Changes In Net Primary Productivity Research Articles

Impacts of urbanisation on vegetation dynamics in Chinese cities

Urbanisation is a global phenomenon that dramatically impacts ecosystems and their services. In recent years, urbanisation in China increased substantially. To improve the urban environment, the Chinese government has implemented several programs to green the cities' central areas and limit forest and grasslands in expanding areas. However, more information is needed about the trend and the factors affecting vegetation status in urban centres and expanding areas. Here we studied the vegetation in these areas in 3345 cities in China using 30 m spatial resolution Annual Net Primary Production (NPP) Change (ANC) and NPP Change Rate (ANCR) in 2000 and 2020. The results showed that most Chinese cities' central areas have a greening trend. However, vegetation cover decreases in expansion areas, mainly in southern cities. The opposite is observed in some urban areas in the country's North. This is mostly attributed to the highest urbanisation trends observed in the South than in the North. The most important factors that affected the ANC trend were mean temperature/precipitation, expansion area, and CO2 changes. ANCR was mainly affected by mean temperature/precipitation. Political decisions such as increasing the greening areas in city centres in China also affected the increase of vegetation in urban cores. In some cities located in the North, the investment in green infrastructure in expansion areas may have also contributed to increased vegetation. The results are important for managers to improve cities' livability, especially in the South of China, where the degradation of the ecosystems is high in expansion areas.

Compensatory response of ecosystem carbon-water cycling following severe drought in Southwestern China

Climate change has increased the frequency and length of droughts, but many uncertainties remain regarding the impacts of this aridification on terrestrial ecosystem function. Vegetation water use efficiency and carbon sequestration capacity are crucial determinants that both respond to and mediate the effects of drought. However, it is important to note that the consequences of drought on these processes can persist for years. A deeper exploration of these “drought legacy effects” will help improve our understanding of how climate change alter ecosystem carbon-water cycling. Here, we investigate the spatial patterns of drought legacy effects on remotely-sensed vegetation greenness (NDVI), net primary productivity (NPP) and water use efficiency (WUE) in southwestern China, a biodiversity hotspot that was impacted by an extreme drought in 2009–2010, with a particular focus on the tradeoff between WUE and NPP. Despite widespread negative drought legacy effects in NDVI (impacting 61.26 % of the study region), there was a general increase in NPP (58.68 %) and a decrease in WUE (67.53 %) in the year following drought (2011). This drought legacy effect was most evident in forests, while drought legacies in grasslands were less common. Drought legacies were also most apparent in relatively warm and humid areas. During the study period (2002 to 2018), we found that drought impacts on WUE also lagged behind changes in NPP by 1–2 years in forests, which highlights how drought legacies may manifest differently across ecosystem processes. Our study demonstrated that severe drought conditions may significantly affect the carbon sequestration capacity and water use efficiency of vegetation in southwestern China, and that forests may compensate for the detrimental effects of water stress by increasing water use and biomass growth after drought episodes.

Spatiotemporal characteristics and influencing factors of net primary production from 2000 to 2021 in China.

With the rapid development of remote sensing, variously high temporal and spatial resolution products of different sensors were gradually applied aspects of researches, which could achieve rapid and low-cost monitors of terrestrial environment. It was meaningful to analyze the latest and long-term changes of net primary production (NPP), which could reflect the human-induced effects on ecological environment. In our study, we used Sen's slope and Mann-Kendall test to analyze the spatiotemporal changes of NPP. Then, we used fluctuation model and Moran model to reveal the stability and clusters of NPP, respectively. Next, we quantitatively analyzed NPP changes in the perspectives of land use types and provinces. Finally, we used geographically weighted regression (GWR) model to analyze effects of different factors on NPP. The result showed that NPP presented significant increase in most areas of China from 2000 to 2021. Especially, Loess Plateau showed obvious NPP increase. Meanwhile, "high-high" cluster of NPP difference were mainly distributed in the ecological policies-influenced areas. The slope in 5-15° has the highest growth trend, and the slope > 25° has the slowest growth trend. Cropland, forests, and shrub revealed an obvious improvement of NPP, which indicated afforestation and intensive farmed played a key role. Temperature, precipitation, population density, and elevation had significant effects on NPP (p < 0.05) in 2000, 2011, and 2021. The degree of effects of human activities was gradually increase in GWR model. In this scenario, related ecological policies had vita influencing on NPP improvement. Our study could provide a help for monitor of ecological environment and government policies.

Divergent Responses of NPP to Climate Factors among Forest Types at Interannual and Inter-Monthly Scales: An Empirical Study on Four Typical Forest Types in Subtropical China

Identifying the main climatic controls of productivity differences in different forest ecosystems is beneficial for revealing the patterns of carbon cycle changes in terrestrial ecosystems. Moreover, exploring the differences in their productivity responses to temperature and precipitation at interannual and inter-monthly scales is conducive to identifying the mechanisms of carbon cycle changes in forest ecosystems, which can enhance the study of the forest ecosystem carbon cycle. In addition, the effect of climate factor changes on the net primary productivity (NPP) of typical forest types in subtropical areas remains unclear. Here, we simulated the dynamic changes in the NPP of four typical forest types (evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), bamboo forest (BF), and evergreen needleleaf–broadleaf mixed forest (ENBMF)) in the Poyang Lake Basin from 1970 to 2021 using the Biome-BGC model where its parameters were adjusted according to the ecophysiological characteristics of forest types in this study. Then, the correlation between the NPP of the four typical forest types and climate factors was examined at interannual and inter-monthly scales; the response of the NPP of four typical forest types to future temperature and precipitation changes was explored. The results revealed that NPP showed a fluctuating downward trend after 2000 in ENF, EBF, and ENBMF. The NPP of the ENF was primarily driven by precipitation at the interannual and inter-monthly levels (p &lt; 0.01). The impact of precipitation and accumulated temperature (AT) on NPP of EBF is not significant interannually; the NPP of BF demonstrated a minor correlation with AT and no significant correlation with precipitation. However, the NPP variation of EBF and BF was significantly affected by the temperature at the inter-monthly scale (p &lt; 0.01), with R2 of 0.85 and 0.92, respectively. At the interannual scale, the NPP of ENBMF was mainly driven by precipitation (p &lt; 0.01); at the inter-monthly scale, it was driven by precipitation (p &lt; 0.01) and AT (p &lt; 0.01), with R2 of 0.74 and 0.62, respectively. Under designed climate scenarios, the precipitation changes will have a greater impact on NPP of ENF; the NPP changes in the EBF, BF, and ENBMF will be mainly controlled by temperature increase, and only a 10% change in precipitation leads to its smaller impact on their NPP changes.

Identifying key landscape pattern indices influencing the NPP: A case study of the upper and middle reaches of the Yellow River

Human activities and climate change directly affect the composition, structure, and function of ecosystems and, consequently, their net primary productivity (NPP). In this study, we explored the response relationships between landscape pattern indices and NPP changes in three major vegetation types (forest, grassland, and shrubland) in the upper and middle reaches of the Yellow River Basin using a random forest model. The results showed that landscape fragmentation increased, leading to higher landscape heterogeneity and edge effects. Patch shapes became more irregular, and spatial distribution became more dispersed. From 2000 to 2015, both vegetation types and NPP showed significant spatial heterogeneity in the study area. The number of patches (NP), largest patch index (LPI), and percent-like adjacency (PLADJ) metrics were used to determine the core landscape characteristics to assess the NPP changes in forest, shrubland, and grassland, respectively. This study provides a basis for understanding the relationships among landscape patterns, vegetation types, and NPP and serves as a reference for developing NPP predictive models in the Loess Plateau region.

Quantitative analysis of spatiotemporal changes and driving forces of vegetation net primary productivity (NPP) in the Qimeng region of Inner Mongolia

Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest.

Evaluation of Future Trends Based on the Characteristics of Net Primary Production (NPP) Changes over 21 Years in the Yangtze River Basin in China

As the third largest river basin in the world, the Yangtze River basin in China has vegetation ecosystems in its plain, mountain, and alpine regions. Studying the change characteristics of the vegetation’s net primary productivity (NPP) and its relationship with natural factors and human activities can aid with understanding, to a certain extent, the response of the ecosystem to global climate change. Based on a total of 21 years of MOD17A3 data products from 2000 to 2020, this paper analyzed the spatial variation characteristics and future trends of the NPP in this region by using the coefficient of variation (CV), trend analysis (β), and Hurst index (H) methods. Meanwhile, correlation analysis was used to explore the influence of natural factors and human activities on the NPP. The results show the following: (1) the total amount of the NPP in the Yangtze River Basin was relatively high, and the overall change trend is rising, while the inter-annual fluctuation is evident. The total amount of NPP ranges from 0.786 PgC (2000) to 1.024 PgC (2020), and the annual average was 0.932 PgC. This increase was mainly caused by the increase in the average NPP of forest land, cultivated land, and construction land. (2) The mean value of the NPP in the different regions of the Yangtze River Basin ranged from 0 (construction land) to 1902.89 gC/m2·a. The mean value of the NPP in the Yangtze River Basin was high in the south and low in the north, as well as high in the middle and low in the east and west. The main high-value areas were located in the Hengduan Mountains and the Yunnan-Guizhou Plateau. The coefficient of variation (CV) was 0.0009–0.9980, and the mean CV was only 0.1126. Regarding the future development trend, 77.90% and 22.10% of the regions showed an increase, 22.10% showed a decrease, and 75.25% showed an anti-sustainable state. (3) The effect of human activities on the NPP was generally negative, and the loss of NPP due to land use change in 2020 was around 9.85 TgC when compared with the same in 2000. (4) The rainfall and temperature in the Yangtze River basin both showed a non-significant increase, and the correlation coefficient between the NPP and rainfall was between −0.874 and 0.910. Furthermore, the correlation coefficient of the temperature ranged from −0.928 to 0.929, with a positive correlation overall and a negative correlation locally, and the NPP changes were more susceptible to the influence of temperature than rainfall.

Impacts of climate, phenology, elevation and their interactions on the net primary productivity of vegetation in Yunnan, China under global warming

Both net primary productivity (NPP) and vegetation phenology play essential roles in influencing the carbon sequestration of terrestrial ecosystems. However, the relationship between vegetation phenology and NPP remains unclear under the effects of global warming. This study used Geodetector to analyze the interaction mechanisms between climate, phenology, elevation, and NPP in Yunnan, China. The results are as follows. (1) NPP is positively correlated with NDVI, LOS, EOS, TEMP, PREC and SRAD, and negatively correlated with ELEV, NDBI and SOS. However, the main influencing factors leading to the variation of NPP differ with the region. (2) The spatial distribution of NPP and LOS in Yunnan Province is mainly influenced by the monsoon, showing a pattern of high in the southwest and low in the northeast, and the interaction between PREC and LOS is the main reason for the variation of NPP. (3) In the high-altitude region of northwest Yunnan, the interaction between altitude and PREC is the primary factor affecting NPP variation. (4) In the tropical monsoon forest region of southern Yunnan, the interaction between SRAD and PREC is the main cause of NPP changes. (5) Despite having a small effect on NPP, SOS was the only phenological factor that showed a significant linear relationship with NPP. This study reveals the complex and diverse interactions between climate, phenology, elevation, and NPP in different regions of Yunnan, and provides a new perspective for understanding the carbon cycle and ecological processes of terrestrial ecosystems under global warming.

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Different nitrogen saturation thresholds for above-, below-, and total net primary productivity in a temperate steppe.

Identifying the thresholds for the positive responses of total net primary productivity (NPP) to nitrogen (N) enrichment is an essential prerequisite for predicting the benefits of N deposition on ecosystem carbon sequestration. However, the responses of below-ground NPP (BNPP) to N enrichment are unknown in many ecosystems, which limits our ability to understand the carbon cycling under the scenario of increasing N availability. We examined the changes in above-ground NPP (ANPP), BNPP, and NPP of a temperate meadow steppe across a wide-ranging N addition gradient (0, 2, 5, 10, 20, and 50 g N m-2 year-1 ) during 5 years. Both ANPP and NPP increased nonlinearly with N addition rates. The N saturation threshold for ANPP (TA ) and NPP (TN ) was at the rate of 13.11 and 6.70 g N m-2 year-1 , respectively. BNPP decreased with increasing N addition when N addition rates ˃5 g N m-2 year-1 , resulting in much lower TN than TA . Soil N enrichment played a key role in driving the negative impacts of high N addition rates on BNPP, and consequently on the earlier occurrence of N saturation threshold for NPP. Our results highlight the negative effects of soil N enrichment on NPP in natural grasslands super-saturated with N. Furthermore, by considering ANPP and BNPP simultaneously, our results indicate that previous findings from above-ground might have over-estimated the positive effects of N deposition on primary productivity.

Net Primary Productivity Estimation of Terrestrial Ecosystems in China with Regard to Saturation Effects and Its Spatiotemporal Evolutionary Impact Factors

The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001–2020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (kNDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C/m2, with a mean value of 72.75 g C/m2, showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001–2005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010–2015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China.

Evolution of spatiotemporal patterns in vegetation net primary productivity and the driving forces on the Loess Plateau

In this study, we determined whether changes in vegetation net primary productivity (NPP) can be used to characterize the quality of terrestrial ecosystems, which is critical for global change and carbon balance. We first explored the spatial correlation of NPP and its impact on vegetation restoration. MOD17A3 remote sensing products were used to analyze the temporal and spatial changes in NPP on the Loess Plateau (LP) over the last two decades (2000–2020). The resulting spatial autocorrelation indices identified cold and hot spots in the spatial clustering patterns. The effects of climate change and human activities on the anomalous clustering of NPP were assessed using Pearson correlation analysis and multi-temporal land use land cover data. The results indicate that i) Temporally, from 2000 to 2020, the NPP of the LP increased significantly by 6.88 gCm−2yr−1 and so did the proportion of revegetated land area &amp;gt;400 gCm−2yr−1 from 4% to 37%. Spatially, NPP showed an increasing trend from northwest to southeast. ii) The vegetation NPP on the LP showed a strong positive global spatial autocorrelation (p&amp;lt; 0.01). The hot and cold regions were polarized; the cold spots were clustered in the northwest, while the hot spots in the south and east. The spatial clustering patterns were dominated by high-high (HH) and low-low (LL) clusters. Abnormal patterns existed mainly in the transition areas between HH and LL clusters and insignificant regions, which were jointly affected by human activities and climate change. iii) Precipitation was the dominant climatic factor (86%) affecting the NPP variation in the LP, with the annual minimum precipitation showing a significantly positive relationship with the interannual variability in NPP, while the maximum precipitations greatly influenced the variation in local spatial anomaly patterns. This suggests that climatic extremes affect vegetation. Our study helps to facilitate green ecological management and high-quality development in the LP.

Quantifying the independent contributions of climate and land use change to ecosystem services

Ecosystem services (ESs) are critical to human welfare and play an important role in supporting sustainable social and economic development. Climate change (CLC) and land use change (LUCC) are two of the most important factors influencing ESs. However, few studies have tried to distinguish the independent contributions of CLC and LUCC to ESs. Using meteorological, soil, land use, and remote sensing data for Liaoning Province (China) from 2000 to 2020, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and Carnegie-Ames Stanford Approach (CASA) model were used to construct scenario simulation frameworks based on three hypothetical scenarios: 1) CLC effect only; 2) LUCC effect only; 3) a combined effect of CLC and LULC. The three scenario simulation frameworks were then used to determine the independent contributions of CLC and LUCC to net primary productivity (NPP), water yield (WY), soil retention (SR), and their temporal dynamics. Results showed that under the combined effect of CLC and LULC, NPP, WY and SR in Liaoning Province exhibited a trend of gradual increase from 2000 to 2020, increasing by 124.62 gC/m2, 30.64 mm/a and 0.63 t/km2, respectively. Under the CLC only scenario, WY and SR changed by 6.24% and 2%, respectively, exhibiting a more significant effect than under the LUCC only scenario. In contrast, NPP changed by 25.71% under the LUCC only scenario, which was more significant than under the CLC only scenario. Overall, CLC was found to be the dominant factor affecting changes in WY and SR in Liaoning Province, with independent contribution rates of 81.79%-84.02% and 73.57%-85.44%, respectively, whereas LUCC was the dominant factor affecting changes in NPP with an independent contribution rate of 86.12%-92.50%. Decreased precipitation levels and an increase in temperature were the two primary factors driving fluctuations in the independent contributions of CLC to WY and SR, while large-scale forest land area damage and rapid urbanization were the two primary factors driving fluctuations in the independent contribution of LUCC to NPP. The results of this study identify the specific effects of different climatic conditions on ESs and highlight the conflict between urbanization and ecosystem service provision, providing a theoretical foundation for improving and increasing ESs and regional sustainable development in Liaoning Province.

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.

Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018

The net primary productivity (NPP) of vegetation is an important indicator used to evaluate the quality of terrestrial ecosystems and characterize the carbon balance of ecosystems. In this study, the spatiotemporal distribution and dynamic change in NPP in Africa from 1981 to 2018 were analyzed using the long time series data of NPP. The results of the trend and fluctuation analysis showed that the NPP in the Sahara arid region in northern Africa and the arid region in South Africa exhibited a significant reduction and a high degree of fluctuation; most of the NPP in the tropical rainforests in central Africa and the deciduous broadleaved forests and deciduous needle-leaved forests on the north and south sides of the tropical rainforests increased and showed a low degree of fluctuation; the Congo basin, Gabon, Cameroon, Ghana, Nigeria, Tanzania, and other regions were affected by human activities, while the NPP in these regions exhibited a significant reduction and a high degree of fluctuation. Anomaly analysis showed that the NPP in Africa generally exhibited a slow upward trend during the period from 1981 and 2018. The trend was basically consistent in different seasons, and can be segmented into three phases: (1) a phase of descent from 1981 to 1992, with the NPP below the average value in most years; (2) a phase of steady growth from 1993 to 2000, reaching a peak in 2000; (3) a phase of fluctuations from 2001 to 2018, where the NPP value was above the average value in all years except 2015 and 2016, when the NPP value was low due to abnormally high temperatures and drought. The Mann–Kendall test further showed that the annual and seasonal NPP in Africa exhibited a significant upward trend, and the mutation time points occurred around 1995. The wavelet time series analysis revealed obvious periodic changes in the time series of NPP in Africa. The annual and seasonal NPP showed clear oscillations on time scales of 7, 20, 29, and 55 years. The 55-year period had the strongest signal, and was the first main period. The study can provide a scientific gist for the sustainable development of environmental ecology, agricultural production, and the social economy in Africa.

Spatiotemporal Distribution of Carbon Sink Indicators—NPP and Its Driving Analysis in Ordos City, China

Ordos City is an important energy supply city for Chinese provinces and cities, providing a secure energy supply for China while also generating corresponding environmental pollution. Examining the spatiotemporal patterns of net primary productivity (NPP) in Ordos City and its driving factors is relevant to the realization of the carbon emission policy in Inner Mongolia. This study was undertaken to analyze NPP and its driving factors in Ordos City from 2000 to 2019 using NPP data, CO2 spatial grid data, meteorological data and statistical yearbook data accordingly. The NPP in Ordos City increased significantly from 2000 to 2019, mainly showing low values of NPP in the northwest and high values in the southeast. The usable grassland area and annual mean precipitation had a significant positive correlation with NPP, whereas the other factors had a more significant negative correlation. The usable grassland area had the largest influence on NPP, and fixed asset investment had the smallest influence on NPP. The total NPP–anthropogenic factor regression model and the mean NPP–natural factor regression model constructed allow for the prediction of NPP. Anthropogenic carbon emissions, population growth and usable grassland area were the main causes of NPP changes. Planting and protecting green plants and scientific and effective energy extraction plans are measures that enhance the degree of carbon sequestration in Ordos City.

Analysis and Dynamic Evaluation of Eco-Environmental Quality in the Yellow River Delta from 2000 to 2020

With the rapid development of urbanization and population growth, the ecological environment in the Yellow River Delta has undergone significant changes. In this study, Landsat satellite data and Google Earth Engine (GEE) were utilized to dynamically evaluate the changes in eco-environmental quality in the Yellow River Delta region using the remote sensing ecological index (RSEI). Additionally, the CASA model was used to estimate net primary productivity (NPP) and explore the relationship between vegetation NPP, land-use and land-cover change (LUCC), and eco-environmental quality to reveal the complexity and related factors of eco-environmental quality changes in this region. The results show that: (1) Over the past 20 years, the eco-environmental quality in the Yellow River Delta region has changed in a “V” shape. The eco-environmental quality near the Yellow River Basin is relatively better, forming a diagonal “Y” shape, while the areas with poorer eco-environmental quality are mainly distributed in the coastal edge region of the Yellow River Delta. (2) The response of vegetation NPP to eco-environmental quality in the Yellow River Delta region is unstable. (3) Urban construction land in the Yellow River Delta region is strongly correlated with RSEI, and the absolute value of the dynamic degree of land use is as high as 8.78%, with significant land transfer changes. The correlation between arable land and RSEI is weak, while coastal mudflats are negatively correlated with RSEI, with the minimum absolute value of the dynamic degree of land use being −1.01%, and significant land transfer changes. There is no correlation between forest land and RSEI. Our research results can provide data support for the eco-environmental protection and sustainable development of the Yellow River Delta region and help local governments to take corresponding measures.

Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau

In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (NPP) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest &gt; meadow steppe &gt; coniferous forest &gt; cropland &gt; shrub &gt; typical steppe &gt; sandy land &gt; alpine steppe &gt; desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) &gt; precipitation (0.33) &gt; soil moisture (0.16) &gt; temperature (0.14) &gt; solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.

Time Effects of Global Change on Forest Productivity in China from 2001 to 2017

With global warming, the concentrations of fine particulate matter (PM2.5) and greenhouse gases, such as CO2, are increasing. However, it is still unknown whether these increases will affect vegetation productivity. Exploring the impacts of global warming on net primary productivity (NPP) will help us understand how ecosystem function responds to climate change in China. Using the Carnegie-Ames-Stanford Approach (CASA) ecosystem model based on remote sensing, we investigated the spatiotemporal changes in NPP across 1137 sites in China from 2001 to 2017. Our results revealed that: (1) Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) were significantly positively correlated with NPP (p < 0.01), while PM2.5 concentration and CO2 emissions were significantly negatively correlated with NPP (p < 0.01). (2) The positive correlation between temperature, rainfall and NPP gradually weakened over time, while the negative correlation between PM2.5 concentration, CO2 emissions and NPP gradually strengthened over time. (3) High levels of PM2.5 concentration and CO2 emissions had negative effects on NPP, while high levels of MAT and MAP had positive effects on NPP.

Assessing restoration and degradation of natural and artificial vegetation in the arid zone of Northwest China

Assessing vegetation restoration and degradation trends is important for regional ecological conservation and sustainable development, yet few studies have examined the characteristics of these trends in natural and artificial vegetation in arid zones. In this study, we develop an assessment framework based on two common ecological indicators, Net Primary Productivity (NPP) and Water Use Efficiency (WUE). We discuss the restoration and degradation trends of natural and artificial vegetation in China’s Northwest Arid Region (NAR) and analyze the similarities and differences between the changes in the two. Our results reveal the following: (1) Both natural vegetation (Nav) NPP and artificial vegetation (Arv) NPP in the NAR are dominated by significant growth, with precipitation being the most influential factor. Arv NPP changes are greater than Nav NPP. (2) WUE and NPP have similar spatial distribution characteristics, with precipitation and temperature dominating WUE changes in the Qilian Mountains and s southern Xinjiang, respectively. In the near future, Nav WUE is expected to be dominated by improvement to degradation, while Arv WUE will continue to improve under human intervention. These two indices respond differently to the environmental factors that cause their changes. (3) Nav and Arv exhibit similar restoration and degradation trends, mainly dominated by early recovery with Nav displaying a slightly more prominent restoration trend than Arv. The NPP-WUE assessment framework will help to rapidly assess vegetation degradation and restoration at large scales, providing new perspectives for research in this field.