q.; Yi, S.; Liu, J.; Su, X.; Xu, T.; Jiang, Y. The Link between Landscape Characteristics and Soil Losses Rates over a Range of Spatiotemporal Scales: Hubei Province, China. Int. J. Environ. Res. Public Health
**2021**, 18, 11044. [Google Scholar] [CrossRef] [PubMed] - Chen, J.; Song, X.; Zang, L.; Mao, F.; Yin, J.; Zhang, Y. Spatio-temporal association mining of intercity PM2. 5 pollution: Hubei Province in China as an example. Environ. Sci. Pollut. Res.
**2023**, 30, 7256–7269. [Google Scholar] [CrossRef] [PubMed] - Zhang, Y.; Guo, L.; Chen, Y.; Shi, T.; Luo, M.; Ju, Q.; Zhang, H.; Wang, S. Prediction of Soil Organic Carbon Based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China. Remote Sens.
**2019**, 11, 1683. [Google Scholar] [CrossRef] - Guo, H.; Wang, B.; Niu, X. A Plan for a Forest Ecosystem Observation Research Network, Based on GIS. Hubei Prov. Acta Ecol. Sin. China
**2015**, 35, 6829–6837. [Google Scholar] - Peng, S. 1-Km Monthly Precipitation Dataset for China (1901–2022); National Tibetan Plateau Data Center: Beijing, China, 2020. [Google Scholar]
- Peng, S. 1-Km Monthly Mean Temperature Dataset for China (1901–2022); National Tibetan Plateau Data Center: Beijing, China, 2019. [Google Scholar]
- Wang, K. Homogeneous Grid Dataset of Chinaese Land Surface Observation (Surface Solar Radiation, Surface Wind Speed, Relative Humidity and Land Surface Evapotranspiration); National Tibetan Plateau Data Center: Beijing, China, 2022. [Google Scholar]
- Yang, J.; Huang, X. The 30 m Annual Land Cover Datasets and Its Dynamics in China from 1985 to 2022. 2023. Available online: https://zenodo.org/records/8176941 (accessed on 25 February 2024).
- Chen, Z.; Yu, B.; Yang, C.; Zhou, Y.; Yao, S.; Qian, X.; Wang, C.; Wu, B.; Wu, J. An Extended Time Series (2000–2018) of Global NPP-VIIRS-like Nighttime Light Data from a Cross-Sensor Calibration. Earth Syst. Sci. Data
**2021**, 13, 889–906. [Google Scholar] [CrossRef] - Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc.
**1968**, 63, 1379–1389. [Google Scholar] [CrossRef] - Lie, Q.; Hu, Z.; Wang, J.; Zhang, Y.; Wu, G. Spatiotemporal Dynamics of NDVI in China from 1985 to 2015: Ecosystem Variation, Regional Differences, and Response to Climatic Factors. Acta Ecol. Sin.
**2023**, 43, 6378–6391. [Google Scholar] - Alavi, N.; King, D. Evaluating the Relationships of Inter-Annual Farmland Vegetation Dynamics with Biodiversity Using Multi-Spatial and Multi-Temporal Remote Sensing Data. Remote Sens.
**2020**, 12, 1479. [Google Scholar] [CrossRef] - Jiang, W.; Yuan, L.; Wang, W.; Cao, R.; Zhang, Y.; Shen, W. Spatio-Temporal Analysis of Vegetation Variation in the Yellow River Basin. Ecol. Indic.
**2015**, 51, 117–126. [Google Scholar] [CrossRef] - Rivas-Tabares, D.A.; Saa-Requejo, A.; Martín-Sotoca, J.J.; Tarquis, A.M. Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain. Remote Sens.
**2021**, 13, 568. [Google Scholar] [CrossRef] - Duo, A.; Zhao, W.; Gong, Z.; Zhang, M.; Fan, Y. Temporal analysis of climate change and its relationship with vegetation cover on the north China plain from 1981 to 2013. Acta Ecol. Sin.
**2016**, 37, 576–592. [Google Scholar] - Wang, F.; Ma, Y.; Darvishzadeh, R.; Han, C. Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s. Remote Sens.
**2023**, 15, 2475. [Google Scholar] [CrossRef] - Jinfeng, W.; Chengdong, X. Geodetector: Principle and Prospective. Acta Geogr. Sin.
**2017**, 72, 116–134. [Google Scholar] - Wang, J.-F.; Zhang, T.-L.; Fu, B.-J. A Measure of Spatial Stratified Heterogeneity. Ecol. Indic.
**2016**, 67, 250–256. [Google Scholar] [CrossRef] - Fan, J.; Xu, Y.; Ge, H.; Yang, W. Vegetation Growth Variation in Relation to Topography in Horqin Sandy Land. Ecol. Indic.
**2020**, 113, 106215. [Google Scholar] [CrossRef] - Feng, X.; Zeng, Z.; Jing, M.; Gao, K.; Xiao, Y. Influence of driving factors under different vegetation indices of NDVI and EVI in Guangdong-Hong Kong-Macao Greater Bay Area. J. Huazhong Agric. Univ.
**2023**, 42, 116–124. [Google Scholar] - Chen, X.; Zhao, X.; Zhang, J.; Wang, R.; Lu, J. Variation of NDVI spatio-temporal characteristics and its driving factors based on geodetector model in Horqin Sandy Land, China. Chin. J. Plant Ecol.
**2023**, 47, 1082–1093. [Google Scholar] [CrossRef] - Qu, S.; Wang, L.; Lin, A.; Yu, D.; Yuan, M.; Li, C. Distinguishing the Impacts of Climate Change and Anthropogenic Factors on Vegetation Dynamics in the Yangtze River Basin, China. Ecol. Indic.
**2020**, 108, 105724. [Google Scholar] [CrossRef] - Seto, K.C.; Fragkias, M. Quantifying Spatiotemporal Patterns of Urban Land-Use Change in Four Cities of China with Time Series Landscape Metrics. Landsc. Ecol.
**2005**, 20, 871–888. [Google Scholar] [CrossRef] - Tang, J.; Xu, M.; Mo, Y.; Wu, W.; Zhang, J.; Li, Z.; Bao, Y. Spatial and temporal variation in normal difference vegetation index of vegetation in Liaoning Province from the perspective of ecogeographic zoning. Chin. J. Appl. Ecol.
**2023**, 34, 3271–3278. [Google Scholar]

**Figure 8.**Spatial distribution of partial correlation coefficient (

**A**–

**C**) and partial correlation significance (

**D**–

**F**) between NDVI and climate factors in Hubei Province from 2000 to 2022.

**Figure 9.**Spatial distribution of dominant climate factors of NDVI change in Hubei Province from 2000 to 2022.

**Figure 10.**Population Density and night Light Index Distribution and partial correlation between NDVI and them in Hubei Province from 2000 to 2022.

**Figure 12.**Influence of driving factors (q value) for Normalized Difference Vegetation Index (NDVI) in Hubei Province. ((

**A**) q value in 2000, 2006, 2011, 2017and 2022. (

**B**) Mean value of q during 2000–2022. X1–X10, driving factors name, see Table 3. * p < 0.05; ** p < 0.01).

**Figure 13.**Interaction of driving factors in Hubei Province. (X

_{1}–X

_{10}, driving factors’ name, see Table 3. Circle size denotes the magnitude of the interaction, numbers in circle indicates q value of driving factors interaction).

Data Type | Code | Data Content | Year | Resolution | Data Source | Product |
---|---|---|---|---|---|---|

Remote sensing data | Y | MODIS NDVI | 2000–2022 | 1 km | NASA website (https://ladsweb.modaps.eosdis.nasa.gov/search/ accessed on 25 February 2024) | MOD13Q |

Climate data | X_{1} | Total annual precipitation | 2000–2022 | 1 km | National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn accessed on 25 February 2024) | Peng, S. [35] |

X_{2} | Annual mean temperature | 2000–2022 | 1 km | National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn accessed on 25 February 2024) | Peng, S. [36] | |

X_{3} | Solar radiation | 2000–2022 | 1 km | National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn accessed on 25 February 2024) | Wang, K. [37] | |

Topographic data | X_{4} | elevation | 2022 | 1 km | NASA website (https://ladsweb.modaps.eosdis.nasa.gov/search/ accessed on 25 February 2024) | SRTM 30 m DEM |

X_{5} | Slope | 2022 | 1 km | Derived from elevation data | / | |

X_{6} | Aspect | 2022 | 1 km | Derived from elevation data | / | |

Land cover data | X_{7} | Soil type | 2022 | 1 km | Usda website (https://www.usda.gov/ accessed on 25 February 2024) | OpenLandMap |

X_{8} | Land cover type | 2000–2022 | 1 km | Zenodo (https://www.zenodo.org/ accessed on 25 February 2024) | Jie Yang, & Xin Huang [38] | |

Anthropogenic data | X_{9} | Population density | 2000–2020 | 1 km | The Word Pop (https://hub.worldpop.org/ accessed on 25 February 2024) | WorldPop Global Project Population Data |

2021–2022 | 1 km | Hubei statistical yearbook (https://tjj.hubei.gov.cn/tjsj/ accessed on 25 February 2024) | Provincial statistical yearbook | |||

X_{10} | nighttime light | 2000–2022 | 1 km | National Earth System Science Data Center (http://www.geodata.cn/ accessed on 25 February 2024) | Chen Zuoqi et al. [39] |

$\mathit{\beta}$ | $\left|\mathit{Z}\right|$ | NDVI Trend Characteristics |
---|---|---|

$\beta $ > 0 | 2.58≤ | Extremely significant improvement |

1.96≤ | Significant improvement | |

<1.96 | non-significant improvement | |

0 < $\beta $ | <1.96 | non-significant degradation |

1.96≤ | Significant degradation | |

2.58≤ | Extremely significant degradation |

Interaction Types | Interaction Criteria |
---|---|

Bivariate-enhancement | $q\left({x}_{1}\cap {x}_{2}\right)>\mathrm{M}\mathrm{a}\mathrm{x}[q\left({x}_{1}\right),q\left({x}_{2}\right)]$ |

Nonlinear-enhancement | $q\left({x}_{1}\cap {x}_{2}\right)>q\left({x}_{1}\right)+q\left({x}_{2}\right)$ |

Univariate-weakening | $\mathrm{M}\mathrm{i}\mathrm{n}[q\left({x}_{1}\right),q\left({x}_{2}\right)]q\left({x}_{1}\cap {x}_{2}\right)\mathrm{M}\mathrm{a}\mathrm{x}[q\left({x}_{1}\right),q\left({x}_{2}\right)]$ |

Nonlinear-weakening | $q\left({x}_{1}\cap {x}_{2}\right)<\mathrm{M}\mathrm{i}\mathrm{n}[q\left({x}_{1}\right),q\left({x}_{2}\right)]$ |

Independent | $q\left({x}_{1}\cap {x}_{2}\right)=q\left({x}_{1}\right)+q\left({x}_{2}\right)$ |

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**MDPI and ACS Style**

Chen, P.; Pan, H.; Xu, Y.; He, W.; Yao, H.
Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022. *Forests* **2024**, *15*, 719.
https://doi.org/10.3390/f15040719

**AMA Style**

Chen P, Pan H, Xu Y, He W, Yao H.
Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022. *Forests*. 2024; 15(4):719.
https://doi.org/10.3390/f15040719

**Chicago/Turabian Style**

Chen, Peng, Hongzhong Pan, Yaohui Xu, Wenxiang He, and Huaming Yao.
2024. "Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022" *Forests* 15, no. 4: 719.
https://doi.org/10.3390/f15040719