Fractional Vegetation Cover Change Research Articles

Spatiotemporal Dynamics and Driving Factors of Vegetation Greenness in Typical Tourist Region: A Case Study of Hainan Island, China

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China.

Spatial and Temporal Characteristics of Fractional Vegetation Cover and Its Response to Urbanization in Beijing

Exploration of the spatiotemporal changes in fractional vegetation cover （FVC） and its response characteristics to urbanization is of great significance for urban ecological protection and planning in Beijing. This study analyzed the spatiotemporal characteristics of vegetation cover changes in Beijing from 2000 to 2020 using the Theil-Sen Median and Mann-Kendall methods based on a long-term time series vegetation cover dataset. Then, this study used the urbanization index as a key indicator of spatial urbanization and utilized the transect line and global grid analysis methods to investigate the response characteristics of FVC to different urbanization gradients. The results indicated that： ① FVC changes showed spatial and temporal heterogeneity. From 2000 to 2020, Beijing was predominantly covered by high vegetation, accounting for 65.22% of the total area, which was mainly distributed in ecological conservation areas consistent with the Jundu, Xishan, and Yaji Mountain ranges. The FVC presented an overall positive development trend, with a decreasing trend of areas with low FVC. The increase in FVC was significant （by 28.68%）, mainly distributed in ecological conservation areas and within a range of 10-12 km in concentric circles centered around Tiananmen Square. The urbanization index and FVC change rate were relatively high in Haidian District, Chaoyang District, Fengtai District, Shijingshan District, and Changping District. ② The artificial land surface in 2000, 2010, and 2020 was 9.69%, 13.64%, and 21.19%, respectively, with significant spatial agglomeration and strong spatial heterogeneity. During the urbanization process in Beijing, the increase in artificial land surface reached 11.5%, with the conversion from arable land to artificial land surface accounting for 53.83% of the total land use conversion area. ③ There was a significant negative correlation between FVC and the urbanization index, indicating that urbanization had a negative impact on regional FVC. However, as the urbanization process stabilized, this negative correlation tended to gradually weaken. Although the central urban areas were mainly characterized by low FVC, there was a significant increasing trend in the FVC, indicating a positive development in the FVC and an improvement in regional ecological quality, which was closely related to the governance of the mountain-water-forest-field-lake-grass-sand system. The results of the study can provide a basis for the development of vegetation restoration programs and ecological management measures in Beijing.

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth.

As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the south-eastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in long-term historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed. This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m). Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation.

Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019

Studying the spatiotemporal evolutionary characteristics of vegetation and the effect of precipitation changes is necessary for understanding the regional ecological environment. We used trend analysis, partial correlation analysis, significance tests, and residual trend analysis to analyze the spatiotemporal evolution characteristics and driving factors of fractional vegetation cover (FVC) in the Jinghe River Basin (JRB) from 1998 to 2019. The results showed that vegetation coverage in the JRB significantly improved from 1998 to 2019. FVC showed an increasing trend in 90.64% of the areas in the JRB, and the overall annual change in FVC was extremely significant (p ≤ 0.01). However, precipitation in the JRB showed an insignificant increasing trend; the annual distribution of precipitation developed in a uniform direction and the precipitation centroid tended to move backward. The area with a significant correlation between the FVC change and the precipitation concentration index accounted for the largest proportion (18.47%). Precipitation changes in the JRB generally favored vegetation recovery; however, the effect was limited and non-precipitation factors dominated the changes in FVC. Our study contributes to a more comprehensive understanding of the effects of changes in precipitation patterns on vegetation cover to facilitate regional ecological protection.

Spatiotemporal characteristics of vegetation cover change in the Central Yunnan urban agglomeration from 2000 to 2020 based on Landsat data and its driving factors

The study of dynamic changes in vegetation coverage has significant implications for urban clusters in terms of ecological environment protection, climate change research, water resource management and urban planning. A thorough understanding of the driving mechanisms behind vegetation coverage helps provide scientific basis and policy recommendations for the ecological environment protection, sustainable development and decision-making of urban clusters. Taking the central Yunnan urban cluster as an example, this study utilizes long-term Landsat remote sensing image data from 2000 to 2020 on the Google Earth Engine platform. The fractional vegetation cover (FVC) is estimated using a pixel-based binary model. The Sen’s slope, Mann–Kendall trend analysis and Hurst index methods are employed to investigate the spatiotemporal dynamic characteristics of FVC. The impacts of climate change and human activities on vegetation coverage are explored through partial correlation analysis and residual analysis. Finally, considering natural factors such as topography and climate, as well as socioeconomic factors, the geographic detector is used to quantitatively analyze the driving factors behind FVC changes. The results indicate that: (1) From 2000 to 2020, the FVC in the central Yunnan urban cluster changed significantly, showing an overall improvement trend. The average FVC is 0.496 with a growth rate of 0.0024 per year, and it exhibits a distribution pattern of higher values in the west and lower values in the east. (2) In terms of percentage distribution, areas with low FVC and high FVC account for a relatively high proportion. The trend of FVC changes is as follows: improvement (49.6%) > degradation (26.4%) > no change (24%). The average Hurst value is 0.45, indicating that future FVC changes in the study area will be opposite to the past. (3) FVC shows a positive correlation with precipitation and a negative correlation with temperature. Human activities have a positive impact on FVC in the study area, accounting for 55.1% of the regional proportion. (4) Slope and nighttime light contribute the most to FVC. The explanatory power of the interaction between slope and precipitation/temperature is the most significant.

A study of spatial distribution and dynamic change in monthly FVC of urban parks.

The study on the spatial distribution and dynamic change in monthly Fractional Vegetation Cover (FVC) of parks provides a scientific basis for vegetation management and optimization in urban parks. This research focuses on two comprehensive parks located in Xinxiang, China-People's Park and Harmony Park, using multi-spectral Unmanned Aerial Vehicle (UAV) images as the data source and considering monthly periods. Monthly FVC data was obtained using the method of Dimidiate Pixel Model based on the Normalized Difference Vegetation Index (NDVI). The dynamic changes of monthly FVC at regional scale were described through the dynamic changes in the monthly FVC mean and in the FVC areas at various scales, and the dynamic changes in the monthly FVC were analyzed using the coefficient of variation and curve change trends. Furthermore, the dynamic changes in FVC areas at various scales in the parks were analyzed using standard deviation and curve change trends. Subsequently, the differential method was used to analyze the monthly FVC dynamic changes at pixel scale. The results indicate: (1) In terms of the spatial distribution characteristics in monthly FVC of urban parks, both parks exhibit the highest ratio of bare area in January and February. The proportions of FVC for People's Park are 59.17% and 64.46%, while for Harmony Park they are 69.10% and 51.92%, showing the most distinct spatial distribution characteristics. The high and very high coverage areas in each month are mainly distributed on the outskirts of the park, while the medium, medium-low, and low coverage areas are mainly located in the central and middle parts of the park. The overall FVC of the park shows a trend of high coverage on the periphery and low coverage in the center. (2) In the spatial-temporal dynamic change in FVC at regional scale, the average monthly FVC changes exhibit an overall "∩" -shaped pattern. The peak and minimum FVC values for different parks occur at different times. The peak FVC for People's Park appears in August, while for Harmony Park it appears in June, with corresponding FVC values of 0.46 and 0.50, respectively. The minimum FVC for People's Park occurs in February, and for Harmony Park it occurs in January, with FVC values of 0.17 and 0.15, respectively. Among the dynamic change in FVC areas at various scales, the areas of bare and highest-coverage exhibit the greatest fluctuations, with the ascending and descending changes and rates of bare and highest-coverage areas generally showing opposite trends. (3) In terms of the spatial-temporal dynamic changes in FVC at pixel scale in urban parks, overall, FVC shows moderate improvement from February-August, and moderate degradation from January-February and from August-December. The degradation and improvement are primarily slight. The most significant improvement in monthly FVC occurs in March-April, with a predominant type of significant improvement in FVC changes. People's Park and Harmony Park show the most significant degradation in FVC during September-October and October-November, respectively, with a predominant type of significant degradation in FVC changes. During the periods of most significant improvement and degradation in monthly FVC, the spatial distribution of significant improvement and degradation areas primarily occurs in the periphery and middle parts of the parks. FVC in urban parks decreases from January to February and from August to December, while it increases from February to August, with relatively good conditions from June to August. Vegetation optimization should consider: balancing recreational and ecological functions overall, controlling the proportion of bare land, and enhancing the canopy structure of vegetation in low coverage areas or the coverage of hard surfaces; locally increasing the proportion of evergreen plants and moderately increasing planting density. In addition, parks should strengthen management to reduce the impact of flooding and maintain the health of vegetation.

Spatiotemporal variation of habitat quality and its response to fractional vegetation cover change and human disturbance in the Loess Plateau

It is of great practical significance to regional ecological conservation and restoration to explore the spatiotemporal variation characteristics of habitat quality in the ecologically fragile Loess Plateau. This study firstly explored the habitat quality in the Loess Plateau during 2000-2020 with the Integrated Valuation of Ecosystem Services and Trade-offs model. Then this study revealed the response characteristics of habitat quality to the fractional vegetation cover (FVC) change and human disturbance with the geographically weighted regression (GWR) model. Results showed habitat quality tended to improve in 51.16% of the study area, and area of high or very high habitat quality increased by 1.78%. Besides, FVC showed dominantly significant increase (62.42%) and high stability (69.66%) in the study area, and human disturbance increased remarkably in 18.11% of the study area but maintained the same level in 91.83% of the study area. Additionally, areas with positive correlation between habitat quality change and FVC and between habitat quality change and human disturbance change accounted for 52.56% and 37.38% of the study area, respectively, indicating FVC played dominant role in affecting the regional habitat quality variation. This study can provide important decision support information for the future ecological conservation of the Loess Plateau.

Modern pollen-vegetation coverage relationships and its application for Holocene vegetation reconstructions in the central Tianshan Mountains

The Tianshan Mountains are the largest mountain system in Central Asia and comprise islands of humidity within a widespread arid zone. In this study, we improve knowledge of the quantitative relationship between pollen and vegetation coverage in the region, to quantitatively reconstruct Holocene vegetation coverage. Specifically, we statistically analyzed 1058 modern pollen samples from the central Tianshan Mountains and neighbouring regions, utilizing redundancy analysis (RDA), and boosted regression trees (BRT), stepwise regression (Stepwise), the modern analogy technique (MAT), weighted average partial least squares (WA-PLS) and local weighted average (LWWA) methods, to construct a series of modern calibration sets between pollen and fractional vegetation cover (FVC). The results show that (1) FVC and modern pollen assemblages display a strong correlation (correlation coefficient of 0.66) and (2) BRT outperforms Stepwise, MAT, WA-PLS and LWWA under leave-one-out cross-validation, bootstrap, and spatial autocorrelation tests, making the BRT model the best choice. Applied to Holocene datasets, we infer that from 11.4 to 4.6 cal kyr BP, FVC changes were mainly influenced by natural climate changes. However, after 4.6 cal kyr BP, FVC changes were influenced by a combination of natural climate changes (with humidity playing a dominant role) and the intensity of human activities.

Spatial and temporal variations of vegetation cover and its influencing factors in Shandong Province based on GEE.

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

Assessing the impact of fractional vegetation cover on urban thermal environment: A case study of Hangzhou, China

The large-scale urbanization has changed the surface characteristics of cities, seriously affected the urban heat balance, and worsened the urban thermal environment. The regulation of vegetation coverage on urban climate has received extensive attention. This study aims to assess and predict changes in the urban thermal environment of Hangzhou by monitoring the variation of the urban thermal field variance index (UTFVI) affected by fractional vegetation cover (FVC) changes. The results indicate that the area of high level FVC will likely decrease by 52.91%, while the area of lower level FVC will likely increase by 33.83% from 1990 to 2030. Furthermore, the area of the strongest level UTFVI shows a significant trend of expansion, increasing by 467.299 km2 from 1990 to 2030, while the area of other levels UTFVI shows different degrees of decrease. Specifically, the area of strong and stronger levels UTFVI decreased by 149.641 km2 and 271.464 km2, respectively. The spatial correlation between the FVC and UTFVI changes is significant and negative, with p-values less than 0.01 and negative coefficients. The absolute value of the coefficient will decrease from 0.416 to 0.246 from 1990 to 2030, and the correlation between FVC and UTFVI changes shows a decreasing trend. The linear regression analysis demonstrates a significant negative correlation between the FVC and UTFVI, with coefficients ranging from −0.470 to −0.280 and p-values less than 0.001, further indicating that the FVC has a mitigating effect on the urban thermal environment. This study provides new insights into thermal environment governance and urban sustainable development.

Spatiotemporal Variations in Fractional Vegetation Cover and Their Responses to Climatic Changes on the Qinghai–Tibet Plateau

The alpine vegetation of the Qinghai–Tibet Plateau (QTP) is extremely vulnerable and sensitive to climatic fluctuations, making it an ideal area to study the potential impacts of climate on vegetation dynamics. Fractional vegetation cover (FVC) is regarded as one of the key indicators in monitoring semiarid and arid ecosystems due to its sensitive responses to vegetation behavior under climatic changes. Although many studies have analyzed the responses of vegetation on the QTP to climatic change, limited information is available on the influence of climatic variables on FVC changes in this area. In this study, we used satellite images and meteorological data to investigate the spatiotemporal variations of FVC during the growing season (FVCGS) during 1998–2018 and evaluated the responses to changes in climatic variables. Results showed that FVCGS displayed an overall fluctuating rise of 0.01/10 a (p < 0.01) over the study period. The FVCGS variation was spatially heterogeneous, with a general trend of greening in the northern and browning in the southern QTP. Obvious correlations were observed between the average FVC, average temperature, and total precipitation of the growing season, with precipitation being the primary controlling factor for vegetation growth. Some regions in the northwestern and northeastern QTP showed greening trends due to the positive influence of precipitation. Some areas in the southwestern QTP experienced browning trends due to water shortages caused, probably, by the weakening of the Indian monsoon. Browning in the southeastern parts was likely caused by drought and permafrost degradation resulting from high temperature. The inconsistent trend of vegetation change on the QTP is relatively high considering the continuous warming and changing atmospheric circulation patterns. FVC in most regions of the QTP has 0–1 month temporal responses to precipitation and temperature. Moreover, the one-month lagged effects of temperature and precipitation had a greater influence on steppe and desert vegetation than on other vegetation types. This research provides new perspectives for understanding the QTP vegetation response to climatic changes and a basis for making reasonable vegetation conservation and management policies.

Spatial and temporal trends and drivers of fractional vegetation cover in Heilongjiang Province, China during 1990-2020.

Fractional vegetation cover (FVC) is a quantitative indicator for vegetation growth conditions and ecosystem change. Clarifying the spatial and temporal trends and driving factors of FVC is an important research content of global and regional ecological environment. Based on Google Earth Engine (GEE) cloud computing platform, we estimated FVC in Heilongjiang Province from 1990 to 2020 using the pixel dichotomous model. We analyzed the temporal and spatial trends and drivers of FVC using Mann-Kendall mutation test, Sen's slope analysis with Mann-Kendall significance test, correlation analysis, and structural equation model. The results showed that the estimated FVC based on the pixel dichotomous model had high accuracy (R2>0.7, root mean square error <0.1, relative root mean square error <14%). From 1990 to 2020, the annual average FVC in Heilongjiang was 0.79, with a fluctuating upward trend (0.72-0.85) and an average annual growth rate of 0.4%. The annual average FVC at the municipal administrative districts level also showed different levels of increase of FVC. The area with extremely high FVC dominated the Heilongjiang Province with a gradual increase proportion. The area with increasing trend of FVC accounted for 67.4% of the total area, whereas the area with decreasing trend only accounted for 26.2%, and the rest remained unchanged. The correlation of human activity factor on annual average FVC was higher than that of growing season monthly average meteorological factor. The human activity factor was the main driver for FVC change in Heilongjiang Province, followed by land use type. The total effect of monthly average meteorological factor during the growing season on FVC change was negative. The results would serve as technical support for long-term FVC monitoring and driving force analysis in Heilongjiang Province, and provide a reference for ecological environment restoration and protection, as well as the formulation of related land use policy.

Study on fractional vegetation cover dynamic in the Yellow River Basin, China from 1901 to 2100

Increasing climate change makes vegetation dynamic. At the same time, dynamic changes in vegetation not only have a feedback effect on climate change, but also affect the hydrological cycle process. Therefore, understanding the vegetation change and its response to climate change is a priority for predicting future climate change and studying the impact of vegetation change on the hydrological cycle. In this study, the Yellow River Basin in China is the study area. Based on the analysis of the evolution characteristics of meteorological elements and fractional vegetation cover (FVC), the delta downscaling Coupled Model Intercomparison Project Phase 6 (CMIP6) models are optimized. The empirical orthogonal function (EOF) and singular value decomposition (SVD) methods are used to investigate the impact of climate change on vegetation in the Yellow River Basin. The results show that: (1) in the four scenarios (SSP126, SSP245, SSP370, and SSP585), FVC in the Yellow River Basin from 2022 to 2100 shows an increasing trend, SSP370 (0.017 10a–1) &amp;gt; SSP126 (0.014 10a–1) &amp;gt; SSP245 (0.0087 10a–1) &amp;gt; SSP585 (0.0086 10a–1). Spatially, FVC in most regions of the Yellow River Basin show an increasing trend under the four scenarios, and the degraded areas are concentrated in a small part of the Yellow River headwaters. (2) There is a significant positive correlation between FVC and precipitation (Pre) and temperature (Tem) under four scenarios in the Yellow River Basin from 2022 to 2100. Under the same scenario, the annual average temperature can be considered as the dominant factor of FVC change in the Yellow River Basin. Under different scenarios, the impact of climate change on FVC under the high emission scenarios is greater than that under the low emission scenarios. This study will help to better understand the response of vegetation to climate change and provide a scientific basis for formulating ecological protection measures to cope with future climate change in the Yellow River Basin.

Unmixing the coupling influence from driving factors on vegetation changes considering spatio-temporal heterogeneity in mining areas: a case study in Xilinhot, Inner Mongolia, China

Considering the spatio-temporal heterogeneity, this study resolved the coupling influence of a variety of driving factors on vegetation changes in mining areas and discovered the influencing characteristics of the respective driving factors, especially mining activities. First, the spatio-temporal characteristics of FVC (fractional vegetation cover) variation were analyzed in the Sheng-Li mining area. Second, the quantitative relationships among the natural factors (temperature, precipitation, and elevation), artificial factors (mining activities, urban activities), and FVC were constructed by GTWR (geographically and temporally weighted regression) to quantify the contribution of each factor to the change in FVC. Third, the influencing characteristics of the respective driving factors, especially mining activities, were analyzed and summarized. The results show that (1) the FVC change was mainly influenced by natural factors in the areas far from mines and towns and artificial factors in the areas close to mines and towns. (2) The contribution of mining activities to vegetation change (C-Mine) was spatially characterized by two features: (a) distance attenuation characteristics: C-Mine showed logarithmic decrement with distance; (b) directional heterogeneity: C-Mine varied significantly in different directions. In particular, there was a high C-Mine area located near multiple mining areas, and the range of this area shifted to include the mine with more production over time. Overall, unmixing the coupling influence from driving factors with spatio-temporal heterogeneity and achieving a quantitative description of the influencing characteristics in mining areas were the main contributions of this study. The quantification methods and results in this paper provide important support for decision-making on ecological protection and restoration in mining areas.

Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and -accumulation effects

Quantifying the contributions of climate change (CC) and human activities (HA) to vegetation change is crucial for making a sustainable vegetation restoration scheme. However, the effects of extreme climate and time-lag and -accumulation effects on vegetation are often ignored, thus underestimating the impact of CC on vegetation change. In this study, the spatiotemporal variation of fractional vegetation cover (FVC) from 2000 to 2019 in northern China (NC) as well as the time-lag and -accumulation effects of 15 monthly climatic indices, including extreme indices, on the FVC, were analyzed. Subsequently, a modified residual analysis considering the influence of extreme climate and time-lag and -accumulation effects was proposed and used to attribute the change in the FVC contributed by CC and HA. Given the multicollinearity of climatic variables, partial least squares regression was used to construct the multiple linear regression between climatic indices and the FVC. The results show that: (1) the annual FVC significantly increased at a rate of 0.0268/10a from 2000 to 2019 in all vegetated areas of NC. Spatially, the annual FVC increased in most vegetated areas (∼81.6 %) of NC, and the increase was significant in ∼54.6 % of the areas; (2) except for the temperature duration (DTR), climatic indices had no significant time-lag effects but significant time-accumulation effects on the FVC change. The DTR had both significant time-lag and -accumulation effects on the FVC change. Except for potential evapotranspiration and DTR, the main temporal effects of climatic indices on the FVC were a 0-month lag and 1–2-month accumulation; and (3) the contributions of CC and HA to FVC change were 0.0081/10a and 0.0187/10a in NC, respectively, accounting for 30.2 % and 69.8 %, respectively. HA dominated the increase in the FVC in most provinces of NC, except for the Qinghai and Neimenggu provinces.

Vegetation cover changes in China induced by ecological restoration-protection projects and land-use changes from 2000 to 2020

Vegetation-coverage research shows China’s significant 25% contribution to global greening. Vegetation is the link between water, soil and atmosphere, making it an important indicator of changes in anthropogenic factors. Anthropogenic attribution analysis of vegetation change helps us to identify and estimate the relationship between vegetation change and major ecological projects, and their corresponding relationship may be the antecedents and consequences of vegetation dynamics. This study assessed the increment and change rate of fractional vegetation cover (FVC) in 2000–2020 in China. The influences of land types and major ecological projects were systematically compared to assess the vegetation-human nexus. China has experienced progressive greening in the study period with regional variations in patterns and causes. The FVC changes and spatio-temporal variations were induced by notable human activities such as land-use conversion, China’s afforestation programs (CAP), Ant Forest Project (AFP), and Conversion of Cropland to Forest Program (CCFP). The North region recorded the highest change rate. The area changes in cropland, grassland, and forest were the main FVC drivers. The average FVC change rate of CAP changed very high in 21 years. The AFP exerted significant impacts on FVC changes. The CCFP effectively promoted FVC improvements from the low, medium and high grades to the very high coverage grade. These comparative trends illustrated the intricate relationships between anthropogenic factors and greening. The findings could enhance the prediction and evaluation of vegetation-cover dynamics under anthropogenic changes and the implementation and management of afforestation programs.

Simulation of solar-induced chlorophyll fluorescence in a heterogeneous forest using 3-D radiative transfer modelling and airborne LiDAR

Solar-induced chlorophyll fluorescence (SIF) provides a means to estimate plant photosynthetic activities and to detect early plant stress. The accurate quantification of SIF emitted by various scene components (tree crowns and background) may significantly improve the interpretation of top-of-canopy SIF (SIFtoc) measurements made over heterogeneous canopies. To do so, a three-dimensional (3-D) canopy SIF model (FluorFLiES) was introduced by coupling the excitation-fluorescence matrices (EF-matrices) with a 3-D Monte Carlo canopy radiative transfer model (Forest Light Environmental Simulator, FLiES). A tool was developed to construct forest canopy scene components from LiDAR data and enable their simulated contributions in structurally complex forest scenes. FluorFLiES is able to quantify SIF measurements with good accuracy at both half-hourly (R2 = 0.72, RMSE = 0.26 mW m−2 sr−1 nm−1) and daily (R2 = 0.83, RMSE = 0.19 mW m−2 sr−1 nm−1) scales. This study showed that non-photosynthetic elements in tree crowns, the fractional vegetation cover (FVC), and the background (including understory vegetation and soils) had a strong influence on SIFtoc intensity. Non-photosynthetic woody material suppressed the propagation of photons within crowns, thereby decreasing SIFtoc by around 10%. The canopy background made a significant contribution to SIFtoc in the NIR region by scattering downward SIF photons upward, and the background contribution increased rapidly with decreasing FVC: SIFtoc increased two-fold from 0.15 to 0.3 mW m−2 sr−1 nm−1 when ground leaf area index increased from 0.5 to 1.5 m2/m2. The results showed that the fluorescence escape ratio (fesc), a key variable relating observed SIFtoc to photosynthesis CO2 rate, contained a contribution from the background with a magnitude of 42%, even for relatively dense forest canopies. Assuming fesc simulated by the FluorFLiES model as a reference value, this study demonstrated that the current reflectance-based approach may cause large uncertainties (29%) when understory vegetation and/or FVC changes, largely due to neglecting the contribution of background elements. This study highlights the need to separate scene components and to consider multiple scattering within/among these components in interpreting the SIFtoc signal when working with heterogeneous ecosystems. The source code of FluorFLiES is available for further benchmarking.

Evaluation of spatio-temporal variations of FVC and its relationship with climate change using GEE and Landsat images in Ganjiang River Basin

Fractional vegetation cover (FVC) is an important feature of the ecosystem, and continuous evaluation of vegetation status is one of the key issues in the ecological monitoring of river basins. This article divided the Ganjiang River Basin (GRB) into 73 subbasins, and quantitatively evaluated the spatio-temporal FVC changes at multiple scales from 2000 to 2019 using Google Earth Engine and Landsat series images. Theil-Sen slope estimator and Mann-Kendall algorithm were used to monitor the spatio-temporal change trend of the FVC in the basin and subbasins, respectively. Mann-Kendall test was executed to analyze the abrupt change of FVC. Hurst exponent and Theil-Sen Slope were integrated to evaluate the consistency of FVC change and predict its spatio-temporal evolution trend. Grey correlation analysis and Pearson correlation analysis were selected to quantify the relationship between FVC and terrain, climate factors in each subbasin, and further explore the effect of single factor and multiple-factor combination on FVC change. The results showed that: (1) FVC in the GRB was in good condition, and increased with the elevation and slope, but the FVC in the subbasins with elevation less than 200 m ranged from 25.95% to 72.86%. From 2000 to 2019, the area of high coverage increased significantly compared with other coverage grades. The FVC of most subbasins varied from 45% to 80%, and the change range fluctuated from 9.4% to 36.53%. (2) The FVC has presented a fluctuating growth trend in the past 20 years. The area with increasing FVC reached 68.93%, and the area of a significant improvement in FVC was 11.65% more than the area with the significant degradation. The FVC in the 64.38% of subbasins has abrupt changed, and 20.55% of subbasins has changed dramatically with 7 abrupt change points, which demonstrated that the subbasin-scale abrupt change analysis could accurately monitor the spatio-temporal change of FVC. (3) The 76% of basin appeared the weak consistency in the FVC, and the northern and southern of the basin present a trend of degradation in the future. (4) The spatio-temporal distribution of FVC in GRB was greatly affected by terrain. The effect of temperature and relative humidity on the FVC change was greater than that of precipitation. The synergistic effect of three climatic factors on FVC change was more significant than that of a single factor.

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

After 2000, China’s vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO2 to vegetation in China’s 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China’s 32 major cities and quantified the effects of CO2, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO2 were the major factors that affected FVC change. The average contributions of climate factors, CO2, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development.

Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector

Although previous studies have shown that the construction of the China-Myanmar Economic Corridor (CMEC) has caused critical deforestation, few studies have monitored the long-term vegetation dynamics in corridor regions. Recent studies have revealed vegetation greening trends at different scales but have failed to further explore whether these trends are due to the restoration of natural vegetation or the large-scale planting of artificial crops. To investigate the vegetation dynamics and influencing factors during the growing season between 2001 and 2020 in the CMEC area, trend analysis, the random forest (RF) classifier at Google Earth Engine platform, and the geographic detector were employed to quantitatively analyze the patterns of distribution and change in vegetation coverage and its driving factors. Note that we distinguished between natural vegetation areas and artificial crop areas using the RF classification algorithm in an attempt to improve the drawback that most previous studies confused the contribution of natural vegetation and artificial vegetation to macro vegetation change. We discovered that (1) the growing-season average fractional vegetation cover (FVC) was 0.7033, which was relatively low in central and southern regions. The FVC increased by 0.21% yr−1 from 2001 to 2020, and areas with an increasing trend in FVC were 16.67% more than those with a decreasing trend. (2) Although forests and grasslands were reduced by 0.68%, the cropland that prominently clustered in the central and southern regions contributed 50.37% to an increase in FVC, and the decreasing effects on natural vegetation were offset by the increase in artificial crops. (4) The factors that influence FVC distribution were ranked in descending order: land use/land cover (LULC) > climate > topography > anthropogenic activity; factors that influence FVC change were ranked as: anthropogenic activity > climate > LUCC. Note that the grain yield influence on the FVC distribution increased 0.31% yr -1, which was also the most important factor driving FVC change, indicating that agricultural activities increasingly facilitated vegetation greening.