Forestry, Faculty of
Non UBC
DSpace
Remote Sensing 17 (8): 1433 (2025)
Chen, Lv
Li, Chong
Pan, Chunyu
Yan, Yancun
Jiao, Jiejie
Zhou, Yufeng
Wang, Xiaoxian
Zhou, Guomo
Zhejiang Province, a pivotal economically developed region within China’s Yangtze River Delta, requires systematic investigation of spatiotemporal vegetation dynamics and their drivers to formulate targeted ecological protection policies and optimize vegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, this study applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vegetation responses to climate variability and human activities in Zhejiang Province from 2000 to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis (Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correlation analysis. The results show: (1) The kNDVI exhibited a significant upward trend (0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1% of vegetation changes exhibited anti-sustainability characteristics, with future vegetation degradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Against the backdrop of climate change, kNDVI demonstrated a positive partial correlation with temperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coefficient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetation dynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change and human activities.
Academic Editor: Lenio Soares GalvaoReceived: 12 February 2025Revised: 21 March 2025Accepted: 29 March 2025Published: 17 April 2025Citation: Chen, L.; Li, C.; Pan, C.; Yan,Y.; Jiao, J.; Zhou, Y.; Wang, X.; Zhou, G.Estimating the Effects of Natural andAnthropogenic Activities onVegetation Cover: Analysis ofZhejiang Province, China, from 2000 to2022. Remote Sens. 2025, 17, 1433.https://doi.org/10.3390/rs17081433Copyright: © 2025 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleEstimating the Effects of Natural and Anthropogenic Activitieson Vegetation Cover: Analysis of Zhejiang Province, China, from2000 to 2022Lv Chen 1,2, Chong Li 1,2,*, Chunyu Pan 1,3 , Yancun Yan 3, Jiejie Jiao 4, Yufeng Zhou 1,2, Xiaoxian Wang 5and Guomo Zhou 1,21 Key Laboratory of Carbon Sequestration and Emission Reduction in Agriculture and Forestry of ZhejiangProvince, Zhejiang A&F University, Hangzhou 311300, China; chentao@stu.zafu.edu.cn (L.C.);chunyu.pan@ubc.ca (C.P.); zhouyuf@zafu.edu.cn (Y.Z.); zhougm@zafu.edu.cn (G.Z.)2 School of Environmental and Resources Science, Zhejiang A&F University, Hangzhou 311300, China3 Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;yancun@student.ubc.ca4 Zhejiang Academy of Forestry, Hangzhou 310023, China; 2023202012007@stu.zafu.edu.cn5 College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;wxiaoxian2024@lzu.edn.cn* Correspondence: chongli@zafu.edu.cnAbstract: Zhejiang Province, a pivotal economically developed region within China’sYangtze River Delta, requires systematic investigation of spatiotemporal vegetation dy-namics and their drivers to formulate targeted ecological protection policies and optimizevegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, thisstudy applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vege-tation responses to climate variability and human activities in Zhejiang Province from 2000to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis(Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correla-tion analysis. The results show: (1) The kNDVI exhibited a significant upward trend(0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1%of vegetation changes exhibited anti-sustainability characteristics, with future vegetationdegradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activ-ities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Againstthe backdrop of climate change, kNDVI demonstrated a positive partial correlation withtemperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coeffi-cient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetationdynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change andhuman activities.Keywords: human activities; climate change; vegetation monitoring; google earth engine;remote sensing1. IntroductionVegetation is a vital component of terrestrial ecosystems [1], playing a crucial rolein carbon balance [2], climate regulation [3], water conservation, and biodiversity main-tenance [4]. However, it demonstrates a high degree of sensitivity to both global climatechange and human activities [5]. Current research focuses on long-term, large-scale vegeta-tion dynamic monitoring and the investigation of its driving mechanisms [6,7]. QuantifyingRemote Sens. 2025, 17, 1433 https://doi.org/10.3390/rs17081433Remote Sens. 2025, 17, 1433 2 of 21the relative contribution of climate and human activities holds significant scientific value inclarifying vegetation growth patterns, spatial heterogeneity, and the evolution of regionalecological quality [8,9].Recently, many scholars have investigated the influence of climate change and hu-man activities on plant growth within large-scale and long-term time series [10,11]. Aseries of research results indicate that temperature and rainfall are the primary drivingfactors influencing vegetation growth and change [12,13]. Diverse plant types demonstratevarying sensitivities and response characteristics to diverse climate conditions [14]. Pre-vious studies have reported that plant growth in humid zones is primarily influenced bytemperature, whereas the growth of vegetation in arid regions is primarily influenced byrainfall [15,16]. Identifying the driving factors of vegetation changes in distinct regions isvital for ecological protection. Research has found that the influence of human productionactivities on vegetation change exhibits duality [17,18]. In particular, human activitiescan promote vegetation growth and recovery, such as implementing ecological restorationprojects and improving agricultural production methods, which positively affect vegetationgrowth [19,20]. Besides, rapid urban expansion and indiscriminate deforestation haveseriously disrupted the vegetation regeneration process and become the main factors ofvegetation degradation [21–23]. However, previous studies on the spatiotemporal correla-tion between vegetation conditions and human activities in Zhejiang Province have beenrelatively scarce and have failed to precisely analyze the influences of human activitiesand climate change on plant growth [24,25]. Additionally, there have been inadequacies indistinguishing the relative impacts of climate factors on vegetation variations [26,27].The Normalized Difference Vegetation Index (NDVI) is one of the most commonlyused vegetation indices. It has achieved certain results in the study of spatiotemporalchanges in vegetation coverage in China and globally, and has been widely used in relatedfield research on vegetation coverage monitoring [28,29]. However, NDVI exhibits lowsensitivity and lacks response to photosynthesis in high-density regions, demonstrating asaturation phenomenon that impacts the accuracy of vegetation change detection [30,31].Camps-Valls et al. [32], 2021, proposed the kNDVI based on the principles of artificialintelligence learning and kernel method theory and compared its performance with NDVIand Near-Infrared Reflectance vegetation (NIRv). They believed that kNDVI outper-formed NDVI in terms of biological communities, climate zones, and phenological cycles.Compared to traditional vegetation indices, kNDVI demonstrates higher stability androbustness, with a greater ability to accurately capture vegetation growth changes andextract vegetation information, thereby providing a valuable precise reflection of plantcondition [33]. Numerous researches confirmed that kNDVI is superior in representingvegetation changes in natural ecosystems [34,35].As an important part of the Yangtze River Delta Economic Zone, Zhejiang Provincehas become a sensitive region under the gradual global climate change background due toits unique geographical location and obvious climate differences [36]. Previous studies onvegetation growth changes in Zhejiang Province have predominantly utilized the EnhancedVegetation Index (EVI) or the Moderate Resolution Imaging Spectroradiometer (MODIS)NDVI [37,38], but this method underestimates the accuracy of vegetation monitoring inareas with high vegetation coverage. In addition, there are still deficiencies in using kNDVIto explain the coupling mechanism and attribution analysis of the dynamic changes ofvegetation in Zhejiang Province under the combined influence of complex climate andhuman activities. Therefore, this study conducted a spatiotemporal evolution investigationof kNDVI, aiming to overcome the saturation effect of traditional vegetation indices inareas with high vegetation coverage, improve the accuracy of vegetation monitoring, andprovide new ideas for quantifying the driving attribution of climate change and humanRemote Sens. 2025, 17, 1433 3 of 21activities on vegetation dynamics. This study also fills a research gap and expands theexisting knowledge base in this area.In summary, this study established a kNDVI time series for Zhejiang Province from2000 to 2022 using MOD09GA data and the Google Earth Engine (GEE) system. Theresearch analyzed the spatiotemporal evolution laws of vegetation cover in ZhejiangProvince between 2000 and 2022 and examined the contributions and effects of climateshifts and human activities on fluctuations in kNDVI of vegetation in this province. Theresearch findings have enhanced our understanding of the laws governing vegetationdynamic changes in Zhejiang Province and provide support and reference for formulatingecological plans and policies in the province.2. Materials and Methods2.1. Research AreaZhejiang Province is located at 27◦01′N–31◦10′N, 118◦01′E–123◦08′E and is an eco-nomically developed province in eastern China. Situated in the Yangtze River Delta regionin southeastern China, Zhejiang Province covers an area of 105,500 square kilometers andhas a population of 55 million [39]. The terrain is predominantly mountainous, hilly, plain,and basin, with higher elevations in the southwest and lower elevations in the northeast(Figure 1) [40]. During the study period (2000–2022), the average annual temperature was17.2 ◦C, fluctuating within the range of 15.6–18.3 ◦C; the average annual precipitation was1619.4 mm, fluctuating within the range of 1190 mm–2000 mm [41,42].Remote Sens. 2025, 17, x FOR PEER REVIEW 3 of 22 monitoring, and provide new ideas for quantifying the driving attribution of climate change and human activities on vegetation dynamics. This study also fills a research gap and expands the existing knowledge base in this area. In summary, this study established a kNDVI time series for Zhejiang Province from 2000 to 2022 using MOD09GA data and the Google Earth Engine (GEE) system. The re-search analyzed the spatiotemporal evolution laws of vegetation cover in Zhejiang Prov-ince between 2000 and 2022 and examined the contributions and effects of climate shifts and human activities on fluctuations in kNDVI of vegetation in this province. The research findings have enhanced our understanding of the laws governing vegetation dynamic changes in Zhejiang Province and provide support and reference for formulating ecolog-ical plans and policies in the province. 2. aterials and ethods 2.1. Research rea hejiang Province is located at 27°01′ –31°10′ , 118°01′E–123°08′E and is an eco-no ically developed province in eastern hina. Situated in the angtze iver elta region in so theastern hina, hejiang rovince covers an area of 105,500 sq are kilo eters an has a o lation of 55 illion [39]. The terrain is predo inantly ountainous, hilly, lain, a basi , it ig er elevatio s i t e so t est a lo er elevatio s i t e ort east ( i re 1) [40]. ri t e st eri (2000–2022), t e a era e a al te erat re as 17.2 ° , fl ct ati it i t e ra e f 15.6–18.3 ° ; t e a era e a al reci itati as 1619.4 , fl ct ati it i t e ra e f 1190 –2000 [41,42]. Figure 1. Zhejiang Province’s geographical location and terrain conditions. 2.2. Data and Methods The technical approach of this study is illustrated in Figure 2; (I) kNDVI data were calculated and selected based on the MOD09GA dataset within the Google Earth Engine (GEE) system, and kNDVI, temperature, rainfall, and human footprint activity data were batch processed using ArcGIS version 10.8 software; (II) According to the kNDVI data of Figure 1. Zhejiang Province’s geographical location and terrain conditions.2.2. Data and MethodsThe technical approach of this study is illustrated in Figure 2; (I) kNDVI data werecalculated and selected based on the MOD09GA dataset within the Google Earth Engine(GEE) system, and kNDVI, temperature, rainfall, and human footprint activity data werebatch processed using ArcGIS version 10.8 software; (II) According to the kNDVI data ofZhejiang from 2000 to 2022, Sen MK analysis was employed to research the interannualand spatiotemporal change trend of vegetation in the study area; (III) The influence ofRemote Sens. 2025, 17, 1433 4 of 21climate shift and human activities on kNDVI in Zhejiang was explored using multivariateregression remainder analysis, and the potential main driving factors affecting kNDVIchanges in Zhejiang Province were analyzed under the climate change background usingcorrelation analysis and partial correlation analysis.Remote Sens. 2025, 17, x FOR PEER REVIEW 4 of 22 Zhejiang from 2000 to 2022, Sen MK analysis was employed to research the interannual and spatiotemporal change trend of vegetation in the study area; (III) The influence of climate shift and human activities on kNDVI in Zhejiang was explored using multivariate regression remainde an lysis, d the poten ial mai driving factors affe ing kNDVI changes in Zhejiang Prov nce were analyzed u der the climate change background using correlation analysis and parti l correlation analysis. Figure 2. Technical route of this research. Maximum Value Composition (MVC) represents the high-est value synthesis; Climatic Research Unit (CRU) stands for temperature and rainfall information that comes from the UK’s National Center; Tmp and Pre represent temperature and precipitation data, respectively. 2.2.1. Data Source and Preprocessing The MODIS MOD09GA data offered by the GEE platform (Table 1), the remote sensing data with cloud cover ≤ 10% of Zhejiang Province from April to October (the vegetation growing season) for all images from 2000 to 2022, were reserved and preprocessed, including, clipped, projection, and resampling. The maximum value compositing [43] approach was employed to determine the kNDVI of Zhejiang Province for each year from 2000 to 2022 (a total of 23 images). Temperature and rainfall information were separately downloaded from the British National Center for Atmospheric Science (NCAS) 1km resolution monthly average data (Table 1). The temperature and precipitation data from 2000 to 2022 were preprocessed (a total of 46 images). This study selects the human activity footprint, calculated as a weighted average of the built environment, roads, cultivated land, railways, navigable waterways, pastures, population density, and nighttime light, to reflect human activities [44]. ArcGIS version 10.8 software was utilized to standardize all data to a 1km resolution for uniformity in spatial resolution. The data employed in this research are shown in Table 1. Figure 2. Technical route of this research. Maximum Value Composition (MVC) represents the highestvalue synthesis; Climatic Research Unit (CRU) stands for temperature and rainfall informationthat comes from the UK’s National Center; Tmp and Pre represent temperature and precipitationdata, respectively.2.2.1. Data Source and PreprocessingThe MODIS MOD09GA data offered by the GEE platform (Table 1), the remote sensingdata with cloud cover ≤ 10% of Zhejiang Province from April to October (the vegetationgr wing season) for all images from 2000 to 2022, were reserved and pr processed, includ-ing, clipped, projection, and resampling. The maximum value compositing [43] approachwas employed to determine the kNDVI of Zhejiang Province for each year from 2000 to2022 (a total of 23 images). Temperature and rainfall information were separately down-loaded from the British National Center for Atmospheric Science (NCAS) 1km resolutionmonthly average data (Table 1). The temperature and precipitation data from 2000 to 2022were preprocessed (a total of 46 images). This study selects the human activity footprint,calculated as a weighted average of the built environment, roads, cultivated land, railways,navigable waterways, pastures, population density, and nighttime light, to reflect humanactivities [44]. ArcGIS version 10.8 software was utilized to standardize all data to a 1 kmresolution for uniformity in spatial resolution. The data employed in this research areshown in Table 1.Remote Sens. 2025, 17, 1433 5 of 21Table 1. Data source and explanation.Dataset Type Spatial Res-olution/mTime-Resolution/Year Data SourceMODIS MOD09GA Raster 500 2000–2022 https://www.usgs.gov/ (accessed on 15January 2025)Temperature dataset Raster 1000 1991–2022 https://crudata.uea.ac.uk/cru/data/hrg/(accessed on 15 January 2025)Precipitation dataset Raster 1000 1991–2022 https://crudata.uea.ac.uk/cru/data/hrg/(accessed on 15 January 2025)Human footprintdataset Raster 1000 2000–2022https://doi.org/10.6084/m9.figshare.16571064(accessed on 15 January 2025)2.2.2. Kernel Normalized Vegetation IndexMOD09GA data are commonly used to monitor surface reflectivity, especially invegetation changes and ecosystem research, providing essential data resources for remotesensing research and environmental monitoring [45]. The kNDVI can be determinedthrough the following formula:kNDVI =k(n , n)− k(n , r)k(n , n)+k(n , r)(1)Here, n and r represent the near-infrared (NI R) band and red band reflectivity, respectively,and k presents the kernel function. k (n , r) = exp(−(n− r)2)/2σ2, σ is the spectraldistance between NIR and red [32]. After sorting out Formula (1), we get Formula (2):kNDVI =1− k(n, r)1+ k(n, r)= tan h((n− r)22σ)(2)tanh is the hyperbolic tangent function, given that σ is a long-scale parameter that linearlyscales with the mean reflectance of NIR and Red, a reasonable approach is to take theaverage of σ, denoted as σ = 0.5(n + r), the calculation method was transformed intoFormula (3):kNDVI = tanh(NDVI2)(3)2.2.3. Trend AnalysisIn the field of academic research, Theil Sen trend approach is a robust non-parametricslope estimation approach (Equation (4)). The core idea of that is to predict the trend ofdata based on computing the median value, which is not affected by outliers or invalidvalues [46].B= Median( kNDVIj − kNDVIij− i), 2000 < i < j < 2022 (4)Here, B refers to the interannual trend of kNDVI. If its value range is above 0, it suggestsan increasing trend in kNDVI; if its value is equal to 0, it signifies no change in the trend ofkNDVI; when it is below 0, it suggests that the kNDVI value is decreased.The Mann Kendall (MK) significance test (Equation (5)) is a widely used nonpara-metric statistical method for trend prediction in time series data. It does not need specificassumptions about the data distribution when conducting trend tests and demonstratesgreater robustness to missing values and outliers in the sample [47]. This approach isRemote Sens. 2025, 17, 1433 6 of 21commonly used to assess the significance of dynamic changes in vegetation over a longtime series.S =n−1∑i=1n∑j=i+1sign(kNDVIj − kNDVIi)(5)where sign( ) is called the sign function, with the expression given below:sign(kNDVIj − kNDVIi)=1 sign(kNDVIj − kNDVIi)> 00 sign(kNDVIj − kNDVIi)= 0−1 sign(kNDVIj − kNDVIi) < 0 (6)Using the test statistic G, the range of G is (−∞, +∞):G =S−1√Var(S)(S > 0)0(S = 0)S+1√Var(S)(S < 0)(7)Var(S) =n(n − 1) (2 n+5)18(8)In which Var( ) is a function that quantifies the variability of a random variable, where nrepresents the quantity of years in the research period. In this study, the numerical value ofn is 23.At a given significance level (p level), the Sen slope evaluation and Mann Kendall(MK) test can be integrated to determine the significance of the increasing trend in thevegetation kNDVI. In this study, we select p < 0.05 as the significance level under a 95%confidence interval (the critical value for a two-sided test is ±1.96) [48], meaning that at a5% significance level, we would reject the null hypothesis. According to the results of thesignificance test, the trend and significance of vegetation kNDVI are categorized into fivecategories, as shown in the following Table 2.Table 2. Theil–Sen median trend analysis result (p < 0.05).B G Trend FeaturesB > 0G > 1.96 Significantly increasedG < 1.96 Slightly increasedB = 0 G = 0 Basically stableB < 0G > −1.96 Slightly reducedG < −1.96 significantly reduced2.2.4. Hurst Predictive AnalyticsThe Hurst index is an indicator that measures the long-term memory ability of dataand can also reflect the duration or anti-sustainability of the kNDVI trend over a periodof time [49]. This research applies the R/S analysis method to determine the Hurst index,which is used to predict the development trend of vegetation kNDVI (denoted as ∆kNDVIin the formula) in Zhejiang Province [50]. The principle is as follows:Time series (t), then there is ∆kNDVI = kNDVIt − kNDVIt−1, where kNDVIt rep-resents the observation value at time t. For every positive integer m, (m ≥ 1), there is amean sequence.Mean series:∆kNDVI(m) =1m∑mt=1 ∆kNDVIt , m = 1, 2, . . . , (9)Remote Sens. 2025, 17, 1433 7 of 21Cumulative difference sequence:χ(t,m) =∑mt=1(∆kNDVI − ∆kNDVI(m)) , 1 ≤ t ≤ m (10)Range sequence R(m):R(m) = max1≤t≤mχ(t,m) − min1≤t≤mχ(t,m), m = 1, 2, . . . , (11)Standard deviation sequence S (m):S(m) =[1m∑mt=1(∆kNDVI − ∆kNDVI(m))2] 12m = 1, 2, . . . , (12)where, R(m), S(m), m has the following relationship:R(m)S(m)= cmH (13)Each R(m) is standardized by S(m), then, R/S is defined as:log(R(m)S(m))= logc+Hlogm (14)R(m)S(m)represents the rescaled range; H represents the Hurst index, ranging form (0–1), and crepresents the constant quantity. The estimated value of the Hurst index, H, can be gainedthrough the least squares approach [51]. When 0.5 < H < 1, it indicates that the prospectivechange tendency of ∆kNDVI is the same as the previous change trend. When 0 < H < 0.5,it indicates that the future change trend of ∆kNDVI is anti-sustaining. When H = 0.5, itindicates that it is randomly arranged on the time scale, and it is impossible to judge thefuture change trend of ∆kNDVI [52].2.2.5. Multiple Regression Residual AnalysisA multiple linear regression model is constructed with kNDVI taken as the inde-pendent variable and temperature (T) and precipitation (P) as the dependent variablesto explain the impact of climatic factors on kNDVI (Equation (15)). The residual valuekNDVIHA is obtained by subtracting the predicted value (kNDVICC) from the actual valuekNDVIobs observed through remote sensing. kNDVIHA is utilized to represent the effect ofhuman actions on the dynamic changes of vegetation kNDVI (Equation (16)) [53].kNDVICC = a× T + b×P+c (15)kNDVIHA = kNDVIobs − kNDVICC (16)where a, b, and c represent the model coefficients; T represents the ◦C; and P represents theaccumulated precipitation (mm).2.2.6. Correlation Analysis and Partial Association AnalysisThe Pearson parameter [54] are used to research the response of kNDVI to humanactivities:rXY =∑Ni=1 [(Xi −-X)(Yi −-Y)]√∑Ni=1 (Xi −-X)2√∑Ni=1 (Yi −-Y)2(17)Remote Sens. 2025, 17, 1433 8 of 21In which rXY refers to the level of association in x and y; i represents the year; Xi and Yimean the values of the two variables X and Y. Moreover,-X and Y refer to the sample meansof X and Y.In the context of multiple influencing factors, other variables often influence the changeof a certain variable. Conditional correlation analysis can be employed to analyze kNDVIwith a single meteorological factor to eliminate the influence of other meteorological factorson kNDVI [55]:Rxy. z1 z2...zi =Rxy. z1 z2 ...zi−1 − Rxzi. z1 z2 ...zi−1 Ryzi. z1z2 ...zi−1√(1− R2xzi. z1z2 ...zi−1)(1− R2yzi. z1z2 ...zi−1) (18)where Rxy. z1 z2 ...zi is the partial association parameter, conditional on the control variablez1 z2 . . . zi. Rxzi. z1 z2 ...zi−1 represents the partial association parameter in x and zi, condi-tional on the control variable z1 z2 . . . zi−1. Ryzi. z1z2 ...zi−1 represents the partial correlationparameter in y and zi, controlling for the impact of variables z1 z2 . . . zi−1.3. Results3.1. NDVI Saturation VerificationThere is a nonlinear relationship between NDVI and green biomass, and vegetationcover tends to saturate in areas with dense vegetation, making accurate capture of growthdynamics challenging. Therefore, this study selected the kNDVI during the vegetationgrowing season to investigate vegetation cover changes from 2000 to 2022 (Figure 3), andcompared its resistance to saturation with NDVI during the same period. The resultsindicated that during the vegetation growing season, which kNDVI exhibited better anti-saturation ability in high vegetation coverage areas and more accurately reflected vegetationdynamic changes.Remote Sens. 2025, 17, x FOR PEER REVIEW 8 of 22 Rxy. z1 z2...zi = Rxy. z1 z2...zi-1-Rxzi. z1 z2...zi-1Ryzi. z1z2...zi-1ට൫1-Rxzi. z1z2...zi-12 ൯൫1-Ryzi. z1z2...zi-12 ൯ (18)where 𝑅௫௬. ௭భ ௭మ...௭೔ is the partial association parameter, conditional on the control variable 𝑧ଵ 𝑧ଶ. . . 𝑧௜. 𝑅௫௭௜. ௭భ ௭మ...௭೔షభ represents the partial association parameter in x and 𝑧௜, condi-tional on the control variable 𝑧ଵ 𝑧ଶ. . . 𝑧௜ିଵ. R௬௭௜. ௭భ௭మ...௭೔షభ represents the partial correlation parameter in y and 𝑧௜, controlling for the impact of variables 𝑧ଵ 𝑧ଶ. . . 𝑧௜ିଵ. 3. Results 3.1. NDVI Saturation Verification There is a nonlinear relationship between ND I an gre i cover tends to saturate in areas with dense vegetatio , i t dynamics challenging. Therefore, t is st s l t growing season to investigate vegetation cover changes fr compared its resistance t ith NDVI during the same period. The result in-dicated that uring the e etation gro ing season, hich k VI exhibited better anti-saturation ability in high vege ation coverage are s and more accurately r fl cted vegeta-tion dynamic changes. Figure 3. Verification of vegetation NDVI saturation in Zhejiang Province. 3.2. Spatiotemporal Evolution Characteristics of kNDVI The single linear regression model was used to explore the spatiotemporal evolution trend of vegetation in Zhejiang Province (Figure 4). The kNDVI in Zhejiang Province in-creased from 0.62 in 2000 to 0.66 in 2022, showing a total linear fitting difference of 0.04 (Figure 4a). The minimum value of 0.62 occurred in 2008, while the highest value of 0.67 was recorded in 2015. Across the temporal dimension, the average kNDVI in Zhejiang Province demonstrated a progressive upward trajectory with an annual growth rate of 0.1×10-2y-1 (𝑝 ൏ 0.01). The increases were particularly rapid during 2014–2015 and 2019–2021, whereas there were gradual declines during 2003–2008 and 2017–2019. From the perspective of spatial distribution, kNDVI exhibited an increasing trend in 61.47% of the study area (Figure 4b). This upward trend was accompanied by 30.42% of the area showing an obvious upward trend, primarily located in the mountainous and hilly regions of Quzhou City and Lishui City, and the eastern areas of Jinhua City and Shaoxing City. Conversely, 28.37% of the study area exhibited a decreased trend, which Figure 3. Verification of vegetation NDVI saturation in Zhejiang Province.3.2. Spatiotemporal Evolution Characteristics of kNDVIThe single linear regression model was used to explore the spatiotemporal evolutiontrend of vegetation in Zhejiang Province (Figure 4). The kNDVI in Zhejiang Provinceincreased from 0.62 in 2000 to 0.66 in 2022, showing a total linear fitting difference of 0.04(Figure 4a). The minimum value of 0.62 occurred in 2008, while the highest value of 0.67was recorded in 2015. Across the temporal dimension, the average kNDVI in ZhejiangProvince demonstrated a progressive upward trajectory with an annual growth rate ofRemote Sens. 2025, 17, 1433 9 of 210.1 × 10−2y−1 (p < 0.01). The increases were particularly rapid during 2014–2015 and2019–2021, whereas there were gradual declines during 2003–2008 and 2017–2019.Remote Sens. 2025, 17, x FOR PEER REVIEW 9 of 22 was more pronounced in urban areas. Within these urban areas, 13.98% of the region showed an obvious downward trend, primarily distributed in the eastern part of Huzhou City, Jiaxing City, and the northeastern part of Hangzhou City. Additionally, this down-ward trend was also observed in central areas such as Jinhua City, as well as in Ningbo City, the eastern part of Taizhou City, and the eastern part of Wenzhou City in the eastern coastal regions. Figure 4. Characteristics of Spatiotemporal evolution of kNDVI in Zhejiang Province from 2000 to 2022. (a) Trend changes of kNDVI in Zhejiang Province from 2000 to 2022; (b) Spatial shift trend and area statistics of kNDVI in Zhejiang from 2000 to 2022. 3.3. Hurst Index and Sustainability Analysis of Future Changes To better understand the sustainability of the prospective change trend of kNDVI in Zhejiang, we determined the Hurst index of kNDVI at the pixel level in Zhejiang and combined it with the Sen trend change results that passed the significance test. This ap-proach provided insights into the relationship between the prospective change trend of kNDVI and past changes in vegetation (Figure 5). The Hurst index of kNDVI in Zhejiang ranges from 0.1279 to 0.9321, with a mean value of 0.4348 (Figure 5a). The kNDVI area with a Hurst index less than 0.5 constituteds 69.06% of the total area, while the kNDVI area with a Hurst index greater than 0.5 constituteds 20.76% of the province’s area. These results indicate that the overall changes in vegetation in Zhejiang Province exhibit anti-sustainable development characteristics. The closer the Hurst index is to 0.5, the weaker the association is between the duration of prospective vegetation changes and past changes. Based on the trend and intensity of persistence [56], we classify the Hurst index into four categories: high reverse persistence (0.35 < H < 0.5), low-intensity reverse persis-tence (0.5 < H < 0.65), low-intensity co-directional persistence (0.65 < H < 1), and high con-tinuity in the same orientation (0.65 < H < 1). By integrating the Hurst index with the kNDVI change trend, we can obtain the future development trend and intensity of kNDVI (Table 3). Figure 4. Characteristics of Spatiotemporal evolution of kNDVI in Zhejiang Province from 2000 to2022. (a) Trend changes of kNDVI in Zhejiang Province from 2000 to 2022; (b) Spatial shift trend andarea statistics of kNDVI in Zhejiang from 2000 to 2022.From the perspective of spatial distribution, kNDVI exhibited an increasing trend in61.47% of the study area (Figure 4b). This upward trend was accompanied by 30.42% of thearea showing an obvious upward trend, primarily located in the mountainous and hillyregions of Quzhou City and Lishui City, and the eastern areas of Jinhua City and ShaoxingCity. Conversely, 28.37% of the study area exhibited a decreased trend, which was morepronounced in urban areas. Within these urban areas, 13.98% of the region howed anobvi us downward trend, primarily distri uted in the eastern part of Huzhou City, JiaxingCity, and the northeastern p rt of Hangzhou City. Additionally, this dow ward trend wasals observed in central areas such as Jinhua City, as well as in Ningbo City, the easternpart of Taizhou City, d the ea ern part of Wenzhou City in the eastern coastal regions.3.3. Hurst Index and Sustainability Analysis of Future ChangesTo better underst nd the su tainability of the p ospective change tr nd of kNDVIin Zhejiang, w det rmined the Hurst index of kNDVI at the pixel lev l in Zhejiang andcombined it with the Sen trend change e ults that passed the significance test. Thisapproach provided insights into the relationship between the prospecti change trend ofkNDVI and past cha g s in vegetation (Figure 5). The Hurst index of kNDVI in Zhejiangra ges from 0.1279 to 0.9321, with a mean value of 0.4348 (Figure 5a). The kNDVI ar a witha Hurst ind x less th 0.5 co stituteds 69.06% of the total area, whil the kNDVI area witha Hurst index greater than 0.5 constituteds 20.76% of the province’s area. These resultsindicate that the overall changes in vegetation in Zhejiang Province exhibit anti-sustainabledevelopment characteristics. The closer the Hurst index is to 0.5, the weaker the associationis between the duration of prospective vegetation changes and past changes. Based on thetrend and intensity of persistence [56], we classify the Hurst index into four categories:high reverse persistence (0.35 < H < 0.5), low-intensity reverse persistence (0.5 < H < 0.65),Remote Sens. 2025, 17, 1433 10 of 21low-intensity co-directional persistence (0.65 < H < 1), and high continuity in the sameorientation (0.65 < H < 1). By integrating the Hurst index with the kNDVI change trend,we can obtain the future development trend and intensity of kNDVI (Table 3).Remote Sens. 2025, 17, x FOR PEER REVIEW 10 of 22 According to Figure 5 and Table 3, Zhejiang Province’s development trend is pro-jected to follow a benign trajectory in the foreseeable future, with 58.61% of the total area experiencing benign improvement (Figure 5b). Continuous improvement areas account for 10.97%, comprising 10.5% of weak sustainable improvement and 0.46% of strong sus-tainable improvement. Approximately 17.18% of previously deteriorated regions are ex-pected to transition to an improving trend, with 13.83% showing anti-weakness degrada-tion and 3.35% experiencing anti-strong continuous decline. The proportion of continu-ously degraded areas is 8.7%, with 1.16% exhibiting strong persistent degradation and 7.54% showing weak persistent decline. Regions that have improved but may deteriorate in the future account for 47.65%, with 11.82% showing anti-strength sustained improve-ment and 35.83% experiencing anti-weakness continued improvement. In Zhejiang Prov-ince, continuously improving areas are mainly located in western Ningbo, northern and southwestern Taizhou, northeastern Wenzhou, and eastern Jinhua, with this area ex-pected to expand in the future. Distribution patterns of continuously degraded areas in Zhejiang Province show no clear trend, but strong sustained degradation is anticipated to account for a small proportion, particularly in urban areas. This indicates that human ac-tivities have regulated ecosystems, such as forests. Regions transitioning from degrada-tion to improvement are more prominent in coastal urban fringes, suggesting potential increases in vegetation coverage in coastal areas. However, past progress in certain re-gions may lead to future deterioration, including northeastern Hangzhou, southwestern Jiaxing, southeastern Huzhou, northeastern Quzhou, and coastal areas such as eastern Ningbo and southeastern Taizhou. In the northeastern part of Zhejiang Province and coastal urban circles, urban development may impact vegetation coverage. The western regio is dominated by tural forests, with limited human impact o this area. These cha ges may result from shifts in climate factors, but changes in climate fa tors could be indirectly influenced by human activities. Figure 5. (a) Hurst index of vegetation in Zhejiang Province kNDVI. (b) Future Trend Changes of Vegetation in Zhejiang Province. Figure 5. (a) Hurst index of vegetation in Zhejiang Province kNDVI. (b) Future Trend Changes ofVegetation in Zhejiang Province.Table 3. Future change trends of kNDVI in Zhejiang Province.Progression Direction Trends in the Future PercentageKeeping degradation Strong persistent degeneration 1.16%Weak persistent degeneration 7.54%Previous improved, future will decreased Anti-strength continuous improvement 11.82%Anti-weakness and continuous improvement 35.83%Previous degradated and will improvein futureAnti-weak persistent degradation 13.83%Anti-strong persistent degradation 3.35%Continuous improvement Weak sustained improvement 10.5%Strong continuous improvement 0.46%Basically no change Basically unchanged 5.35%According to Figure 5 and Table 3, Zhejiang Province’s development trend is projectedto follow a benign trajectory in the foreseeable future, with 58.61% of the total area expe-riencing benign improvement (Figure 5b). Continuous improvement areas account for10.97%, comprising 10.5% of weak sustainable improvement and 0.46% of strong sustain-able improvement. Approximately 17.18% of previously deteriorated regions are expectedto transition to an improving trend, with 13.83% showing anti-weakness degradationand 3.35% experiencing anti-strong continuous decline. The proportion of continuouslydegraded areas is 8.7%, with 1.16% exhibiting strong persistent degradation and 7.54%showing weak persistent decline. Regions that have improved but may deteriorate in thefuture account for 47.65%, with 11.82% showing anti-strength sustained improvement andRemote Sens. 2025, 17, 1433 11 of 2135.83% experiencing anti-weakness continued improvement. In Zhejiang Province, contin-uously improving areas are mainly located in western Ningbo, northern and southwesternTaizhou, northeastern Wenzhou, and eastern Jinhua, with this area expected to expandin the future. Distribution patterns of continuously degraded areas in Zhejiang Provinceshow no clear trend, but strong sustained degradation is anticipated to account for a smallproportion, particularly in urban areas. This indicates that human activities have regu-lated ecosystems, such as forests. Regions transitioning from degradation to improvementare more prominent in coastal urban fringes, suggesting potential increases in vegetationcoverage in coastal areas. However, past progress in certain regions may lead to future de-terioration, including northeastern Hangzhou, southwestern Jiaxing, southeastern Huzhou,northeastern Quzhou, and coastal areas such as eastern Ningbo and southeastern Taizhou.In the northeastern part of Zhejiang Province and coastal urban circles, urban developmentmay impact vegetation coverage. The western region is dominated by natural forests, withlimited human impact on this area. These changes may result from shifts in climate factors,but changes in climate factors could be indirectly influenced by human activities.3.4. Analysis of Driving Factors of kNDVIThe dynamic changes of plant cover are jointly influenced by climate and humanactivities, resulting in the spatial heterogeneity of kNDVI in Zhejiang Province (Figure 6). Toeliminate the influence of water bodies on vegetation, the area of water bodies accounts for5.57% (Figure 6a). Influenced by climate factors, the proportion of regions with improvedvegetation is 45.03%, located in the northern parts of Lishui, southern parts of Wenzhou,southeastern parts of Shaoxing, and eastern parts of Jinhua (Figure 6a). The proportionof regions where vegetation remains relatively stable is 31.53%, located in the southernparts of Lishui, northern parts of Quzhou, and western parts of Ningbo (Figure 6a). Theproportion of regions where vegetation exhibits a declining trend is 23.44%, located in thenortheastern parts of Hangzhou, Jiaxing, eastern parts of Huzhou, Jinhua, northern partsof Ningbo, southeastern parts of Taizhou, and eastern parts of Wenzhou. Degradation ismore pronounced in the urban areas of Hangzhou and its surroundings (Figure 6a).From the perspective of the impact of human activities, the area with improvedvegetation accounts for 59.57% of the total area and is scattered in the mountainous andhilly areas of Quzhou, Lishui, western Hangzhou, Shaoxing, eastern Jinhua, westernTaizhou, and southern Wenzhou (Figure 6b). In contrast, the area with stable vegetationaccounts for 27.73% of the total area and is scattered in the southeastern regions of Jiaxing,eastern Taizhou, and southeastern Wenzhou (Figure 6b). The area with degraded vegetation,accounting for 12.70% of the total study area, is mainly concentrated in the northeasternregions of Hangzhou, Jiaxing, and Huzhou, as well as the central regions of Quzhou andJinhua (Figure 6b). Degradation is more pronounced in urban areas within these regions.Remote Sens. 2025, 17, 1433 12 of 21Remote Sens. 2025, 17, x FOR PEER REVIEW 12 of 22 Figure 6. Spatial distribution of the impact of climate change and human activities on kNDVI in Zhejiang Province from 2000 to 2022. (a) The effect of climate change on kNDVI in Zhejiang; (b) The effect of human activities on kNDVI in Zhejiang. 3.5. Relative Contributions of Climatic Variations and Anthropogenic Activities to kNDVI Figure 7 demonstrates the impact of climate and human activity data on vegetation change in Zhejiang Province. The positive contribution of climate change to the kNDVI in Zhejiang Province is 52.05%, with 5.79% of regions showing a contribution rate exceeding 80%, primarily located in Wenzhou and eastern Lishui (Figure 7a). In contrast, the nega-tive contribution of climate change to kNDVI in Zhejiang Province amounts to 47.95%, predominantly concentrated in southern Jiaxing, northern Huzhou, northeastern Hang-zhou, northern Ningbo, eastern Taizhou, eastern Wenzhou, central Jinhua, and western Quzhou (Figure 7b). The positive contribution of human activities to kNDVI in Zhejiang Province is 59.47%, and 29.98% of the regions exhibit a contribution rate exceeding 80%, mainly distributed in the mountainous and hilly areas of Quzhou, western Hangzhou, Lishui, Shaoxing, and western Ningbo (Figure 7b). The impact of human activities on kNDVI in Zhejiang Province accounts for 40.53%, with the majority of the contribution concentrated in eastern Huzhou, southern Jiaxing, northeastern Hangzhou, northern Ningbo, eastern Taizhou, and Wenzhou. Therefore, human activities have a more signifi-cant impact on vegetation change in Zhejiang Province than does climate change. Figure 6. Spatial distribution of the impact of climate change and human activities on kNDVI inZhejiang Province from 2000 to 2022. (a) The effect of climate change on kNDVI in Zhejiang; (b) Theeffect of human activities on kNDVI in Zhejiang.3.5. Relative Contributions of Climatic Variations and Anthropogenic Activities to kNDVIFigure 7 demonstrates the impact of climate and human activity data on vegetationchange in Zhejiang Province. The positive contribution of climate change to the kNDVI inZhejiang Province is 52.05%, with 5.79% of regions showing a contribution rate exceeding80%, primarily located in Wenzhou and eastern Lishui (Figure 7a). In contrast, the negativecontribution of climate change to kNDVI in Zhejiang Province amounts to 47.95%, pre-dominantly concentrated in southern Jiaxing, northern Huzhou, northeastern Hangzhou,northern Ningbo, eastern Taizhou, eastern Wenzhou, central Jinhua, and western Quzhou(Figure 7b). The positive contribution of human activities to kNDVI in Zhejiang Provinceis 59.47%, and 29.98% of the regions exhibit a contribution rate exceeding 80%, mainlydistributed in the mountainous and hilly areas of Quzhou, western Hangzhou, Lishui,Shaoxing, and western Ningbo (Figure 7b). The impact of human activities on kNDVI inZhejiang Province accounts for 40.53%, with the majority of the contribution concentratedin eastern Huzhou, southern Jiaxing, northeastern Hangzhou, northern Ningbo, easternTaizhou, and Wenzhou. Therefore, human activities have a more significant impact onvegetation change in Zhejiang Province than does climate change.Remote Sens. 2025, 17, 1433 13 of 21Re ote Sens. 2025, 17, x FOR PEER REVIEW 13 of 22 Figure 7. Spatial distribution of the relative contributions of climate change and human actions to kNDVI in Zhejiang. (a) The relative contribution of climate change to kNDVI. (b) That of human activities to kNDVI. 3.6. Spatiotemporal Evolution Laws of kNDVI Associated with Climate Change Figure 8 demonstrates that the climate in Zhejiang Province has become warmer and wetter from 2000 to 2022, which is conducive to vegetation growth, recovery, and repro-duction. During this period, the average annual precipitation was 1619 mm, increasing at a rate of 0.76 × 10-1mm·y-1. The precipitation varied between 1190 mm and 2039 mm (Fig-ure 8a). Additionally, the average temperature during this period was 17.2 °C, rising at a rate of 2.52 × 10-2℃·y-1. The average temperature increased from 16.6 °C to 18 °C (Figure 8b). Figure 8. Rainfall and temperature changes in Zhejiang from 2000 to 2022. (a) Rainfall variation trend in Zhejiang from 2000 to 2022. (b) Temperature variation trend in Zhejiang from 2000 to 2022. Figure 7. Spatial distribution of the relative contributions of climate change and hu an actions tokNDVI in Zhejiang. (a) The relative contribution of climate change to kNDVI. (b) That of humanactivities to kNDVI.3.6. Spatiotemporal Evolution Laws of kNDVI Associated with Climate ChangeFigure 8 demonstrates that the climate in Zhejiang Province has become warmerand wetter from 2000 to 2022, which is conducive to vegetation growth, recovery, andreproduction. During this period, the average annual precipitation was 1619 mm, increasingat a rate of 0.76× 10−1 mm·y−1. The precipitation varied between 1190 mm and 2039 mm(Figure 8a). Additionally, the average temperature during this period was 17.2 ◦C, risingat a rate of 2.52× 10−2 ◦C·y−1. The average temperature increased from 16.6 ◦C to 18 ◦C(Figure 8b).Remote Sens. 2025, 17, x FOR PEER REVIEW 13 of 22 Figure 7. Spatial distribution of the relative contributions of climate change and human actions to kNDVI in Zhejiang. (a) The relative contribution of climate change to kNDVI. (b) That of human activities to kNDVI. 3.6. Spatiotemporal Evolution Laws of kNDVI Associated with Climate Change Figure 8 demonstrates that the climate in Zhejiang Province has becom warmer and wetter from 2000 to 2 22, which is conducive to vegetation growth, recovery, and repro-ducti n. During this period, the average annual preci itation was 1619 mm, increasing at a rate of 0.76 × 10-1mm·y-1. The precipitation varied between 1190 mm and 2039 mm (Fig-ure 8a). Additionally, the average temperature d ring this period was 17.2 °C, rising at a rate of 2.52 × 10-2℃·y-1. The average temperature increased from 16.6 °C to 18 °C (Figure 8b). Figure 8. Rainfall and temperature changes in Zhejiang from 2000 to 2022. (a) Rainfall variation trend in Zhejiang from 2000 to 2022. (b) Temperature variation trend in Zhejiang from 2000 to 2022. Figure 8. Rainfall and te perature changes in Zhejiang from 2000 to 2022. (a) Rainfall variation trendin Zhejiang from 2000 to 2022. (b) Temperature variation trend in Zhejiang from 2000 to 2022.Remote Sens. 2025, 17, 1433 14 of 21Analysis reveals that the partial correlation parameter of kNDVI with temperatureis 0.44, and the proportion of positive correlation with temperature is 63.68%, which ispredominantly located in the southwest of Shaoxing, the eastern regions of Jinhua, Lishui,Wenzhou, and the northeast of Taizhou (Figure 9a). Similarly, the partial correlationparameter with precipitation is 0.056, and the proportion of positive correlation withprecipitation is 53.58%, which is primarily situated in the mountainous and hilly areas ofthe west of Quzhou, Jinhua, and the northeast of Lishui (Figure 9b).Remote Sens. 2025, 17, x FOR PEER REVIEW 15 of 22 Figure 9. Partial association in kNDVI and climate. (a) Partial association of kNDVI with tempera-ture. (b) Partial association between kNDVI and precipitation. 3.7. Spatiotemporal Evolution Characteristics of kNDVI Under the Impact of Human Activities Figure 10a shows the spatial distribution pattern of human impact in Zhejiang Prov-ince from 2000 to 2022. The human activity footprint is densely distributed in cities such as Hangzhou, Ningbo, Taizhou, and Wenzhou. According to global human activity foot-print data, the human activity footprint in Zhejiang Province increased gradually from 2000 to 2022, with an increase of 0.1842 (Figure 10b). The Pearson correlation index method was used to study the relationship between kNDVI and human activity footprint in Zhejiang Province (Figure 10c). The results showed that 42.65% of the areas exhibited a positive correlation with both kNDVI and human activity footprint. Further correlation and significance superposition analysis revealed that 8.03% of the areas were significantly positively correlated with both kNDVI and human activity footprint. The areas where kNDVI and human activity footprint were negatively correlated accounted for 57.35%, with 24.01% of these areas being significantly negatively correlated. The negatively corre-lated areas were primarily distributed in the northeast of Hangzhou, the southwest of Jiaxing, the east of Huzhou, Ningbo, and Taizhou. i re 9. artial association in kNDVI and climate. (a) Partial association of kNDVI with temperature.(b) Partial association between kNDVI and precipitation.A bilateral T-test was conducted on the partial correlation analysis results of NDVI,temperature, and precipitation from 2000 to 2022 to assess their significance. Accordingto the critical value table of the T-test, extremely significant and significant correspondto p < 0.01 and p < 0.05, respectively, and the T value is classified into six categories:extremely significant negative correlation (T < −2.831), significant negative correlation(−2.831 < T < −2.08), extremely significant positive correlation (T > 2.831), significant pos-itive correlation (2.08 < T < 2.831), insignificant negative correlation (−2.08 < T < 0), andinsignificant positive correlation (0 < T < 2.08). In the significance level test of temperatureand partial correlation, the areas with extremely significant positive correlation and signifi-cant positive correlation accounted for 6.75% and 3.47% of the total study area, respectively.The area of insignificant positive correlation accounted for 53.46%, the area of extremelysignificant negative correlation and significant negative correlation accounted for 2.75%,and the area of insignificant negative correlation accounted for 33.57%. The study findingsindicate that temperature positively influenced kNDVI during the vegetation growingseason, but the effect was not statistically significant. In the significance level test of precipi-tation and partial correlation, the areas of insignificant positive correlation and insignificantnegative correlation accounted for 51% and 43.5% of the study area, respectively. Thedifference between the positive and negative correlation areas was minimal, with positivecorrelation areas accounting for 2.58% and negative correlation areas accounting for 2.91%(extremely significant positive correlation: 0.42%, significant positive correlation: 2.16%,Remote Sens. 2025, 17, 1433 15 of 21extremely significant negative correlation: 0.61%, significant negative correlation: 2.31%).These results suggest that precipitation had no statistically significant effect on kNDVI inZhejiang Province, and its influence was inhibitory. Furthermore, the partial correlationcoefficient between kNDVI and temperature during the vegetation growing season inZhejiang Province was higher in areas that passed the significance test (10.22%) comparedto precipitation (2.58%). This indicates that, during the study period, vegetation growthwas more influenced by temperature in areas that passed the significance test.3.7. Spatiotemporal Evolution Characteristics of kNDVI Under the Impact of Human ActivitiesFigure 10a shows the spatial distribution pattern of human impact in Zhejiang Provincefrom 2000 to 2022. The human activity footprint is densely distributed in cities such asHangzhou, Ningbo, Taizhou, and Wenzhou. According to global human activity footprintdata, the human activity footprint in Zhejiang Province increased gradually from 2000 to2022, with an increase of 0.1842 (Figure 10b). The Pearson correlation index method wasused to study the relationship between kNDVI and human activity footprint in ZhejiangProvince (Figure 10c). The results showed that 42.65% of the areas exhibited a positivecorrelation with both kNDVI and human activity footprint. Further correlation and signifi-cance superposition analysis revealed that 8.03% of the areas were significantly positivelycorrelated with both kNDVI and human activity footprint. The areas where kNDVI andhuman activity footprint were negatively correlated accounted for 57.35%, with 24.01% ofthese areas being significantly negatively correlated. The negatively correlated areas wereprimarily distributed in the northeast of Hangzhou, the southwest of Jiaxing, the east ofHuzhou, Ningbo, and Taizhou.Remote Sens. 2025, 17, x FOR PEER REVIEW 16 of 22 Figure 10. Correlation between spatiotemporal changes in human activity footprints and kNDVI. (a) Spatial distribution of anthropogenic activity footprints; (b) Change trend of human activity footprint from 2000 to 2022; (c) Correlation between anthropogenic activity and kNDVI; (d) anthro-pogenic activity footprint and kNDVI importance. 4. Discussion 4.1. Spatiotemporal Evolution Laws of kNDVI This research investigated the spatiotemporal evolution laws of kNDVI in Zhejiang province from 2000 to 2022. The annual trend of kNDVI increased at a rate of 0.001 (Figure 4a), indicating a gradual improvement in vegetation and an enhancement of ecological benefits in recent years. The research findings align well with existing studies [38,57]. The improvement in vegetation conditions is closely related to the implementation of the na-ture reserve strategy by the Zhejiang Provincial Government. According to the forest re-source inventory data from Zhejiang Province, the forest coverage rate was 58.79%, 59.67%, and 61.15% in 2000, 2010, and 2022, respectively [58]. The forest coverage rate increased by 0.88% from 2000 to 2010 and by 1.48% from 2010 to 2022. Over a 23-year period, the overall forest coverage rate increased by 2.36%. Long-term nature reserve management has played a positive role in improving vegetation [59]. Additionally, the implementation of a millionmu greening plan by the Zhejiang Provincial Government has also demonstrated a positive impact on vegetation improvement [60]. (“mu” is a conven-tional unit of land area in China, with one mu commonly defined as equal to 667 m2 [61].) The spatial distribution of vegetation in Zhejiang Province shows that the area with in-creased vegetation coverage from 2000 to 2022 accounted for 61.47% (Figure 4b), primarily located in the western part of Quzhou, northeastern Lishui, eastern Jinhua, and central Shaoxing in Zhejiang Province. Factors such as rising temperatures, increased precipita-tion, and high spatial humidity in these regions are conducive to vegetation development [62], which has led to an increase in vegetation coverage and an upward trend in coverage. 4.2. Hurst Index and Trend Analysis Fig re 10. orrelation bet een spatiote poral changes in h an activity footprints an k I.Spatial distribution of anthropogenic activity footprints; (b) Change tr nd of human activity foot-print from 2000 to 2022; (c) Correlation between anthropogenic activity and kNDVI; (d) anthropogenicactivity footprint and kNDVI importance.Remote Sens. 2025, 17, 1433 16 of 214. Discussion4.1. Spatiotemporal Evolution Laws of kNDVIThis research investigated the spatiotemporal evolution laws of kNDVI in Zhejiangprovince from 2000 to 2022. The annual trend of kNDVI increased at a rate of 0.001(Figure 4a), indicating a gradual improvement in vegetation and an enhancement of ecolog-ical benefits in recent years. The research findings align well with existing studies [38,57].The improvement in vegetation conditions is closely related to the implementation of thenature reserve strategy by the Zhejiang Provincial Government. According to the forest re-source inventory data from Zhejiang Province, the forest coverage rate was 58.79%, 59.67%,and 61.15% in 2000, 2010, and 2022, respectively [58]. The forest coverage rate increasedby 0.88% from 2000 to 2010 and by 1.48% from 2010 to 2022. Over a 23-year period, theoverall forest coverage rate increased by 2.36%. Long-term nature reserve management hasplayed a positive role in improving vegetation [59]. Additionally, the implementation of amillionmu greening plan by the Zhejiang Provincial Government has also demonstrated apositive impact on vegetation improvement [60]. (“mu” is a conventional unit of land areain China, with one mu commonly defined as equal to 667 m2 [61].) The spatial distributionof vegetation in Zhejiang Province shows that the area with increased vegetation coveragefrom 2000 to 2022 accounted for 61.47% (Figure 4b), primarily located in the western partof Quzhou, northeastern Lishui, eastern Jinhua, and central Shaoxing in Zhejiang Province.Factors such as rising temperatures, increased precipitation, and high spatial humidity inthese regions are conducive to vegetation development [62], which has led to an increase invegetation coverage and an upward trend in coverage.4.2. Hurst Index and Trend AnalysisThe Hurst index was used to predict the sustainability of future vegetation changetrends and their spatial distribution in Zhejiang Province (Figure 5). The areas with con-tinuous improvement in the future are mainly distributed in the central part of Shaoxing,eastern Jinhua, and western Taizhou (Figure 5b). The region is mostly mountainous andhilly, with suitable temperatures and sufficient precipitation, providing good conditionsfor the continued growth of vegetation [63]. In addition, China’s carrying out ecologicalrestoration projects have also promoted the improvement of vegetation, which will con-tinue to improve in the future [64]. The areas that have deteriorated in the past and willimprove in the future are mainly distributed in Ningbo, Taizhou, and Wenzhou, with morenoticeable changes in urban areas. Continuously degraded areas are mostly distributedin the northeastern part of Hangzhou, Shaoxing, and the eastern part of Huzhou, withmore prominent changes in Hangzhou City. According to the 2022 urban constructionstatistics, Hangzhou’s population and industries are increasing year by year [65], andhuman activities may degrade the vegetation in the area. Therefore, in the future, we needto pay attention to the ecological environment of the region, strengthen the monitoring ofthe area, formulate effective protection and restoration measures, and alleviate degradation.4.3. Analysis of kNDVI Driving Force Factors4.3.1. Relative Contributions of Climate Change and Anthropogenic Activities to kNDVIThe contribution percentage of climate change to kNDVI in Zhejiang province showsa positive regional proportion of 52.05% (Figure 7a). The climate along the eastern coast isgradually transitioning towards a warmer and wetter trend, which may benefit vegetationgrowth [66]. The proportion of areas anthropogenic activities to the kNDVI in Zhejiangprovince accounts for 59.47% (Figure 7b). This is attributed to Zhejiang Province’s launch ofthe Precision Forest Quality Improvement Project targeting ten million mu of forests, whichhas accelerated the restoration of vegetation patterns in the province [67]. In addition,Remote Sens. 2025, 17, 1433 17 of 21implementing a nature reserve system with national parks as the mainstay, along withenhanced policies for resource restoration and protection in Zhejiang province, has laid thefoundation for vegetation restoration in the province [67].4.3.2. Evolution Laws of kNDVI Under the Impact of Climate ChangeFrom 2000 to 2022, the temperature rising percentage in Zhejiang Province is2.52× 10−2 ◦C·y−1, as well as the rainfall rising percentage is 0.76 × 10−1 mm·y−1(Figure 8), indicating that the climate in Zhejiang is gradually transitioning towards warm-ing and humidification. Partial correlation analysis indicates that temperature is themain factor among climate change factors, which aligns with existing research [36–38,57](Figure 9). In the mountainous and hilly regions of southern Zhejiang, such as Wenzhouand eastern Lishui, as well as the central areas of Shaoxing and eastern Jinhua, a positivecorrelation exists between temperature and kNDVI. Increased temperature lengthens thevegetation growth cycle, and enhanced vegetation photosynthesis and water use efficiencycontribute to better vegetation growth [68,69]. Conversely, in the northeastern part of Zhe-jiang, including eastern Hangzhou, Shaoxing, and eastern Huzhou, and the eastern coastalareas of Zhejiang, such as Ningbo, eastern Taizhou, and eastern Wenzhou, precipitationis negatively correlated with kNDVI. Increased precipitation leads to soil erosion, whichreduces organic substances in the soil and limits vegetation growth [70,71].4.3.3. Evolution Laws of kNDVI Under the Impact of Human ActivitiesFrom 2000 to 2022, the human activity footprint in Zhejiang province gradually in-creased, with a rate of 1.842× 10−1y−1 (Figure 10b). Additionally, Pearson correlationanalysis (Figure 10b,c) reveals that 42.65% of the regions exhibit a positive correlationbetween kNDVI and human activity footprint, primarily located in hilly and mountainousareas across Quzhou, Wenzhou, Shaoxing, and Jinhua. These regions are characterizedby high altitudes and extensive mountainous terrain, providing favorable natural envi-ronments for vegetation growth. Furthermore, the Zhejiang Provincial Government hasimplemented pilot urban agglomerations in central Zhejiang to create green ecologicalnetworks, promoting improvements in vegetation quality [72]. In contrast, 57.35% of re-gions show a negative correlation between kNDVI and human activity footprint, mainlyconcentrated in the northeastern parts of Hangzhou, Shaoxing, Huzhou, Lishui’s southwest,and eastern coastal areas. Factors such as frequent human activities and urban expansionhave inhibited vegetation growth in these areas [73,74].4.4. Limitations and Future WorkThis research reveals the effects of climate shift and anthropogenic activities on plantvariations in Zhejiang Province over a long-term period. However, there are notabledeficiencies. First, it is unclear how to reasonably choose climate factors using the multipleregression residual analysis method. Additionally, the vegetation growth process is alsoinfluenced by factors such as topography, humidity, runoff, drought, evaporation, andsunshine hours [75,76]. Future studies should include more of these factors to analyzevegetation changes. Second, this study only examined the overall vegetation coverage inZhejiang Province, and different vegetation types exhibit varying sensitivities to climatechange and human activities. Therefore, differentiating the responses of different vegetationtypes to climate change and human activities will be the focus of the next research.5. ConclusionsThis study used kNDVI as an indicator to monitor long-term vegetation coveragechanges in Zhejiang Province and examined the impacts of climate change and humanRemote Sens. 2025, 17, 1433 18 of 21activities on vegetation coverage changes in Zhejiang Province and their driving factors.The results showed that:(1) During 2000–2022, kNDVI in Zhejiang Province showed an upward trend, accountingfor 61.47% of the region with an rise percentage of 0.1× 10−2y−1, and was mostlylocated at western and central and eastern regions of Zhejiang. Analysis of theHurst index and kNDVI trend in Zhejiang Province shows that the vegetation changetrend in Zhejiang Province is mainly anti-sustainable, accounting for 69.06% of thearea (0 < Hurst < 0.5). Future trend analysis shows that in future, the area of plantdegradation in Zhejiang Province (56.35%) will be greater than the improvementtrend (28.14%).(2) Climate shift and anthropogenic activities exhibit obvious spatial heterogeneity invegetation variations in Zhejiang Province. The contribution rates of climate shiftand anthropogenic activities to the increase in plant in Zhejiang Province accountedfor 42.89% and 57.11%, respectively. Anthropogenic activities are the main factorimpacting vegetation changes.(3) Zhejiang province has evident warming and humidification phenomena. Amongclimate factors, temperature is the primary factor impacting vegetation variations.There is a positive association between temperature and the kNDVI in the northwest-ern, southern, and southeastern regions of Zhejiang province. Precipitation in thecentral hilly, mountainous, and northern and eastern coastal areas negatively corre-lates with kNDVI. Overall, because of the effect of human activities and climate shift,plant coverage in Zhejiang Province has risen yearly, and the natural environmenthas improved.Author Contributions: L.C.: Writing—original draft, Writing—review & editing, Investigation,Conceptualization. J.J.: Writingoriginal draft, Writing—review & editing, Investigation, Conceptu-alization. C.P.: Writing—review & editing. Y.Y.: Writing—review & editing. X.W.: Software, Datacuration. Y.Z.: Supervision, Project administration. G.Z.: Funding acquisition, Project administration.C.L.: Writing—review & editing, Formal analysis, Supervision, Conceptualization, Methodology,Project administration. All authors have read and agreed to the published version of the manuscript.Funding: The present research obtained support from the “Pioneer” and “Leading Goose” R&DProgram of Zhejiang (2022C03039), This study was supported by the National Natural ScienceFoundation of China (grant number: 32401573).Data Availability Statement: The data presented in this study are available on reasonable requestfrom the corresponding author.Conflicts of Interest: The authors declare no conflict of interest.References1. Seddon, A.W.R.; Macias-Fauria, M.; Long, P.R.; Benz, D.; Willis, K.J. Sensitivity of global terrestrial ecosystems to climatevariability. Nature 2016, 531, 229–232. [CrossRef]2. Piao, S.; Fang, J.; Ciais, P.; Peylin, P.; Huang, Y.; Sitch, S.; Wang, T. The carbon balance of terrestrial ecosystems in China. Nature2009, 458, 1009–1013.3. Anderson-Teixeira, K.J.; Snyder, P.K.; Twine, T.E.; Cuadra, S.V.; Costa, M.H.; DeLucia, E.H. 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Estimating the Effects of Natural and Anthropogenic Activities on Vegetation Cover : Analysis of Zhejiang Province, China, from 2000 to 2022
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