Spatial-temporal Variation Characteristics Research Articles

Analysis of spatial-temporal variations of grassland gross ecosystem product based on machine learning algorithm and multi-source remote sensing data: A case study of Xilinhot, China

The Gross Ecosystem Product (GEP) of grassland ecosystems refers to the total value of the final products and services provided by the grassland of a particular region for human well-being each year. GEP serves as an important indicator for assessing the health of ecosystems. This study focused on Xilinhot, investigating the feasibility of integrating machine learning (ML) algorithms such as stepwise linear regression (SLR), random forest (RF), support vector regression (SVR), and k-nearest neighbor regression (KNN) with multi-source remote sensing data to model grassland GEP. Based on this, the study extracted the spatial-temporal variation characteristics and driving factors of GEP in the study area over the past 20 years. The results: (1) RF demonstrates significant predictive accuracy (R2=0.6409, RMSE=0.15), making it suitable for simulating grassland GEP in the study area. This underscores the potential of employing ML in conjunction with multi-source remote sensing data for grassland GEP estimation. (2) GEP exhibited a distribution pattern that gradually increased from the northwest to the southeast. Over time, there has been a consistent upward trend in GEP, peaking at 23.6 billion CNY in 2020. Among them, the proportion of the material and cultural service values increased annually, while the regulating service value remained stable. This indicates effective protection of the Xilinhot grassland ecosystem. (3) The driving force from climate change is greater than that from human activities, and it predominantly interacts with factors such as terrain and altitude, exerting significant effects on grassland ecosystems. This study can provide technical reference for the evaluation of grassland ecosystem services.

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan–Guizhou Plateau

Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan–Guizhou Plateau

Analysis of spatial-temporal trends and causes of vapor pressure deficit in China from 1961 to 2020

Understanding the spatial-temporal trends and variability of vapor pressure deficit (VPD) is important for promoting climate protection and water management. This study was based on long-term series data of maximum/minimum air temperature (Tmax/Tmin) and relative humidity (RH) from 1740 meteorological stations in China from 1961 to 2020 to jointly analyze the spatial-temporal variation characteristics of VPD at different time scales, climate zones and extreme events and their influencing factor. The results showed an overall highly significant increasing trend in VPD, Tmax, and Tmin, with Sen's slope (SS) values of 0.0178 kPa/decade, 0.22 °C/decade, and 0.32 °C/decade, respectively. The opposite was true for RH (SS = −0.41%/decade). Moreover, the trends all changed abruptly around the year 2000. The VPD exhibited a highly significant upward trend in spring as well as at the junction of climatic zones and along the southeastern coast. This was particularly evident in the semi-arid steppe of Inner Mongolia (IM), the humid tropical south China (SC), and the semi-humid warm-temperate north China (NC) regions. For the frequency distribution of VPD extremes in each climate zone, the arid desert of northwest China (NWC)> > IM > NC. The frequency range was 758–910 days/decade, 249–410 days/decade, and 170–300 days/decade, respectively. Moreover, their average frequency of extreme events was 88 times/decade, 55 times/decade, and 38 times/decade respectively. The main meteorological factors affecting VPD taken together were Tmax, RH, Tmin, sunshine duration (N), and wind speed (U2) with mean importance values of 0.612, 0.351, 0.020, 0.016, and 0.001 respectively. Meanwhile, we found that VPD and reference crop evapotranspiration (ET0) were positively correlated and negatively correlated with soil water content and altitude. The maximum growth rate of VPD was observed in Cropland and Impervious environments. This research reveals the spatial-temporal characteristics of meteorological elements in China and provides a reference for scholars to formulate countermeasures against climate change.

Analysis on spatial-temporal variation characteristics of climate in Qinling-Huaihe demarcation zone since 1961

The emerging global environmental crisis, intensified by global warming, necessitates a deeper understanding of climate variations, especially in critical geographical divisions like the Qinling-Huaihe demarcation zone, which acts as a natural division between northern and southern mainland China. This study investigates the spatial–temporal differentiation of climatic elements within the Qinling-Huaihe demarcation zone from 1961 to 2018, employing data accumulated from 144 meteorological stations. Utilizing methodologies including the univariate linear regression, the Mann-Kendall test, and the Kriging interpolation, this investigation unveils the spatial–temporal variation characteristics of precipitation, relative humidity, and temperature. Temporally, the climatic alterations in the zone are predominantly marked by warming trends, significantly pronounced since the 1990s, with the elevation in minimum temperature being the most notable. Both precipitation and relative humidity exhibit a fluctuating ascending trend. Spatial analysis reveals a west-to-east increase in precipitation, while relative humidity presents lower values in the central region, escalating towards the eastern and western edges. The spatial distribution patterns for average, maximum, and minimum temperatures align closely, showcasing higher temperatures in the central region, extending to the Huaihe River area, with the Qinling Mountains exhibiting the lowest temperatures, particularly noticeable north of the Qinling Mountains.

Spatial-temporal variation, source apportionment and risk assessment of lead in surface river sediments over ∼20 years of rapid industrialisation in the Pearl River Basin, China

Lead (Pb) is a highly toxic element and is not essential to the human body. Lead pollution caused by human activities and a high geological background is considered a global environmental issue. According to the China Geochemical Baseline (CGB) project, the Pearl River Basin had the highest Pb content in alluvial sediments of 30 first-level basins in China. For this reason, it is of great significance to determine the temporal and spatial variations in Pb and their influencing factors in the Pearl River Basin. In this study, 956 stream sediment samples collected in the 1980 s (early stage) and 129 river sediment samples collected from 2008 to 2010 (late stage) were used to study the background value and spatial-temporal variation characteristics of Pb in river sediments in the Pearl River Basin. The Pb source apportionment and an ecological risk assessment were also carried out. The background value of Pb (36.2 mg·kg–1) in the river sediments of the Pearl River Basin was significantly higher than that in China (22.1 mg·kg–1). The parent rocks determine the Pb background in sediments and the high Pb background areas mainly comprised carbonate rocks and acid volcanic rocks. Over 20 years of rapid industrialisation, the average Pb increased from 43.3 to 68.3 mg·kg–1 in the Pearl River Basin. The BCR analysis revealed that Pb mainly existed in the reducible phase (48 % on average) and residue phase (42 % on average). The enrichment factor and geo-accumulation index indicated that the late-stage sediments experienced more Pb pollution than the early-stage sediments. However, the risk assessment code (RAC) showed that there was a low ecological risk of Pb in the late-stage sediments. The factor analysis results for the two rounds of data were significantly different. The Pb content in early-stage sediments was closely related to Al2O3 and Zr, while Pb in the late-stage sediments was mainly related to Zn, As, Sb, Au and Hg, indicating that the increase in Pb in the later samples was mainly influenced by human activities. The Pb isotope composition of the late-stage sediments confirmed that low Pb content was mainly controlled by natural sources, while high Pb content was significantly affected by anthropogenic sources. Combining the results of spatial-temporal variation, chemical speciation and source apportionment indicated that the rapid rise of Pb in late-stage sediments in certain areas could be attributed to mining and smelting activities during the process of industrialisation over 20 years. The anthropogenic exogenous Pb could be immobilised by Fe-Mn (hydro)oxides when it entered the soil, so although there was a high background the ecological risk of Pb in river sediments was low. In the future, Pb pollution control and remediation needs to be strengthened in the Pearl River Basin to avoid the outbreak of potential ecological risks linked to Pb.

Spatial–Temporal Variation Characteristics and Driving Factors of Net Primary Production in the Yellow River Basin over Multiple Time Scales

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.

Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect.

Spatial-temporal variations of one-year ice in Antarctic different regions, 1988–2020

In order to increase the comparability of Antarctic sea ice changes, we proposed a new method to quantitatively assess the spatial–temporal variation characteristics of Antarctic one-year ice based on daily Antarctic sea ice concentration data provided by the National Snow and Ice Data Center (NSIDC) from 1988 to 2020. According to the threshold value of 15% sea ice concentration, the definition of one-year ice and multiyear ice, we divided the Antarctic into 5 regions (seawater regions, stable one-year ice regions, one-year ice and seawater coexistence regions, multiyear ice regions, and one-year ice and multiyear ice coexistence regions) for 33 years and finally conducted the spatio-temporal analysis of the one-year ice in three regions (stable one-year ice regions, one-year ice and seawater coexistence regions, and one-year ice and multiyear ice coexistence regions). We examined the changes in sea ice parameters over 33 years. The results showed that from 1988 to 2020, in the stable one-year ice regions the Antarctic melt onset date had a slight delay trend with 0.2504 days year−1 and the melt end date had a slightly advance trend with 0.0524 days year−1, in the one-year ice and seawater coexistence regions the Antarctic annual average sea ice concentration had a weak decreasing trend with a rate of 0.0003/year (The maximal sea ice concentration is 0.238724 in 2010, and the minimal sea ice concentration is 0.166427 in 2019), in one-year ice and multiyear ice coexistence regions the multiyear ice increased at a rate of 0.0039 × 106 km2 and the annual melt index had a slight upward trend with 0.0022 × 108 km2 year−1.

High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

Spatial-temporal variation characteristics and coupling coordination of the “water resources – water environment – water ecology” carrying capacity in the Three Gorges Reservoir Area

The coordinated control and management of water resources, the water environment, and water ecology are essential for optimizing the management of water resources, improving the water environment, rehabilitating water ecosystems, and achieving sustainable development. Most studies conducted to investigate the water carrying capacity have focused primarily on water resources or water environment. However, it is challenging to conduct such research in areas where the interactions between water resources, the environment, and ecology are intricate. In this study, an evaluation indicator system for the water resources - water environment - water ecology carrying capacity (WCC) was constructed, and a coupling coordination model and GeoDetector were employed to systematically analyze the coupling coordination indices (CCI) among the water resource carrying capacity (WRECC), water environment carrying capacity (WENCC), and water ecology carrying capacity (WECCC), as well as the main influencing factors in the Three Gorges Reservoir Area (TGRA) from 2005 to 2018. The results showed that the numbers of counties and districts with a high CCI class gradually increased over this period. The percentage of Class V fluctuated from 0% to 7.7%, and the stability of the water system steadily increased. Spatially, the overall distribution pattern gradually increased from southwest to northeast, and both the single and interactive effects of anthropogenic factors on water systems gradually increased. The CCI was predominantly affected by factors such as the amount of surface water resources per capita, indices of water conservation, forest coverage, discharge of domestic sewage per capita, and water replenishment of the ecosystem. These results show that coordination between the WCC components should be strengthened to further improve water use and domestic water treatment efficiency. To achieve superior-class, effective, and environmentally friendly scientific governance, the productivity of water resources should be increased in areas where the disparity between water availability and the demand for socioeconomic development is severe, and sewage treatment processes should be closely regulated. It is also important to combine resource development and utilization with water ecological protection and management.

Improving extraction phenology accuracy using SIF coupled with the vegetation index and mapping the spatiotemporal pattern of bamboo forest phenology

Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R2 values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.

Spatial and Temporal Characteristics of Rainstorm Events in Southwest China from 1961 to 2021

The rainfall distribution in southwest China is uneven, and the rainstorm threshold cannot use in a unified standard. This paper synthesizes a calculation method for the extremely heavy precipitation threshold and the provision of the rainstorm threshold in meteorological operation. It calculates the daily precipitation rainstorm threshold at 400 national ground stations in southwest China. The rainstorm events from 1961 to 2021 were statistically analyzed using the rainstorm threshold and analyzing the spatial-temporal variation characteristics. The results show that the number of single-station rainstorm events and the average precipitation of single-station rainstorm events in southwest China decreased from east to west. The number and frequency of single-station rainstorm events in Guizhou, Sichuan, Tibet, and Chongqing are increasing, while the number of single-station rainstorm events in Yunnan is decreasing. There is no apparent spatial distribution pattern for the continuous rainstorm events in the southwest region. From 1961 to 2021, the number and frequency of rainstorm events at a single station in southwest China followed an upward trend. The number of rainstorm events at a single station increased by 16.7 times · (10a)−1, and the frequency of rainstorms increased by 9.9% · (10a)−1. The continuous rainstorm events show an increasing trend, with an increase of 0.1 times · (10a)−1. Using the rainstorm threshold in southwest China, the early warning threshold for rainstorm disasters can be adjusted. The temporal and spatial characteristics of rainstorm events since 1961 can analyze the changes occurring in rainstorm events under global warming and provide data to support the response of southwest China to climate change.

Spatial-temporal Variation in Water Quality and Its Response to Precipitation and Land Use in Baiyangdian Lake in the Early Stage of the Construction of Xiong'an New Area

To assess the water quality of Baiyangdian Lake in the early stage of construction of the Xiong'an New Area, the water quality data in Baiyangdian Lake from December 2020 to November 2021 were collected from five typical monitoring stations. Using the remote-sensing interpretation of land use types, Pearson correlation analysis, comprehensive pollution index, and principal component analysis, the spatial-temporal variation characteristics of the water quality and its response to precipitation and land use structure at different spatial scales were explored in Baiyangdian Lake. The results showed that:① the water quality of Baiyangdian Lake was the best in spring and the worst in summer. Nutrient pollution was the heaviest at Nanliuzhuang, which was greatly affected by the Fuhe River. Organic pollution was the heaviest at Caiputai. ② The comprehensive pollution index of Nanliuzhuang was the highest, followed by that of Shaochedian, Guangdianzhangzhuang, Caiputai, and Quantou. The main excessive pollutant was TN, and the pollution index of 1.55 reached the level of severe pollution. The water quality of Baiyangdian Lake was significantly better than that before the construction of the Xiong'an New Area. ③ The construction land and cropland within the 1.5 km buffer zone had the greatest impact on turbidity, NH4+-N, and TP. While the reed wetland acted as the "sink" for intercepting and absorbing pollutants, it also released nitrogen into the 800 m buffer zone, which was the "source" of the pollutants. ④ The correlation between water quality indicators and land use structure in the rainy season was lower than that in the dry season. This was due to the dilution effect of the upstream reservoir discharging in the rainy season, which weakened the impact of pollutants in the rainfall runoff on the water quality of Baiyangdian Lake.

Retracted: Spatial-Temporal Variation Characteristics of Soil Moisture and Nutrients Based on Nanomaterials in the Root Zone of Haloxylon ammodendron Seedlings

Retracted: Spatial-Temporal Variation Characteristics of Soil Moisture and Nutrients Based on Nanomaterials in the Root Zone of Haloxylon ammodendron Seedlings

Spatiotemporal Variation in Land Use and Ecosystem Services during the Urbanization of Xining City

Based on the five phases of land use data from Xining corresponding to 1980, 1990, 2000, 2010, and 2020, we used the equivalent factor method to analyze the values of spatial-temporal variation characteristics of land use and ecosystem services in Xining. The results showed that (1) farmland and developed areas were the most active types of land, and the continuous occupation of farmland and developed areas led to the formation of a kind of “cross shape” in the spatial pattern of conversion of land use types along the Huangshui River, Beichuan River, and Nanchuan River from south to north and from northwest to southeast, respectively, with the central urban area serving as a core of the pattern; (2) the transformation between different land use types mainly occurred in the land-slope range between 0–15° and altitudes between 2000–2750 m; and (3) the ESV of Xining increased by RMB 2165.26 × 106 in the past 40 years. The period from 2000 to 2020 was the main growth period of the ecosystem service value of Xining. Urbanization had a great impact on the variation in land use types and the evolution of ecosystem service values. In the middle and late stages of urbanization, different types of land use changed significantly within each county and district. The ecosystem service values of the central districts were low, with those of the marginal districts and counties being higher, forming a “core-periphery” trend and a phenomenon of hollowing ESV. (4) The spatial agglomeration effect of the ESV per unit area was continuously enhanced in Xining. The high-high (slope-altitude) type of area was distributed in the north and west of Xining, whereas the low-low type of area was distributed in the urban area at the intersection of major rivers in the southeast of Xining.

Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020

Scientific evaluation of ecological environmental quality is the premise of realizing regional ecological sustainable development. Taking Landsat series satellite images from 1990 to 2020 as the data source, on the basis of the entropy remote sensing ecological index (E-RSEI), combining the Mann-Kendall significance test, Theil-Sen Median analysis, Hurst exponent, and stability analysis, the spatial-temporal variation characteristics of ecological environmental quality in typical ecological areas of the Yellow River Basin were analyzed in the context of multi-spatiotemporal scales. In addition, the effects of eight environmental and human factors on the change in E-RSEI were quantified using a geodetector. The results showed that:① in the past 31 years, the average value of E-RSEI was 67.5%, which showed an increasing trend on the time scale, with an average increase of 0.066·(10 a)-1. On the spatial scale, E-RSEI was higher in the west and the south lower in the east and the north. ② The ecological environmental quality will continue to improve in the future, but 9.33% of the areas have potential risks of degradation. ③ Precipitation was the dominant environmental factor that affected the spatial distribution of E-RSEI in this area, and the influence of human factors was low. Compared with that of single factors, the interaction of factors had a stronger impact on ecological environmental quality, and the interaction between precipitation and other factors played a leading role. The results of this study can provide a scientific reference for the sustainable development of ecological environmental quality in the ecological zone of the Yellow River Basin.

Spatial-Temporal Variation Characteristics and Obstacle Factors of Resilience in Border Cities of Northeast China

The study of border resilience is an effective means to analyze the development status of border cities. This study constructs a border city resilience assessment framework, measures the urban resilience level of 13 prefecture-level administrative regions in China’s northeastern border region from 2010 to 2019, reveals their temporal and spatial differentiation characteristics, analyzes the main obstacles, and proposes development strategies. This study reveals the following: (1) The overall resilience of the northeast border cities is at a medium level, and differences between regions and cities are large. (2) The resilience of the northeast border cities increased rapidly between 2010 and 2015, especially in the western area; the improvement effect was poor between 2015 and 2019, and except for the increased resilience of individual cities in the eastern area, the development of other cities was slow and some even declined. (3) At the city scale, the development potential, economic structure, residents’ security, and environmental governance capabilities are the main factors restricting improvement in the basic resilience of the northeast border cities. Insufficient national policy support and the poor development of neighboring countries have become obstacles to cities’ exclusive resilience.

Spatio-temporal variation of soil microplastics as emerging pollutant after long-term application of plastic mulching and organic compost in apple orchards

Microplastics (MPs) pollution in agroecosystems have aroused great alarm and widespread concern. However, the spatial distribution and temporal variation characteristics of MPs in apple orchards with long-term plastic mulching and organic compost input are still poorly understood. This study investigated MPs accumulation characteristics and vertical distribution after applying plastic mulch and organic compost in apple orchards for 3 (AO-3), 9 (AO-9), 17 (AO-17), and 26 (AO-26) years on the Loess Plateau. The clear tillage (no plastic mulching and organic composts) area was used as a control (CK). At a soil depth of 0–40 cm, AO-3, AO-9, AO-17, and AO-26 treatments increased the abundances of MPs, and the black fibers and fragments of rayon and polypropylene were dominant. In the 0–20 cm soil layer, the abundances of MPs increased with the treatment time; the abundance was 4333 pieces kg−1 after 26 years of treatment, gradually decreasing with soil depth. In different treatments and soil layers, the percentages of MPs <1000 μm were dominant (>50%). The AO-17 and AO-26 treatments significantly increased the MPs with the size of 0–500 μm at 0–40 cm and the abundances of pellets in 0–60 cm soil. In conclusion, the long-term (≥17 years) application of plastic mulching and organic composts increased the abundances of small particles at 0–40 cm, and plastic mulching contributed the most to MPs, while organic composts increased the complexity and diversity of MPs.

Remote sensing evaluation of Chinese mainland's comprehensive natural resources carrying capacity and its spatial-temporal variation characteristics

With the rapid economy development and population growth, China is facing a pressure on the high demand in natural resources. However, most of the natural resource carrying capacity (NRCC) studies considered only on a single natural element, and the data sources showed some limitations. The traditional assessment approach is difficult to provide a comprehensive level of natural resources rapidly for China. Therefore, this study aims to develop a multiple factors NRCC assessment framework by using both remote sensing (RS) satellite data and Geographic Information System (GIS) and to explain the distribution characteristics and spatial-temporal variation characteristics of NRCC from multiple perspectives. The impacts of natural and anthropogenic factors on NRCC in mainland China from 2003 to 2019 were analyzed. The results show that most of the provinces are on an upward trend in the NRCC changes and is significantly higher in the eastern part than in the western part of mainland China. The areas with rapid and significant changes are mainly distributed at the junction of Shaanxi and Shanxi. The overall coefficient of variation of the study area is 0.09, with stable changes, but the stability is worse in the unused land distribution area. The analysis of influencing factors showed that precipitation and temperature were positively correlated with NRCC. The positive correlation was stronger in the regions of bad NRCC, unused land, and grassland. The NRCC values decreased significantly with the increasing elevation. In addition, there was a significant positive correlation between NRCC and human activity pressure from the provincial analysis. This study provides a relatively complete framework for regional natural resource evaluation, which is conducive to promoting sustainable development in mainland China and can effectively evaluate the results of natural resources conservation work and provide effective suggestions for the next step of restoration work.