Response of net primary production to land use and land cover change in mainland China since the late 1980s

Unraveling the enigma of NPP variation in Chinese vegetation ecosystems: The interplay of climate change and land use change

Global carbon emissions have exacerbated the greenhouse effect, exerting a profound impact on ecosystems worldwide. Gaining an understanding of the fluctuations in vegetation net primary productivity (NPP) is pivotal in the assessment of environmental quality, estimation of carbon source/sink potential, and facilitation of ecological restoration. Employing MODIS and meteorological data, we conducted a comprehensive analysis of NPP evolution in Chinese vegetation ecosystems (VESs), employing Theil-Sen median trend analysis and the Mann-Kendall test. Furthermore, utilizing scenario-based analysis, we quantitatively determined the respective contributions of climate change and land use change to NPP variations across various scales. The overall NPP exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 5.83 gC·m-2·year-1. Forestland ecosystem (FES) displayed the highest rate of increase (9.40 gC·m-2·year-1), followed by cropland ecosystem (CES) (4.00 gC·m-2·year-1) and grassland ecosystem (GES) (3.40 gC·m-2·year-1). Geographically, NPP exhibited a spatial pattern characterized by elevated values in the southeast and diminished values in the northwest. In addition, climate change had elevated 76.39 % of CES NPP, 90.62 % of FES NPP, and 71.78 % of GES NPP. At the national level, climate change accounted for 83.14 % of the NPP changes, while land use change contributed 14.14 %. Notably, climate change emerged as the primary driving force behind NPP variations across all VEGs, with land use change exerting the most pronounced influence on CES. At the grid scale (2 km × 2 km), land use change played a substantial role in all VEGs, contributing 60.01 % in CES, 54.20 % in FES, and 55.61 % in GES of the NPP variations.

Past and projected future patterns of fractional vegetation coverage in China

With the intensifying climate change and the strengthening ecosystem management, quantifying the past and predicting the future influence of these two factors on vegetation change patterns in China need to be analyzed urgently. By constructing a framework model to accurately identify fractional vegetation coverage (FVC) change patterns, we found that FVC in China from 1982 to 2018 mainly showed linear increase (29.5 %) or Gaussian decrease (27.4 %). FVC variation was mainly affected by soil moisture in the Qi-North region and by vapor pressure deficit in other regions. The influence of environmental change on FVC, except for Yang-Qi region in the southwest (-2.0 %), played a positive role, and weakened from the middle (Hu-Yang region: 2.7 %) to the northwest (Qi-North region: 2.4 %) to the east (Hu-East region: 0.8 %). Based on five machine learning algorithms, it was predicted that under four Shared Socioeconomic Pathways (SSPs, including SSP126、SSP245、SSP370、SSP585) from 2019 to 2060, FVC would maintain an upward trend, except for the east, where FVC would rapidly decline after 2039. FVC in the eastern region experienced a transition from past growth to future decline, suggesting that the focus of future ecosystem management should be on this region.

Analysis and simulation of the driving mechanism and ecological effects of land cover change in the Weihe River basin, China

Land cover change (LCC) is both a consequence and a cause of global environmental change. This paper attempts to construct a framework to reveal the driving mechanism and ecological effects of different ecological factors under LCC and to explore the ecological characteristics of future LCC. A rule-mining framework based on a land expansion analysis strategy (LEAS) in the patch-generating land use simulation (PLUS) model was used to analyze the drivers of LCC. Neighborhood analysis and ecological effect index were used to investigate multiple ecological effects of LCC. Remote sensing-based ecological indices (RSEI) and the PLUS and stepwise regression model were introduced to explore and predict the integrated ecological effect of LCC. Focusing on the Weihe River basin, study's main drivers of LCC were precipitation, temperature, elevation, population, water table depth, proximity to governments and motorways, GDP, and topsoil organic carbon were the main drivers of LCC. Change directionality were similar for the effects of greenness and biomass formation but opposite for summertime and wintertime temperature. In addition, the conversion of land cover types to cropland had the most significant integrated ecological effect, followed by forest, grassland-shrubland, and other types. The RSEI is predicted to rise to 0.77 in 2030, and the areas where the ecological quality grade will improve and decrease are concentrated on the east and west sides of Ziwuling Mountain, respectively. The findings of this study have practical significance for land management and ecological protection.

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.

Changes and net ecosystem productivity of terrestrial ecosystems and their influencing factors in China from 2000 to 2019

Changes in net ecosystem productivity (NEP) in terrestrial ecosystems in response to climate warming and land cover changes have been of great concern. In this study, we applied the normalized difference vegetation index (NDVI), average temperature, and sunshine hours to drive the C-FIX model and to simulate the regional NEP in China from 2000 to 2019. We also analyzed the spatial patterns and the spatiotemporal variation characteristics of the NEP of terrestrial ecosystems and discussed their main influencing factors. The results showed that (1) the annual average NEP of terrestrial ecosystems in China from 2000 to 2019 was 1.08 PgC, exhibiting a highly significant increasing trend with a rate of change of 0.83 PgC/10 y. The terrestrial ecosystems in China remained as carbon sinks from 2000 to 2019, and the carbon sink capacity increased significantly. The NEP of the terrestrial ecosystem increased by 65% during 2015-2019 compared to 2000-2004 (2) There was spatial differences in the NEP distribution of the terrestrial ecosystems in China from 2000-2019. Taking the line along the Daxinganling-Yin Mountains-Helan Mountains-Transverse Range as the boundary, the NEP was significantly higher in the eastern part than in the western part. Among them, the NEP was positive (carbon sink) in northeastern, central, and southern China, and negative (carbon source) in parts of northwestern China and the Tibet Autonomous Region. The spatial variation of NEP in terrestrial ecosystems increased from 2000 to 2009. The areas with a significant increase accounted for 45.85% and were mainly located in the central and southwestern regions. (3) The simulation results revealed that vegetation changes and CO2 concentration changes both contributed to the increase in the NEP in China, contributing 85.96% and 36.84%, respectively. The vegetation changes were the main factor causing the increase in the NEP. The main contribution of this study is to further quantify the NEP of terrestrial ecosystems in China and identify the influencing factors that caused these changes.

Assessing the Effects of Human Activities on Terrestrial Net Primary Productivity of Grasslands in Typical Ecologically Fragile Areas

Global enhanced human activities have deeply influenced grassland ecosystems. Quantifying the impact of human activities on grasslands is crucial to understanding the grassland dynamic change mechanism, such as grassland degradation, and to establishing ecosystem protection measures. In this study, potential net primary productivity (PNPP), actual NPP (ANPP), and the forage harvest NPP (HNPP) were employed to establish the human activities index (HAI) to reveal the spatiotemporal changes of the effects of human activities on grassland ecosystems in eastern Inner Mongolia from 2000 to 2017, and to further explore the relationship between human activities and grassland degradation. The results showed that the total average PNPP, ANPP, and HNPP of grasslands in eastern Inner Mongolia were 187.2 Tg C yr^-1, 152.3 Tg C yr^-1, and 8.9 Tg C yr^-1, respectively, during the period of 2000 to 2017. The HAI exhibited a clear decreasing trend during the study period, with annual mean values ranging from 0.75 to 0.47, which indicates that the NPP loss induced by human activities is weakening, and this trend is dominated by the difference between potential NPP and actual NPP. About 42.4% of the study area was non-degraded grassland, and the declining grassland degradation index (GDI) indicated that the degradation grade in eastern Inner Mongolia improved from moderate to light degradation. A positive relationship was found between HAI and GDI. This relationship was more significant in Xilingol League, which is a typical ecologically fragile area, than that in Xing'an League and Hulunbuir City.

Water quality analysis based on phytoplankton and metal indices: a case study in the Sauce Grande River Basin (Argentina)

The increasing landscape alterations due to anthropogenic activities is of global concern since it affects aquatic ecosystems, often resulting in compromise of the ecological integrity and the water quality. In this sense, the evaluation, monitoring, and prediction of the aquatic ecosystem quality becomes an important research subject. This study presents the first integrated water quality assessment of the Sauce Grande River Basin, in Argentina, based on the spatial distribution of the phytoplankton community, the physicochemical parameters, and the metal concentrations (Cd, Cu, Cr, Fe, Mn, Ni, Pb, and Zn) found in the particulate fraction. According to the trophic indices and the phytoplankton abundance, composition, and diversity, the water quality showed significant deterioration in the lower basin after the Sauce Grande lake. The trophic state index indicated that water was oligotrophic in over 75% of the sampling sites, increasing downstream, where two sites were characterized as mesotrophic, and one described as hypertrophic. The phytoplankton community was dominated by diatoms in zones with low anthropogenic impact and conductivity, whereas high densities of Euglenophyta, Chlorophyta, and Cyanobacteria were found in the middle-lower basin, associated with higher organic matter and eutrophication. The conductivity, turbidity, and most metal concentrations also increased towards the downstream area, even exceeding recommended levels for the metals Cu, Cr, Mn, and Pb in the middle and lower reaches of the basin (Cu: 3.5 µg L^-1; Cr: 2.4 µg L^-1; Pb: 1.2 µg L^-1; Mn 170 µg L^-1). This study generates a database for the water quality of the Sauce Grande River Basin and sets an example of how the water quality varies along a basin that crosses different topographic environments, land covers, and anthropogenic influences.

Responses of Forest Net Primary Productivity to Climatic Factors in China during 1982-2015

Forest ecosystems play an important role in the global carbon cycle. Clarifying the large-scale dynamics of net primary productivity (NPP) and its correlation with climatic factors is essential for national forest ecology and management. Hence, this study aimed to explore the effects of major climatic factors on the Carnegie−Ames−Stanford Approach (CASA) model-estimated NPP of the entire forest and all its corresponding vegetation types in China from 1982 to 2015. The spatiotemporal patterns of interannual variability of forest NPP were illustrated using linear regression and geographic information system (GIS) spatial analysis. The correlations between forest NPP and climatic factors were evaluated using partial correlation analysis and sliding correlation analysis. We found that over thirty years, the average annual NPP of the forests was 887 × 1012 g C/a, and the average annual NPP per unit area was 650.73 g C/m2/a. The interannual NPP of the entire forest and all its corresponding vegetation types significantly increased (p < 0.01). The increase in the NPP of evergreen broad-leaved forests was markedly substantial among forest types. From the spatial perspective, the NPP of the entire forest vegetation gradually increased from northwest to southeast. Over the years, the proportions of the entire forest and all its corresponding vegetation types with a considerable increase in NPP were higher than those with a significant decrease, indicating, generally, improvements in forest NPP. We also found climatic factors variably affected the NPP of forests over time considering that the rise in temperature and solar radiation improved the interannual forest NPP, and the decline in precipitation diminished the forest NPP. Such varying strength of the relationship between the interannual forest NPP and climatic factors also varied across many forest types. Understanding the spatiotemporal pattern of forest NPP and its varying responses to climatic change will improve our knowledge to manage forest ecosystems and maintain their sustainability under a changing environment.

How Does Different Cropland Expansion Trajectories Affect Cropland Fragmentation? Insights From Three Urban Agglomerations in Yangtze River Economic Belt, China

A clear understanding of cropland expansion dynamics and their effects is vital for cropland protection and food security. However, the trajectories of cropland expansion have been less discussed. This study referred to the modes of landscape expansion and assessed the cropland expansion trajectory in three urban agglomerations in the Yangtze River Economic Belt and its impact on cropland fragmentation. Specifically, we identified three cropland expansion trajectories using the landscape expansion index, namely, infilling, edge-expansion, and outlying. Moreover, the surface relief amplitude model was employed to characterize the relief amplitude effect on cropland expansion trajectories. By coupling landscape metrics (e.g., patch density, landscape shape index, the largest patch index, and aggregation index) and Spearman correlation analysis, the relationship between cropland expansion trajectories and cropland fragmentation was assessed. Results show that (1) three urban agglomerations experience cropland expansion, in which the edge-expansion trajectory is primary, followed by infilling and outlying trajectories; (2) the cumulative frequency curve indicates that infilling and edge-expansion trajectories are likely to be distributed in low topographic relief amplitude regions, while the outlying trajectory is located in relatively higher topographic relief amplitude regions; and (3) infilling and edge-expansion trajectories contribute to a significantly positive relationship with the decrease of cropland fragmentation, while the outlying trajectory has a negative relationship with cropland fragmentation. This research highlights that cropland protection policies should considerably focus on the trajectory of cropland expansion, not only request the total area of cropland in a dynamic balance.

Spatiotemporal Variations of Chinese Terrestrial Ecosystems in Response to Land Use and Future Climate Change

Terrestrial ecosystems in China are threatened by land use and future climate change. Understanding the effects of these changes on vegetation and the climate-vegetation interactions is critical for vegetation preservation and mitigation. However, land-use impacts on vegetation are neglected in terrestrial ecosystems exploration, and a deep understanding of land-use impacts on vegetation dynamics is lacking. Additionally, few studies have examined the contribution of vegetation succession to changes in vegetation dynamics. To fill the above gaps in the field, the spatiotemporal distribution of terrestrial ecosystems under the current land use and climate baseline (1970–2000) was examined in this study using the Comprehensive Sequential Classification System (CSCS) model. Moreover, the spatiotemporal variations of ecosystems and their succession under future climate scenarios (the 2030s–2080s) were quantitatively projected and compared. The results demonstrated that under the current situation, vegetation without human disturbance was mainly distributed in high elevation regions and less than 10% of the national area. For future vegetation dynamics, more than 58% of tundra and alpine steppe would shrink. Semidesert would respond to climate change with an expansion of 39.49 × 104 km2, including the succession of the steppe to semidesert. Although some advancement of the temperate forest at the expense of substantial dieback of tundra and alpine steppe is expected to occur, this century would witness a considerable shrinkage of them, especially in RCP8.5, at approximately 55.06 × 104 km2. Overall, a warmer and wetter climate would be conducive to the occurrence and development of the CSCS ecosystems. These results offer new insights on the potential ecosystem response to land use and climate change over the Chinese domain, and on creating targeted policies for effective adaptation to these changes and implementation of ecosystem protection measures.

Frontier of Rural Revitalization in China: A Spatial Analysis of National Rural Tourist Towns

As the national economic situation improves, concerns about rural issues in China, a large agricultural country, are gradually increasing. Hence, rural tourism has been thrust into the limelight. This research is based on the National Rural Tourist Towns of China (NRTTC). It aims to analyze the spatial structure, influencing factors and their relevance to rural tourism development. Initially, this research examines the spatial distribution pattern in terms of kernel density. Subsequently, the imbalance index and Lorenz curve are used to distinguish the differences in spatial distribution. The Gini coefficient is used to explore the clustered regional distribution. The results indicate the following: (1) the number of NRTTC in each province is relatively even; and (2) the spatial distribution is highly uneven. The degree of aggregation is bounded by the Hu Huanyong boundary, with more in the southeast and less in the northwest. The capital circle is the core density area. Additionally, those NRTTC in the eastern and southeastern regions have a large distribution density and a more comprehensive radiation range. This study additionally analyzed the factors influencing the spatial distribution characteristics of NRTTC, and found four crucial aspects, namely, the national development strategy, the social environment, the geographical environment, and historical development. This research can provide a reference for the construction of rural tourist towns in different countries and regions.

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if considering their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms.

To Preserve Green Buffer under Polarization and Diffusion Effects of a Fast-Developing Megalopolis

The polarization and diffusion effects of landscape patterns are important features of megalopolis development. Under the urbanized effects, green space is a key spatial unit in delivering vital ecosystem services for sustainable urban planning. However, currently, fast urban developing is swamping the green space. In this study, by tracing landscape pattern changes of a fast-developing megalopolis, the Chengdu-Chongqing Megalopolis in the southeast of China, and using land-use data from 1980 to 2020, we aimed to determine the polarization and diffusion effects of the megalopolis and their impacts on the green space within and between the cities. We found that: (1) during the past four decades, spatial expansion of the megalopolis mainly occupied grassland and farmland, triggering an increase in landscape fragmentation; (2) based on socio-economic indicators, the spatial-attraction network analysis showed a significant polarization effect; however, based on the natural landscape, this analysis demonstrated a more scattered pattern; (3) importantly, the megalopolis developed at quite a similar pace, which caused the green rural area between the central cities demonstrating an encroached trend by the urbanization. To promote sustainability of the fast-developing megalopolis, we suggest that the boundary of the green space should be broadened to form a green network in which natural green space and urban green space are interconnected, improving the connectivity of habitats within the megalopolis for urban biodiversity. Our study implied that maintaining the green buffer shall be considered in advance for sustainable megaregional planning and establishing resilience of the fast-developing megalopolis.

Coupling coordination relationship between geology-geomorphology and ecology in Northeast China

Northeast China is an important ecological barrier and commodity grain base in China. The coupling coordination relationship between geology–geomorphology and ecology has become a critical background condition for ecosystem protection and sustainable development. Taking Northeast China as a case (accounting for about 13% of China’s land area), 9 divisions are divided according to the characteristics of regional ecology and geology–geomorphology, and 17 indicators are selected to build an evaluation index system. Methods of analytic hierarchy process, entropy weight and game theory are used to determine the index weights. Based on the coupling coordination degree (CCD) model, the spatial coupling coordination characteristics of geology–geomorphology and ecology are studied. The variation characteristics of the Normalized Difference Vegetation Index (NDVI) are evaluated by Sen+Mann–Kendall (Sen+MK) method. Our results are as follows. (1) The coupling between geology–geomorphology and ecology is strong, but the spatial differentiation of CCD is obvious. Nine divisions are evaluated as two high–level, three medium–level and three low–level coordination types and one mild imbalance type. (2) The plain divisions Ⅰ and Ⅳ where the typical black soil belt is located are high coordination types. Restricted by geology–geomorphological conditions or ecological conditions, mountain divisions Ⅲ and Ⅶ and plain division Ⅴ are moderate coordination types, mountain divisions Ⅱ and Ⅷ and plateau division Ⅸ are low coordination types, and mountain division Ⅵ is mild imbalance type. (3) The variation trend of NDVI shows a significant increase in divisions Ⅲ, Ⅴ, Ⅰ, Ⅱ and Ⅶ. it shows a significant decrease in part of divisions Ⅳ, Ⅵ, Ⅷ and Ⅸ, and ecological management and construction should be strengthened in these divisions. The research shows that the CCD model method is feasible for evaluating the relationship between geology–geomorphology and ecology and can provide eco–geological background information for Northeast China.

The Dominant Driving Force of Forest Change in the Yangtze River Basin, China: Climate Variation or Anthropogenic Activities?

Under the combined effect of climate variations and anthropogenic activities, the forest ecosystem in the Yangtze River Basin (YRB) has experienced dramatic changes in recent decades. Quantifying their relative contributions can provide a valuable reference for forest management and ecological sustainability. In this study, we selected net primary productivity (NPP) as an indicator to investigate forest variations. Meanwhile, we established eight scenarios based on the slope coefficients of the potential NPP (PNPP) and actual NPP (ANPP), and human-induced NPP (HNPP) to quantify the contributions of anthropogenic activities and climate variations to forest variations in the YRB from 2000 to 2015. The results revealed that in general, the total forest ANPP increased by 10.42 TgC in the YRB, and forest restoration occurred in 57.25% of the study area during the study period. The forest degradation was mainly observed in the Wujiang River basin, Dongting Lake basin, and Poyang Lake basin. On the whole, the contribution of anthropogenic activities was greater than climate variations on both forest restoration and degradation in the YRB. Their contribution to forest restoration and degradation varied in different tributaries. Among the five forest types, shrubs experienced the most severe degradation during the study period, which should arouse great attention. Ecological restoration programs implemented in YRB have effectively mitigated the adverse effect of climate variations and dominated forest restoration, while rapid urbanization in the mid-lower region has resulted in forest degradation. The forest degradation in Dongting Lake basin and Poyang Lake basin may be ascribed to the absence of the Natural Forest Conservation Program. Therefore, we recommend that the extent of the Natural Forest Conservation Program should expand to cover these two basins. The current research could improve the understanding of the driving mechanism of forest dynamics and promote the effectiveness of ecological restoration programs in the YRB.

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Exploring the changes of ecosystem services value caused by land use transformation driven by urbanization is crucial for ensuring the safety of the regional ecological environment and for enhancing the value of ecosystem services. Based on the land use remote sensing data during the rapid urbanization development period of Hubei Province from 1995 to 2015, this study analyzed the characteristics of land use/land cover change and land use transformation. The spatial-temporal response characteristics and evolution of ecosystem services value (ESV) to land use transformation driven by urbanization were measured by equivalent factor method, spatial autocorrelation analysis, hot spot analysis and gravity model. We found that: (1) Driven by urbanization, the most significant feature of land use transformation in Hubei Province was the expansion of the built-up land and the significant reduction of cropland and forest, among which 90% of the new built-up land was converted from cropland and forest. (2) This land use transformation became the main source of ESV losses. Especially, the sharp increase of the built-up land from 2010 to 2015, occupying cropland and forest, resulted in ESV losses of nearly USD 320 million. The service capacity of climate regulation, soil conservation, gas regulation and food production undertaken by cropland and forest decreased. (3) The ecosystem services value in the study area showed spatial distribution characteristics of high in the west and low in the middle and east regions. The center of gravity of ESV shifted from northwest to southeast. Due to the sharp increase of the built-up land from 2010 to 2015, the center of gravity shift rebounded. This study can help policymakers better understand the trade-offs between land use transformation and ecosystem services driven by urbanization.

Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions.

Long-term ecological effects of two artificial forests on soil properties and quality in the eastern Qinghai-Tibet Plateau

Afforestation is an essential process of ecological restoration, landscape reconstruction, and environmental improvement. While large-scale plantations have restored the fragile ecosystems of the Qinghai-Tibet Plateau, they have also changed local soil characteristics. A 30-year-old typical planted forest on the eastern Qinghai-Tibet Plateau was selected to determine the long-term ecological effects of artificial forests on the soil in this study. Physicochemical soil characteristics at varying soil depths and relative soil parameters, such as element stoichiometry and growing stock, were quantified on the different plantations. This soil quality information was used to construct an MDS-SQI Model. Our findings revealed that soil TN, TK, TP, and AP content was higher than pre-afforestation baseline values, while SOC and pH values were lower. Amounts of soil nutrients SOC, TN, TP, TK, AP, and AK, were positively correlated in the artificial forests. The ratio of soil C/N was higher and ratios C/P and N/P were lower in poplar than the Chinese pine plantation. The soil quality index values calculated from the MDS model were 0.31 and 0.40 for poplar and Chinese pine plantations in the top 30 cm and 0.55 and 0.46 in the 100 cm depth, respectively, which indicated that the two plantations had low-quality soil. LiDAR satellite imagery was used to estimate a growing stock of 7723 m³ and 435 m³ in the poplar and Chinese pine plantations. The results suggest that the artificial forest improves soil properties overall but that different stand forests have discrete effects on the soil environment.

Evaluation of NPP using three models compared with MODIS-NPP data over China

Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.

Accelerated increase in vegetation carbon sequestration in China after 2010: A turning point resulting from climate and human interaction

China has increased its vegetation coverage and enhanced its terrestrial carbon sink through ecological restoration since the end of the 20th century. However, the temporal variation in vegetation carbon sequestration remains unclear, and the relative effects of climate change and ecological restoration efforts are under debate. By integrating remote sensing and machine learning with a modelling approach, we explored the biological and physical pathways by which both climate change and human activities (e.g., ecological restoration, cropland expansion, and urbanization) have altered Chinese terrestrial ecosystem structures and functions, including vegetation cover, surface heat fluxes, water flux, and vegetation carbon sequestration (defined by gross and net primary production, GPP and NPP). Our study indicated that during 2001-2018, GPP in China increased significantly at a rate of 49.1-53.1 TgC/yr² , and the climatic and anthropogenic contributions to GPP gains were comparable (48%-56% and 44%-52%, respectively). Spatially, afforestation was the dominant mechanism behind forest cover expansions in the farming-pastoral ecotone in northern China, on the Loess Plateau and in the southwest karst region, whereas climate change promoted vegetation cover in most parts of southeastern China. At the same time, the increasing trend in NPP (22.4-24.9 TgC/yr² ) during 2001-2018 was highly attributed to human activities (71%-81%), particularly in southern, eastern, and northeastern China. Both GPP and NPP showed accelerated increases after 2010 because the anthropogenic NPP gains during 2001-2010 were generally offset by the climate-induced NPP losses in southern China. However, after 2010, the climatic influence reversed, thus highlighting the vegetation carbon sequestration that occurs with ecological restoration.

Evaluating the Drought-Monitoring Utility of GPM and TRMM Precipitation Products over Mainland China

The purpose of this study was to evaluate the applicability of medium and long-term satellite rainfall estimation (SRE) precipitation products for drought monitoring over mainland China. Four medium and long-term (19 a) SREs, i.e., the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 post-real time Final Run precipitation products (IMF6), Global Rainfall Map in Near-real-time Gauge-calibrated Rainfall Product (GSMaP_Gauge_NRT) for product version 6 (GNRT6) and gauge-adjusted Global Satellite Mapping of Precipitation V6 (GGA6) were considered. The accuracy of the four SREs was first evaluated against ground observation precipitation data. The Standardized Precipitation Evapotranspiration Index (SPEI) based on four SREs was then compared at multiple temporal and spatial scales. Finally, four typical drought-influenced regions, i.e., the Northeast China Plain (NEC), Huang-Huai-Hai Plain (3HP), Yunnan–Guizhou Plateau (YGP) and South China (SC) were chosen as examples to analyze the ability of four SREs to capture the temporal and spatial changes of typical drought events. The results show that compared with GNRT6, the precipitation estimated by GGA6, IMF6 and 3B42V7 are in better agreement with the ground observation results. In the evaluation using SPEI, the four SREs performed well in eastern China but have large uncertainty in western China. GGA6 and IMF6 perform superior to GNRT6 and 3B42V7 in estimating SPEI and identifying typical drought events and behave almost the same. In general, GPM precipitation products have great potential to substitute TRMM precipitation products for drought monitoring. Both GGA6 and IMF6 are suitable for historical drought analysis. Due to the shorter time latency of data release and good performance in the eastern part of mainland China, GNRT6 and GGA6 might play a role for near real-time drought monitoring in the area. The results of this research will provide reference for the application of the SREs for drought monitoring in the GPM era.

Vegetation dynamics and its linkage with climatic and anthropogenic factors in the Dawen River Watershed of China from 1999 through 2018

The Dawen River Watershed (DRW), an important sub-basin of the Yellow River, has been experiencing substantial climatic and anthropogenic stresses. Identifying how stressors relate to shifts in vegetation growth is critical for maintaining the health and stability of its regional ecosystems. To address this, we constructed a 20-year dataset (1999-2018) reflecting changes in satellite-based normalized difference vegetation index (NDVI), climate variables, and land use in the DRW. We then used time series, principal component, and partial correlation analyses to detect spatial and temporal patterns in vegetation dynamics over time, as well as linkages with temperature, precipitation, and anthropogenic activities. Over 20 years, the DRW exhibited a warming-greening trend and experienced four regime shifts in its climate-vegetation system, roughly centered on 2001, 2006, 2013, and 2016. Both the average and maximum NDVI increased in all seasons, likely due to favorable changes in seasonal climatic conditions. Temperature was the dominant factor promoting vegetative growth in spring, autumn, and throughout the growing season. Precipitation had a considerable positive effect on the average NDVI during the summer. Spatial analyses indicated that 67.94% of the study area exhibited significant increase in NDVI values over time, mainly locating in the mountains and in Dongping County; Significant NDVI decrease was generally located in the urban expansion areas around cities and counties. Land cover types and annual growth cycles appeared to govern spatial patterns and the extent of variation in vegetation growth, followed by land use-related drivers and climate anomalies. These findings offer an insight on appropriate ecological management and climatic adaptation within the Dawen River Watershed.

An improved trend vegetation analysis for non-stationary NDVI time series based on wavelet transform

The aim of this paper is to improve trend analysis for non-stationary Normalized Difference Vegetation Index (NDVI) time series (TS) over different areas in Tunisia based on the wavelet transform (WT) multi-resolution analysis (MRA-WT), statistical test, and meteorological data. The MRA-WT was applied in order to decompose the TS into different components. However, the most challenge for TS analysis using MRA-WT laid in the selection of two optimum parameters: the level of decomposition and mother wavelet (MW). In this work, both factors were investigated. Firstly, the level of decomposition was calculated for 18 different MWs, and secondly the energy to Shannon entropy ratio criterion was investigated to choose the most suitable MW. The Mann-Kendall test (MK) and Sen's slope were applied to the last approximation component in order to analyze long-term vegetation changes. Finally, the influence of meteorological data for trend was analyzed. The results were first computed for different sites in Tunisia using MODIS NDVI TS from 2001 to 2017. The obtained results proved the importance of MW selection. Level 5 was considered for the TS as the best level of decomposition for long-term vegetation changes. The Daubechies and Symlets MWs (db9 and sym4) showed the highest energy to entropy ratio for three selected vegetation canopies. A combination of the two MW was proposed to derive a trend vegetation analysis at image level. A degradation in the forest area and a few increases in cropland and vegetation area were presented.

Using urban landscape pattern to understand and evaluate infectious disease risk

COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82∼63.56 ‱ and 3.00∼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951∼181.261. The adjusted R² in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide.

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

Response of non-point source pollution to landscape pattern: case study in mountain-rural region, China

Landscape patterns have a substantial effect on non-point source (NPS) pollution in watersheds. Facilitating sustainable development of mountain-rural areas is a major priority for China. Knowledge of the impacts of various landscapes on water quality in these areas is critical to meeting environmental goals. This study applied the Soil and Water Assessment Tool (SWAT) to create a hydrologic and water quality model of the study watershed; then, the relationship between water quality and landscape patterns was investigated using multiple linear regression and redundancy analysis. The results show that the western sub-basins had higher nitrogen pollution loads, and the total nitrogen concentration reached a maximum value of 3.91 mg/L; the eastern sub-basins had a higher pollution load of phosphorous featured by maximum total phosphorous concentration of 2.15 mg/L. The water quality of the entire watershed in all scenarios tended to deteriorate over time. Landscape metrics accounted for 81.7% of the total variation in pollutant indicators. The percentage of forest landscape was negatively correlated with NPS pollution, while other types of landscape showed a positive correlation. The patch density, landscape shape index, and largest patch index of urban and agricultural lands were negatively correlated with pollutant concentrations. Upland landscapes contributed more pollutants than paddy fields. Some measures, e.g., returning grassland and farmland to forest in steep regions and replacing upland crops with paddy fields, were recommended for mitigating NPS pollution in the study watershed.

Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data

Increasing frequency and intensity of extreme drought events have harmed the environment, ecosystem, and agricultural productivity. However, the characteristics of agricultural drought in China have not been well understood. The remote sensing (RS) based gridded monthly precipitation, soil moisture, land surface temperature (LST), and normalized difference vegetation index (NDVI) datasets over 1982-2018 were utilized to derive standardized precipitation index (SPI), standardized soil moisture index (SSI), multivariate standardized drought index (MSDI), and vegetation health index (VHI). The variation patterns and trends of SPI, SSI, and MSDI at the 1-, 3-, and 6-month timescales against monthly VHI anomaly were compared to identify the best agricultural drought index in China. The drought variations in the four sub-regions (northwest, north, Qinghai-Tibet area, and south area) were also investigated. The results showed that: (1) Temporal patterns of VHI anomaly were similar to relative soil moisture and slightly different from precipitation. The spatial patterns of MSDI matched with VHI the best than SPI and SSI. (2) All three indices showed positive correlations with VHI at the three timescales. The highest correlation coefficients (r) between MSDI and VHI ranged from 0.25 to 0.67, 0.22 to 0.78, 0.23 to 0.69, and 0.19 to 0.74 in northwest China, north China, Qinghai-Tibet Plateau, and south China, respectively. (3) The connections between monthly VHI and the three drought indices were weaker at the 1-month timescale (0.16 < r < 0.25) than the 3-month (0.39 < r < 0.78) and 6-month (0.26 < r < 0.68) timescales. (4) The VHI significantly increased in most of China except north China. Overall, MSDI showed better performance for monitoring agricultural drought in China's mainland.

China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC

New types of remote sensed land cover datasets provide key evidence for understanding global environmental change. However, low data consistency makes understanding the changes unclear. China has become a hot spot of land cover change in the world due to climate change and a series of human measures, such as ecological engineering, land consolidation, and urbanization. However, due to the inconsistencies in interpretation of signs and thresholds, the understanding of yearly-continued land cover changes in China is still unclear. We aim to produce China’s land cover fraction dataset from 2001 to 2015 by weighted consistency analysis. We compare the Moderate-resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC) datasets, and a new land cover fraction dataset named China-LCFMCD-CCI, produced with a 1 km resolution. The obvious increased forest areas only accounted for 4.6% of the total forest areas, and were mainly distributed in northeast China. Approximately 75.8% of the grassland and shrubland areas decreased in size, and these areas were relatively concentrated in northeast and south China. The obvious increased areas of cropland (3.7%) were equal to the obvious decreased areas (3.6%), and the increased cropland areas were in northwest China. The change in bare land was not obvious, as the obvious increased areas only accounted for 0.75% of the bare land areas. The results not only prove that the data fusion of the weighted consistency method is feasible to form a land cover fraction dataset, but also helps to fully reveal the trends in land cover fraction change in China.

Effects of climatic factors on the net primary productivity in the source region of Yangtze River, China

The ecosystem of the Source Region of Yangtze River (SRYR) is highly susceptible to climate change. In this study, the spatial–temporal variation of NPP from 2000 to 2014 was analyzed, using outputs of Carnegie–Ames–Stanford Approach model. Then the correlation characteristics of NPP and climatic factors were evaluated. The results indicate that: (1) The average NPP in the SRYR is 100.0 gC/m² from 2000 to 2014, and it shows an increasing trend from northwest to southeast. The responses of NPP to altitude varied among the regions with the altitude below 3500 m, between 3500 to 4500 m and above 4500 m, which could be attributed to the altitude associated variations of climatic factors and vegetation types; (2) The total NPP of SRYR increased by 0.18 TgC per year in the context of the warmer and wetter climate during 2000–2014. The NPP was significantly and positively correlated with annual temperature and precipitation at interannual time scales. Temperature in February, March, May and September make greater contribution to NPP than that in other months. And precipitation in July played a more crucial role in influencing NPP than that in other months; (3) Climatic factors caused the NPP to increase in most of the SRYR. Impacts of human activities were concentrated mainly in downstream region and is the primary reason for declines in NPP.

Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China from 2001 to 2012

The 2001–2012 MODIS MCD12Q1 land cover data and MOD17A3 NPP data were used to calculate changes in land cover in China and annual changes in net primary productivity (NPP) during a 12-year period and to quantitatively analyze the effects of land cover change on the NPP of China’s terrestrial ecosystems. The results revealed that during the study period, no changes in land cover type occurred in 7447.31 thousand km2 of China, while the area of vegetation cover increased by 160.97 thousand km2 in the rest of the country. Forest cover increased to 20.91%, which was mainly due to the conversion of large areas of savanna (345.19 thousand km2) and cropland (178.96 thousand km2) to forest. During the 12-year study period, the annual mean NPP of China was 2.70 PgC and increased by 0.25 PgC, from 2.50 to 2.75 PgC. Of this change, 0.21 PgC occurred in areas where there was no land cover change, while 0.04 PgC occurred in areas where there was land cover change. The contributions of forest and cropland to NPP exhibited increasing trends, while the contributions of shrubland and grassland to NPP decreased. Among these land cover types, the contributions of forest and cropland to the national NPP were the greatest, accounting for 40.97% and 27.95%, respectively, of the annual total NPP. There was no significant correlation between changes in forest area and changes in total annual NPP (R2 < 0.1), while the correlation coefficient for changes in cropland area and total annual NPP was 0.48. Additionally, the area of cropland converted to other land cover types was negatively correlated with the changes in NPP, and the loss of cropland caused a reduction in the national NPP.

Reservoir-Induced Hydrological Alterations Using Ecologically Related Hydrologic Metrics: Case Study in the Beijiang River, China

Anthropogenic activities have a tremendous impact on water ecosystems worldwide, especially in China. To quantitatively evaluate the hydrological alteration connected with aquatic lives and river ecological risks, we took the Beijiang River located in South China as the case study and used ecosurplus (defined as ecological carrying capacity exceeding ecological consumption)/ecodeficit (defined as ecological consumption exceeding carrying capacity) and Indicators of Hydrological Alterations to evaluate hydrological changes. The Ecologically Relevant Hydrologic Indicators were employed to select the key indices of Indicators of Hydrological Alterations, and the eco-environmental water demand calculation provide an effective way for the reservoir operation. Results showed that: (1) High flows contributed more to the ecodeficit, while low flows contributed more to the ecosurplus; (2) the ecodeficit in some parts of the river basin might exceed the ecosurplus after reservoir construction, especially along the main stream; and (3) the determination of eco-environmental water demand is a feasible way for improving the environment by controlling reservoirs. The current study can help guide the optimization of hydrological operation in the basin toward making the ecosystem healthier and has potential to further provide a reference for other basins in terms of hydrological alterations driven by anthropogenic activities.

Impact of variations in vegetation on surface air temperature change over the Chinese Loess Plateau

Studying the drivers and combating the effects of climate change is more urgent than ever, particularly in regions with limited water and sensitive ecosystems. This study evaluated the effect of vegetation variation on surface air temperature (SAT) change in the Chinese Loess Plateau over 1982-2015 based on the 'observation minus reanalysis' (OMR) method. Observed temperature, ERA-Interim reanalysis temperature, and Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) 3rd generation were used to analyze the relationship between OMR temperature (representing vegetation impact on SAT) and NDVI. Results showed that the Loess Plateau, especially its central-east areas, has undergone a rapid increase in NDVI and rapid decrease in OMR temperature during 1982-2015. This implies a strong cooling effect of vegetation restoration on SAT change. The mean annual NDVI (M_NDVI) and NDVI trend (Slope_NDVI) were negatively correlated with OMR temperature trend (Slope_OMR) on the Loess Plateau (P < 0.001). However, the relationships between M_NDVI (Slope_NDVI) and Slope_OMR varied among the arid, semi-arid, and semi-humid regions. As a result, the impacts of restoration of vegetation condition on SAT change during 1982-2015 were estimated to be 0.04, -0.01, and -0.07 °C decade^-1 in the arid, semi-arid, and semi-humid regions, respectively. For the entire Loess Plateau, the restoration of its vegetation condition led to a cooling effect of -0.02 °C decade^-1 during 1982-2015 and a cooling effect of -0.05 °C in the period following the implementation of the Grain for Green Project (GGP). Moreover, among the three major land use types of the Loess Plateau (i.e., grassland, farmland, and forest), vegetation restoration of forest demonstrated the most obvious cooling effect (-0.06 °C decade^-1 during 1982-2015). These results are the first quantitative estimation of the impact of vegetation variation on SAT across the entire Loess Plateau, and demonstrate the ecological effect of afforestation efforts in the southeastern areas in terms of climate warming alleviation.

Spatial and temporal effects of drought on Chinese vegetation under different coverage levels

Land surface vegetation dynamics are strongly affected by drought. Thus, understanding the responses of vegetation to drought can inform measures to increase biome stability. In this study, the normalized difference vegetation index (NDVI) and the Palmer drought severity index (PDSI) were utilized to investigate the relationship between vegetation activity and drought across different drought regions and ecological community types from 1982 to 2015. Our results showed that the highest correlation between monthly NDVI and PDSI at different timescales (1-36 months) indicated the degree of drought impact on vegetation. There were diverse responses of vegetation to drought according to the drought features and climatic environment. The northern grassland, cropland, and desert ecosystems were strongly impacted by drought. These vegetation ecosystems had a low sensitivity to drought in southern China. Drought had the strongest impact on grassland in summer, which is the high frequency drought season. The most susceptible ecosystem types to drought were those with homogenous vegetation, especially under long-term drought conditions (such as the Inner Mongolia Plateau dominated by grassland). Under global warming, drought with high-temperature characteristics is expected to become more frequent and severe. Such drought could threaten the survival of plateau grassland, arid plain grassland, and rain-fed cropland, as high temperatures accelerate evaporation, leading to water deficit. However, moist forests showed little threat under normal drought. We suggest that future research should focus on vegetation activity in northern and southwestern China, where the vegetation shows the greatest sensitivity to drought.

The Impact of Reforestation Induced Land Cover Change (1990–2017) on Flood Peak Discharge Using HEC-HMS Hydrological Model and Satellite Observations: A Study in Two Mountain Basins, China

Understanding the effect of land use and land cover (LULC) type change on watershed hydrological response is essential for adopting applicable measures to control floods. In China, the Grain to Green Program (GTGP) and the Natural Forest Conservation Program (NFCP) have had a substantial impact on LULC. We investigate the effect of these conservation efforts on flood peak discharge in two mountainous catchments. We used a series of Landsat images ranging from 1990 to 2016/2017 to evaluate the LULC changes. Further to this, the hydrological responses at the basin and sub-basin scale were generated by the Hydrologic Modeling System (HEC-HMS) under four LULC scenarios. Between 1990 and 2016/2017, both catchments experienced an increase in forest and urban land by 18% and 2% in Yanhe and by 16% and 8% in Guangyuan, respectively. In contrast, the agricultural land decreased by approximately 30% in Yanhe and 24% in Guangyuan, respectively. The changes in land cover resulted in decrease in flood peak discharge ranging from 14% in Yanhe to 6% in Guangyuan. These findings provide a better understanding on the impact of reforestation induced LULC change on spatial patterns of typical hydrological responses of mountainous catchment and could help to mitigate flash flood hazards in other mountainous regions.

Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China

Net primary productivity (NPP) of terrestrial ecosystems is an important metric of ecosystem functioning; however, the understanding of response mechanism of NPP to influencing factors and driving mechanisms are still limited. In this study, taking Anhui province as an example, spatio-temporal changes of NPP and its response to influencing factors were investigated for evaluating the effects of climate change and land use and land cover change (LUCC) on regional NPP. The Carnegie-Ames-Stanford Approach (CASA) model was employed for NPP simulation by using the MODIS normalized difference vegetation index (NDVI) data and meteorological data over 2001-2016. Combined domestic LUCC, the spatiotemporal distribution pattern and dynamic change characteristics of NPP under a long time series and its response to climate factors and human activities were analyzed in the Anhui province. The results indicated that from 2001 to 2016, total NPP had a fluctuated and decreased trend with the variation range between 30.52 and 38.07 TgC in Anhui province. The multi-year average of total NPP was about 34.62 TgC. The highest value was in 2008 and the lowest value was in 2011. Among them, amount of forestland NPP was the most. The spatial distribution of NPP shows that the high value area was mainly distributed in southern Anhui mountain areas and western Anhui Dabie mountain areas; the lower value was distributed in the middle in the study area. The area of which the NPP showed a slight decrease and essentially unchanged accounted for 59.35% and 31.82%, respectively. In general, the correlation between vegetation NPP and temperature was greater than that between precipitation. The vegetations NPP of eight land use types were all positively correlated with temperature. However, the other seven types of land use were negatively correlated with precipitation except cultivated land. In the past 16 years, the decrease of cultivated land areas and the increase of urban and construction land areas contributed a lot to the decrease of vegetation NPP in Anhui province. The NPP changes of different land use types were closely related to climatic factors, land cover area, and vegetation types.

Impacts of 1.5 °C and 2 °C Global Warming on Net Primary Productivity and Carbon Balance in China’s Terrestrial Ecosystems

Assessing potential impacts of 1.5 °C and 2 °C global warming and identifying the risks of further 0.5 °C warming are crucial for climate adaptation and disaster risk management. Four earth system models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a process-based ecosystem model are used in this study to assess the impacts and potential risks of the two warming targets on the carbon cycle of China’s terrestrial ecosystems. Results show that warming generally stimulates the increase of net primary productivity (NPP) and net ecosystem productivity (NEP) under both representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios. The projected increments of NPP are higher at 2 °C warming than that at 1.5 °C warming for both RCP4.5 and RCP8.5 scenarios; approximately 13% and 19% under RCP4.5, and 12.5% and 20% under RCP8.5 at 1.5 °C and 2 °C warming, respectively. However, the increasing rate of NPP was projected to decline at 2 °C warming under the RCP4.5 scenario, and the further 0.5 °C temperature rising induces the decreased NPP linear slopes in more than 81% areas of China’s ecosystems. The total NEP is projected to be increased by 53% at 1.5 °C, and by 81% at 2 °C warming. NEP was projected to increase approximately by 28% with the additional 0.5 °C warming. Furthermore, the increasing rate of NEP weakens at 2 °C warming, especially under the RCP8.5 scenario. In summary, China’s total NPP and NEP were projected to increase under both 1.5 °C and 2 °C warming scenarios, although adverse effects (i.e., the drop of NPP growth and the reduction of carbon sequestration capacity) would occur in some regions such as northern China in the process of global warming.

Quantitative Assessment of the Impact of Human Activities on Terrestrial Net Primary Productivity in the Yangtze River Delta

The continuous growth of the economy and population have promoted increasing consumption of natural resources, and raised concerns regarding the upper limits of the terrestrial ecosystems with biomass accessible for humanity. Here, human appropriation of net primary production (HANPP) was employed to assess the influence of human activities on terrestrial net primary production (NPP), and a detailed method was introduced to simulate the magnitude and trends of HANPP in the Yangtze River Delta. The results showed that the total HANPP of the Yangtze River Delta increased from 102.3 Tg C yr−1 to 142.2 Tg C yr−1, during 2005–2015, with an average of 121.3 Tg C yr−1. NPP changes induced by harvest (HANPPharv) made the dominant contribution of 79.9% to the total HANPP, and the increase of HANPPharv in cropland was the main driver of total HANPP growth, which was significantly correlated with the improvement in agricultural production conditions, such as total agricultural machinery power and effective irrigation area. The proportion of HANPP ranged from 59.3% to 72.4% of potential NPP during 2005–2015 in the Yangtze River Delta, and distinguishable differences in the proportions were found among the four provinces in the Yangtze River Delta. Shanghai had the largest proportion of 84.3%, while Zhejiang had the lowest proportion of 32.0%.

Severe drought events inducing large decrease of net primary productivity in mainland China during 1982-2015

The analysis of the impact of drought events on terrestrial net primary productivity (NPP) is significant to understand the effects of droughts on regional/global carbon cycling. During the past three decades, terrestrial ecosystems in mainland China have been frequently impacted by drought events. However, quantitative analyses of the variation of NPP induced by droughts are still not enough. Therefore, this study explored the response of NPP to drought events from 1982 to 2015 based on the standardized evapotranspiration deficit index (SEDI) and an NPP dataset obtained from the Carnegie-Ames-Stanford Approach model. We first identified drought events and analyzed the characteristics of drought events using a three-dimensional clustering algorithm. Subsequently, we determined the NPP variations in the drought-affected areas during the droughts and explored the correlation between the NPP variation and the drought characteristics. The results showed that 152 persistent drought events lasting at least 3 months were identified. Most events had durations between 3 and 5 months, and 19 events lasted >9 months. A negative NPP was detected in >60% of the drought-affected areas during long-term (>6 months) and severe (>4 × 10⁶ km² month) drought events and the total NPP showed a clear decrease during these events. In general, strong drought events reduced the total NPP by >30 TgC in the Northern Region, South Region, Southwest Region, and Northeast Region. The substantial decrease was mainly caused by the NPP anomaly from April to September. The NPP responses to drought events exhibited differences due to different drought characteristics. Although a high proportion of the drought-affected areas experienced a decrease in NPP during most short-term (<5 months) and less severe droughts (<2 × 10⁶ km² month), the total NPP did not exhibit a large change during these events.

Applicability of long-term satellite-based precipitation products for drought indices considering global warming

This study evaluates the applicability of using long-term satellite rainfall estimate (SRE) precipitation products in drought monitoring over mainland China under global warming conditions. Two widely used drought indices, the self-calibrating Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI), were selected as study cases; both indices consider global warming but based on different mechanisms. Two popular long-term SREs were selected to calculate the indices: the Precipitation Estimation from Remotely Sensed Information using the Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). A ground-based gridded observation dataset known as the China monthly Precipitation Analysis Product (CPAP) was used as a reference for the evaluation. Research results showed that on a grid cell scale, the SPEI based on both SREs was consistent with observations in eastern China (correlation coefficient over 0.9), while the scPDSI was much less accurate (correlation coefficient of only 0.5) and its accuracy patterns were highly spatially heterogeneous. However, on a regional scale, after spatial errors were offset by spatial averaging, the performance of the SRE-based scPDSI improved, and it showed the same ability as the SPEI in temporally detecting the timing, intensity, and magnitude of drought. The self-calibrating procedure of the scPDSI was determined as the most probable cause of its poorer performance and high heterogeneity, which would increase instability and enlarge the uncertainty of the SREs. It is thus considered that the SPEI should be the first choice for use in monitoring global-warming related drought, primarily because of the high uncertainty and instability of the scPDSI.

Spatial Heterogeneity of the Carbon Emission Effect Resulting from Urban Expansion among Three Coastal Agglomerations in China

Land-use change, particularly urban expansion, can greatly affect the carbon balance, both from the aspects of terrestrial ecosystems and anthropogenic carbon emissions. Coastal China is a typical region of rapid urban expansion, and obvious spatial heterogeneity exists from the north to south. However, the different urban change characteristics and the effect on carbon balance remain undetermined. By unifying the spatial-temporal resolution of carbon source and sink data, we effectively compared the carbon budgets of three coastal urban agglomerations in China. The results show that all of the three urban agglomerations have undergone an obvious urban expansion process, with the built-up area increasing from 1.03 × 104 km2 in 2000 to 3.06 × 104 km2 in 2013. For Beijing–Tianjin–Hebei (BTH), the built-up area gradually expanded. The built-up area in the Yangtze River Delta (YRD) gradually changed before 2007 but rapidly grew thereafter. The built-up expansion of the Pearl River Delta (PRD) passed through three growing stages and showed the largest mean patch size. Carbon emission spatial patterns in the three urban agglomerations are consistent with their economic development, from which the net ecosystem production (NEP) spatial patterns are very different. Compared to carbon emissions, NEP has a carbon sink effect and can absorb some carbon emissions, but the amounts were all much lower than the carbon emissions in the three urban agglomerations. The carbon sink effect in the Yangtze River Delta is the most obvious, with the Pearl River Delta following, and the lowest effect is in Beijing–Tianjin–Hebei. Finally, a scientific basis for policy-making is provided for viable CO2 emission mitigation policies.

Roles of Climate Change and Increasing CO2 in Driving Changes of Net Primary Productivity in China Simulated Using a Dynamic Global Vegetation Model

Net primary productivity (NPP) is the key component of the terrestrial carbon cycle, and terrestrial NPP trends under increasing CO2 and climate change in the past and future are of great significance in the study of the global carbon budget. Here, the LPJ-DGVM was employed to simulate the magnitude and pattern of China’s terrestrial NPP using long-term series data to understand the response of terrestrial NPP to increasing CO2 concentration and climate change. The results showed that total NPP of China’s terrestrial ecosystem increased from 2.8 to 3.6 Pg C yr−1 over the period of 1961–2016, with an annual average of 3.1 Pg C yr−1. The average NPP showed a gradient decrease from the southeast to northwest. Southwest China and Northwest China, comprising mostly arid and semi-arid regions, exhibited the largest increase rate in total NPP among the six geographical regions of China. Additionally, large interannual variability around the NPP trends was presented, and NPP anomalies in China’s terrestrial ecosystem are strongly associated with the El Niño-Southern Oscillation (ENSO). Southwest China made the largest contribution to the interannual variability of national total NPP. The total NPP of China’s terrestrial ecosystem continuously increased with the concurrent increase in the CO2 concentration and climate change under different scenarios in the future. During the period from 2091 to 2100, the average total NPP under the A2 and RCP85 scenarios would reach 4.9 and 5.1 Pg C yr−1 respectively, higher than 4.2 and 3.9 Pg C yr−1 under the B1 and RCP45 scenarios. Forests, especially temperate forests, make the largest contribution to the future increase in NPP. The increase in CO2 concentration would play a dominant role in driving further NPP increase in China’s terrestrial ecosystems, and climate change may slightly attenuate the fertilization effect of CO2 on NPP.

Response of net primary productivity to vegetation restoration in Chinese Loess Plateau during 1986-2015

Land use and land cover change induced by large scale ecological restoration programs has a significant impact on the terrestrial ecosystem carbon cycle, especially on the net primary productivity (NPP) in arid and semi-arid regions. This study investigated the change in NPP caused by the large-scale ecological restoration in the Chinese Loess Plateau (LPR) region from 1986 to 2015 based on land cover datasets and NPP calculated using the Carnegie-Ames-Stanford Approach model. The results indicated that the annual total NPP exhibited a significant uptrend (P < 0.01) throughout the whole vegetation restoration region during the last 30 years, with an annual increase of 0.137 Tg C. A significant abrupt change was detected in 2006 for the annual total NPP series. Over half of the restoration region showed an increase in NPP in the past three decades, however, about 30~40% of the vegetation restoration region exhibited NPP loss before 2006, but subsequently NPP loss was found in only approximately 20% of the study region. Overall, the increase in NPP attributed to the vegetation restoration reached 51.14 Tg C in the past three decades, indicating that these large-scale vegetation restoration programs increased the carbon sequestration capacity of terrestrial ecosystems in the Loess Plateau. The findings of this study improve our understanding of the effects of the green campaign on terrestrial ecosystems.

Spatiotemporal Variability of Actual Evapotranspiration and the Dominant Climatic Factors in the Pearl River Basin, China

Evapotranspiration is a vital component of the land surface process, thus, a more accurate estimate of evapotranspiration is of great significance to agricultural production, research on climate change, and other activities. In order to explore the spatiotemporal variation of evapotranspiration under global climate change in the Pearl River Basin (PRB), in China, this study conducted a simulation of actual evapotranspiration (ETa) during 1960–2014 based on the variable infiltration capacity (VIC) model with a high spatial resolution of 0.05°. The nonparametric Mann–Kendall (M–K) test and partial correlation analysis were used to examine the trends of ETa. The dominant climatic factors impacting on ETa were also examined. The results reveal that the annual ETa across the whole basin exhibited a slight but not significant increasing trend during the 1960–2014 period, whereas a significant decreasing trend was found during the 1960–1992 period. At the seasonal scale, the ETa showed a significant upward trend in summer and a significant downward trend in autumn. At the spatial scale, the ETa generally showed a decreasing, but not significant, trend in the middle and upper stream of the PRB, while in the downstream areas, especially in the Pearl River Delta and Dongjiang River Basin, it exhibited a significant increasing trend. The variation of the ETa was mainly associated with sunshine hours and average air pressure. The negative trend of the ETa in the PRB before 1992 may be due to the significant decrease in sunshine hours, while the increasing trend of the ETa after 1992 may be due to the recovery of sunshine hours and the significant decrease of air pressure. Additionally, we found that the “paradox” phenomenon detected by ETa mainly existed in the middle-upper area of the PRB during the period of 1960–1992.

Response of agricultural multifunctionality to farmland loss under rapidly urbanizing processes in Yangtze River Delta, China

Agricultural multifunctionality is increasing interest and importance under the environmental change, which influences the sustainability of agricultural systems. However, research on how the agricultural multifunctionality is being temporally adjusted under the process of rapid urbanization remains limited. Here, we use the Yangtze River Delta, one of the newest metropolitan agglomerations globally, as study area to investigate the threats of modern urbanization to traditional agriculture. This study assessed changes to farmland area and the agricultural multifunctionality of 16 cities in the delta during 1995-2015. The results show that: (1) 87.1% (690, 200 hm²) of farmland area was lost because of urban sprawl over the last 20 years; (2) the total value of agricultural multifunctionality in the delta had increased by 23.2%, which was mainly attributed to a significant increase in food provision and cultural leisure values; (3) the key factor affecting the spatial differentiation of agricultural multifunctionality changed from agricultural labour in 1995 to gross domestic product in 2005 and 2015; and (4) Socio-economic conditions and natural resources determined the adaptive change model of agricultural multifunctionality in different groups of cities. These results illustrate that agricultural multifunctionality is being adjusted to rapid urbanization through the intensification and trade-off of the multiple functions in agricultural system. Therefore, to foster the sustainable development of agriculture in metropolitan agglomerations, future land use policy should focus on both urban control and promoting agricultural multifunctionality. Ongoing transformation practices, such as land consolidation, should aim to improve the bio-physical and socio-economic functions of farmland in the delta. Future research should focus on developing locally suitable strategies based on the adaptive mechanisms of agricultural multifunctionality under changing environments in different cities.

Spatiotemporal variations of vegetation and its determinants in the National Key Ecological Function Area on Loess Plateau between 2000 and 2015

China defined 25 National Key Ecological Function Areas in 2010 and adopted various measures to support ecosystem restoration in these areas. During the process of environment policymaking, it is important to observe the variation of vegetation and its driving factors. In this paper, we chose the National Key Ecological Function Area (NKEFA) on Loess Plateau as the study area. Based on MODIS-NDVI data between 2000 and 2015, the trend analysis was used to depict the change in NDVI and the stepwise regression analysis method was used to quantitatively assess its determinants. The results show that: (a) The vegetation coverage in study area was low in the northwest and high in the southeast, corresponding to the distribution of precipitation and temperature. (b) NDVI in the growing season increased remarkably from 0.2841 in 2000 to 0.4199 in 2015 with a linear tendency of 0.085/10a. About 71.22% of the study area experienced an extremely significant increasing of NDVI, while only 0.03% of the total area suffered from significant decreasing of NDVI. (c) Compared to climatic factors, ecosystem conservation policies, and labor transfer contributed more to the vegetation changes in the study area. In order to ensure ecological security and sustainable development in these areas, it is necessary to maintain the continuity of ecological compensation policy. Moreover, developing targeted eco-compensation policies and encouraging farmers to participate in nonfarm employment are effective ways to reach a win-win outcome of reducing the ecosystem pressure and improving the welfare of rural households.

Impacts of Chinese Grain for Green program and climate change on vegetation in the Loess Plateau during 1982-2015

Remote sensing based vegetation index provides a practical method for the monitoring of vegetation dynamics at regional and global scales. Here, using a long-term remotely sensed normalized difference vegetation index (NDVI) dataset, we quantified the vegetation changes in the Loess Plateau (LP) over the last three decades (1982-2015), which includes the period before the Chinese"Grain for Green Program"(GGP) was launched (1982-1999), and the period after the GGP (1999-2015). The correlations between the NDVI and four climate related variables, i.e., precipitation, temperature, root-soil moisture (RSM), and a drought proxy-standardized evapotranspiration deficit index (SEDI), were also examined. The results indicated that, (i) the GGP strongly changed the vegetation in the LP. The growing-season mean NDVI (GSM-NDVI) and the annual mean NDVI (AM-NDVI) decreased slightly before the GGP launched in 1999, with slopes of -3.38×10^-3 and-8.00×10^-4year^-1, respectively. However, they showed slight and significant (p<0.05) increases after the GGP, with slopes of 4.75×10^-3 and 2.32×10^-3year^-1, respectively. (ii) Climate change (i.e., warming and drying) resulted in adverse effects on vegetation in the LP during the period before the GGP. However, the observed changes (i.e., wetting and reduced drought) exerted positive effects on the vegetation during the period after the GGP. (iii) Inter-annual variations of spatially averaged NDVI over the LP were primarily determined by RSM rather than any other climate related variables. In the southeastern LP, the inter-annual variation of GSM-NDVI was mainly determined by precipitation and SEDI, while the inter-annual variation of AM-NDVI was mainly caused by SEDI and RSM. Inter-annual variations of both GSM-NDVI and AM-NDVI were mainly determined by SEDI and RSM in the northwestern LP, and by temperature in the southwestern LP.

Response of net primary production to land use and climate changes in the middle-reaches of the Heihe River Basin

Net primary production (NPP) supplies matter, energy, and services to facilitate the sustainable development of human society and ecosystem. The response mechanism of NPP to land use and climate changes is essential for food security and biodiversity conservation but lacks a comprehensive understanding, especially in arid and semi-arid regions. To this end, taking the middle-reaches of the Heihe River Basin (MHRB) as an example, we uncovered the NPP responses to land use and climate changes by integrating multisource data (e.g., MOD17A3 NPP, land use, temperature, and precipitation) and multiple methods. The results showed that (a) land use intensity (LUI) increased, and climate warming and wetting promoted NPP. From 2000 to 2014, the LUI, temperature, and precipitation of MHRB increased by 1.46, 0.58°C, and 15.76 mm, respectively, resulting in an increase of 14.62 gC/m2 in annual average NPP. (b) The conversion of low-yield cropland to forest and grassland increased NPP. Although the widespread conversion of unused land and grassland to cropland boosted both LUI and NPP, it was not conducive to ecosystem sustainability and stability due to huge water consumption and human-appropriated NPP. Urban sprawl occupied cropland, forest, and grassland and reduced NPP. (c) Increase in temperature and precipitation generally improved NPP. The temperature decreasing <1.2°C also promoted the NPP of hardy vegetation due to the simultaneous precipitation increase. However, warming-induced water stress compromised the NPP in arid sparse grassland and deserts. Cropland had greater NPP and NPP increase than natural vegetation due to the irrigation, fertilizers, and other artificial inputs it received. The decrease in both temperature and precipitation generally reduced NPP, but the NPP in the well-protection or less-disturbance areas still increased slightly.