Spatiotemporal dynamics of fractional vegetation cover and its relationship with climatic factors in the Yarkand River Basin

Vegetation is seen as a sensitive indicator of global change because of its crucial role in connecting the atmosphere, soil, and water. Fractional vegetation cover (FVC), in turn, is an important indicator of vegetation status. Qingyang is a typically ecologically sensitive region, with a range of changes in vegetation in the last decade as a result of climatic and non-climatic factors. However, the exact impact of climate change and human activities remains unclear. Satellite observations can help to clarify that impact, allowing us to assess trends in vegetation change in the last two decades (2000–2019). In this study, daily and composite time series vegetation variations were derived from moderate resolution imaging spectroradiometer (MODIS) data and the impact of climate and human activity factors was examined for different administrative districts. By deploying multiple regression models, the research revealed that human activity has contributed 46% to the FVC variation, while the remaining 54% was led by climate factors. In areas where FVC was increasing, human activity contributed 55.89% while climate factors contributed 44.11%. In areas where FVC was decreasing, human activity and climate factors contributed 24.58% and 75.42%, respectively. The study also looks at the impacts of El Nino/IOD events in FVC dynamics in the study site. The FVC inversion result from MODIS proved capable of capturing long-term and seasonal vegetation patterns and thus provide a valuable archive for decadal-scale vegetation dynamics in the study area. Moreover, the improvement in FVC was a dual effect of climatic and human activities, while the latter owns a higher contribution especially for the implementation of ecological construction projects.

Generating spatiotemporally consistent fractional vegetation cover at different scales using spatiotemporal fusion and multiresolution tree methods

Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year−1 from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution.

As an important indicator to characterize the surface vegetation, fractional vegetation cover (FVC) with high spatio-temporal resolution is essential for earth surface process simulation. However, due to technical limitations and the influence of weather, it is difficult to generate temporally continuous FVC with high spatio-temporal resolution based on a single remote-sensing data source. Therefore, the objective of this study is to explore the feasibility of generating high spatio-temporal resolution FVC based on the fusion of GaoFen-1 Wide Field View (GF-1 WFV) data and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Two fusion strategies were employed to identify a suitable fusion method: (i) fusing reflectance data from GF-1 WFV and MODIS firstly and then estimating FVC from the reflectance fusion result (strategy FC, Fusion_then_FVC). (ii) fusing the FVC estimated from GF-1 WFV and MODIS reflectance data directly (strategy CF, FVC_then_Fusion). The FVC generated using strategies FC and CF were evaluated based on FVC estimated from the real GF-1 WFV data and the field survey FVC, respectively. The results indicated that strategy CF achieved higher accuracies with less computational cost than those of strategy FC both in the comparisons with FVC estimated from the real GF-1 WFV (CF:R2 = 0.9580, RMSE = 0.0576; FC: R2 = 0.9345, RMSE = 0.0719) and the field survey FVC data (CF: R2 = 0.8138, RMSE = 0.0985; FC: R2 = 0.7173, RMSE = 0.1214). Strategy CF preserved spatial details more accurately than strategy FC and had a lower probability of generating abnormal values. It could be concluded that fusing GF-1 WFV and MODIS data for generating high spatio-temporal resolution FVC with good quality was feasible, and strategy CF was more suitable for generating FVC given its advantages in estimation accuracy and computational efficiency.

Qinghai Lake Basin is the largest endorheic basin in the northeastern part of the Qinghai-Tibet Plateau (QTP). The vegetation dynamics are subject to dual pressures from climate change and human activities. Previous studies have neglected the interactions among driving factors, as well as the impact of climate factors on vegetation under the regulatory role of topographic elements. The present study utilises MODIS-EVI data from 2001 to 2022 to estimate Fractional Vegetation cover (FVC) and to reveal the spatiotemporal dynamics of vegetation cover through trend analysis and other methods. Furthermore, it elucidates the effect of topographical factors on vegetation distribution. Finally, geographic detectors and the partial least squares structural equation model (PLS-SEM) were employed to quantify the impact intensity of driving factors (including climate, human activities, topography, and soil) and analyze their interactive effects and influence pathways on vegetation cover. The results suggested that (1) FVC in the Qinghai Lake Basin increased significantly (1.38×10-³/a); notably, low-grade FVC areas exhibiting high volatility. (2) The terrain effect displays clear differentiation characteristics. FVC peaks in the elevation range of 3500–3800 m, FVC dispersion increased with slope, and semishady/shady slopes dominated FVC distribution. The vegetation improvement type is concentrated on low-elevation, flat slopes and shady slopes, whereas the vegetation degradation type is distributed on middle- and low-elevation slopes and semipositive slopes. (3) Climatic factors primarily exert a direct positive influence on FVC. As far as climate factors are concerned, the effects of temperature and precipitation on FVC do not act independently, but act together through synergistic effects, with temperature showing a more significant driving effect. Topography primarily affects FVC indirectly by regulating water and heat conditions (temperature and precipitation). Each factor possesses an optimal range (elevation: 3400–4100 m, precipitation: 325–550 mm, temperature: −6 to 0°C). When changes in these driving factors exceed the optimal range, FVC is suppressed. On a temporal scale, climate change and human activities are the dominant factors influencing the FVC in the Qinghai Lake Basin. The positive effects of human factors on FVC have strengthened.

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.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于SPOT-VEGETATION数据的神农架林区1998-2013年植被覆盖度格局变化 DOI: 10.5846/stxb201704240739 作者: 作者单位: 中国科学院植物研究所植被与环境变化国家重点实验室;中国科学院大学,中国科学院植物研究所植被与环境变化国家重点实验室;中国科学院大学,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院科技服务网络计划（KFJ-SW-STS-167） Dynamics and analysis of vegetation fraction changes in Shennongjia Forest District during 1998 to 2013 by using SPOT-VEGETATION NDVI data Author: Affiliation: State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences; University of Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences; University of Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental; Change,Institute of Botany,Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于1998-2013年的SPOT-VEGETATION归一化植被指数（normalized differential vegetation index，NDVI）数据，利用二分模型法、相关性分析和空间分析的方法，结合同期降水量和平均温度数据，估算了神农架林区及神农架国家级自然保护区的植被覆盖度，并分析了空间格局及植被覆盖度变化的影响因素。结果表明，1998-2013年间，神农架林区平均植被覆盖度为66.8%，年最大植被覆盖度为93.8%，保护区内最大植被覆盖度显著高于保护区外；林区植被覆盖度变化率为1.45%，保护区植被覆盖度变化率为2.26%，植被整体呈增加的趋势，保护区保护效果较好。温度、降水量、年最低气温、距道路和居民地距离的远近是影响植被覆盖度变化的重要因子，而海拔对植被覆盖度变化无影响。 Abstract:The fractional vegetation cover (FVC) is a very important parameter for describing forest vegetation dynamics and forest ecosystems. Among all the methods used for measuring FVC, the remote sensing method has its own advantages because of the vast spatiotemporal scale of satellite data. Shennongjia Forest District, considered as one of the well-preserved primary forest distribution areas in central China, has diverse species and forest resources. However, the forest was adversely influenced to a large extent by human activities in the 1970s and 1980s, because of the rapidly growing population and remarkable commercial logging. The anthropogenic disturbance has been mitigated and improved since a nature reserve was established, and related protecting policies were implemented. The protecting efficiency of the National Nature Reserve was determined by using a dimidiate model to measure the fractional vegetation cover over the Shennongjia Forest District from 1998 to 2013 by using the 1 km resolution, ten-day NDVI serial data of SPOT-VEGETATION. Yearly precipitation and average temperature in the same period, as well as elevation, distance to residential areas and main roads were included, and the main influencing factors were determined by conducting correlation analysis. The annual average FVC of the study area was 66.8%, whereas the annual maximum FVC was 93.8%, which was higher inside the reserve. During 1998-2013, FVC showed increasing tendencies both over the entire district and inside the reserve, and it increased by 1.45% and 2.26% for the Shennongjia Forest District and National Reserve, respectively. The National Reserve had a better protecting efficiency for forest vegetation. The correlation analysis for the main influencing factors showed that environmental factors, including yearly precipitation, average temperature, and extreme cold temperature, were positively correlated with FVC, whereas elevation did not show a significant correlation. The socioeconomic factors, including the distance to main roads and residential areas, had remarkable impacts on the changes of FVC. FVC near residential areas has increased owing to the urbanization and greening processes. FVC near roads showed both increasing and decreasing tendencies in space, because of the simultaneous construction of new roads and afforestation. This study revealed the changing tendency in Shennongjia Forest District and the National Reserve and indicated that both natural and socioeconomic factors had remarkable impacts on dynamics of fractional vegetation cover and forest ecosystems, which might provide detailed scientific basis for ecosystem management. 参考文献 相似文献 引证文献

Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

呼伦贝尔市作为中国北方的重要生态屏障，其生态状况的变化及影响因素一直备受关注。基于1998-2018年SPOT/VEGETATION NDVI数据，结合地形、气候和社会经济数据利用像元二分模型、趋势分析法，并根据数据特点，综合多种统计方法，对呼伦贝尔市植被覆盖度（FVC）时空变化及驱动力进行定性与定量分析。结果表明：（1）21年间研究区FVC在低波动中缓慢增长，以4-5年为周期，周期内FVC先减少后增长，在空间上呈"西减东增"的变化格局；（2） FVC类型以极高和高为主，中、低、极低面积依次递减且总和仅占10%-15%，FVC增加、变化不显著及减少区域面积分别为135720.57 km²（53.56%）、107140.74 km²（42.28%）、10569.06 km²（4.17%）；（3）地形因素奠定了FVC"西低东高"的空间分布格局，气候和人类活动因素影响FVC的年际变化。位于研究区西南部的新巴尔虎右旗、新巴尔虎左旗、鄂温克族自治旗、陈巴尔虎旗和东南部的阿荣旗以气候因素为主导，位于中部大兴安岭上的牙克石市、额尔古纳市、根河市和扎兰屯以人类活动因素为主导，满洲里、海拉尔和鄂伦春自治旗则受气候因素与人类活动因素的综合影响。驱动力因素对FVC变化的影响具有差异性和双向性，在合理的政策和规划下，可以实现社会经济发展与生态环境可持续发展的双赢。;With the rapid development of the global economy, the ecological environment is deteriorating, and it is urgent to carry out environmental change monitoring. The rise of remote sensing technology has made it possible. As an important ecological barrier in northern China, Hulunbuir City has been receiving much attention. Currently, the ecological environment monitoring of Hulunbuir focuses on the use of multi-period remote sensing images, combining with climate data, and a single driving force analysis method to study the changes in local vegetation and to explore the response of vegetation changes to the natural climate. However, there are few studies on the impacts of human activities on vegetation changes. Most of them are based on qualitative analysis or human activities as a whole, which is difficult to determine the specific driving force components and the influence direction and the extent of each component factor. This paper is based on SPOT/VEGETATION NDVI data from 1998-2018, combined with the terrain, climate and Human activity data, and comprehensively applying the trend analysis, coefficient of variation method, pixel decomposition model, principal component analysis, multiple linear regression, ridge regression and so on to carry out the qualitative and quantitative analysis of the spatio-temporal changes and driving forces of Hulunbuir's fraction of vegetation cover (FVC). The results show:(1) during the 21 year, the FVC in the study area slowly increases in low fluctuations, with a period of 4-5 years, during which the FVC first decreases and then increases, and there is a change pattern of decreasing in the west and increasing in the east in space. The FVC types are mainly extremely high and high, and the areas of medium, low, and extremely low are decreasing, and the sum only accounts for 10%-15% of the study area. The increase of extremely low and low type area is relatively large, which are 36.80% and 98.46%, respectively. The main source is the degradation of medium and high type, and the increase of extremely high type area is relatively large, which is 33786.03 km², and the main source is the further improvement of high type. (2) The areas with significant and extremely significant increases account for 53.56% (135720.57 km²), the areas with insignificant changes account for 42.28% (107140.74 km²), and the areas with significant and extremely significant decreases only account for 4.17% (10569.06 km²) of the research area. (3) There are obvious differences in the driving force of the FVC in different regions. Topographical factors lay the spatial distribution pattern of low west and high east and climatic factors affect the temporal distribution pattern of FVC. New Barag Right Banner, New Barag Left Banner, Ewenki Autonomous Banner, Arun Banner and Chen Barag Banner are dominated by climatic factors. Yakeshi, Erguna, Genhe City and Zhalantun is dominated by human activities factors, while Manzhouli, Hailar and Oroqen Autonomous Banner are affected by climatic factors and human activities factors. The influence of driving factors on Hulunbuir's FVC changes is bidirectional. With the correct policies and reasonable planning, it is possible to achieve a win-win situation for socio-economic development and sustainable development of the ecological environment. The research results provide the theory and data support for Hulunbuir's sustainable development of ecological environment and grassland protection policy.

Abstract. Terrestrial vegetation dynamics are closely influenced by both climate and by both climate and by land use and/or land cover change (LULCC) caused by human activities. Both can change over time in a monotonic way and it can be difficult to separate the effects of climate change from LULCC on vegetation. Here we attempt to attribute trends in the fractional green vegetation cover to climate variability and to human activity in Ejina Region, a hyper-arid landlocked region in northwest China. This region is dominated by extensive deserts with relatively small areas of irrigation located along the major water courses as is typical throughout much of Central Asia. Variations of fractional vegetation cover from 2000 to 2012 were determined using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data with 250 m spatial resolution over 16-day intervals. We found that the fractional vegetation cover in this hyper-arid region is very low but that the mean growing season vegetation cover has increased from 3.4% in 2000 to 4.5% in 2012. The largest contribution to the overall greening was due to changes in green vegetation cover of the extensive desert areas with a smaller contribution due to changes in the area of irrigated land. Comprehensive analysis with different precipitation data sources found that the greening of the desert was associated with increases in regional precipitation. We further report that the area of land irrigated each year can be predicted using the runoff gauged 1 year earlier. Taken together, water availability both from precipitation in the desert and runoff inflow for the irrigation agricultural lands can explain at least 52% of the total variance in regional vegetation cover from 2000 to 2010. The results demonstrate that it is possible to separate the satellite-observed changes in green vegetation cover into components due to climate and human modifications. Such results inform management on the implications for water allocation between oases in the middle and lower reaches and for water management in the Ejina oasis.

The present study explored both the temporal variation and spatial distribution of fractional vegetation cover (FVC) and land surface temperature (LST) in the Greater Khingan Mountains, a location distinguished by four types of surface cover formations and strong gradients in meteorological conditions. Furthermore, we assessed the relationships between FVC and LST in different time-space dimensions. We measured the spatio-temporal variability in LST through a harmonic analysis of time series (HANTS) for 8 days of LST time series product data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the FVC was extracted through linear spectral mixture analysis (LSMA). The results show that the time series image data reconstructed via HANTS are effective for quantifying the morphological dynamics of the thermal environment and enabling sound calculations of environmental variables, such as vegetation abundance. We found significant differences in within-year and interannual variation among different eco-geographical regions. The within-year maximum value of vegetation coverage was observed in July, whereas the surface temperature was highest in June. This finding suggests that the decrease in warming can be mostly attributed to the increase in evapotranspiration associated with increased vegetation activity. In addition, a strong negative correlation was found between the FVC and LST throughout the study area only from April to September, whereas a triangular relationship was found in other months. This study investigated the time series variations in different eco-geographical regions for 2000 – 2015 and revealed that the most obvious LST and FVC variations occurred at the region II and junction between regions III and IV. An analysis of the average FVC and LST from April to September indicated that 86.9% of the entire study area showed a negative correlation, such that when the FVC increased by 10%, the LST showed a maximum average decrease of 2.76°C. We conclude that the increase in vegetation activity is the main cause of the reduction in the LST and that surface activities and latitude zonality are the main driving factors of the LST differentiation in the Greater Khingan Mountains. These findings will serve as a foundation for future studies seeking to better understand climate change processes and to estimate ecosystem responses to changing climatic conditions.

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.

Estimation of fractional vegetation cover dynamics based on satellite remote sensing in pakistan: A comprehensive study on the FVC and its drivers

The impacts of climate and the need to improve resilience to current and possible future climate are highlighted in the UN’s Sustainable Development Goal (SDG) 13. Vegetation in the Amur River Basin (ARB), lying in the middle and high latitudes and being one of the 10 largest basins worldwide, plays an important role in the regional carbon cycle but is vulnerable to climate change. Based on GIMMS NDVI3g and CRU TS4.01 climate data, this study investigated the spatiotemporal patterns of fractional vegetation cover (FVC) in the ARB and their relationships with climatic changes from 1982 to 2015 varying over different seasons, vegetation types, geographical gradients, and countries. The results reveal that the FVC presented significant increasing trends (P < 0.05) in growing season (May to September) and autumn (September to October), but insignificant increasing trends in spring (April to May) and summer (June to August), with the largest annual FVC increase occurring in autumn. However, some areas showed significant decreases of FVC in growing season, mainly located on the China side of the ARB, such as the Changbai mountainous area, the Sanjiang plain, and the Lesser Khingan mountainous area. The FVC changes and their relationships varied among different vegetation types in various seasons. Specifically, grassland FVC experienced the largest increase in growing season, spring, and summer, while woodland FVC changed more dramatically in autumn. FVC correlated positively with air temperature in spring, especially for grassland, and correlated negatively with precipitation, especially for woodland. The correlations between FVC and climatic factors in growing season were zonal in latitude and longitude, while 120° E and 50° N were the approximate boundaries at which the values of mean correlation coefficients changed from positive to negative, respectively. These findings are beneficial to a better understanding the responses of vegetation in the middle and high latitudes to climate change and could provide fundamental information for sustainable ecosystem management in the ARB and the northern hemisphere.

Long-term global land surface fractional vegetation cover (FVC) data are essential for global climate modeling, earth surface process simulations, and related applications. However, high quality and long time series global FVC products remain scarce, although several FVC products have been generated using remote sensing data. This study aims to use the previously proposed Global LAnd Surface Satellite (GLASS) FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data (denoted as GLASS-MODIS FVC) to generate a long term GLASS FVC product from advanced very high resolution radiometer (AVHRR) data (denoted as GLASS-AVHRR FVC) back to year 1981. The GLASS-AVHRR FVC algorithm adopted the multivariate adaptive regression splines method, which was trained using samples extracted from the GLASS-MODIS FVC product and the corresponding red and near-infrared band reflectances of the preprocessed AVHRR reflectance data from 2003 over the global sampling locations. The GLASS-AVHRR FVC product has a temporal resolution of eight days and a spatial resolution of 0.05°. Through comparison of the GLASS-AVHRR and GLASS-MODIS FVC products from 2013, good temporal and spatial consistencies were observed, which confirmed the reliability of the GLASS-AVHRR FVC product. Furthermore, direct validation using field FVC measurement based reference data indicated that the performance of the GLASS-AVHRR FVC product (R2 = 0.834, RMSE = 0.145) was slightly superior to that of the popular long term GEOV1 FVC product (R2 = 0.799, RMSE = 0.174).