Theil-Sen Median Trend Analysis Research Articles

Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

Analysis of spatio-temporal changes in net primary productivity and their driving factors in ecological functional areas of the Yellow River Basin

The Yellow River Basin (YRB) is an important region for both ecological and economic growth in China, and a healthy and stable ecological environment is fundamental to achieving high-quality development. Investigating the spatio-temporal changs of vegetation carbon sinks in the YRB is necessary to improve understanding of the regional carbon cycle. This study analyzed the YRB using the Google Earth Engine (GEE) platform. The basin was divided into sections based on ecological function regionalization schemes. We applied Theil-Sen median trend analysis and Spearman correlation analysis, along with the lasso regression model, to examine the NPP's temporal and spatial patterns and its driving factors in these regions. The objective was to examine the NPP’ s temporal and spatial patterns and its driving factors in these regions, identifying commonalities within regions and differences between regions, while analyzing in a targeted manner the lack of vegetation carbon sequestration in ecological areas. This study highlighted the importance of ecological engineering in ecological civilization and carbon neutrality, providing scientific theoretical support and a decision-making basis for the realization of carbon neutrality. The research results indicate significant upward trends (p<0.001) in both the average annual NPP (AANPP) growth rate (57.98%) and the total annual NPP (TANPP) growth rate (41.9%) in the YRB from 2001 to 2020. The multi-year average NPP (MYANPP) exhibit a spatial distribution with high values in the south and low values in the north. The spatial distribution of NPP exhibits notable spatial aggregation and heterogeneity, where regions demonstrating improvement trends constitute 93.01% of the area, and approximately 79.73% of this region displays low volatility. A high-value area is concentrated in the Agroecological Zone of the Fenwei Basin (I-11), the Loess Plateau Agriculture and Grassland Ecological Zone (I-12), and the Qinba Mountains Deciduous and Evergreen Broad-leaved Forest Ecological Zone (I-15), with significant spatial differences. Temperature, precipitation, and forests are the foremost elements affecting NPP in the YRB, exhibiting positive correlations. Conversely, farmland displays negative correlations. Overall, NPP shows an increasing trend in all ecological function areas, and the NPP of the YRB shows a trend of improvement as a whole, as the ecological environment of the YRB has improved significantly.

A novel potential cause of extreme precipitation in the northwest China

The northwest China is a climate change-sensitive and ecologically vulnerable area. Under the backdrop of global warming, this region exhibits clear characteristics of warming and wetting. In recent years, the causes of climate change in the northwest China has become a widely-focused topic. Previous research has mainly attributed the increase in precipitation to changes in the westerly belt and enhanced local convective activity caused by global warming. South Asia, beside the Tibetan Plateau from the northwest China, is one of the regions with the fastest growth in global atmospheric pollutant emissions. This study utilizes reanalysis data such as ERA5 and MERRA-2. Statistical methods, including Theil-Sen Median trend analysis and Singular Value Decomposition analysis, are employed to analyze the spatiotemporal characteristics of South Asian aerosols, extreme precipitation in the northwest China, and the correlation between the two. The study reveals the existence of two key aerosol-precipitation response areas. Synthetic analysis of the meteorological elements of the response events in both regions is conducted to explore the possible physical mechanisms behind the correlation between South Asian aerosols and precipitation in the northwest China. The result of this study is to provide a new perspective on the causes of extreme precipitation in the arid region of northwest China.

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

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

Quantitative Assessment of the Impact of Climate Change on the Growing Season of Vegetation Gross Primary Productivity in the Middle and Lower Reaches of the Yangtze River

Quantitatively determining the direct, indirect, and comprehensive effects of climatic factors on the growing season of the vegetation GPP (GPPGS) in the middle and lower reaches of the Yangtze River at the regional and vegetation type scales can provide a scientific basis for the management and restoration of regional vegetation resources under the background of global climate change. Using MODIS GPP data, meteorological data, and vegetation type data, combined with Theil-Sen Median trend analysis and the Mann-Kendall significance test, the spatiotemporal characteristics of the GPPGS in the middle and lower reaches of the Yangtze River were investigated at different temporal and spatial scales. Path analysis was used to further reveal the direct, indirect, and comprehensive effects of climate factors on GPPGS variation in different vegetation types. The results showed that:① from 2000 to 2021, the vegetation GPPGS in the middle and lower reaches of the Yangtze River showed a fluctuating upward trend, with a rising rate (in terms of C, same below) of 2.70 g·(m2·a)-1 (P<0.01). The GPPGS of different vegetation types all showed a significant upward trend (P<0.01), with shrubs having the highest upward rate of 3.31 g·(m2·a)-1 and cultivated vegetation having the lowest upward rate of 2.54 g·(m2·a)-1. ② The proportion of the area with an upward trend in GPPGS in the middle and lower reaches of the Yangtze River was 88.11%. The proportion of the area with an upward trend in GPPGS was greater than 84% for all different vegetation types, with shrubs (49.76%) and cultivated vegetation (44.36%) having significantly higher proportions of the area with an upward trend than that in other vegetation types. ③ The path analysis results showed that precipitation and the maximum temperature had a significant positive direct effect on vegetation GPPGS (P<0.05), whereas solar radiation had a non-significant positive effect (P ≥ 0.05). The indirect effects of maximum temperature, precipitation, and solar radiation on vegetation GPPGS were all non-significantly negative (P ≥ 0.05). Under the combined effects of direct and indirect influences, precipitation and maximum temperature had a non-significant positive effect on vegetation GPPGS (P ≥ 0.05), whereas solar radiation had a non-significant negative effect on vegetation GPPGS (P ≥ 0.05). Among different vegetation types, precipitation was the main climate factor affecting the changes in GPPGS of cultivated vegetation, whereas the maximum temperature was the main climate factor affecting the changes in GPPGS of coniferous forests, broad-leaved forests, shrubs, and grasslands. ④ The changes in vegetation GPPGS in the middle and lower reaches of the Yangtze River were mainly influenced by the direct effects of maximum temperature, precipitation, and solar radiation, with the direct effect of precipitation dominating 56.72% of the changes in GPPGS. The research results can provide a reference for quantifying the carbon sequestration potential of vegetation in the middle and lower reaches of the Yangtze River and formulating ecological restoration governance policies tailored to local conditions under the background of global climate change.

Spatio-Temporal Dynamics of Normalized Difference Vegetation Index and Its Response to Climate Change in Xinjiang, 2000–2022

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, and the coefficient of variation. Meanwhile, the persistence of the NDVI distribution was analyzed by combining the trend results and Hurst index. Finally, partial correlation analysis was used to deeply explore the response mechanisms of interannual and seasonal-scale NDVI and climatic factors in Xinjiang, and the characteristics of multi-year vegetation distribution were comprehensively analyzed with the help of human footprint data. The findings indicate the following: (1) The NDVI of interannual and seasonal vegetation in Xinjiang showed a significant increasing trend during the 23-year period, but the spatial distribution was heterogeneous, and the improvement of the vegetation condition in the southern part of the region was remarkable. (2) The NDVI is relatively stable across the region. Unlike in other regions, in general, it is difficult to maintain the existing trend in NDVI in the study area for a long period of time, and the reverse trend is more persistent. (3) On the interannual scale, both precipitation and temperature are positively correlated with the NDVI, and the influence of temperature (80.94%) is greater than that of precipitation (63.82%). Precipitation was dominantly positively correlated with the NDVI in spring, summer, and the growing season, while it was negatively correlated with it in autumn. Temperature and NDVI were positively correlated, with the greatest influence in the spring. (4) Human activities had the greatest impact on the areas with low vegetation cover and areas with medium–low vegetation cover, and there was a high degree of overlap between the areas where the interannual human footprints and NDVI showed an increasing trend. The percentage of human footprints that significantly correlated with interannual NDVI was 34.79%. In the future, the protection and management of ecologically fragile areas should be increased to increase desert-vegetation cover.

Spatial and Temporal Variation Characteristics of Ecological Environment Quality in China from 2002 to 2019 and Influencing Factors

Since the beginning of the new century, there has been a notable enhancement in China’s ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the spatial and temporal characteristics of China’s EEQ, and its responses to both climate change and human activities over the past two decades, have remained largely elusive. In this study, we harnessed a combination of multi-source remote-sensing data and reanalysis data. We employed Theil–Sen median trend analysis, multivariate regression residual analysis, and the Hurst index to examine the impacts and changing patterns of climatic factors and human activities on China’s EEQ during the past two decades. Furthermore, we endeavored to forecast the future trajectory of EEQ. Our findings underscore a significant improvement in EEQ across most regions of China between 2002 and 2019, with the most pronounced enhancements observed in the Loess Plateau, Northeast China, and South China. This transformation can be attributed to the combined influence of climatic factors and human activities, which jointly accounted for alterations in EEQ across 78.25% of China’s geographical expanse. Human activities (HA) contributed 3.93% to these changes, while climatic factors (CC) contributed 17.79%. Additionally, our projections indicate that EEQ is poised to continue improving in 56.70% of China’s territory in the foreseeable future. However, the Loess Plateau, Tarim Basin, and Inner Mongolia Plateau are anticipated to experience a declining trend. Consequently, within the context of global climate change, the judicious management of human activities emerges as a critical imperative for maintaining EEQ in China. This study, bridging existing gaps in the literature, furnishes a scientific foundation for comprehending the evolving dynamics of EEQ in China and informs the optimization of management policies in this domain.

Grassland biomass spatiotemporal patterns and response to climate change in eastern Inner Mongolia based on XGBoost model estimates

Aboveground biomass (AGB) is a key index that reflects grassland health and is of great importance for grassland conservation and sustainable utilization. Many previous studies on grassland AGB were based on the analysis of the whole study period, can’t reflect the change of grassland in the short term, which was not conducive to the implementation and revision of policies. And it is still a challenge to accurately describe the response of grassland AGB to climate factors. In this study, we used four machine learning algorithms (kNN, k-nearest neighbor; RF, Random Forest; SVR, Support Vactor Regression; and XGBoost, eXtreme Gradient Boosting) to construct the estimation model of grassland AGB in eastern Inner Mongolia based on grassland AGB measurement data, MODIS(MOD13Q1) data and environmental data (climate and topography). The average distribution of grassland AGB from 2003 to 2021 was calculated based on the optimal grassland AGB estimation model. The changes of grassland AGB from 2003 to 2021 and in every five-year period during the study series were comprehensively analyzed by the Theil–Sen Median trend analysis and the Mann–Kendall test. The response of grassland AGB to climatic factors was investigated using sensitivity analysis. The results showed that (1) the XGBoost model constructed by NDVI, longitude, latitude, temperature, precipitation, elevation, slope, and aspect (NXYTPESA) is the optimal grassland AGB model (R2 = 0.87, RMSE = 14.27 g/m2). (2) The 20-year average grassland AGB in Eastern Inner Mongolia showed heterogeneous, with higher values in the northeast and lower values in the southwest. (3) The inter-annual variation fluctuated substantially, and the overall trend was increasing. Compared with other time periods, most grassland AGBs were in an increasing trend during 2008–2012 (79 %) and 2018–2021 (84 %). (4) The grassland AGB in the study area was regulated by temperature and precipitation, and the influence of precipitation was greater than that of temperature. This study provides a new method for estimating grassland AGB and its changes and a scientific basis for the sustainable development of grassland ecosystems in eastern Inner Mongolia.

How Do Driving Factors Affect Vegetation Coverage Change in the Shaanxi Region of the Qinling Mountains?

Understanding the effects of natural and human disturbance factors on fractional vegetation coverage (FVC) is significant in the promotion of ecological and environmental protection. However, most of the relevant studies neglect to consider differences in the effect of driving factors on areas with different vegetation change characteristics. In this paper, we have combined Theil-Sen median trend analysis and Mann-Kendall testing to identify degraded and restored areas. Differences in the impact of various factors on FVC in terms of degradation, restoration, and the whole region were distinguished quantitatively using the geodetector model. Additionally, the constraint line approach was used to detect the influence thresholds of factors on FVC. The results are shown as below: (1) FVC showed an overall improving trend, and vegetation restoration and degradation areas accounted for 69.2% and 22.0%, respectively. (2) The two dominant factors affecting FVC were Digital Elevation Model (DEM) and temperature for both degraded and restored regions. However, the explanatory power of precipitation was noticeably different between regions. (3) Most natural factors had a “convex” constraint effect on FVC, which gradually weakened with an increase in the variable below the threshold and vice versa. Human disturbance factors negatively constrained FVC, and the constraint effect increased with increased human activity. This study can help decision-makers optimize specific implementation policies relating to ecological restoration and sustainable development.

Investigating the spatio-temporal pattern evolution characteristics of vegetation change in Shendong coal mining area based on kNDVI and intensity analysis

Alterations in vegetation cover serve as a significant indicator of land ecology. The Shendong Coal Mining Area, being the largest coal base globally, holds significant importance for national energy security. Moreover, it has gained recognition for its environmentally conscious approach to coal mining, characterized by the simultaneous implementation of mining activities and effective governance measures. In order to assess the ongoing vegetation recovery and the temporal changes in vegetation within the Shendong Coal Mining Area, we initially utilized Landsat TM/ETM+/OLI remote sensing data. Using the Google Earth Engine (GEE), we developed a novel kernel-normalized vegetation index (kNDVI) and subsequently generated a comprehensive kNDVI dataset spanning the years 2000 to 2020. In addition, the Sen (Theil-Sen median) trend analysis method and MK (Mann-Kendall) test were utilized to examine the temporal trends over a span of 21 years. Furthermore, the Hurst exponent model was employed to forecast the persistent changing patterns of kNDVI. The utilization of the intensity analysis model was ultimately employed to unveil the magnitude of vegetation dynamics. The findings indicated a notable positive trend in the overall kNDVI of vegetation within the study area. In relation to the analysis of changing trends, the vegetation in the region underwent a slight improvement from 2000 to 2010, followed by a significant improvement from 2010 to 2020. During this transition period, a total of 289.07 km2, which represents 32.36% of the overall transition area, experienced a shift in vegetation. The predictive findings from the Hurst model indicate that while the majority of areas within the mining region will exhibit an upward trend in vegetation growth, there will be certain areas that will demonstrate a decline. These declining areas account for 39.08% of the total transition area. Furthermore, the intensity analysis results reveal notable disparities in the characteristics of vegetation growth and evolution between the periods of 2000-2010 and 2010-2020. Throughout the entirety of the transformation process, the transition from slight improvement to significant improvement prevails in terms of both relative intensity and absolute intensity, surpassing alternative transformation processes. Various trend transitions display diverse intensity characteristics that adhere to the overarching principles governing shifts in vegetation growth. Furthermore, the utilization of the intensity analysis framework and intensity spectrum employed in this study demonstrates their efficacy in elucidating the temporal dynamics of vegetation changes. Furthermore, this study plays a pivotal role in the surveillance and assessment of the efficacy of ecological restoration in mining regions. It carries substantial implications for comparable land ecological restoration efforts in mining and reclamation, thereby furnishing a theoretical foundation.

Evaluation and quantitative characterization for the ecological environment impact of open pit mining on vegetation destruction from landsat time series: A case study of Wulishan limestone mine

Quantitatively evaluating the ecological environment impacts of vegetation destruction due to open-pit mining activities is vital for enhancing the green mining standard and cost management capabilities of mining enterprises. Based on the Landsat time series, this study proposes an ecological environment impact assessment and quantitative characterization method for vegetation destruction in mining areas resulting from open-pit mining activities. First, the modified normalized difference water index and the normalized difference vegetation index time series data were calculated. The water body thresholds and the fraction of vegetation coverage were ascertained using the K-means clustering algorithm and the dimidiate pixel model, respectively, to determine the area of direct vegetation destruction in mining areas. Second, utilizing the Theil-Sen Median trend analysis and the Mann-Kendall test, the indirect impact area of vegetation in the mining region was identified. Lastly, by integrating vegetation's net primary productivity with the Chinese Emission Allowance price index, the total carbon emission cost of vegetation destruction due to mining activities over 20 years was calculated to be about 2.122 million yuan. The findings indicated that the ecological environmental impact of open-pit mining activities on vegetation destruction cannot be ignored. From 2000 to 2020, open-pit mining at the Wulishan limestone mine in Anhui Province, China, increased the area of direct vegetation destruction by 9.072 × 105 m2, and the indirect impact area on vegetation was 7.371 × 105 m2. The carbon emission cost of vegetation destruction in the direct destruction area was about 104,000 yuan per year, and the carbon emission cost of vegetation damage in the indirect impact area was approximately 2,082.53 yuan per year. This research provides a scientific foundation for ecological environmental protection, regulations, green mining, and cost management for mining enterprises, promoting the harmonious progress of both the economy and environmental protection.

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.

Estimation of grassland aboveground biomass and its response to climate changes based on remote sensing inversion in Three-River-Source National Park, Tibet Plateau, China

Three-River-Source (TRS) National Park stands as one of China’s earliest established national parks, dedicated to significant ecological responsibilities that include conserving soil and water resources in the Tibetan Plateau region. Research on climate change’s influence on the TRS region’s grasslands is of great significance in our efforts to comprehend and conserve the grassland ecosystem. The most effective random forest (RF) model was chosen to invert the aboveground biomass (AGB) of grassland in the previous 6 years (2015−2020) and predict the grassland AGB in the following 20 years (2021−2040) by comparing linear regression and multivariate nonlinear regression models such as RF, support vector machine, decision tree, and artificial neural network. A Theil–Sen median trend analysis and a Mann–Kendal test were then used to examine the trends of grassland AGB. The results showed that (1) RF outperformed other models in estimating grassland AGB, with a test set decision coefficient of multiple determination (R2) of 0.722, a root mean square error of 42.596 g/m2, and a mean absolute error of 35.619 g/m2; (2) over 6 years, the grassland AGB in TRS National Park had a spatial trend of a steady rise from the northwest to the southeast. The average annual grassland AGB was 247.333 g/m2, with averages of 44.836 g/m2, 92.601 g/m2, and 120.217 g/m2 in the Yangtze River, Yellow River, and Lancang River source parks respectively. The trend of the grassland AGB was primarily stabilized and slightly recovered, with a small portion of the slightly deteriorated areas; (3) climate change significantly affected grassland AGB, and when temperature and precipitation conditions were adequate, grassland AGB values increased with temperature and precipitation. In the scenarios of ssp119, ssp245, and ssp585, grassland AGB is projected to exhibit a dynamic upward trend over the next 20 years. Global warming is expected to boost grassland AGB. Comprehensive measures are essential to maintain grassland health and ensure a positive impact on global carbon and ecological balance. The study’s findings hold great importance for the ecological security of the TRS region and contribute to our global understanding of sustainable grassland development.

Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) &gt; GDP (0.326) &gt; precipitation (0.255) &gt; elevation (0.167) &gt; slope (0.136) &gt; temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.

Climatic and Anthropogenic Contributions to Vegetation Changes in Guangdong Province of South China

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions.

Space-time dynamics and potential drivers of soil moisture and soil nutrients variation in a coal mining area of semi-arid, China

The spatial–temporal variability of soil moisture plays a pivotal role in the reconstruction and management of ecosystems in mining areas. However, the actual driving mechanisms behind long-term, continuous mining disturbances remain unclear due to a lack of continuous data. To address this gap, a network of instrumented sites, known as Disturbed Critical Zone Observatories (DCZO), has been established for ecological monitoring in mining areas. In this study, we used Sentinel-1A images of the Dafosi Mine from 2014 to 2022 and one year of continuous DCZO monitoring data. We employed Theil-Sen median trend analysis, empirical orthogonal function (EOF), and structural equation modeling (SEM) to quantitatively explore the factors influencing the variability of soil moisture and soil nutrients under continuous mining disturbances. The results showed: (1) Inter-annual variations in soil moisture differed among coal seams mined in various years, with the steepest decline observed in seams mined in 2021–2022 > 2018–2020 > 2015–2017 > 2013–2014. (2) Mining areas exhibited a significant reduction in Available Nitrogen (AN), Available Phosphorus (AP), and Available Potassium (AK), with decreases of 25.12 %, 25.38 %, and 22.59 %, respectively, compared to non-mining areas. (3) The first two EOFs explained 80.42 % of the observed spatiotemporal soil moisture pattern, 77.69 % of AN, 70.21 % of AP, and 88.32 % of AK, accounting for the total variability in soil moisture and soil nutrients. (4) The contribution rates of mining to soil moisture and nutrient variation were 27.31 % and 13.91 %, with subsidence having the most pronounced negative impact. Additionally, our study revealed that while soil moisture can partially recover through natural processes, soil nutrients require artificial reclamation during mining. This study offers valuable insights for sustainable mining management.

Study on Accuracy Evaluation of MCD19A2 and Spatiotemporal Distribution of AOD in Arid Zones of Central Asia

The Central Asian arid zone is the largest non-territorial arid zone in the world, so it is particularly important to understand the optical properties of aerosols in this region. In this paper, we validate the MCD19A2 atmospheric aerosol optical depth (AOD) remote sensing data by using ground-based data and measured data. To explore the spatial and temporal changes in aerosols in the Central Asian arid zone as well as the interannual variations and seasonal variations, we characterize the spatial and temporal distributions of the AOD over 20 years. Finally, we analyze the spatial and temporal variations of the AOD in the Central Asian arid zone by using three methods, namely, the Theil–Sen median trend analysis combined with the Mann–Kendall test, coefficient of variation, and Hurst index; analyze the characteristics of the spatial and temporal variations of the AOD in the Central Asian arid zone; and explore the relationships among the AOD, wind speed, and NDVI. This study reveals the characteristics of the long-term changes in the aerosol optical properties in the Central Asian arid zone and provides a scientific basis for estimating the factors affecting climate change.

Water Deficit Caused by Land Use Changes and Its Implications on the Ecological Protection of the Endorheic Dalinor Lake Watershed in Inner Mongolia, China

Dalinor Lake, the second-largest endorheic salt lake in Inner Mongolia, has shown a shrinking trend given the lack of a significant decrease in precipitation (PRE). Based on high-spatial-resolution datasets, we employed a linear regression model, Theil–Sen median trend analysis, the Mann–Kendall test, and a land use transfer matrix to identify the spatio-temporal distribution and trends of PRE and actual evapotranspiration (AET) at the watershed scale during 2001–2019; then, the water deficit (WD) caused by land use changes in different surface lithology zones was analyzed. The results showed that the annual PRE and WD of the Dalinor Lake watershed showed insignificant upward trends, while the annual AET showed a significant upward trend. Spatially, about 89% of the watershed showed a significant upward trend for AET, while 12% showed a weak significant upward trend for PRE. The WDs of the aeolian sand zone and the sand, gravel, and silt accumulation zone were most heavily affected by the new increased land use from 2001 to 2019, accounting for 43.14% and 25.56% of the total WD of the watershed, respectively. Specifically, the WD of the aeolian sand zone caused by the new increased grassland and farmland in 2019 accounted for 41.92% and 18.52% of the total WD of the zone, respectively. The WD of the sand, gravel, and silt accumulation zone caused by the new increased grassland and farmland in 2019 accounted for 37.07% and 35.59% of the total WD of the zone, respectively. The WD caused by the new increased land use was increased by 7.78 million m3 in 2019 compared with the corresponding land use type in 2001, which would decrease the water yield. It is necessary to strengthen the protection of regional forest ecosystems in the granite and terrigenous clastic rock zone; standardize pasture management and reduce farmland reclamation in the sand, gravel, and silt accumulation zone, the aeolian sand zone, and the basalt platform zone; and reduce unnecessary impervious land construction in the aeolian sand zone.

Spatiotemporal Characteristics Prediction and Driving Factors Analysis of NPP in Shanxi Province Covering the Period 2001–2020

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm−2a−1. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage.

Spatiotemporal Evolution Characteristics and Driving Factors of Water Conservation Service in Jiangxi Province from 2001 to 2020

Water conservation services are key indicators of ecological services. Against the backdrop of frequent extreme weather events and water scarcity caused by global climate change and intensified human activities, assessing these services and their drivers are crucial tasks for regional ecological security and sustainable development. Jiangxi Province is one of the first national ecological civilization pilot zones in China, representing an important ecological barrier in southern China. Exploring the characteristics of spatial and temporal changes in water conservation and their driving factors can facilitate the rational development and utilization of regional water resources and the construction of ecological civilizations. Therefore, based on long time series data, the InVEST model was used to explore the spatiotemporal evolution characteristics of water conservation services, and to elucidate the trend of their change through the Theil–Sen median trend analysis and the Mann–Kendall test; then, the geographic detector and geographically weighted regression model were used to further analyze the drivers of spatial variability of water conservation services. The results showed the following: (1) The average depth of water conservation was 103.18 mm, showing a spatial pattern of “low in the middle, high in the surroundings, high in the north and low in the south”. (2) Slight improvements were primarily observed (77.49%), with only 1.60% of the area showing significant improvements. (3) Land use was the main driver of the spatial differentiation, and the interaction between precipitation and forestland had a significantly greater effect on spatial heterogeneity than any single factor. (4) Obvious spatial heterogeneity occurred in the driving factor impacts, with natural factors (precipitation, evapotranspiration, forestland, and grassland) having a positive impact on water conservation services, and land-use factors (construction land and cropland) and socioeconomic factors (population density and land area) having a negative effect. This study provides a reference for water-conservation-based ecosystem construction and policy formulation in Jiangxi Province.