Vegetation Net Primary Productivity Research Articles

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.

Vegetation carbon input moderates the effects of climate change on topsoil organic carbon in China

Climate change affects soil organic carbon (SOC) and contributes to the uncertainty of terrestrial carbon sinks in the global carbon budget. The response of SOC to climate change and its associated control processes are still not well understood at the national level due to limited surface monitoring and control experiments. With changes in vegetation growth and the regulation of its carbon input, the feedback of topsoil SOC to climate change is likely to become more complex at broad geographical scales. Here, on the basis of the long-term satellite remote sensing data from 2000 to 2019, multiple linear regression and pathway analyses were applied to the raster scale to investigate the importance of vegetation carbon input relative to climate change impacts on topsoil SOC in China and quantify the direct effects of climate change on topsoil SOC and the indirect effects through vegetation net primary productivity (NPP). The results show that the overall trend of increase in topsoil SOC in China from 2000 to 2019 is significant (P < 0.05), and most regions show a good development trend in the future, except for some areas in the Central South, East, and Northeast regions, where a risk of degradation exists. Relative to climate change, the dominant areas of NPP impact on topsoil SOC in China occupy a considerable proportion, especially in the North, Northwest, and Central South regions, with area proportions of 68.15 %, 49.52 %, and 47.99 %, respectively. The indirect positive impacts of climate change on changes in topsoil SOC in China through vegetation carbon input offset the direct negative impacts in most areas. These results have important implications for enhanced prediction of future SOC changes and provide references for terrestrial carbon sinks management strategies in the context of response to climate change.

Optimizing Ecological Spatial Network Topology for Enhanced Carbon Sequestration in the Ecologically Sensitive Middle Reaches of the Yellow River, China

The destruction of vegetation structure and quantity leads to the weakening of the carbon sequestration capacity of the ecosystem. Building an ecological spatial network is a potent method for studying vegetation spatial distribution structures. The relationship between the spatial distribution structure of vegetation networks and carbon sequestration, as approached from the perspective of complex network theory, is understudied. This study uses the minimum resistance model (MCR) and morphological spatial pattern analysis (MSPA) to study the eco-space network and ecological node spatial structure and topological characteristics of vegetation in the ecologically sensitive area of the middle reaches of the Yellow River (ESAMRYR). Based on the Carnegie-Ames-Stanford approach (CASA) model, the vegetation Net Primary Productivity (NPP) of the study area is calculated, and the ecological carbon sequestration function of the ecological node is estimated, and the relationship between the ecological node and the topological indicators is analyzed. The study shows that the forest land carbon storage in the regions situated toward the south and east of the Yellow River ecologically sensitive area is the highest, accounting for twice the proportion of the area, and is very important in terms of increasing carbon storage. Most of the ecological sources in the study area have a higher topological importance than functional importance, and the sources with low coordination are mainly distributed in the southwest and northeast. We construct a topology and function coupling optimization model (TFCO) to explore the coordination between vegetation structure and carbon sequestration function, to determine the network optimization direction, and to propose optimization solutions. Analysis of network robustness and carbon sequestration capacity shows that the sturdiness and carbon sequestration of the enhanced network are significantly improved. This study provides strategies and methods for protecting ecological sensitive areas, optimizing vegetation spatial distribution, and enhancing carbon sequestration capacity.

Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China

Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km2 during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m−2·a−1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m−2·a−1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.

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.

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Aerosols impact vegetation productivity by increasing diffuse radiation and changing temperature and humidity conditions. In this study, climate simulations of the Jinghe River Basin in 2020 based on aerosol and aerosol−free scenarios were carried out using the control variable method and the aerosol optical depth parameter as the external input data of Weather Report Forecast (WRF)−solar. These two output results were used as input data for the Carnegie Ames Stanford Approach (CASA) model to calculate the impact of aerosols on vegetation productivity. The results showed that WRF−solar accurately simulated changes in meteorological factors such as temperature, rainfall, solar radiation, and relative humidity in the Jinghe River Basin, with a correlation coefficient above 0.85. Aerosols significantly change the ratio of diffuse to direct radiation, act as a cooling function to reduce temperature, and affect rainfall by interacting with clouds. The scenario simulation results showed that under the influence of aerosols, the total solar radiation was reduced by 224.98 MJ/m2, accounting for 3.44% of the total annual radiation. Correspondingly, the average net primary productivity of vegetation in the Jinghe River Basin in 2020 decreased by 26.64 gC/m2, which was not conducive to vegetation photosynthesis and carbon fixation in the basin.

Response of net primary productivity of vegetation to drought: A case study of Qinba Mountainous area, China (2001–2018)

Drought can significantly affect the carbon cycle of ecosystems. The Qinba Mountains region has a high potential for developing carbon sink forestry due to its strong carbon fixation capacity. This study applied the Carnegie-Ames-Stanford approach (CASA) model calculate monthly net primary productivity (NPP) in the region from 2001 to 2018. The standardized precipitation evapotranspiration indices (SPEI1-12) at various time scales were also analyzed by the Thornthwaite method, along with the test of significance and sustainability (Sen-MK-Hurst) model, to examine the persistent characteristics of dry and wet changes in the Qinba Mountains. The results of the correlation analysis between NPP and SPEI at multiple time scales showed that regional vegetation NPP is significantly impacted by drought, with the most sensitive months being July and October. On average, there is a four-month cumulative effect of drought on NPP. About 45.49% and 19.99% of NPP in the Qinba Mountains region are extremely or heavily sensitive to drought, with grasses being the most sensitive, followed by cultivated plants, deciduous broadleaved forests, mixed forests, sparse forests, and shrublands. The study also found that 40.99% of the region is expected to become wetter and 14.9% is expected to become drier in the future. Understanding the response of NPP to drought in this region can help with the management of regional climate change for environmental regulation.

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.

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.

Prediction of Changes to the Suitable Distribution Area of Fritillaria przewalskii Maxim. in the Qinghai-Tibet Plateau under Shared Socioeconomic Pathways (SSPs)

The Qinghai–Tibet Plateau has given birth to many indigenous highland plants due to its special geographical location and sensitivity to climate change. Relevantly, the impact of climate change on species distribution has been a hot issue for research in biogeography. Using the maximum entropy (MaxEnt) model, the spatial distribution of habitat suitability for Fritillaria przewalskii Maxim. (FPM) on the Tibetan Plateau was predicted and ranked by combining ecological data and information on its actual current geographical distribution. The potential distribution and trends of FPM on the Tibetan Plateau from 2021 to 2040, 2041 to 2060, 2061 to 2080 and 2081 to 2100 under four current and future climate scenarios (SSP126, SSP245, SSP370 and SSP585) were also predicted. The predictions were found to be highly accurate with AUC values of 0.9645 and 0.9345 for the training and test sets, respectively. A number of conclusions could be drawn from the results. Firstly, the main ecological factors limiting the growth distribution of FPM were the Vegetation types, NPP (net primary production), Soil types, Bio7 (temperature annual range), Pop (population), Slope, GDP, Aspect, Bio1 (annual mean temperature) and Elevation, with a cumulative contribution of 97.6%. Secondly, in the recent past period of 1970–2000, the total suitable distribution area of FPM accounted for 5.55% of the plateau’s total area, which was about 14.11 × 104 km2, concentrated in its eastern and central regions. Thirdly, compared to the previous period, the aforementioned distribution area will, for the period spanning 2021–2040, increase by 14.48%, 16.23%, 16.99%, and 21.53% in the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. This comes with an overall expansion trend, and the areas predicted to be affected are concentrated in the eastern and central-western parts of the Tibetan Plateau. The other three future periods 2041–2060, 2061–2080, and 2081–2100 also show increases in these total areas to varying degrees. It is noteworthy that in the future periods 2061–2080 and 2081–2100, under the SSP370 and SSP585 scenarios, the area of high suitable distribution decreases or even disappears. Lastly, under the four climate scenarios, the FPM suitable distribution area will shift towards the western part of the Tibetan Plateau.

Impacts of Climate Change and Land Use/Cover Change on the Net Primary Productivity of Vegetation in the Qinghai Lake Basin.

The Qinghai Lake Basin acts as a natural barrier, preventing the western desert from spreading eastward. This is an important link in preserving the ecological stability of the northeastern region of the Qinghai-Tibet Plateau (QTP). Therefore, quantitative research into the net primary productivity (NPP) of vegetation and its driving force in the Qinghai Lake Basin is required. The effects of land use/cover change (LUCC) and climate change on NPP in the Qinghai Lake Basin were studied using R-contribution ratio and partial correlation analysis methods using MOD17A3H products, Land Use/Land Cover (LULC) data, and meteorological data. (1) The LULC of the Qinghai Lake Basin showed a trend that "the area of grassland, cultivated land, and unused land continued to decrease, while the area of other LULC types increased" from 2000 to 2020, according to the study's findings. Grassland, water bodies, construction land, and unused land dominated the mutual transformation of LULC types. (2) The NPP of the basin showed a growing trend, with a growth rate of 3.93 gC·m-2·a-1 before 2010 and 0.88 gC·m-2·a-1 after 2010. Significant regional heterogeneity was found in NPP, with gradients decreasing from southeast to northwest. (3) The impact of LUCC on overall NPP changes had gradually increased. Climate change has been the primary driver of NPP changes in the Qinghai Lake Basin over the last 20 years.

Changes in the Spatiotemporal of Net Primary Productivity in the Conventional Lake Chad Basin between 2001 and 2020 Based on CASA Model

Accurate estimation of vegetation Net Primary Productivity (NPP) has important theoretical and practical significance for ecological environment governance, carbon cycle research, and the rational development and utilization of natural resources. In this study, the spatial characteristics, temporal changes, and driving factors of NPP in the Conventional Lake Chad Basin (CLCB) were based on MODIS data by constructing a Carnegie Ames Stanford Approach (CASA) model and using a combination of Residual trends (RESTREND) and correlation analysis. The results showed that from 2001 to 2020, the NPP of the CLCB decreased annually (1.14 g C/m2), mainly because of overgrazing, deforestation, and large-scale irrigation. We conducted a driving factor analysis and found that the main influencing factor of the NPP of the CLCB is high-intensity human activities, including farmland reclamation and animal husbandry. Although the impact of climate change on NPP is not obvious in the short term, climate change may help recover NPP in the long term. The continued reduction in NPP has greatly increased the difficulty of regreening the Sahel; the increase in population density and rapid urbanization have led are major contributing factors to this. Our findings have important implications for the continued implementation of stringent revegetation policies. However, owing to limited data and methods, only the overall change trend of NPP was obtained, and comprehensive follow-up studies are needed.

Multi-source remote sensing data shows a significant increase in vegetation on the Tibetan Plateau since 2000

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 ( p &lt; .01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively ( p &lt; .05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions.

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

Multiple effects of climate changes and human activities on NPP increase in the Three-north Shelter Forest Program area

In the framework of ecological engineering, vegetation net primary productivity (NPP) has grown dramatically in the Three-north Shelter Forest Program area (TSFPA), which is influenced by both human activities and climate change, as well as their interaction. However, it remains unclear how the individual (pure effects) and interactive (shared and coupling effects) impacts affect and maintain the growing vegetation NPP trend. In this study, the monotonically increasing NPP trend from nonlinear trends was detected using Ensemble empirical mode decomposition (EEMD), and its persistence was investigated using boosted regression tree (BRT) models. We investigated the relative contributions of environmental factors and variable interactions, as well as spatially identified the multiple effects of climate change and human activities on the increasing trend and its persistence over TSFPA. The results indicated that: (1) The vegetation NPP was dominated by the monotonically increasing trend (41.08 %), and 30.04 % of all pixels can still maintain NPP increment. (2) Although soil moisture exhibited the highest interaction with the precipitation seasonality (32.21 %) on the monotonically increasing NPP trend, land-use change substantially interacted with soil moisture (26.97 %), snowmelt (23.34 %), and net solar radiation (13.43 %). (3) The monotonically increasing vegetation NPP was caused not only by the pure effects of climate change (23.57 %) and human activities (11.69 %) directly but also by their coupling effects (26.64 %) or shared effects (38.10 %) interactively. (4) The shared effects (50.94 %) dominated the persistently increasing NPP trend, whereas the coupling effects (78.78 %) dominated the non-persistently increasing NPP trend. The findings highlight the importance of the pure, shared, and coupling effects of human activities and climate change interactively for elucidating the driving of the vegetation NPP increment and its persistence over ecological engineering areas.

Impact of Farmland Change on Vegetation NPP in the One River and Two Streams Region of Tibet

Studies on the impacts of a particular land use type change are relatively rare, especially in the Tibetan Plateau region (TP). This study focused on the impacts of farmland use change on grain supply and ecosystem stability in the Yarlung Zangbo river and its two tributaries (also known as One River and Two Streams, ORTS), using net primary productivity (NPP), known as the total amount of organic matter left after removal of carbon absorbed from the atmosphere by vegetation through photosynthesis, as a common proxy for farmland productivity and ecosystem stability. The trend analysis method was applied to measure the inter-annual change of NPP, and an ecological impact index was constructed to quantify the impact of farmland use change on the NPP change in the ORTS region. The results showed that: (1) The total area of farmland decreased by 6.09% from 2000 to 2018. Built-up land occupation and ecological restoration were the main reasons for the decrease of farmland area, while there was also new reclaimed farmland, transferred from ecological land. (2) The NPP in the ORTS region was roughly on an increasing trend, while the trends of NPP in different farmland change areas were not the same. Specifically, the NPP of ecological restoration, newly reclaimed farmland, and unchanged farmland areas all showed a significant increasing trend, while the NPP in the area of farmland occupied by built-up land showed a significant decreasing trend. (3) The impact of farmland changes from 2000 to 2018 contributed 1.22% to the increase of NPP in the ORTS region. This study not only provides a research paradigm in quantifying the production and ecological impacts of a particular land use type change that can be applied in related studies in other regions, but at the same time, the results of the empirical analysis in the ORTS region can also provide suggestions for the rational use and conservation of farmland and the stability and sustainable development of ecosystems for the region and even the TP.

Greener cities cost more green: Examining the impacts of different urban expansion patterns on NPP

Urban expansion patterns whether rather “dispersed” or “compact”, have profound impacts on vegetation net primary productivity (NPP), which substantially alters the ecosystem functioning and its resources. However, there remains limited understanding of which pattern is more conducive towards NPP. Different studies have different and even contradictory views. Hence, to better understand the relationship of NPP and the underlying urban spatial patterns further research is needed. In this study, we compare the impacts of different urban expansion patterns on NPP at varying scales for the time period of 2000–2020. We exemplify this for differing city types in China (Chengdu and Hangzhou) and the USA (Chicago and Raleigh). The results showed cities with dispersed spatial patterns caused higher NPP loss rates (17.93%) than cities with compact spatial patterns (10.40%). The majority of NPP loss (more than 72%) caused by urbanization occurred predominantly in suburban and urban fringe areas. In both, suburban and urban fringe areas, the American cities with low population density and dispersed expansion patterns showed more NPP loss per new urban resident (880.713–8076.308 Mg C 10−4 persons) as well as more NPP loss per square kilometer of built-up land (51.480–881.737 Mg C km−2). The dispersed spatial pattern with high green space ratios significantly alleviated the NPP loss at the local scale but caused more overall NPP loss for the entire city. These findings shed new light on the scale dependence of urbanization-induced impacts on vegetation, and thus, help to better understand the effects of urban expansion patterns on our environment from local to planetary scales.

Spatiotemporal Distribution and Influencing Factors of the Net Primary Productivity in the Datai Mine in Western Beijing

In the context of those countries around the world that are actively promoting sustainable development of the environment, China has formulated a new “double carbon” strategic goal to assume corresponding responsibilities. Vegetation carbon sequestration plays a key role in enhancing the carbon sink capacity toward reaching the carbon peak and carbon neutrality. In order to quantitatively study vegetation carbon sequestration, in this article, we used the net primary productivity (NPP) as an indicator to measure it. In this research, the Datai Coal Mine in western Beijing was used as the study area, and the spatiotemporal distribution characteristics and the influencing factors of carbon sequestration through vegetation were analyzed. Based on the meteorological data, remote sensing images, and the land use data of the mining area, the improved Carnegie–Ames–Stanford Approach (CASA) was used to calculate the net primary productivity (NPP) of vegetation in the Datai mining area from 2013 to 2021, to analyze its temporal and spatial distribution in relation to meteorological factors. The results showed that in the past 9 years, the NPP in the Datai mining area generally increased from 546 gC/m2 to 601 gC/m2. The NPP in the Mentougou District generally decreased and had a significant relationship with precipitation, temperature, and solar radiation. The Mentougou District’s NPP change had a significant positive correlation with the precipitation change (R2 = 0.8). The Mentougou District’s NPP change had no significant relationship with temperature (R2 = 0.98) and solar radiation fluctuations (R2 = 0.75). In conclusion, the vegetation NPP in the Datai Mine regularly changed throughout the year, and its annual vegetation NPP was about twice that of the Mentougou District, which probably due to the low-intensity mining of the Datai Mine. Thus, there is no significant impact on the vegetation carbon in this area.

Investigating the Impacts of Urbanization on Vegetation Net Primary Productivity: A Case Study of Chengdu–Chongqing Urban Agglomeration from the Perspective of Townships

As an emerging national strategic urban agglomeration in China, the changing trend of vegetation net primary productivity (NPP) and the impact of the urbanization level (UL) on carbon cycle functions in the Chengdu–Chongqing urban agglomeration (CUA) have received increasing attention. Previous studies have largely overlooked externalities and the heterogeneity of urbanization effects, and urbanization has also been analyzed in isolation (with focus being on land and population urbanization). In this study, the spatial evolution of NPP was evaluated from 2000 to 2020 at the township level (3859) using multivariate remote sensing data and a comprehensive index (UL) that included population urbanization, land urbanization, and economic urbanization. Bivariate spatial autocorrelation, spatial Durbin models, and geographically weighted regression models were used to analyze the spatial externalities of urbanization impacts and assess the global and local effects. The results show that the region’s mean NPP increased by 177.25 g*c/m2 (annual growth of 1.59%), exhibiting a distribution of “low in the middle and high in the periphery” and low-value clustering along major traffic arteries and rivers. Low-value-NPP areas were mainly located in urban centers, while the high-level areas were in the mountainous region (in the southwest and southeast) and significantly expanded over time. Negative correlation clusters were the main clustering types between the UL and NPP; the “High-Low” negative correlation clusters accelerated outward from the urban centers of Chengdu and Chongqing. Overall, urbanization had negative direct and spillover effects on NPP, exhibiting spatial non-stationarity of the negative driving effect within the urban agglomeration. The results indicate the need to strengthen regional ecological joint governance and adopt more place-based urbanization optimization strategies. This study offers new insights to help to reduce the constraining effects of urbanization on vegetation productivity and ecological functions from the perspectives of population agglomeration, land expansion, and industrial construction.

The establishment of an eco-environmental evaluation model for southwest China and eastern South Africa based on the DPSIR framework

The establishment of a standard and unified evaluation system model and its application to a cross-regional eco-environmental quality evaluation and analysis are required for identifying patterns of change in eco-environmental quality in different regions and the driving factors. This knowledge is important for achieving sustainable development. This study used remote sensing (RS) data and geographic information science (GIS) to construct a unified eco-environmental quality evaluation index system based on the Driving Forces, Pressure, State, Impact and Response (DPSIR) framework and the Analytic Hierarchy Process (AHP), the Entropy method, and Grey Relational Analysis (GRA). The index system was applied to evaluate the eco-environmental quality of central Yunnan Province (CYP) and eastern South Africa (ESA) at the county scale and to identify driving factors. The results showed that: (1) The mean ecological quality index of all counties (districts/cities) in the ESA in 2019 was 0.3696, which was slightly higher than that of 0.3449 in the CYP, indicating similar total ecological quality between the CYP and ESA. (2) The Moran's I value of eco-environmental quality in the CYP and ESA both exceeded 0, with significant spatial agglomerations. (3) The strongest correlations were between state class indicators and eco-environmental quality in both study areas. The grey relational coefficients exceeded 0.9, representing state indicators (such as vegetation coverage, habitat quality index, remote-sensed vegetation water supply index, vegetation net primary productivity, and annual mean precipitation) as the main driving forces of eco-environmental quality.