Vegetation Net Primary Productivity Research Articles

Assessment of vegetation net primary productivity variation and influencing factors in the Beijing-Tianjin-Hebei region

Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m−2a−1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m−2a−1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m−2a−1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a−1 and 0.07 TgC·a−1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.

Evaluation of spatiotemporal variation and impact factors for vegetation net primary productivity in a typical open‐pit mining ecosystem in northwestern China

AbstractThe vegetation net primary productivity (NPP) is a key indicator for evaluating vegetation carbon sequestration. Exploring its spatiotemporal changes and impact factors is essential for coal mining and ecological restoration in open‐pit mining areas. This study utilized the Carnegie‐Ames‐Stanford‐Approach (CASA) model to calculate monthly vegetation NPP in the Xiwan mine area, a typical open‐pit mine in northwestern China. The trend, stability, and persistence analysis were conducted, along with the development of a grading method to examine the vegetation NPP spatiotemporal variation across different land cover types. Statistical grading and correlation analysis were used to explore the relationships between the topographical factors, meteorological factors, human activities, and vegetation NPP. The following results were obtained: (1) The vegetation NPP in the study area exhibited a high stability and anti‐persistent decrease in trend. NPP reached a peak of 143.49 g C/(m2 year) in 2017, but declined to a low of 118.38 g C/(m2 year) in 2021. (2) The vegetation NPP decreases with increasing elevation and slope, and a relatively strong correlation with temperature and precipitation was also observed. (3) The impact intensity of human activities on vegetation NPP exhibited a rising and fluctuating volatile trend. In 2021, the inhibition of vegetation NPP by human activities reached its peak at 166.42 g C/(m2 year), with an impact effect share of 36.9%. This research provides a comprehensive framework for vegetation NPP analysis in open‐pit mining, offering valuable insights for ecological conservation in mining ecosystems.

Exploring the Effects of Urban Development in Ten Chinese Node Cities along the Belt and Road Initiative on Vegetation Net Primary Productivity

Exploring the Effects of Urban Development in Ten Chinese Node Cities along the Belt and Road Initiative on Vegetation Net Primary Productivity

Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China

Human activities and global environmental changes have transformed terrestrial ecosystems, notably increasing vegetation greenness in China. However, this greening is less effective across the Hu Huanyong Line (Hu Line). This study analyzes dynamic changes and driving factors of nine vegetation net primary productivities (NPPs) in regions divided by the Hu Line using remote sensing data, trend analysis, and the Geodetector model. Findings reveal that from 2001 to 2022, 38.22% of regional vegetation NPP in China increased, especially in the Loess Plateau, Sichuan Basin, and Northeast Plains, while 2.39% decreased, primarily in the southeastern region and southern Tibet. Grasslands contributed 39.71% to NPP north of the Hu Line, and cultivated vegetation contributed 50.58% south. The driving explanatory power of factors on vegetation NPP on the north side of the Hu Line is generally greater than that on the south side. Natural factors primarily drive NPP changes, with human activities having less impact. Combined factors, particularly climate and elevation, significantly enhance the driving explanatory power (q, 0–1). The joint effects of elevation and precipitation on grassland NPP dynamics (q = 0.602) are notable. GDP’s influence on broadleaf forests north of the Hu Line (q = 0.404) is significant. Grasslands respond strongly to land use changes and population density, with a combined effect of q = 0.535. Shrubs, alpine vegetation, and meadows show minimal response to individual factors (q < 0.2). These findings offer insights for devising ecological protection measures tailored to local conditions.

Joint Impact of the AO and ENSO on Vegetation NPP Over Indo‐Myanmar in Boreal Winter

AbstractIn this paper, the authors quantitatively investigated the joint impact of the Arctic Oscillation (AO) and El Niño‐Southern Oscillation (ENSO) on vegetation net primary productivity (NPP) over Indo‐Myanmar in boreal winter from 1981 to 2018, and found that there is a significant in‐phase variation between them. When the warm ENSO co‐occurs with the positive AO, the NPP in more than 80% of the grids in the study region significantly increases by approximately 24 gC m−2. For the cold ENSO plus the negative AO, the regionally averaged NPP anomalies are approximately −10 gC m−2, and the local minimum is as low as −60 gC m−2. The combination of AO and ENSO can explain approximately 36% of the total variance of NPP in Indo‐Myanmar. The AO/ENSO linkages to the NPP are very likely due to regional precipitation anomalies through atmospheric circulation. In association with the positive (negative) AO, the Rossby wave, propagating eastward along the subtropical jet stream, brings an anomalous cyclone (anticyclone) over Indo‐Myanmar. This enhances (weakens) Indo‐Myanmar Trough, and resulting in more (less) regional precipitation. During the warm (cold) ENSO, through the Gill‐type response, an anomalous high (low)‐pressure appears in the lower and middle troposphere over Philippine‐South China Sea. The Walker circulation is also weaker (stronger) than normal. These are conducive to anomalous southerly (northerly) winds in Indochina. As a result, in the warm ENSO plus positive AO winters, the mean precipitation anomaly in Indo‐Myanmar increased by 27% as averaged for five data sets. In contrast, when the cold ENSO co‐occurs with negative AO, the precipitation is significantly reduced where the largest anomaly exceeds 100 mm in the ERA5. The precipitation changes are consistent with the local NPP anomalies, whereas the temperature does not seem to be a dominant limiting factor.

Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Tibetan Plateau from 1979 to 2018

The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.

A new method of high-resolution NPP estimation and its implementation in caragana shrublands on the Loess Plateau, China

ABSTRACT Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management.

The functional diversity-productivity relationship of woody plants is climatically sensitive.

Plot-scale experiments indicate that functional diversity (FD) plays a pivotal role in sustaining ecosystem functions such as net primary productivity (NPP). However, the relationships between functional diversity and NPP across larger scale under varying climatic conditions are sparsely studied, despite its significance for understanding forest-atmosphere interactions and informing policy development. Hence, we examine the relationships of community-weighted mean (CWM) and functional dispersion (FDis) of woody plant traits on NPP across China and if such relationships are modulated by climatic conditions at the national scale. Using comprehensive datasets of distribution, functional traits, and productivity for 9120 Chinese woody plant species, we evaluated the distribution pattern of community-weighted mean and functional dispersion (including three orthogonal trait indicators: plant size, leaf morphology, and flower duration) and its relationships with NPP. Finally, we tested the effects of climatic conditions on community-weighted mean/functional dispersion-NPP relationships. We first found overall functional diversity-NPP relationships, but also that the magnitude of these relationships was sensitive to climate, with plant size community-weighted mean promoting NPP in warm regions and plant size functional dispersion promoting NPP in wet regions. Second, warm and wet conditions indirectly increased NPP by its positive effects on community-weighted mean or functional dispersion, particularly through mean plant size and leaf morphology. Our study provides comprehensive evidence for the relationships between functional diversity and NPP under varying climates at a large scale. Importantly, our results indicate a broadening significance of multidimensional plant functional traits for woody vegetation NPP in response to rising temperatures and wetter climates. Restoration, reforestation actions and natural capital accounting need to carefully consider not only community-weighted mean and functional dispersion but also their interactions with climate, to predict how functional diversity may promote ecosystem functioning under future climatic conditions.

Quantitative Assessment of the Impact of the Three-North Shelter Forest Program on Vegetation Net Primary Productivity over the Past Two Decades and Its Environmental Benefits in China

Vegetation net primary productivity (NPP) is a crucial indicator for assessing the carbon balance in terrestrial ecosystems. Qualitative and comparative research on the NPP influenced by human activities, climate change, and their interactions remains insufficient. The Three-North Shelter Forest Program (TNSFP), initiated in 1978, provides a valuable reference for such investigations. This study employs an improved residual trend method to analyze the spatiotemporal patterns, trends, and driving factors of vegetation NPP during the second phase of the Three-North Shelter Forest Program (2001–2020), as well as TNSFP’s contribution to vegetation NPP. The results indicate that (1) from 2001 to 2020, overall vegetation NPP exhibited a significant fluctuating upward trend at a rate of 3.69 g C/m−2 annually; and (2) precipitation, accounting for 1.527 g C/m−2, had a more significant impact on vegetation net productivity compared to temperature (0.002 g C/m−2). Climate factors (76%) significantly influenced vegetation NPP in the Three-North Shelter Forest region more than human activities (24%). In the last decade (2011–2020), the climate contribution rate decreased to 67%, while the human activity contribution rate increased by seven percentage points compared to the previous decade (2001–2010); (3) during 2001–2020, TNSFP contributed 10.9% to the total human activity contribution to vegetation net primary productivity, approximately 2.6% of the overall contribution; (4) After the second phase of TNSFP was enacted, PM2.5 levels decreased by an average of −0.57 μg/m−3/a−1. Concurrently, soil conservation improved from 6.57 t/km2 in 2001 to 14.37 t/km2 in 2020.

Spatiotemporal Dynamics and Influencing Factors of Vegetation Net Primary Productivity in the Yangtze River Delta Region, China

Vegetation Net Primary Productivity (NPP) plays a crucial role in terrestrial carbon sinks and the global carbon cycle. Investigating the spatiotemporal dynamics and influencing factors in the Yangtze River Delta (YRD) region can furnish a solid scientific foundation for green, low-carbon, and sustainable development in China, as well as a reference for other rapidly urbanizing regions. This study focuses on the YRD region as an illustration and utilizes the Carnegie–Ames–Stanford Approach (CASA model) to quantify NPP in this region from 2000 to 2018. Investigation into the spatiotemporal dynamics and influencing factors was conducted using Theil–Sen median trend analysis and scenario analysis. The results indicate that the NPP in the YRD region from 2000 to 2018 exhibited pronounced spatial differentiation characteristics, typically exhibiting a spatial distribution pattern of being high in the south and low in the north, high in the west and low in the east. Additionally, the expansion of built-up areas and the reduction in cultivated land have the potential to reduce NPP in the YRD region. Moreover, the influence of land-use and land-cover change (LULCC) is anticipated to be relatively limited compared to that of climate change. Furthermore, changes in precipitation were found to be positively correlated with changes in NPP, with the effect being relatively more pronounced. The correlation between temperature and NPP demonstrated spatial differentiation, with a mainly positive correlation in the central and southern parts of the YRD and a mainly negative correlation in the northern part. Changes in solar radiation had a negative correlation with changes in NPP. Based on these results, it is recommended that local governments strictly enforce urban development boundaries and manage the disorderly expansion of built-up areas, enhance the regional irrigation infrastructure, and address air pollution, so as to ensure the necessary conditions for the growth of vegetation, reduce greenhouse gas emissions, and control regional temperature rises. This study can provide stronger evidence for revealing the influencing mechanisms of NPP through the control of impact conditions and the exclusion of confounding factors via scenario analysis. The policy implications can offer insights into NPP enhancement and environmental management for the YRD and other rapidly urbanizing regions.

How hydrothermal factors and CO2 concentration affect vegetation carbon sink over time and elevation gradient

How hydrothermal factors and CO2 concentration affect the vegetation carbon sink over time and across elevation gradients is crucial for carbon sequestration and enhancement, which remains unclear. Here, the temporal changes of the interannual relationship of net primary productivity (NPP) with hydrothermal factors and CO2 concentration over elevation were revealed using ensemble empirical mode decomposition (EEMD) method and moving window-based multiple regression analysis during 1981–2018, in a case study of six provinces along the ‘Maritime Silk Road’ of China. We found that: (1) except for low-elevation farmland, the interannual relationship of vegetation NPP with CO2 (RNPP-CO2), precipitation accumulation (RNPP-PA), soil moisture (RNPP-SM), downward surface shortwave radiation (RNPP-SR) and minimum temperature (RNPP-TMN) is mainly positive, while the relationship with maximum temperature (TMAX, RNPP-TMX) and vapor pressure deficit (VPD, RNPP-VPD) is mainly negative, indicating that most hydrothermal factors can increase NPP except TMAX and VPD. Most of the interannual relationship is strengthening, especially for thermal factor, indicating the strengthened impacts of hydrothermal factors and CO2, while the weakening relationship with hydrological factors accounted for large areas, which cannot be ignored. (2) With increasing elevation, the positive RNPP-CO2 diminished, while the positive RNPP-SM and the negative RNPP-VPD amplified, indicating the positive impacts from CO2 decreased, while those from SM and VPD increased. Meanwhile, RNPP-SR, RNPP-TMN, and RNPP-PA reversed from positive to negative, while RNPP-TMX reversals from negative to positive, indicating reversed impacts. (3) With increasing elevation, the temporal changes of positive RNPP-CO2 and RNPP-SM varied from weakening to strengthening and from strengthening to weakening, respectively, while those of RNPP-SR and RNPP-TMN together with RNPP-PA varied from strengthening positive to strengthening negative and weakening negative, respectively. Meanwhile, the strengthening negative of RNPP-TMX, and RNPP-VPD, changed to weakening positive and weakening negative, respectively. Our study highlights the importance of the divergent trends of the interannual relationship of NPP with hydrothermal factors and CO2 concentration across elevation for accurate prediction of future carbon sink potential under global change.

Exploring the relationships between human consumption and environmental pressure: A case study of the Yangtze river economic zone in China.

It is crucial to decouple and coordinate human consumption and its environmental pressure for achieving sustainable development. As an important aspect of United Nations Sustainable Development Goal (SDG12), sustainability on material consuming is still in its early stages of research. To address the research gap in sustainable consumption of vegetation net primary productivity (NPP), this study analyzed the spatio-temporal dynamics of human consumption and environmental pressure in the Yangtze River Economic Zone (YREZ) using consumption-based HANPP (cHANPP) and Human Appropriation of Net Primary Production (HANPP) as indicators. Later, we measured their decoupling relationship using Tapio decoupling approach. We found that distribution of HANPP and cHANPP were regionally separated, with the former mainly concentrated in the middle and upper reaches provinces, while the latter concentrated in the lower reach provinces. From 2004 to 2019, the relationship between HANPP and cHANPP changed from strong negative decoupling to weak decoupling in the YREZ. Furthermore, the relationship was differed among different regions. As a whole, developing regions showed a weak decoupling state, experiencing an increase in environmental pressure (i.e., HANPP) alongside increased human consumption (i.e., cHANPP). In contrast, developed regions showed a strong decoupling state, experiencing a decrease in environmental pressure (i.e., HANPP) alongside increased human consumption (i.e., cHANPP). Our study highlights that different countermeasures should be formulated by regions according to their own situation to realize sustainable regional development.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

Interannual Variations in Terrestrial Net Ecosystem Productivity and Climate Attribution in the Southern Hilly Region of China.

The vegetation ecosystem in the southern hilly region of China (SHRC) plays a crucial role in the country's carbon reservoir. Clarifying the dynamics of net primary productivity (NPP) in this area and its response to climate factors in the context of climate change is important for national forest ecology, management, and carbon neutrality efforts. This study, based on remote sensing and meteorological data spanning the period 2001 to 2021, aims to unveil the spatiotemporal patterns of vegetation productivity and climate factors in the southern hilly region, explore interannual variation characteristics of vegetation productivity with altitude, and investigate the response characteristics of NPP to various climate factors. The results indicate that from 2001 to 2021, the annual average NPP in the southern hilly region had a significant increasing trend of 2.13 ± 0.78 g m-2 a-1. The trend of NPP varies significantly with altitude. Despite a general substantial upward trend in vegetation NPP, regions at lower elevations exhibit a faster rate of increase, suggesting a diminishing difference in the NPP of different elevation ranges. The overall rise in average temperature has positive implications for the southern hilly region, while the impact of precipitation on vegetation NPP demonstrates noticeable spatial heterogeneity. Regions in which vegetation NPP is significantly negatively correlated with precipitation are mainly concentrated in the southern areas of Guangdong, Fujian, and Jiangxi provinces. In contrast, other regions further away from the southeastern coast tend to exhibit a positive correlation. Over the past two decades, there has been an asymmetry in the diurnal temperature variation in the SHRC, with the nighttime warming rate being 1.8 times that of the daytime warming rate. The positive impact of daytime warming on NPP of vegetation is more pronounced than the impact of nighttime temperature changes. Understanding the spatiotemporal patterns of NPP in the SHRC and the characteristics of its response to climate factors contributes to enhancing our ability to protect and manage vegetation resources amidst the challenges of global climate change.

Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020

To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017–2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (−3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (−0.06 %) and PRE (−1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.

Spatio-temporal Variation in Net Primary Productivity of Different Vegetation Types and Its Influencing Factors Exploration in Southwest China

Studying the spatiotemporal variation in vegetation net primary productivity (NPP) and exploring its influencing factors are of considerable practical significance for understanding the spatiotemporal variation in vegetation and for guiding ecological restoration and management projects based on local conditions. Based on MODIS NPP data, combined with in situ meteorological data, land use data, and vegetation type data, this study explores the spatiotemporal variation in different types of vegetation NPP in southwest China via the Mann-Kendall significance test and Theil-Sen Median slope estimator. It reveals the influencing factors of spatial differentiation of different types of vegetation NPP and the interaction between influencing factors in combination with stability analysis and Geo Detectors. The results revealed that on the temporal scale, from 2000 to 2021, vegetation NPP, NPPPre (vegetation NPP exclusively under the influence of climate change), and NPPRes (vegetation NPP exclusively under the influence of human activities) in southwest China showed a fluctuating upward trend. Among different vegetation types, NPP, NPPPre, and NPPRes exhibited an upward trend, except for a minor decline in NPPRes of tree vegetation at a rate of -0.183 g·(m2·a)-1. Among them, NPP, NPPPre, and NPPRes of economic vegetation showed the most significant upward rates, 5.96, 3.09, and 2.94 g·(m2·a)-1, respectively. On the spatial scale, the tree vegetation NPP with the most significant downward trend was mainly distributed in Tibet and southern Yunnan, while the economic vegetation NPP with the highest upward trend was primarily distributed in eastern Sichuan Province. The stability of vegetation NPP in southwest China presented a spatial distribution pattern of "low in the south and high in the north," and the average value of the correlation coefficient increased in the ascending order of arbor vegetation (0.101), shrub vegetation (0.105), herb vegetation (0.110), and economic vegetation (0.114). The interaction between surface temperature and relative humidity was the main influencing factor for spatial differentiation of vegetation NPP, while the interaction between sunshine duration and warmth index had the most significant impact on vegetation in southwest China, with an increasing percentage of 30.91%. Different types of vegetation had different requirements for different climatic factors, but their requirements for surface temperature and warmth index were significantly consistent. When the surface temperature was 21.03-28.49℃, and the warmth index was 106.46-167.2, the NPP of different vegetation types peaked. Under natural succession, the impact of climate change on vegetation was inversely proportional to the stability of the vegetation community. The arbor vegetation community with high stability was less affected, while the herb vegetation community with low stability was highly affected by climate. In contrast, the stability of economic vegetation was directly proportional to the impact of climate due to the influence of human activities. This study establishes a theoretical foundation for evaluating the impact of regional climate on the growth of different vegetation types and can be crucial for formulating ecological restoration and management strategies in southwest China that are adapted to the local conditions.

Impacts of Extreme Climate Events at Different Altitudinal Gradients on Vegetation NPP in Songhua River Basin

Vegetation net primary production (NPP) is an essential index for determining the quality of terrestrial ecosystems and their potential carbon storage ability. The impacts of extreme climate events on vegetation NPP are different under different altitude gradients. However, the research on the impact of extreme climate events on the spatial variation in vegetation NPP and the coupling effects under different altitude conditions remain insufficient. Using the MOD17A3HGF remote sensing data set and RClimDex 1.9 software, the vegetation NPP and 10 extreme climate indices in the Songhua River Basin from 2001 to 2020 were calculated, respectively. The spatial and temporal evolution characteristics of vegetation NPP and its response mechanism to extreme climate events in the Songhua River Basin under different altitude gradients were analyzed by means of trend analysis, correlation analysis, regression analysis, GeoDetector, and relative importance analysis. The results showed that:① the vegetation NPP (calculated by C) in the Songhua River Basin increased significantly at the rate of 4.13 g·(m2·a)-1 from 2001 to 2020 (P < 0.01), and the rates of 3.65, 4.04, 4.70, 5.09, and 4.57 g·(m2·a)-1 at the altitude gradients of 29-255, 255-440, 440-658, 658-935, and 935-2 589 m, respectively (P < 0.01). ② The spatial distribution pattern of vegetation NPP presented "high around and low in the middle," and the fluctuation of vegetation NPP in high altitude areas was more obvious than that in low altitude areas; for example, the average value of vegetation NPP at an altitude gradient from 29 to 255 m had a lower value, whereas the other altitude gradients had higher mean values than the mean value of the basin. ③ The extreme precipitation events in the Songhua River Basin were the main influencing factors of vegetation NPP, i.e., the vegetation NPP in low-altitude areas was mainly affected by extreme precipitation events, whereas the values in high-altitude areas were affected by both extreme precipitation events and extreme temperature events. The results of this research can provide a scientific basis for improving the carbon cycle model of the terrestrial ecosystem in the Songhua River Basin, quantifying the ability of carbon storage of vegetation and formulating policies to deal with climate change.

Quantifying the contributions of climatic and human factors to vegetation net primary productivity dynamics in East Africa

As an important part of the terrestrial ecosystem, vegetation dynamics are subject to impacts from both climate change and human activities. Clarifying the driving mechanisms of vegetation variation is of great significance for regional ecological protection and achieving sustainable development goals. Here, net primary productivity (NPP) was used to investigate the spatiotemporal variability of vegetation dynamics from 2000 to 2020 in East Africa, and its correlations with climate factors. Furthermore, we utilized partial derivatives analysis and set up different scenarios to distinguish the relative contributions of climatic and human factors to NPP changes. The results revealed that NPP exhibited a significant increase with 4.16 g C/m2/a from 2000 to 2020 in East Africa, and an upward trend was detected across 71.06% of the study area. The average contributions of precipitation, temperature, and solar radiation to the NPP inter-annual variations in East Africa were 2.02, −1.09, and 0.31 gC⋅m–2⋅a–1, respectively. Precipitation made the greatest positive contribution among all of the climatic factors, while temperature made strong negative contributions. The contributions of climate change and human activities to NPP changes were 1.24 and 2.34 gC⋅m–2⋅a–1, respectively. Moreover, the contribution rate of human activities to NPP increase was larger than that of climate change, while the role of climate change in NPP decrease was larger than that of human activities. The findings of the study can provide new evidence for a deeper understanding of ecosystem stability and carbon cycling in East Africa, as well as a reference for decision-making and scientific support for ecological environmental protection.

Spatial and temporal variation of net primary productivity of herbaceous marshes and its climatic drivers in China.

Herbaceous marshes are widely distributed in China and are vital to regional ecological security and sustainable development. Vegetation net primary productivity (NPP) is a vital indicator of vegetation growth. Climatic change can significantly affect NPP, but variations in NPP of herbaceous marsh and their responses to climate change in China remain unclear. Using meteorological data and MODIS NPP data during 2000-2020, this study analyzed the spatial and temporal variations of NPP and their responses to climate change in Chinese herbaceous marshes. We found that the annual NPP of herbaceous marshes in China increased significantly at a rate of 3.34 g C/m2/a from 2000 to 2020, with an average value of 336.60 g C/m2. The increased annual total precipitation enhanced the national average NPP, whereas annual mean temperature had no significant effect on the national average NPP. Regionally, precipitation had a significant positive effect on the NPP in temperate semi-arid and arid and temperate semi-humid and humid marsh regions. For the first time, we discovered asymmetry effects of daytime and nighttime temperatures on NPP in herbaceous marshes of China. In temperate humid and semi-humid marsh regions, increased summer daytime temperature decreased the NPP while increased summer nighttime temperature increased the NPP. In the Tibetan Plateau, increased autumn daytime temperature, as well as summer daytime and nighttime temperatures could increase the NPP of herbaceous marshes. This study highlights the different influences of seasonal climate change on the NPP of herbaceous marshes in China and indicates that the differential effects of daytime and nighttime temperatures should be considering in simulating the NPP of herbaceous marshes in terrestrial ecosystem models, especially under the background of global asymmetric diurnal warming.

Analysis of Spatiotemporal Evolution and Influencing Factors of Vegetation Net Primary Productivity in the Yellow River Basin from 2000 to 2022

The Yellow River Basin (YRB) plays a very important role in China’s economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB.