Vegetation Net Primary Productivity Research Articles

The Impact of Seasonal Climate on Dryland Vegetation NPP: The Mediating Role of Phenology

Dryland ecosystems are highly sensitive to climate change, making vegetation monitoring crucial for understanding ecological dynamics in these regions. In recent years, climate change, combined with large-scale ecological restoration efforts, has led significant greening in China’s arid areas. However, the mechanisms through which seasonal climate variations regulate vegetation growth are not yet fully understood. This study hypothesizes that seasonal climate change affects net primary productivity (NPP) of vegetation by influencing phenology. We focused on China’s Windbreak and Sand-Fixation Ecological Function Conservation Areas (WSEFCAs) as representative regions of dryland vegetation. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate vegetation NPP from 2000 to 2020. To extract phenological information, NDVI data were processed using Savitzky–Golay (S–G) filtering and threshold methods to determine the start of season (SOS) and end of season (EOS). The structural equation model (SEM) was constructed to quantitatively assess the contributions of climate change (temperature and precipitation) and phenology to variations in vegetation NPP, identifying the pathways of influence. The results indicate that the average annual NPP in WSEFCAs increased from 55.55 gC/(m2·a) to 75.01 gC/(m2·a), exhibiting uneven spatial distribution. The pathways through which seasonal climate affects vegetation NPP are more complex and uneven. Summer precipitation directly promoted NPP growth (direct effect = 0.243, p < 0.001) while also indirectly enhancing NPP by significantly advancing SOS (0.433, p < 0.001) and delaying EOS (−0.271, p < 0.001), with an indirect effect of 0.133. This finding highlights the critical role of phenology in vegetation growth, particularly in regions with substantial seasonal climate fluctuations. Although the overall ecological environment of WSEFCAs has improved, significant regional disparities remain, especially in northwestern China. This study introduces causal mediation analysis to systematically explore the mechanisms through which seasonal climate change impacts vegetation NPP in WSEFCAs, providing new insights into the broader implications of climate change and offering scientific support for ecological restoration and management strategies in arid regions.

Benefit evaluation of sloping land conversion program in the upper reaches of the Yellow River

This study used forestry statistical data and field survey data from 2000 to 2018 to analyze the ecological restoration effect of the returning farmland to forest project in the upper reaches of the Yellow River, considering four perspectives, namely, project construction goals, ecosystem quality, ecosystem service functions, and ecological restoration driving factors. One-way ANOVA and logit regression analysis methods were used for comparison. The results show that the implementation of the Grain for Green Project has reduced the carrying capacity of rural land, effectively curbed the continuous deterioration of the ecological environment, and protected and benefited the forest ecosystem. (1) Initiatives of the return farmland to forest project have achieved the expected goals, and the forest coverage rate has increased significantly from 5.26 % (in 2000) to 15.63 % (in 2019); the proportion of primary, secondary, and tertiary industries related to forestry has reached a reasonable level, and the economic benefits thereof continue to improve. (2) Indicators such as total forest and grass vegetation coverage, vegetation net primary productivity, and forest and grass health index reflect that the functional quality of the regional forest and grass ecosystem is gradually improving. (3) The ecosystem service function of the project region has improved considerably, and the ecological benefits are significant. (4) In terms of the contribution value of the driving forces for this ecological restoration project, they can be ranked as follows: government investment > reducing land carrying capacity > policy of returning farmland to forest > government management > hydrological factors > meteorological factors. (5) After the implementation of the Grain for Green Project, farmers' income has increased significantly, but the changes in livelihood capital income are not synchronized. As the ecological environment in the Grain for Green project areas gradually improved from deterioration, the natural capital and social capital of farmers' livelihood capital decreased significantly, while the physical capital and financial capital increased significantly, with no significant change in human capital. Farmers' satisfaction with the Grain for Green Project is as follows: natural capital has a significant negative impact, while financial capital and social capital have significant positive impacts, and there are no significant changes in physical capital and human capital. Finally, the consistency test results show that the fit is as high as 95 %, and the accuracy exceeds 90 %. Notably, the implementation of the Grain for Green Project has played a pivotal role in ecological restoration and contributed greatly to ecological benefits. These achievements provide a scientific basis and theoretical support for the country to formulate relevant policies for forestry projects, which holds great significance for the country in addressing the contradiction between economic and social development and ecological protection.

Analysis of spatial and temporal variations of vegetation NPP and TWS in the Yangtze River Basin

Net primary productivity (NPP) is an important parameter reflecting vegetation growth, and water is one of the necessary factors for vegetation growth. Investigating the mutual influence between NPP and water is significant for ensuring the stable development of the ecological environment. This study focuses on the Yangtze River Basin (YRB) as the research area, and based on medium-resolution imaging spectrometer (MODIS) data, climate data, and gravity recovery and climate experiment (GRACE) data, the spatiotemporal evolution characteristics of vegetation NPP and terrestrial water storage (TWS) in the YRB from 2000 to 2022 are explored and analyzes the mutual influence of NPP with climate factors and TWS. The results show that vegetation NPP (4.10 gC·m−2·a−1) and TWS (0.55 mm) in the YRB have exhibited an increasing trend from 2000 to 2022, with a strong correlation between the two, which is related to recent environmental policies. Analysis of the impact of climate factors on NPP reveals that temperature and TWS significantly positively impact NPP changes. Furthermore, comparisons between NPP and TWS indicate that changes in TWS substantially promote plant growth. In addition, the comparison between NPP and TWS indicates that changes in TWS have an important promoting effect on plant growth. Surface water (SWS) and soil water (SM) have a significant promoting effect on plant growth, but with a strong lag, while the consumption of groundwater (GWS) has been promoting plant growth without significant lag.

Spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen’s tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate.

Detecting Drought-Related Temporal Effects on Global Net Primary Productivity

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation.

Attribution of climate change and human activities to spatiotemporal changes of ecological service value in Yunnan Province of China

Quantifying the influence of climate change and human activities on spatiotemporal changes of regional ecological service value (ESV) can provide key information for regional ecosystem protection. However, few studies have distinguished the contributions of climate change and human activities to the total ESV. In this study, Yunnan Province in Southwest China with high biodiversity and ESV was selected as a representative region for the attribution analysis of ESV changes. The Carnegie-Ames Stanford Approach (CASA) was used to calculate the vegetation net primary productivity (NPP), and improved equivalent factor method was used to calculate ESVs of different ecosystem service functions directly from NPP. Then, the multi-factor attribution analysis method was used to quantify the contribution rates of climate change and human activities to changes in ESV. Results show that the ESV in Yunnan Province increased by 12.9 % in 2000–2019 compared to 1980–1999 without considering economic development. Land use and cover change (LUCC) is the main cause for the increase in ESV with the contribution rate of 92.3 %, while that of climate change is only 7.7 %. Major components of ESV change are ESV changes in forests, grasslands, and farmland, which are all mainly caused by LUCC with the contribute rates of over 80 %. ESVs in different cities differ greatly, where the ESV and ESV per unit area in Pu'er City are 5.3 and 2.6 times as those in Zhaotong City, respectively. LUCC is also the main cause of ESV changes in all cities, with contributions rates generally exceeding 80 %. When considering economic development, the ESV in Yunnan has increased by 336.2 % in these two periods. Contribution rate of economic development to ESV increase in Yunnan is 80.3 %, while that of climate change and LUCC is only 19.7 %. The speed of ecological construction in Yunnan Province is far slower than that of economic development, and appropriate measures should be taken to improve the ecological services in the future.

Dynamic Analysis and Risk Assessment of Vegetation Net Primary Productivity in Xinjiang, China

Dynamic Analysis and Risk Assessment of Vegetation Net Primary Productivity in Xinjiang, China

Assessing the Response of the Net Primary Productivity to Snow Phenology Changes in the Tibetan Plateau: Trends and Environmental Drivers

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP.

Spatial-Temporal Variations and Influencing Factors of Vegetation Net Primary Productivity: A Case Study of Yunnan Province, China

Spatial-Temporal Variations and Influencing Factors of Vegetation Net Primary Productivity: A Case Study of Yunnan Province, China

Integrating calibrated PTFs and modified OpenKarHydro framework to map the responses of ecohydrological processes to climate change across the Loess Plateau

Climate change is exacerbating the risk of soil water stress, soil erosion and ecological degradation in the Loess Plateau (CLP). However, the spatiotemporal dynamics of ecohydrological processes, including soil water balance (SWB), soil erosion (SE) and vegetation net primary productivity (NPP), in response to climate change remain isolated and ambiguous. Additionally, existing studies often rely on limited measured data from specific watersheds with particular land use/cover types to establish the pedotransfer functions (PTFs) for single soil hydraulic parameter (SHP). There is a notable lack of comprehensive studies that explore PTFs encompassing all SHPs applicable to the entire CLP, predict the spatiotemporal response of ecohydrological processes to climate change, and identify future risk periods and regions for SWB, SE, and NPP. To address these gaps, we first screened the PTFs applicable for the whole CLP, and then projected the spatiotemporal dynamics of ecohydrological processes from 2020 to 2030 under CMIP6 scenarios by integrating the PTFs with the OpenKarHydro, RUSLE, and CASA model, and finally classified the soil water content, SE and NPP to identify their risk periods and regions. The results showed that the PTFs achieved satisfactory accuracy, with RMSEs for Ks, θs, θr, α, n, θfc and θw being 2.682, 0.109, 0.016, 0.111, 0.897, 0.060 and 0.058, BIASs of 1.365, 0.182, 0.012, 0.031, 0.214, 0.057 and 0.048, R2s of 0.445, 0.500, 0.430, 0.400, 0.694, 0.453 and 0.453, and NSEs of 0.645, 0.737, 0.874, 0.349, 0.567, 0.756 and 0.458, respectively. SE decreased significantly from 2020 to 2030, with an average annual rate of −6.18 %. Soil water storage decreased significantly between 2020 and 2030, and declining from southeast to northwest. The proportion of area in the wet zone decreased by 4 %, while the proportion of area in the dry zone increased by 11.9 % from 2020 to 2030. The average NPP in 2020–2030 is 320.07 gC·m−2·a−1, with the largest in summer and smallest in winter, and decreasing from southeast to northwest. The NPP increased significantly in 2020–2030, with average annual value and rate of 19.61 gC·m−2·a−1 and 70.84 %, respectively. The NPP increased significantly in spring, summer and autumn, but remained stable in winter. This investigation bridges the gap between existing soil properties, missing SHPs, the strong regional applicability of PTFs, and isolated ecohydrological processes. It is promises to provide valuable insights into the response to climate change in the CLP and another water-limited regions.

Analysis of Temporal and Spatial Carbon Stock Changes and Driving Mechanism in Xinjiang Region by Coupled PLUS-InVEST-Geodector Model

Based on the goal of "dual-carbon" strategy, it is important to explore the impacts of land use change on carbon stock and the drivers of spatial differentiation of carbon stock in Xinjiang. Here, we predicted the land use types in Xinjiang in 2035 under different scenarios and analyzed the impacts of land use on carbon stock, which is of great theoretical and practical importance for policy formulation, land use structure adjustment, and carbon neutrality target achievement in Xinjiang. The coupled PLUS-InVEST-Geodector model was used to explore the spatial and temporal patterns of carbon stock change under the scenarios of rapid development, natural change, arable land protection, and ecological protection in Xinjiang in 2035 and to quantitatively reveal the attribution of influences on the changes in carbon stock from the perspectives of land use change and the combination of nature-socioeconomic-accessibility. The results showed that： ① From 1990 to 2020, the area of arable land and construction land in Xinjiang increased, and in terms of the transfer direction, it was mainly shifted from unutilized land to grassland. ② On the time scale, the carbon stock in Xinjiang showed the fluctuation of "decrease-increase-decrease," with an overall increasing trend. The transfer of unutilized land to grassland was the main reason for the increase in carbon stock； on the spatial scale, the carbon stock in the Altai Mountains in the north, the Tianshan Mountains in the middle, and the Kunlun Mountains in the south was higher, whereas the carbon stock in the Tarim Basin and the Junggar Basin was lower. ③ In 2035, the carbon stock of the natural development and rapid development scenarios decreased by 27.24 Tg and 71.17 Tg compared with 2020, respectively, and the ecological protection and arable land protection scenarios increased by 492.55 Tg and 46.67 Tg. The ecological protection scenario could significantly increase the carbon stock of the Xinjiang Region compared with that in the other scenarios, and the distribution pattern of the carbon stock in the four scenarios was more or less the same as that in 2020. In addition to land transformation, soil erosion intensity was the main driver of spatial differentiation of carbon stocks in Xinjiang （q value of 0.3501）, followed by net primary productivity of vegetation. The results of multifactor interactions showed that the spatial differentiation of carbon stocks in Xinjiang was the result of the joint action of multiple factors. All the factors had a synergistic enhancement under the interactions. The interaction between soil erosion intensity and the net primary productivity of vegetation was the main driver of the spatial differentiation of carbon stocks in Xinjiang.

Characteristics of Vegetation and Soil of Degraded Grasslands and Their Relationships in Xizang

To investigate the characteristics of grassland degradation on a regional scale in Xizang, data on grassland degradation from the second grassland survey of Xizang and 12 vegetation and soil indicators from the National Tibetan Plateau Data Center were collected. Using ArcMap, 10 000 random sample points were selected on raster data （excluding non-grassland, desertification, and salinization data, leaving 7 949 valid sample points）. The multi-value extraction to-point method was applied to extract degradation and indicator data for each sample point. The characteristics of degraded grassland vegetation and soil and their relationships were analyzed in Xizang. Moreover, random forest modeling was conducted to predict the trend of grassland ecosystem changes. The results indicated that： ① The grasslands in Xizang were primarily composed of alpine steppe and alpine meadow types, accounting for 45.83% and 41.15% of the valid sample points, respectively. ② With the intensification of grassland degradation, the number of steppe-type species among the 17 grassland types gradually decreased, and the proportion of steppe dominated by species such as Stipa purpurea and Carex moorcroftii decreased, whereas the proportion of miscellaneous grasses and Dasiphora fruticosa increased. ③ As the degree of degradation increased, vegetation indicators generally showed a declining trend, with soil total nitrogen, total phosphorus, total potassium, and organic carbon decreasing, whereas soil pH and bulk density increased, and soil moisture content was not significant. ④ A positive correlation exists between soil moisture content, total nitrogen, total phosphorus, total potassium, organic carbon, vegetation cover, net primary productivity of vegetation, normalized difference vegetation index, aboveground biomass, and habitat quality. However, there was a negative correlation between pH and soil bulk density, and the correlation coefficients among various indicators decreased with the intensification of degradation. ⑤ The random forest simulation results showed that during the degradation process, the contribution rates of soil bulk density and habitat quality both exceeded 12%, with the model prediction accuracy reaching 78%. The study revealed that grassland degradation in Xizang was closely related to soil bulk density and habitat quality, indicating that higher soil bulk density or lower habitat quality may correspond to more severe grassland degradation. This provides a scientific basis for future grassland conservation and management.

Exploring Spatial Non-Stationarity and Scale Effects of Natural and Anthropogenic Factors on Net Primary Productivity of Vegetation in the Yellow River Basin

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB.

Spatial spillover effects of ecosystem service values in northeast China tiger and leopard national park based on spatial Durbin model

National parks are public green spaces planned and managed by the state, with the primary purpose of protecting natural ecosystems and cultural heritage. Evaluating the Ecosystem Service Value (ESV) of national parks and clarifying their ecological benefits can provide important scientific basis for the ecological protection and restoration of national parks. Therefore, taking the Northeast China Tiger and Leopard National Park (NCTLNP) as the study area, this research constructs an ESV assessment model based on Land Use and Land Cover Change (LUCC), using vegetation net primary productivity（NPP）as a biomass correction factor to modify the Equivalent Factor Approach (EFA). It aims to explore the spatial–temporal pattern changes of the ESV in the NCTLNP from 2000 to 2022. Additionally, the Future Land Use Simulation (FLUS) model is utilized to simulate the future LUCC pattern and ESV evolution trend under ecological protection. Finally, the spatial Durbin model (SDM)is employed, combining socio-economic and environmental factors such as population, GDP, and PM10 and so on, to reveal the driving mechanism of spatial spillover effects of ESV in the NCTLNP. The results indicate that:①The accuracy of the FLUS model is 93.12% with a Kappa coefficient of 0.87. The main LUCC of the NCTLNP is forest. Before 2020, the ecological land had a shrinking trend, while impervious is in a significant increasing trend. After 2020, there is an opposite trend.The most significant changes in LUCC were found in Wangqing County and Hunchun City. ②The ESV of the NCTLNP shows a fluctuating and increasing trend from 2000 to 2022. It increased from 69.315 billion yuan to 69.809 billion yuan. The main service function of the ecosystem in the study area is the regulatory services, which provide 58.03% of the ESV to the NCTLNP. The Hunchun City and Wangqing County have the fastest increasing rate. ③The ESV of the NCTLNP had a significant negative spillover effect, with spatial spillover coefficients of −0.135 and −0.236, respectively, which led to a siphoning phenomenon in the NCTLNP. Overall, by assessing the ESV of the NCTLNP and revealing the spatial spillover effects in the study area, it can provide a vital scientific foundation for the conservation and management of the national park, which will help to fulfill the sustainable development of the ecosystem and the conservation of biodiversity.

Spatio-Temporal Changes and Driving Mechanisms of Vegetation Net Primary Productivity in Xinjiang, China from 2001 to 2022

Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in Xinjiang and projects future trends using Theil-Sen trend analysis, the Mann–-Kendall test, and the Hurst Index. By integrating meteorological data, this study employs partial correlation analysis, the Miami model, and residual analysis to explore the driving mechanisms behind NPP changes influenced by climatic factors and human activities. The results indicate that: (1) The average NPP in Xinjiang has increased over the years, displaying a spatial pattern with higher values in the north and west. Regions with increasing NPP outnumber those with declining trends, while 75.18% of the area shows un-certain future trends. (2) Precipitation exhibits a stronger positive correlation with NPP compared to temperature. (3) Climate change accounts for 28.34% of the variation in NPP, while human activities account for 71.66%, making the latter the dominant driving factor. This study aids in monitoring ecological degradation risks in arid regions of China and provides a scientific basis for developing rational coping strategies and ecological restoration initiatives.

Spatiotemporal Variation Characteristics and Driving Factors of Vegetation NPP in the Ulansuhai Nur Basin from 2001 to 2020

Vegetation net primary productivity （NPP） represents the ability of plants to fix ecosystem carbon, which is a key indicator to determine the health status and sustainable development of ecosystems. Its spatial and temporal changes and driving factors play an important role in revealing the status of vegetation restoration and guiding ecological restoration. Based on MODIS17A3 NPP data, land use, and meteorological data from 2001 to 2020, the temporal and spatial variation characteristics and driving factors of vegetation NPP in the Ulansuhai Nur Basin of Inner Mongolia were explored by using the methods of coefficient of variation, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and Geodetector. The results showed that： ① From 2001 to 2020, the vegetation NPP in the Ulansuhai Nur Basin showed a fluctuating upward trend, with an average value （in terms of C） of 141.03 g·（m2·a）-1 and an average increase rate of 2.33 g·（m2·a）-1. The vegetation NPP had obvious spatial differentiation, which was characterized by high in the southwest and low in the northeast and high in Hetao Plain and low in sandy land and mountainous areas. ② NPP mainly showed an increasing trend, and the area proportions of increasing, decreasing, and unchanged areas were approximately 80%, 3%, and 17%, respectively. The average coefficient of variation of vegetation NPP was 0.149, which mainly showed low fluctuation change, and the area accounted for approximately 51%. The future change trend of NPP was mainly characterized by anti-persistence, with an area ratio of approximately 75%. ③ Land use, altitude, maximum temperature, and slope were the dominant driving factors of variation NPP change in the Ulansuhai Nur Basin, and the q values were all above 0.200. The interaction between altitude and relative humidity had the greatest explanatory power for the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. There were significant differences in the explanatory power of land use and all factors except nighttime light to the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. According to the research results, in the future, we should strengthen the ecosystem management of the Ulansuhai Nur Basin； continue to implement strict ecological protection and restoration policies； and comprehensively consider factors such as climate, topography, and human activities to carry out comprehensive ecological management according to local conditions to improve the quality of ecosystem services.

Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors of Vegetation Net Primary Productivity in Shendong Mining Area

Coal plays a key role in the composition of energy production and consumption in China. The mineral is a crucial resource for human economic and social development. The surface subsidence caused by coal mining has attracted disturbance and damage to the surface ecological environments of mining areas. To quantitatively study vegetation carbon sequestration in coal mining areas, this article takes the Shendong mining area as its research object and analyzes the spatiotemporal distribution characteristics and influencing factors of vegetation net primary productivity (NPP). Based on meteorological data, remote sensing images, and land use data in the mining area, this research analyzed the vegetation NPP and its relationship with meteorological factors in the Shendong mining area from 2000 to 2023. The results show that (1) in the past 24 years, the range of NPP variation in the Shendong mining area has been 0–350 gC/m2. The overall NPP shows a fluctuating upward trend. The annual average change in NPP in the mining area is bounded by the two central dividing lines, with a lower value in the central part and a gradually increasing trend in the western and eastern parts. (2) The average vegetation NPP of the Shendong mining area over 24 years was 0–250 gC/m2; after 2011, the annual average NPP of vegetation was higher than this average level, which shows that the ecological environment of the mining area is gradually improving. Coal mining in the mining area has not caused a significant impact on the overall environment of the mining area. (3) There is a significant relationship between NPP and annual cumulative rainfall, average annual temperature, and average annual Normalized Difference Vegetation Index (NDVI). Overall, it is not significantly related to mining factors. The mining factors affect local areas and have a certain correlation.

Internal response of vegetation growth to degrees of permafrost degradation in Northeast China from 2001 to 2020

ABSTRACT Ecosystems at the southern edge of the permafrost distribution are highly sensitive to global warming. Changes in soil freeze-thaw cycles can influence vegetation growth in permafrost regions. Extant studies mainly focused on analyzing the differences of vegetation dynamics in different permafrost regions. However, the intrinsic drivers of permafrost degradation on vegetation growth remain elusive yet. Based on the top temperature of permafrost (TTOP) model, we simulated the spatial distribution of permafrost in Northeast China (NEC) from 2001 to 2020. Using the data of the vegetation Net Primary Productivity (NPP), vegetation phenology, climate and permafrost phenology, and analytical methods including partial correlation, multiple linear regression, and path analysis, we explored the response of vegetation growth and phenology to soil freeze-thaw changes and climate change under different degrees of permafrost degradation. Overall, the start date of the growing season (SOS) was very sensitive to the start date of soil thaw (SOT) changes, and multiple regression analyses showed that SOT was the main factor influencing SOS in 41.8% of the NEC region. Climatic factors remain the main factors affecting vegetation NPP in NEC, and the results of partial correlation analysis showed that only 9.7% of the regional duration of soil thaw (DOT) had a strong correlation with vegetation NPP. Therefore, we determined the mechanism responsible for the soil freeze-thaw changes and vegetation growth relationship using the path analysis. The results indicated that there is a potential inhibitory effect of persistent permafrost degradation on vegetation growth. Our findings would contribute to the improvement of process-based models of forest dynamics in the boreal region, which would help to plan sustainable development and conservation strategies in permafrost areas.

Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China

Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China

A dataset of vegetation net primary productivity at 250-meter spatial resolution in the Three-River Headwaters Region of Qinghai Province from 2000 to 2018

Net primary productivity (NPP) is a fundamental indicator quantifying photosynthetic production in ecology. With the development of ecosystem process simulation and satellite remote sensing technology, NPP is widely applied to exploring long-term spatiotemporal dynamics and underlying mechanism in terrestrial ecosystems with high spatial heterogeneity. Using the remote sensing ecological process coupling model GLOPEM-CEVSA and incorporating meteorological data interpolated from 2000 to 2018 the remote sensing-based vegetation photosynthetically active radiation absorption (FPAR) data, we obtained a dataset of vegetation net primary productivity at 250-meter spatial resolution for the Three-River Headwaters Region of Qinghai Province from 2000 to 2018. The dataset was made accessible to the public through the National Data Center for Ecological Sciences. The data sharing is expected to provide reliable scientific data to support for the study of spatial and temporal changes and the decision making related to sustainable development in the region.