Distribution Of Net Primary Productivity Research Articles

Variations and influencing factors of vegetation net primary productivity over 31 years in Wuyishan National Park, China

In this study, the Carnegie–Ames–Stanford approach model was used to estimate the net primary productivity (NPP) of Wuyishan National Park from 1990 to 2020. Variation in NPP in parks was examined, and the influencing factors were identified. NPP showed an increasing trend annually with climate change. The value (625.67 gC m−2 a−1) obtained in this study was higher than the regional value along similar latitudes and coastlines. Affected by the spatial distribution characteristics of vegetation, NPP in the study area exhibited significant spatial heterogeneity, with a distribution of higher values in the northwestern region and lower values in the southeastern region. However, the growth rates showed the opposite trend. The higher growth rate of NPP in the ecological restoration zone was attributed to the ecological conservation efforts undertaken in Wuyishan National Park. Among the various environmental factors, the vapor pressure deficit contributed substantially to NPP temporal distribution, accounting for 25.71%. Terrain factors, which are influenced by the spatial distribution characteristics of vegetation and environmental factors, are important variables that affect the spatial distribution of NPP within the study area. The highest vegetation NPP in Wuyishan National Park was observed in regions with an elevation of approximately 1000 m, slopes ranging from 20° to 40°, and an aspect of approximately 150°. Compared with slope and aspect, elevation provides a stronger explanatory power (R2 = 0.93) for the spatial distribution patterns of vegetation NPP in Wuyi Mountain National Park. This study suggests that Wuyishan National Park has considerable carbon sequestration potential. In the context of climate warming, the favorable aspects of climate warming should be considered, and ecological restoration measures tailored to local conditions should be implemented.Article title mismatch between manuscript and publisher provided information, so we have follow the manuscript. Kindly check and confirm.Variations and influencing factors of vegetation net primary productivity over 31 years in Wuyishan National Park, China

Net primary productivity and litter decomposition rates in two distinct Amazonian peatlands.

Measurements of net primary productivity (NPP) and litter decomposition from tropical peatlands are severely lacking, limiting our ability to parameterise and validate models of tropical peatland development and thereby make robust predictions of how these systems will respond to future environmental and climatic change. Here, we present total NPP (i.e., above- and below-ground) and decomposition data from two floristically and structurally distinct forested peatland sites within the Pastaza Marañón Foreland Basin, northern Peru, the largest tropical peatland area in Amazonia: (1) a palm (largely Mauritia flexuosa) dominated swamp forest and (2) a hardwood dominated swamp forest (known as 'pole forest', due to the abundance of thin-stemmed trees). Total NPP in the palm forest and hardwood-dominated forest (9.83 ± 1.43 and 7.34 ± 0.84 Mg C ha-1 year-1, respectively) was low compared with values reported for terra firme forest in the region (14.21-15.01 Mg C ha-1 year-1) and for tropical peatlands elsewhere (11.06 and 13.20 Mg C ha-1 year-1). Despite the similar total NPP of the two forest types, there were considerable differences in the distribution of NPP. Fine root NPP was seven times higher in the palm forest (4.56 ± 1.05 Mg C ha-1 year-1) than in the hardwood forest (0.61 ± 0.22 Mg C ha-1 year-1). Above-ground palm NPP, a frequently overlooked component, made large contributions to total NPP in the palm-dominated forest, accounting for 41% (14% in the hardwood-dominated forest). Conversely, Mauritia flexuosa litter decomposition rates were the same in both plots: highest for leaf material, followed by root and then stem material (21%, 77% and 86% of mass remaining after 1 year respectively for both plots). Our results suggest potential differences in these two peatland types' responses to climate and other environmental changes and will assist in future modelling studies of these systems.

Prediction of net primary productivity in the middle-to-high latitudes of Eurasia based on snow and soil temperature

Net primary productivity (NPP) is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change. Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction, this paper develops a statistical prediction model for NPP from April to June (AMJ) across the middle-to-high latitudes of Eurasia. The model introduces two physically meaningful predictors: the snow water equivalent (SWE) from February to March (FM) over central Europe and the FM local soil temperature (ST). The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves, leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia. This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes. The ST is closely related to the AMJ temperature and precipitation. With positive ST anomalies, the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region. The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor. Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP. The predictive skills of the multi-factor prediction schemes can be improved by about 13 % if the ST predictor is included. The findings confirm that local ST is a predictor that must be included for NPP prediction.摘要净初级生产力 (NPP) 是植被通过光合作用积累机物质的净效益, 是探索植被对气候变化响应的关键指标. 考虑到陆面热力异常通过陆气相互作用和大尺度环流对植被生长的影响, 本文研制了欧亚中高纬地区4–6月NPP预测模型. 该模型引入了两个具有明确物理意义的预测因子: 欧洲中部2–3月的雪水当量 (SWE) 和2–3月局地土壤温度 (ST). SWE的正异常会触发异常东传的Rossby波, 导致下游出现位势高度负异常并引起降温. 通过雪和温度的正反馈, 这种异常低温持续到春季并使得NPP下降. ST与随后季节的温度和降水密切相关, 当ST出现正异常时, 该地区的4–6月温度和降水呈现出东西向偶极子异常分布.对比各方案的预测效果发现, ST比SWE有更好的预测能力. 2009–2014年独立后报结果显示, ST方案对NPP的空间分布和年际变率都有很好的预测效果. 交叉检验的结果显示, 多因子方案中引入ST后能提高模型13 %的预测技巧.

Spatio-Temporal Variation and Future Sustainability of Net Primary Productivity from 2001 to 2021 in Hetao Irrigation District, Inner Mongolia

The Hetao Irrigation District in Inner Mongolia, a vital grain-producing region in northern China, faces growing environmental challenges. Studying net primary productivity (NPP) is essential for understanding spatiotemporal vegetation shifts and guiding locally adapted restoration and management efforts. Utilizing MOD17A3/NPP data, this study applies the Theil–Sen median trend, Mann–Kendall significance, and the Hurst index to scrutinize the spatiotemporal distribution patterns of NPP from 2001 to 2021 and forecast future changes in the area. The findings reveal cyclic temporal trends, forming a “∧” shape with initial increases followed by decreases, notably during the July to August period each year. The multi-year average NPP exhibits a slight upward fluctuation trend, averaging 172.40 gCm−2a−1. Peaks occur approximately every three years, reaching the highest average in 2012 at 218.96 gCm−2a−1. Spatially, NPP distribution stays consistent over the years, influenced by various land cover types, especially cropland, shaping the spatial patterns. Monthly and yearly NPP trends over the 21 years indicate a significant decrease in May and June, with other months mostly showing a non-significant increase. The Hurst index for monthly and yearly NPP changes over 21 years shows relatively high weak anti-persistence. In summary, over the past 21 years, the NPP trend in the study area has not significantly improved and is expected to decline in the future. This study offers data support and a scientific foundation for refining the carbon cycle model, quantifying vegetation carbon sequestration capacity, addressing climate change policies, and striving for carbon peak and neutrality in the Hetao Irrigation District.

DISTRIBUTION AND VARIATION OF NET PRIMARY PRODUCTIVITY IN FORESTLAND AND GRASSLAND OF CHINA

Net primary productivity (NPP) can characterize the state and service functions of terrestrial ecosystems and reflect the responses of plants to climate fluctuations and human disturbances. Many studies have analysed changes in NPP and its influencing factors in some parts of China, but few studies have made in-depth comparisons between different regions, decades, and land cover types across China. Based on uniform NPP products and land cover data, the distribution, difference and variation of NPP in forestland and grassland in China were investigated with GIS spatial analysis and least-squares regression methods, and results indicated that average NPP increased gradually from northwest to southeast, and the regions with average NPP of 1-200 gC m-2 yr-1 occupied the largest proportion. During 1981–2018, the regions where decadal average NPP increased were larger than those where NPP decreased, and the areas with inter-decadal difference of 1-50 gC m-2 yr-1 were the largest. In different land cover types or regions, NPP generally exhibited a rapid rising trend in the 1990s and a slow or declining trend in the 2000s. Annual NPP increased at a rate of 13.2 gC m-2 decade-1 in the entire study area during 1981–2018, and the NPP in forestland and grassland increased at a speed of 15.6 and 11.1 gC m-2 decade-1, respectively. Spatially, NPP increased at a rate of 1-100 gC m-2 decade-1 in most areas, and the increasing trend was statistically significant in Qinghai-Tibetan Plateau, most of Northwest China and eastern Northeast China. The area with a significant increase in NPP over the past 38 years accounted for 21.0% of the total study area. Our results highlight the consistent and upward variation of NPP in general, but there are some distinctions among different land cover types and different regions of China, and higher quality datasets and more reliable methods should be fully explored in further studies. Keywords: Net primary productivity, spatial distribution, linear trend, forestland and grassland, China.

The urban hierarchy and agglomeration effects influence the response of NPP to climate change and human activities

In recent years, global climate changes and human activities have significantly reshaped ecosystems, posing a challenging aspect for research—accurately understanding how climate and human activities affect vegetation. This study employs the Carnegie–Ames–Stanford Approach (CASA) model to analyze the spatiotemporal distribution of Net Primary Productivity (NPP) in the Central Yunnan Urban Agglomeration (CYUA) from 1982 to 2019. It measures human activity intensity using the Comprehensive Human Activity Intensity Index (CHAII), which is derived from human activity intensity of land surface, population density, economic conditions, and remote sensing data. Spatial autocorrelation analysis is used to assess human activity clustering. The study employs Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) to quantify the impacts of climate change and human activities on NPP in CYUA across different spatiotemporal scales. The findings are as follows: (1) Human activities in CYUA exhibit a spatial distribution radiating from Kunming City and display distinct spatial autocorrelation characteristics. (2) Increasing human activity reduces NPP. In areas with higher and more concentrated human activity, it suppresses NPP, while positively affecting neighboring areas. The threshold of human activity intensity affecting NPP varies across space and time, with higher thresholds in cities with greater human activity intensity compared to those with lower intensity. (3) NPP and precipitation exhibit a closely positive correlation. Reduced precipitation weakens this correlation. Rising temperatures since 2000 have negatively impacted NPP, especially in regions with higher human activity intensity. (4) This study provides a new institutional perspective for the analysis and formulation of adaptive strategies in regions where ecosystems are jointly impacted by climate change and human activities. Future research can further refine the study of NPP responses to human activities in cities with varying development levels, diverse urban organizational structures, and different urban expansion patterns.

Multi-source data-driven estimation of urban net primary productivity: A case study of Wuhan

Amidst urban expansion, numerous ecological challenges have surfaced, notably the swift alterations in the net primary productivity (NPP), presenting complexities in discerning their scale and spatial distribution. Leveraging meteorological data, multi-modal remote sensing datasets, field sampling data, and a digital elevation model (DEM), this research amalgamates field assessments, model simulations, and cutting-edge remote sensing techniques to craft a city-scale NPP estimation model for vegetation rooted in the Carnegie-Ames-Stanford Approach (CASA). This improved CASA model facilitated the computation of monthly NPP for Wuhan over 2017–2020. Our analysis divulged NPP spatiotemporal trends and distribution nuances in Wuhan, and assessed meteorological influences on the same. Salient conclusions encompass: (1) Wuhan's annual NPP maximum between 984.40 and 1310.51 gC/(m2 a), averaging from 316.78 to 430.32 gC/(m2 a), and culminating in totals from 170.15 to 231.60 × 1010 gC/a. (2) Monthly NPP trajectories in Wuhan exhibited a unimodal pattern: ascending from January to June, peaking in July–August, and tapering off through September-December, with a spatial distribution characterized by lower NPP values centrally and higher values peripherally. (3) Regionally, temperature markedly influenced NPP over precipitation. This research underpins urban ecological planning, offering empirical insights and strategic guidance for fostering a sustainable coexistence between humans and their environment, and charting future urban development paradigms.

Dynamic changes in net primary productivity of marsh wetland vegetation in China from 2005 to 2015

Wetlands are one of the world's three major ecosystems. Marsh wetland vegetation, with its unique characteristics, is playing an increasingly prominent role in absorbing atmospheric carbon dioxide and mitigating climate warming. This study estimated the annual, seasonal, and monthly trends of marsh wetland vegetation net primary productivity (NPP) in China from 2005 to 2015 based on the Carnegie–Ames–Stanford approach (CASA) model. The temporal and spatial characteristics of NPP were analyzed and the driving factors were discussed. The results showed that the annual marsh wetland vegetation NPP had an increasing trend from 2005 to 2015 on national scale in China, with the lowest and highest values in 2005 (202.03 gCm−2a−1) and 2015 (222.58 gCm−2a−1), respectively. The monthly seasonal variations in marsh wetland vegetation NPP consistent with the typical growth pattern of vegetation observed in China. Monthly marsh wetland vegetation NPP showed a unimodal distribution, and May to September were the main months for NPP accumulation. The highest vegetation NPP values occurred during the summer season. Marsh wetland vegetation types did impact the NPP distribution, Gramineous marsh vegetation contributed most to the total NPP (29%). Spatially, the NPP was higher in east than in the west, and higher in the center than in the south and north. An overall positive impact of precipitation and temperature on the Chinese marsh wetland vegetation NPP was revealed. The study recommended to give greater attention the conservation of Gramineous vegetation alongside the overall protection of Chinese marsh wetlands. Our findings can offer important evaluation results and guidance for the future addressing climate change and conservation and maintenance of the carbon sink capacity of marsh wetland vegetation in China.

Spatial and temporal variation of vegetation NPP and analysis of influencing factors in Heilongjiang Province, China

Vegetation net primary productivity (NPP) plays an important role in investigating carbon cycle and NPP provided by forest constitutes the main component of terrestrial vegetation NPP. Since late 1990s, a series of policies for protecting forest have been implemented in Heilongjiang Province, China, directly leading to the continuous change of forest area and growth environment. As a result, analysis of spatiotemporal characteristics of NPP after the implementation of protection policies was conducted in this study, and influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP was also compared quantitatively, aiming to reveal the changing characteristics and influencing factors of NPP in a large area over a longer time span. In this study, multi-source data including MOD17A3 NPP product, meteorological data, topographic data and land-use data was selected, and then the spatiotemporal characteristics of NPP from 2001 to 2020 were analyzed through commonly-used spatiotemporal analysis indices and methods. Finally, influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP in the study area were quantitatively compared and analyzed. Results show that the total amount of NPP in Heilongjiang Province has a fluctuating trend with an increasing rate of 23.47% over the past twenty years, and the increase/decrease scenarios of NPP usually can be found in forest regions with high forest coverage and urban regions experiencing dramatic urbanization processes, respectively. Meanwhile, this study also finds that there is no absolute dominant factor can be responsible for the difference in spatial distribution of NPP, but the conversion between forest and other land-use types are the main factors leading to the spatiotemporal heterogeneity of NPP in the study area. The conclusions in this study may provide guiding significance for formulating forestry protection policies in forest regions and designing land-use plans in the future, aiming to achieve the balance between ecological protection and urban development.

Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China

The objective of this study is to analyze the spatiotemporal dynamics of net primary production (NPP) change combined with land use and to further explore the driving factors of NPP change, allowing us to provide a scientific reference point for optimizing the land-use structure and improving regional carbon sequestration capacity. The average annual NPP ranged from 200 to 300 gC/m2•a in the period of 2001–2020 in our study area. We used trend analysis and linear regression analysis to explore the spatial and temporal dynamic changes in annual NPP and analyzed the driving mechanism in a semiarid region (western Jilin Province) of China in the period of 2001–2020. The results showed that NPP presented a trend of fluctuating growth, and the spatial distribution of NPP showed that NPP values of cultivated land, forest and grassland were generally higher than those of other land-use types. The high value in the southeastern region and low value in the northwestern region were identified because there were large areas of cultivated land distributed in the southeastern part of the study area in the period of 2001–2020. The main driving factors that affected NPP were annual precipitation, CO2 emissions, GDP and hours of sunshine. NPP was positively correlated with annual precipitation, CO2 emissions and GDP, and it had a significant negative correlation with hours of sunshine. Our study provides important support for research into land-use structure and improvements to the regional carbon sequestration capacity, making an important contribution to regional sustainable development.

Vulnerability Identification and Analysis of Contributors to Desertification in Inner Mongolia

Desertification vulnerability and contributing factors are of global concern. This study analyzed the spatial and temporal distribution of net primary productivity (NPP), precipitation, and temperature from 1985 to 2015. The rain use efficiency (RUE) of vegetation was selected as an indicator; and desertification vulnerability and contributors were evaluated with the Mann−Kendall test (M−K test) and the Thornthwaite−Memorial model. The results showed that NPP was lower in that years that had lower precipitation and higher temperatures, and vice versa. NPP was spatially consistent with precipitation distribution and roughly opposite to the spatial distribution of the annual change rate of temperature. The desertification vulnerability decreased from west to east, among which both the western sub−region (WSR) and the central sub−region (CSR) had the largest proportion of regions with high desertification vulnerability. On the other hand, the eastern sub−region (ESR) mostly comprises areas with extremely low or low desertification vulnerability. The vulnerability contributors for desertification differed among each sub−region. The desertified regions in WSR and ESR were mainly influenced by human activity (HA), but primarily driven by the combined impact of Precipitation−Temperature (PT) and HA in CSR. The south−east part of the CSR was only affected by HA, whereas the lesser affected regions in the study area were affected by PT and HA simultaneously. The study provides recommendations for the improvement of regional ecological environments to prevent future disasters.

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.

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.

Quantitative Impacts of Climate Change and Human Activities on Grassland Productivity in Otog Banner, China from 2001 to 2020

The responses of grassland net primary productivity (NPP) to climate change (CC) and human activities (HA) have received much attention and are inconsistent on different spatial scales. The accurate and quantitative evaluation of the impacts of CC and HA on grassland NPP at a county scale is very important to reveal the external driving factors on grassland NPP and guide the protection of the grassland ecosystem in the arid sandy area of China. In this study, the improved CASA model was adopted to quantify the grassland NPP in Otog Banner, China from 2001 to 2020. The spatiotemporal dynamics of grassland NPP and the relationships between grassland NPP and climate factors in space were analyzed using the methods of simple linear regression and relative sensitivity coefficient. Furthermore, the relative contributions to grassland NPP dynamics caused by CC and HA were explored using the quantitative method based on partial derivative. The results revealed that the mean value of grassland NPP was 175.17 g C m−2 yr−1, and exhibited a significant decrease trend periodically at a rate of 2.14 g C m−2 yr−1 from 2001 to 2020. The spatial distribution of grassland NPP increased from west to east gradually and ranged in 17.48–498.09 g C m−2 yr−1. Grassland NPP exhibited significant linear patterns along the gradients of climate factors, and was the most sensitive to sunshine duration (SSD). Approximately 86.83% of the grassland showed a degradation trend and 39.71% showed a serious degradation trend. The CC contribution to grassland NPP dynamics was 0.593 g C m−2 yr−1, and precipitation was the key driving climate factor, while the contribution of HA was −2.733 g C m−2 yr−1, which was the primary factor leading to large-scale degradation of grassland in Otog banner. This study indicates that the status of the grassland ecosystem in Otog Banner is not optimistic, and measures for grassland ecosystem restoration and improvement need to be further strengthened.

Topography intensifies variations in the effect of human activities on forest NPP across altitude and slope gradients

The distribution pattern of forest net primary productivity (NPP) is mainly driven by biotic (climate) and abiotic (topography and human activity) factors. However, the influence of these factors on NPP's distribution along the elevation and slope gradient is uncertain. Additionally, it remains uncertain whether the pattern of NPP at the regional scale with significant altitude variations is comparable to that at the mountain scale. This study analyzed the NPP distribution pattern in the Yangtze River Basin, a region with high altitude heterogeneity, and investigated the impact of climate and human activity on the NPP along the elevation and slope gradient. The results revealed that as altitude increased, NPP first rose and then declined, reaching a peak at 2200–2300 m above sea level, This is in line with the distribution of NPP in mountainous areas and suggests that regions with large elevation differences may also experience vertical effects. NPP displayed an “N” shaped distribution along the slope gradient and the effect of climatic factors varied at different altitudes. Precipitation has a greater effect on NPP than the temperature in regions with less than 2300 m altitude, whereas temperature has the opposite effect in high-elevation regions (>2300 m). The study also found that afforestation and the returning farmland to forestland mainly occurred in low- and middle-altitude regions and middle-slope regions, leading to rapid NPP growth. These findings reveal that topography influences climate and human impacts on forest NPP, and future forest management strategies should consider the terrain's impact on tree growth and protect forests in high-altitude and steep-slope areas.

NPP Variability Associated with Natural and Anthropogenic Factors in the Tropic of Cancer Transect, China

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region’s net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance.

Detecting Influencing Factor of Vegetation NPP in Southwest China Based on Spatial Scale Effect

Vegetation net primary productivity (NPP) is an important parameter for evaluating the quality of terrestrial ecosystems. It is of great importance to study the spatio-temporal evolution of vegetation NPP and its driving force for regional ecological environment protection and sustainable development. On the basis of MODIS NPP data, meteorological data, DEM data, population density data, GDP data, and land use type data, this study used linear regression analysis, R/S analysis, and a Geodetector model to analyze the spatio-temporal variation in vegetation NPP and its future changing trend on both regional and landform scales and to detect the influencing factors that affect the spatial differentiation of vegetation NPP. The results showed that the vegetation NPP exhibited an extremely significant upward trend in southwest China from 2000 to 2020. On the landform scale, the vegetation NPP had showed an upward trend in all landforms, except for the southern Tibet Plateau; among them, the vegetation NPP in the Sichuan Basin showed the most obvious upward trend. The variation in vegetation NPP exhibited obvious spatial heterogeneity in southwest China, with the changing rate of "high in the east and low in the west." The areas with an upward trend of vegetation NPP were greater than the areas with a downward trend, but the changing trend was dominated by a decreasing trend in the future, both in southwest China and each landform unit. The Geodetector results showed that elevation was the dominant factor controlling the spatial differentiation of vegetation NPP in southwest China and all landform units, except for the Yunan-Guizhou Plateau, in which the spatial differentiation of vegetation NPP was mostly dominated by temperature. The interaction detection results showed that the interaction between the influencing factors was manifested as two-factor enhancement or nonlinear enhancement. The interaction between elevation and temperature showed the highest impact on vegetation NPP distribution. On the landform scale, the spatial differential of vegetation NPP was dominated by the interaction between elevation and climate factors or elevation and GDP in the Guangxi Hills, Sichuan Basin, Zoige Plateau, Hengduan Mountains, and southern Tibet Plateau and between climate factors in the Yunan-Guizhou Plateau. The above results indicated that vegetation NPP variation and the influencing factors that dominate its spatial differential in southwest China showed obvious scale effects. Therefore, exploring the dynamic variation in vegetation NPP and its influencing factors at different spatial scales has practical significance for a comprehensive understanding of the vegetation cover situation and formulating regional ecological restoration plans in southwest China.

Delayed response of net primary productivity with climate change in the Yiluo River basin

Climatic factors are the main drivers that change net primary productivity (NPP). We chose the Yiluo River Basin as the study area to investigate the response of NPP to climatic factors in the Yellow River’s middle reaches. The temporal and spatial distribution of net primary productivity and the trend change of slope in the basin from 2000 to 2020 are analyzed. In addition, we analyzed the correlation between the NPP and climatic factors (precipitation and temperature) and the time-lag effect. The results show that 1) the annual average NPP in the basin is 556.4 gC/m2/a, the maximum value that occurred in 2019. Most of the NPP’s mutation points in the basin’s forest appeared in 2004, while the NPP’s mutation points in arable land mostly appeared in 2008 and 2009. 2) The slope trend analysis result of NPP from 2000 to 2020 shows that the NPP in 67.88% of the basin shows a significant increasing trend, 3.8% of the basin shows a significant decrease, and the other area show no significant change. 3) The correlation analysis between NPP and climatic factors shows a positive correlation between precipitation and NPP in 62.3% of the basin and a positive correlation between air temperature and NPP in 62.5%. The response of NPP to precipitation and temperature in the basin has different degrees of a time-lag effect. The NPP has a time-lag effect on precipitation in 44.8% of the basin, and its mean lag time is 8 days. NPP has a time-lag effect on temperature in 55.7% of the basin, and its mean lag time is 1 day. This study is beneficial to understanding the influence of climatic factor change on vegetation activities in the Yiluo River Basin. Our study illustrates the significance of time lag in analyzing the relationship between NPP and climatic factors. Furthermore, it provides support for scientific vegetation regulation and allocation, as well as adaptation to climate change in areas similar to the Loess Plateau, based on the lag time distribution corresponding to different vegetation types in different regions of the Yiluo River Basin. After that, these areas’ water resources will be fully utilized by vegetation, and ecological restoration could be accelerated.

气候变化和人类活动对祁连山国家公园植被净初级生产力的定量影响

PDF HTML阅读 XML下载 导出引用 引用提醒 气候变化和人类活动对祁连山国家公园植被净初级生产力的定量影响 DOI: 10.5846/stxb202202050295 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院"西部之光"交叉团队项目；国家重点研发计划项目（2020YFA0607702）；国家"万人计划"青年拔尖人才项目；第二次青藏高原综合科学考察研究项目"生态安全屏障功能与优化体系"专题（2019QZKK0405） Quantitative analysis of the impacts of climate change and human activities on vegetation NPP in the Qilian Mountain National Park Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:祁连山国家公园作为西北地区重要的生态安全屏障和水源涵养地，研究其植被变化对西北地区的生态安全具有重要意义。基于2000-2019年祁连山国家公园的MOD17A3遥感数据，利用一元线性回归、偏相关分析、多元线性回归和残差分析等方法，分析了祁连山国家公园植被净初级生产力（NPP）的时空态势及其与降水、气温和人类活动的相关性，在此基础上量化气候变化和人类活动对植被NPP的影响。结果表明：（1）2000-2019年祁连山国家公园植被NPP整体呈波动上升趋势，且空间上呈东高西低的分布格局，其多年平均值为113.14 g C m-2 a-1，年均增长量达1.41 g C m-2 a-1；（2）植被NPP与降水、气温均呈正相关，其中降水对植被NPP影响更为显著；（3）人类活动区植被NPP总体呈增加趋势，与2016年相比，2019年人类活动区植被NPP增加的面积占87%，植被NPP降低的面积占13%；（4）在植被恢复区，气候变化和人类活动对植被恢复分别解释了92%和8%；在植被退化区，气候变化和人类活动对植被退化分别解释了29%和71%。研究结果可为祁连山国家公园生态环境保护与管理提供科学的决策依据。 Abstract:The Qilian Mountain National Park (QMNP) is an important ecological security barrier and water conservation area, and exploring its vegetation Net Primary Productivity (NPP) variation is of great significance to the ecological security in Northwest China. We used methods, including unary linear regression, partial correlation analysis, and multivariate linear regression analysis and residual analysis, to analyze the space distribution of vegetation NPP and its relationship with precipitation, temperature and human activities based on the MOD17A3 remote sensing data in the QMNP from 2000 to 2019. In terms of this, we quantified the vegetation NPP influenced by the climate change and human activities, respectively. The results showed that:(1) from 2000 to 2019 vegetation NPP in the QMNP had an overall fluctuating upward trend and decreased from east to west, with a multi-year average of 113.14 g C m-2 a-1 and an average annual increase of 1.41 g C m-2 a-1; (2) The vegetation NPP was positively correlated with precipitation and temperature, with precipitation having a more significant effect on it; (3) In human activity area, the vegetation NPP presented an overall increasing trend, with 87% area increasing and 13% area decreasing of vegetation NPP in 2019 compared with those in 2016; (4) In the vegetation recovery area, climate change and human activities explained 92% and 8% of the vegetation recovery, respectively. While in the vegetation degradation area, they explained 29% and 71% of the vegetation degradation, respectively. The results can provide a scientific decision basis for ecological and environmental protection and management in QMNP. 参考文献 相似文献 引证文献