Carnegie-Ames-Stanford Approach Research Articles

Quantitative analysis of the impact of climate change and oasification on changes in net primary productivity variation in mid-Tianshan Mountains from 2001 to 2020

Net primary productivity (NPP) has been substantially changed under the intense oasification in the urban agglomerations on the northern slopes of mid-Tianshan Mountain (UANSTM) and climate change. However, the temporal variations of NPP under the oasification remain unclear, and the relative contribution of oasification and climate change on annual NPP variation is still under debate. By using remote sensing data, reanalysis data, modified Carnegie–Ames-Stanford Approach (CASA) model, and a machine learning method, we explored the spatial–temporal variation of NPP in the UANSTM region and quantified the contribution of oasification and climate change to NPP variation from 2001 to 2020. Our study indicated that: (1) the NPP presents an overall increasing trend in the most of region and the region presented decreasing trend mainly due to the cropland conversion to the urban area; (2) the oasification-dominated NPP area concentrated in the built-up land and cropland; (3) during 2001–2020, the NPP increased by about 5.4 Tg·C, and the contribution of climatic and oasification to NPP increase were quantified (73.1% and 26.9%, respectively); (4) water-related factors was the main driver of NPP variation in the UANSTM region.

Improvement of water yield and net primary productivity ecosystem services in the Loess Plateau of China since the “Grain for Green” project

The Loess Plateau of China is one of the regions with the most serious soil erosion globally. The ambitious “Grain for Green” project was there implemented to restore degraded ecosystems, profoundly changing the water yield and carbon sequestration ecosystem services. To understand the changes of water- and carbon-related ecosystem services (hereafter WCES) in the Loess Plateau, this study combined the CASA (Carnegie-Ames-Stanford Approach) model and InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to thoroughly reveal the changes and intrinsic linkages of water yield and net primary productivity since the “Grain for Green” project in the Loess Plateau. The results show that the WCES of the Loess Plateau were increasing since 2001, dominated by both the climate change and human afforestation. The water yield and net primary productivity showed strong synergistic effects in 80% of the Loess Plateau, mainly resulting from the rapid increase of precipitation in recent years. The increasing atmospheric precipitation contributed to more WCES provisions, which may have offset the trade-off between WCES caused by large-scale afforestation. In view of the synergistic effects of WCES, enhancing water input into the ecosystem may be an effective measure to respond with future WCES degradation in dry years, such as artificial basin-cross water transfer project. Despite the low correlation between teleconnection factors and WCES, the low water yield and reduction of net primary productivity may be associated with a strong El Niño event in 2015. This study assists in a wise decision-making on the future “Grain for Green” and ecological management in the Loess Plateau.

Impacts of Climate Change and Anthropogenic Activities on the Net Primary Productivity of Grassland in the Southeast Tibetan Plateau

Climate change and anthropogenic activities have had a profound effect on the variation in grassland productivity in the Tibetan Plateau in recent decades. Quantifying the impacts of climatic and anthropogenic variables on grassland productivity is a necessary step in making the management policies of a sustainable grassland ecosystem. Net primary productivity (NPP) is an important part of the terrestrial carbon cycle and can be used to assess vegetation growth. Based on the Carnegie–Ames–Stanford Approach model and statistical analysis method, in this study we estimated the variations in grassland potential NPP (PNPP), actual NPP (ANPP) and human-induced NPP (HNPP) in the Northwest Sichuan Plateau (NWSP) of the Southeast Tibetan Plateau from 2001 to 2020. Also, we assessed the contribution of climatic change and anthropogenic activities to grassland ANPP. The results showed that the average values of grassland ANPP, PNPP and HNPP in the whole NWSP increased at the rates of 3.81, 9.14 and 7.18 g C m−2 a−1, respectively. Grassland ANPP increased in 91.7% of the total area. Climate-oriented impacts led grassland ANPP to increase in 82.6% of the area, and temperature increase was the dominant factor. Additionally, anthropogenic activity was the major reason for the grassland ANPP’s decline (5.4% of the total area). Overall, our findings are beneficial for the formulation of practical countermeasures regarding climate change adaption and damaged grassland recovering in the plateau.

NPP and Vegetation Carbon Sink Capacity Estimation of Urban Green Space Using the Optimized CASA Model: A Case Study of Five Chinese Cities

Urban area is a major source of CO2 and other greenhouse gases. Urban green space (UGS) is an essential element to increase carbon sequestration directly and reduce emission indirectly. In this study, the net primary production (NPP) and net ecosystem productivity (NEP) was monitored in order to enhance the carbon sequestration function of UGS and promote urban low-carbon development. Based on the Sentinel-2 L2A satellite images, meteorological data, and vegetation type data in 2019, we used the optimized Carnegie Ames Stanford Approach (CASA) model to estimate the NPP values of UGS types including attached green space, park green space, protective green space, and regional space in Beijing, Guangzhou, Shanghai, Shenyang, and Xi’an. The NEP values were evaluated based on NPP and soil heterotrophic respiration (RH) to quantify the vegetation carbon sink capacity. The accuracy test shows that the estimated NPP values based on the optimized CASA model are effective. The results indicate that the average NPP values (1008.5 gC·m−2·a−1) and vegetation carbon sink capacity (771.49 gC·m−2·a−1) of UGS in Beijing rank first among the cities, which is followed by the values in Guangzhou. The regional green space and park green space in five cities function as carbon sinks with high NPP values and have vegetation carbon sink capacity, whereas the attached green space in Shanghai and Xi’an as well as the protective green space in Guangzhou and Xi’an function as carbon sources. Moreover, the NEP distribution shows obvious spatial aggregation characteristics, that is, the high NEP values of UGS are clustered in mountainous forest areas in the west and north of Beijing, Northeast Guangzhou, and South Xi’an whereas the low NEP values are mostly concentrated in the urban built-up areas under strong influences of human activities. This research provides a new method for NPP and NEP estimation of UGS at the city scale and the scientific basis for the improvement of the vegetation carbon sink capacity of UGS.

Analysis of Spatial and Temporal Variations in Ecosystem Service Functions and Drivers in Anxi County Based on the InVEST Model

Background: Exploring the spatial and temporal evolution patterns of regional ecosystem service functions and their main drivers can provide effective support for formulating regional ecological conservation policies and coordinating sustainable economic–ecological development. Methods: This study quantifies the service functions of the water production, soil conservation, carbon storage, habitat quality, and net primary productivity (NPP) in the study area based on the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and the Carnegie–Ames–Stanford Approach (CASA) model and constructs a comprehensive index for ecosystem services (CES) based on the analytic hierarchy process (AHP) to reflect the total supply of various ecosystem services spatially and explore the main driving mechanisms of their spatial variation. The main driving mechanisms of the spatial variation were investigated. Results: (1) Water production in the study area from 2010 to 2020 showed a trend of increasing before decreasing, soil retention showed a trend of continuously decreasing, carbon storage and biotope quality showed a trend of decreasing before increasing, and the NPP showed a trend of continuously increasing. (2) The mean CES of the study area from 2010 to 2020 (0.5398, 0.5763, 0.5456) showed a trend of increasing before decreasing. The improvement areas were mainly concentrated in the western, southwestern, and northeastern parts of the study area, and the degraded areas were mainly distributed in the southeast and northwest. (3) The fit of the geographically weighted regression (GWR) was higher than that of the ordinary least squares (OLS) in all the periods, and the main driving factors affecting the spatial variation in the CES were the NDVI and tea plantation area (T-Area). Conclusion: This study constructed the CES model, explored the regional CES spatiotemporal evolution pattern and its main driving mechanism, and provided a reference basis for promoting the high-quality development of specialized tea regions.

Quantifying the relationship between urban blue-green landscape spatial pattern and carbon sequestration: A case study of Nanjing’s central city

Against the background of global warming, urban blue–green spaces (UBGS) are important carbon sequestration (CS) carriers that can reduce carbon emissions. However, previous studies on urban CS were mostly limited to urban green or blue spaces separately and lack a comprehensive consideration of the synergistic impact of UBGS on CS. In addition, research on the driving mechanism between urban blue–green spatial patterns (UBGSP) and CS has so far been insufficient. Therefore, in this study, we explored the correlation between UBGSP and CS, identified the affecting UBGSP factors, and proposed suggestions for planning design. We identified the UBGS of the central city of Nanjing based on Landsat-8, calculated the CS using the Carnegie-Ames-Stanford approach (CASA), and analyzed the relationship between the UBGSP and CS at both class and landscape levels by establishing sets of UBGSP indicators. At the class level, the percentage of landscape and aggregation index have positive effects on CS, whereas the landscape division and shape indexes have negative effects. Moreover, edge density and area-weighted patch fractal dimension do not correlate with CS. At the landscape level, connectance can promote the CS of UBGS. Finally, we found that the largest patch index, edge density, Shannon’s evenness index, patch cohesion index, and contagion have no significant effects. The results of this study contribute to the systematic optimization of UBGSPs, promote the integrated development of UBGSs, and provide references for urban planning practices and resource management.

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.

Optimization of Green Spaces in Plain Urban Areas to Enhance Carbon Sequestration

Global climate change and rapid urbanization have placed enormous pressure on the urban ecological environment worldwide. Urban green spaces, which are an important component of urban ecosystems, can maintain ecological and environmental sustainability and benefits, including biodiversity conservation and carbon sequestration. However, land use changes across urban landscapes, especially in plain urban areas with high development pressure, have significantly impacted the carbon sequestration efficiency of urban green spaces. Nevertheless, research examining the impact of land use change and development pressure on urban green spaces and carbon sequestration is relatively scarce. Understanding the carbon sequestration efficiency of urban green spaces and its determining factors will help predict future carbon capture trends within urban ecosystems and formulate more targeted sustainable urban planning and management strategies to improve urban carbon sink efficiency and achieve the goal of carbon neutrality. Therefore, to understand the factors affecting the carbon sequestration efficiency of urban green spaces, this paper used an integrated framework that combined the Carnegie–Ames–Stanford approach (CASA) model, landscape pattern index, multiple linear regression, and Markov–FLUS model. The study explored the impact of urban land use and land cover changes on carbon sequestration within the plain urban areas of Beijing at street scale. The results showed that, at street scale, there was a significant positive and negative correlation between the landscape pattern index and net primary productivity (NPP). In addition, the green spaces located in areas with more complex landscape structures had better carbon sequestration benefits. In addition, multiscenario carbon sequestration efficiency prediction suggested that the sustainable development (SD) scenario could achieve a positive increment of overall NPP. In contrast, the business-as-usual development (BD), the fast development (FD), and the low development (LD) scenarios showed a downward trend in NPP. This paper also proposed strategies for optimizing and enhancing green spaces within urban plain areas. Based on the strategies, the results guide decision making for sustainable urban green space planning that maintains the ecological, economic, and social integrity of urban landscapes during urbanization.

Trade-Off and Synergy Relationships and Spatial Bundle Analysis of Ecosystem Services in the Qilian Mountains

Significant heterogeneity has been observed among different ecosystem services (ES). Understanding the trade-offs and synergies among ES and delineating ecological functional zones is crucial for formulating regional management policies that improve human well-being and sustainably develop and maintain ecosystems. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Carnegie–Ames–Stanford Approach (CASA) models to evaluate the spatial distribution patterns of nine ES (food supply, raw material supply, water resource supply, water connotation, climate regulation, soil conservation, water purification, habitat quality, and entertainment tourism) in the Qilian Mountains from 2000 to 2018. We also investigated the trade-offs and synergistic relationships among ES through Spearman correlation analysis, identified ES hotspots through exploratory spatial data analysis, and identified ES bundles (ESB) using K-means clustering. Our results revealed that water purification and habitat quality remained relatively stable, while food supply, raw material supply, water resource supply, water conservation, climate regulation, soil conservation, and entertainment tourism increased by 1038.83 Yuan·ha−1, 448.21 Yuan·ha−1, 55.45 mm, 7.80 mm, 0.60 tc·ha−1, 40.01 t·ha−1 and 4.82, respectively. High-value areas for water resource supply were mainly concentrated in the high-altitude mountainous area, whereas high-value areas for soil conservation were found in the western and eastern parts of the study area. The low-value areas of water purification were primarily located in the east, while the remaining six services were highly distributed in the east and were less common in the west. Correlation analysis showed that water resource supply, water conservation, and soil conservation exhibited a synergistic relationship in the Qilian Mountains. Moreover, food supply, raw material supply, climate regulation, habitat quality, and entertainment tourism showed synergistic relationships. However, there were trade-offs between food supply and water purification as well as water resource supply, and habitat quality showed a tradeoff with water resource supply, water conservation, and soil conservation. We identified four ESB. The food supply bundle consisted mainly of farmland ecosystems, while the windbreak and sand fixation and ecological coordination bundles were dominant in the Qilian Mountains. Notably, the area of the water conservation bundle increased significantly. Our comprehensive findings on ES and ESB can provide a theoretical foundation for the formulation of ecological management policies and the sustainable development of ecosystems in the Qilian Mountains.

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

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

Estimating net primary productivity of semi-arid Crimean pine stands using biogeochemical modelling, remote sensing, and machine learning

The aim of the paper was to predict net primary productivity (NPP) in pure Pinus nigra J.F. Arnold (Crimean pine) stands by consecutively implementing remote sensing, biogeochemical modelling, and machine learning techniques. In this context, NPP was estimated using Carnegie-Ames-Stanford Approach (CASA). Following, NPP was re-modelled with spectral characteristics of the P.nigra using multi-temporal remotely sensed images (Landsat 8 OLI and Sentinel-2), land use, soils and meteorological information in a total of 180 temporary sample plots. The model results were validated using litterfall samples from 30 stations for each forest stand, including needle, branch, cone, bark, male flower, and others. The highest relationship was between NPP and male flowers (r =‐−0.75). In addition, reflectance (R), vegetation indices (VI) and texture (TEX) values (calculated according to filter and degree) for each sample plot were calculated from each sensor. Multiple linear regression (MLR) was applied to define the best subset to model the NPP values with R, VI and TEX values using MLR, support vector machines (SVM) and deep learning (DL) methods. The best prediction accuracy was obtained in TEX data in the SVM method and Sentinel-2 sensor combination. NPP testing determination co-efficiency (R2) values were 0.95. The performance of the male flower litterfall in the validation control was promising for the modelling of NPP in Crimean pine. The TEX properties of the satellite images were well reflected by using different filters, degrees, and functions, resulting in achieving a high success.

Spatio-temporal patterns and driving mechanisms of rice biomass during the growth period in China since 2000

China is a major rice-producing nation and understanding changes in rice planting areas and growth periods and the mechanisms driving rice biomass is important to ensure effective agricultural strategies and food security. In this study, rice planting areas from 2000 to 2021 in China were extracted using Savitzky–Golay filtering and the absolute distance index. Using MODIS remote sensing image data, we determined the rice growth period based on the inflection points of the enhanced vegetation index. In addition, the cumulative biomass of rice during the growth period since 2000 was determined based on the Carnegie–Ames–Stanford approach model. Finally, the key driving mechanisms and coupling relationships among the driving factors of rice biomass during the growth period were determined using geographically weighted regression and structural equation models. The results showed that the accumulation of net primary productivity (NPP) during the rice growth period in Northeast China, the middle and lower reaches of the Yangtze River, and Northwest China increased significantly (P < 0.01). The accumulation of NPP during the rice growth period in Huang-Huai-Hai, South China, and Southwest China decreased significantly (P < 0.01). In addition, Central Inner Mongolia, Qinghai–Tibet Plateau, and Loess Plateau have less rice planting area and no significant variation pattern in the accumulation of NPP during the rice growth period. In the nine major agricultural regions of China, precipitation, temperature, and evapotranspiration were among the most influential meteorological factors affecting rice biomass. For the inland areas with less precipitation, drainage density was an increasingly important influencing factor. Areas with high topographic relief were more likely to be affected by the slope. In areas with relatively poor soil, the fertilizer application rate influenced rice biomass growth. Our results provide insights into the spatio-temporal patterns of rice biomass and its driving mechanisms, and demonstrate the efficacy of using remote sensing data at a macro scale.

Analysis of Net Primary Productivity of Retired Farmlands in the Grain-for-Green Project in China from 2011 to 2020

The Grain-for-Green Project (GFGP), one of the largest ecological restoration projects in China, has made a significant contribution to carbon neutrality. However, the quantitative contribution to climate change and the driving forces of the carbon sequestration of retired farmlands remains unclear. To analyze the carbon dynamics of the retired farmlands and their driving forces, GlobeLand30 databases were used to identify retired farmlands from 2001 to 2020; in addition, net primary productivity (NPP) of the identified lands was estimated with the Carnegie–Ames–Stanford Approach (CASA). Results showed that 131,298 km2 of farmlands were retired from 2001 to 2010 (L01–10), and 130,225 km2 were retired from 2011 to 2020 (L11–20). The largest areas of retired farmlands were South Central China (24%) in L01–10 and Northwest China (22%) in L11–20. The annual NPP increment of retired farmlands was the highest and most significant in Southwest China (26,455–28,783 GgC·year−1 for retired farmlands in L01–10 and 21,320–23,303 GgC·year−1 in L11–20). In this study, NPP had significantly positive correlations with temperature and precipitation as well as significant constraint relationships with rural population density and animal husbandry output value. The findings could provide suggestions for the further implementation of the GFGP and other restoration projects.

Modeling the impact of urbanization and climate changes on terrestrial vegetation productivity in China by a neighborhood substitution analysis

Vegetation plays a vital role in global carbon sink in terrestrial ecosystems and could be affected by climate changes and human activities. In the current context of rapid urban expansion, understanding the impacts of urbanization and climate changes on net primary productivity (NPP) is helpful to sequester more atmospheric carbon and achieve carbon neutrality. We explored the terrestrial spatio-temporal NPP dynamics in China during the years 2000–2010 and 2010–2020, respectively, using the Carnegie-Ames-Stanford Approach (CASA) model based on multi-source remote sensing data. We then proposed a neighborhood substitution model to isolate the effects of urbanization and climate changes on NPP and examined the driving forces for the NPP update. The results revealed that while urbanization was likely to reduce NPP, averagely by 48.57 Tg C and 50.13 Tg C in the two stages, climate changes improved NPP by 97.71 Tg C and 92.46 Tg C, respectively, indicating that the climate changes offset the reduced vegetation productivity from rapid urbanization. The results highlighted that the rapidly urbanizing process reduced vegetation productivity due to the lost vegetated land and degraded vegetation productivity. In addition, the residual effect other than urbanization and the climate changes also played a part on the degraded vegetation carbon sequestration, reducing NPP by an average of 4.29 Tg C year−1 and 3.94 Tg C year−1 in the two stages, which could be related to other human activities. We recommend protecting vegetation cover and making informed land use plan as means to improve carbon sequestration in the context of rapid urban expansion and climate changes.

Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018

The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001–2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (RH) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future.

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.

Estimation of aerosol and cloud radiative effects on terrestrial net primary productivity over northeast Qinghai-Tibet plateau

Processes of plant growth are affected by solar radiation reaching the surface, which is regulated by atmospheric components such as aerosols and clouds. However, there are still large uncertainties in evaluating the impact of aerosol radiative effects (ARE) and cloud radiative effects (CRE) on ecosystems. This study estimated the ARE and CRE on vegetation productivity from 2001 to 2020 over the Northeast Qinghai-Tibet Plateau (QTP) based on MODIS satellite data coupled with the validated Radiative Transfer model (RTM) libRadtran and CASA (Carnegie-Ames-Stanford Approach)model. The results showed that both ARE and CRE have restrained vegetation growth in the last 20 years. The net primary productivity (NPP) of vegetation attenuation by ARE was strongest in summer with an increase of temperature and humidity which caused enhanced air particle transformation and hygroscopic growth, followed by spring and autumn, which were extremely affected by dust, and NPP attenuation by ARE was weakest in winter, accounting for 5.2% of the annual NPP. CRE plays an attenuating role on NPP with increasing cloudiness. The ARE reduced NPP by 18.8 g C/m2/yr, and CRE reduced NPP by 406.9 g C/m2/yr. NPP attenuation by CRE was more than ten times that of NPP attenuation by ARE, which indicated that the CRE has a much stronger weakening of NPP than ARE. The area where the trend of NPP at all-sky conditions increases/decreases had a good match with the areas where the trend of NPP by CRE increases/decreases with R2 being 0.89, indicating that CRE plays a dominant role in attenuating NPP. The increase in NPP is mainly attributed to the decrease in NPP attenuation by CRE over the past two decades. The study results could help to comprehensively understand the ARE and CRE on the terrestrial carbon cycle, and to further access and analyze vegetation ecosystem conditions.

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.