Spatiotemporal Dynamic Changes Research Articles

Early changes in spatiotemporal dynamics of remapped circuits and global networks predict functional recovery after stroke in mice.

Stroke is the leading cause of chronic disability in the United States. How stroke size affects post-stroke repair and recovery is poorly understood. We aim to investigate the effects of stroke size on early repair patterns and determine how early changes in neuronal circuits and networks predict functional outcomes after stroke. We used wide-field optical imaging, photothrombosis, and the cylinder-rearing assay to examine changes in neuronal circuit and network activity in the context of functional recovery after stroke. Larger strokes ablating caused diffuse and widespread forepaw stimulus-evoked cortical activation, including contralesional regions evolving within 4 weeks post-stroke; smaller strokes resulted in more focused ipsilesional activation. Larger strokes decreased neuronal fidelity and bilateral coherence during stimulation of either the affected or unaffected forepaw within this 4-week period. Mice in the larger lesion group demonstrated hyperconnectivity within the contralesional hemisphere at the resting state. Greater degrees of remapping diffusivity, neuronal fidelity degradation, and hyperconnectivity predicted worse 8-week recovery after statistically controlling for the effect of infarct size. These results suggest that diffuse patterns of remapping, and desynchronization and hyperconnectivity of cortical networks, evolving early after stroke may reflect maladaptive plasticity, predicting poor long-term functional recovery.

Characteristics of Spatiotemporal Changes in China's Carbon Budget at Different Administrative Scales

Currently, scientifically and reasonably specifying carbon emission reduction measures in the context of "double carbon" has become a common concern worldwide. China's administrative divisions have a notable impact on the formulation and implementation of relevant policies. Therefore the carbon emissions must be calculated accurately under China's administrative divisions at different scales. The spatiotemporal change characteristics of absorption and carbon emissions can provide scientific basis for the formulation of reasonable and differentiated carbon emission reduction policies in different administrative regions in China. To this end, this study used multi-source data such as remote sensing and statistics and integrated ecological models, statistics, and GIS space analysis and other methods to analyze the spatiotemporal dynamic change characteristics of carbon emissions and carbon absorption at different administrative scales （provinces, cities, and counties） in China. The results showed that： ① The total carbon absorption of vegetation in China continued to increase from 2000 to 2021 and the average value gradually increased. Differences were observed in spatiotemporal changes in carbon emissions at different administrative scales. The spatiotemporal changes at smaller scales were more evident. Carbon emissions showed obvious spatial differences of "high in the north and low in the south, high in the east and low in the west." ② The spatiotemporal distribution of CPI at the administrative scale was similar to that of carbon emissions and the overall trend was increasing annually. The pressure of carbon emissions on carbon absorption gradually weakened from the east to the central and western regions. ③ Spatiotemporal hotspot analysis showed that the overall spatial distribution of cold and hot spots in China's carbon absorption was as follows： In the spatial pattern of "hot in the east and cold in the west," the spatial distribution of cold and hot spots of carbon emissions showed agglomeration characteristics. The provincial scale was primarily oscillating hotspot whereas municipal and county scales were majorly continuous hot spots. Further results revealed that： ① Carbon absorption in different regions and periods in China showed significant variability, especially in the central and eastern regions. The possibility of offsetting carbon emissions by increasing carbon absorption remains. ② At the same scale, administrative regions （such as different provinces） and lower-level administrative regions at another scale （such as different cities in the same province） showed varying degrees of variability in carbon absorption and carbon emissions. Therefore, taking provincial administrative regions as an example for subsequent formulation considering carbon trading, emission reduction, and other policies, we should first consider the coordination of emissions between different cities in the province and then consider the coordination between provinces, which is expected to better promote the implementation of relevant policies.

Spatio-temporal forecasting of landslide hazard in Chongqing National Transmission Protection Regions, China

ABSTRACT Previous studies on landslide susceptibility (LS) and hazard (LH) assessments have overlooked a crucial measure for evaluating validation effectiveness, which involves the use of post-temporal landslide inventories instead of solely relying on historical data. Therefore, this study constructed a regional LH prediction and validation framework at different timescales using the landslide data from 2002 to 2021. Firstly, based on updated landslide data and conditioning factors, the datasets for susceptibility modelling were built for various periods. Then, the performances of traditional ma chine learning, ensemble and deep learning models were compared. Finally, the multi-period rainfall data and susceptibility results were used in innovative LH forecasting. Our study results show that the ensemble learning model (random forest) has stronger generalisation ability in different time periods, with all Area Under Curve (AUC) values exceeding 0.9. The hazard modelling framework based on rainfall erosion intensity and susceptibility can effectively forecast LH, and the shortened timescale for rainfall erosion intensity can improve the accuracy of LH forecasting. At the very high hazard level, the percentage of landslides forecast increased from 25.23% to 42.86%. This study comprehensively explores spatio-temporal dynamic changes in LH, accurately identifying areas posing threats to the National Transmission Line Protection Regions (NTLPR).

Immune checkpoint reprogramming via sequential nucleic acid delivery strategy optimizes systemic immune responses for gastrointestinal cancer immunotherapy

Monoclonal antibodies targeting immune checkpoints have been widely applied in gastrointestinal cancer immunotherapy. However, systemic administration of various monoclonal antibodies does not often result in sustained effects in reversing the immunosuppressive tumor microenvironment (TME), which may be due to the spatiotemporal dynamic changes of immune checkpoints. Herein, we reported a novel immune checkpoint reprogramming strategy for gastrointestinal cancer immunotherapy. It was achieved by the sequential delivery of siPD-L1 (siRNA for programmed cell death ligand 1) and pOX40L (plasmid for OX40 ligand), which were complexed with two cationic polymer brush-grafted carbon nanotubes (dense short (DS) and dense long (DL)) designed based on the structural characteristics of nucleic acids and brush architectures. Upon administrating DL/pOX40L for the first three dosages, then followed by DS/siPD-L1 for the next three dosages to the TME, it upregulated the stimulatory checkpoint OX40L on dendritic cells (DCs) and downregulated inhibitory checkpoint PD-L1 on tumor cells and DCs in a sequential reprogramming manner. Compared with other combination treatments, this sequential strategy drastically boosted the DCs maturation, and CD8+ cytotoxic T lymphocytes infiltration in tumor site. Furthermore, it could augment the local antitumor response and improve the T cell infiltration in tumor-draining lymph nodes to reverse the peripheral immunosuppression. Our study demonstrated that sequential nucleic acid delivery strategy via personalized nanoplatforms effectively reversed the immunosuppression status in both tumor microenvironment and peripheral immune landscape, which significantly enhanced the systemic antitumor immune responses and established an optimal immunotherapy strategy against gastrointestinal cancer.

Spatiotemporal Dynamic Changes and Prediction of Wild Fruit Forests in Emin County, Xinjiang, China, Based on Random Forest and PLUS Model

Spatiotemporal Dynamic Changes and Prediction of Wild Fruit Forests in Emin County, Xinjiang, China, Based on Random Forest and PLUS Model

PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics.

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network's advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated.

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.

Changes in Spatiotemporal Dynamics of Default Network Oscillations between 19 and 29 Years of Age.

The exploration of functional resting-state brain developmental parameters and measures can help to improve scientific, psychological, and medical applications. The present work focussed on both traditional approaches, such as topographical power analyses at the signal space level, and advanced approaches, such as the exploration of age-related dynamics of source space data. The results confirmed the expectation that the third life decade would show a kind of stability in oscillatory signal and source-space-related parameters. However, from a source dynamics perspective, different frequency ranges appear to develop quite differently, as reflected in age-related sequential network communication profiles. Among other discoveries, the left anterior cingulate source location could be shown to reduce bi-directional network communication in the lower alpha band, whereas it differentiated its uni- and bidirectional communication dynamics to sub-cortical and posterior brain locations. Higher alpha oscillations enhanced communication dynamics between the thalamus and particularly frontal areas. In conclusion, resting-state data appear to be, at least in part, functionally reorganized in the default mode network, while quantitative measures, such as topographical power and regional source activity, did not correlate with age in the third life decade. In line with other authors, we suggest the further development of a multi-perspective approach in biosignal analyses.

Spatiotemporal dynamic changes in transpiration in the shoot sheath and its relation to water transportation during rapid growth of Moso bamboo

IntroductionTranspiration is the driving force of water transport, which plays a crucial role in the rapid growth of bamboo shoots. Nonetheless, the transpiration changes that occur in the shoot sheath of Moso bamboo during the leafless stage and the physiological processes involved in water transport from the mother bamboo to bamboo shoots are not completely understood.MethodsThis study investigated the temporal dynamics of the transpiration rate (Tr) and stomatal conductance (gs) of the sheaths from bamboo shoots to culms and diurnal variations in Tr and gs as well as the spatial dynamics of Tr and gs in various parts of the shoot sheaths. Water distribution patterns in bamboo shoots were analyzed using the isotope tracer method, and the water transportation path in bamboo shoots was determined by soaking the shoots in fuchsine dye solution.ResultsWe observed that the Tr was higher in bamboo shoots at heights of 4 and 7 m compared to that at 0.5, 1, 2, and 13 m, with rates ranging from 6.8 mmol/m2/s1 to 8.3 mmol/m2/s. Additionally, the Tr and gs of the shoot sheath were lower at noon, but higher in the morning and evening. The Tr in the lower parts of the culm sheath was higher than that that in the upper part (height: 0.5–4 m). However, when injected into the mother bamboo, D2O was not immediately transported to the shoots via the mother culms but was transported upward through the vascular bundle, with a larger vascular bundle near the pulp cavity transporting high volumes of water.DiscussionThese findings provide the foundation for further studies on the rapid growth of Moso bamboo and establish a theoretical basis for water management during its shoot developmental period.

Efficiency Characteristics and Evolution Patterns of Urban Carbon Metabolism of Production-Living-Ecological Space in Beijing-Tianjin-Hebei Region

This study explored the carbon metabolism efficiency of a production-living-ecological space system, which is of great significance for regional factor integration and spatial optimization. In this study, the material flow analysis method was introduced to establish a framework for evaluating the carbon metabolism efficiency of the production-living-ecological space system, and the super-efficiency DEA model and Malmquist index were used to empirically analyze the spatio-temporal distribution, dynamic change, and evolution patterns of the carbon metabolism efficiency of production-living-ecological space in the Beijing-Tianjin-Hebei Region, China, from 2000 to 2020 on the basis of the urban metabolic perspective. The results showed that:① the carbon metabolism efficiency of the production-living-ecological space showed a fluctuating growth trend, indicating the significant spatial differentiation of carbon metabolism efficiency in each city. There was a low overall carbon metabolism efficiency level, with a distribution pattern of being high in the middle and low in the north and south. ② The Malmquist index showed that the Total Factor Productivity (TFP) of carbon metabolism efficiency was greater than 1, and both the Technical Change (TC) and Pure Efficiency Change (PEC) were less than 1, in which the TFP showed an increasing trend, whereas there was no significant contribution of technological progress or pure technical efficiency to carbon metabolism efficiency. The total factor productivity of more than 50% of the cities showed an improving trend, only 38.46% of which made technological progress in improving carbon metabolism efficiency, and more than half of the urban pure technical efficiency showed a decreasing trend, in which the technical efficiency change and scale efficiency change were greater than 1 in most cities. ③ There were different types of carbon efficiency characteristics in each city, and according to the movement rules of the corresponding points in the quartile map, the evolution patterns of tourism industry efficiency were classified into stable, reciprocating, progressive, and abrupt. Therefore, local governments should adopt differentiated strategies to reasonably allocate spatial resources of production-living-ecological space and improve the technical level and scale efficiency, so as to improve the efficiency of urban carbon metabolism.

Land Use/Cover-Related Ecosystem Service Value in Fragile Ecological Environments: A Case Study in Hexi Region, China

The monetary value assigned to ecosystems and their essential goods and services is known as ecosystem service value (ESV). Fragile ecological environments, susceptible to climate change and human disturbances, require significant ecological protection. This protection is vital not only for stabilizing socio-economic conditions but also for fostering a positive feedback loop within natural systems. Looking ahead, identifying priority conservation areas and regions of particular concern in fragile environments based on ESV changes is a proactive approach. Until now, current studies on ESV in fragile ecological environments have been insufficient, falling short of adequately serving the purpose of ecological protection in such areas. The Hexi region (HXR), situated in the interlaced area of the Qilian Mountains and the Alxa Plateau in northwest China, possesses a fragile ecological environment. Due to the intricate interactions between humans and the environment, it stands out as a crucial area for studying ecosystem services in fragile habitats. Hence, this study aims to systematically analyze the ESV in the HXR over the past 30 years. Here, the values of 11 ecosystem service categories in HXR during 1990–2020 were calculated, clarifying their spatiotemporal difference and dynamic changes based on land use/cover (LULC) products for years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, using an improved equivalent factors method. The findings revealed that the unit standard equivalent factor is 1.51 × 105 CNY·km−2·a−1, and the total ESV value has grown from CNY 182.50 to 185.48 billion from 1990 to 2020. Hydrologic regulation, climate regulation, and soil conservation service values are the principal single ESV categories across HXR. Over the past three decades, ESV changes in HXR have been primarily driven by water area, grassland, and wetland, with the sensitivity of ESV to LULC generally increasing (0.15~9.10%). Natural forcing and anthropogenic perturbations have combined to cause changes in LULC in HXR, influencing ESV fluctuations. Future ecosystem protection activities in HXR should prioritize the maintenance of grasslands, water regions, and wetlands to ensure the preservation of ESV. This study clarifies the impacts of LULC change on ESV in fragile natural environments and highlights the significance of focusing on ESV changes for the health promotion of natural-social systems. The findings provide a foundation for constructing a sustainable development model that is in harmony with both people and the environment.

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Research on the Spatiotemporal Characteristics of Block Vitality based on LBS Data and the Impact Mechanisms of the Built Environment -Taking the Third Ring Road in Fuzhou City as an example

Using location-based services (LBS) data from the third ring area in Fuzhou city, this study analyzes the spatiotemporal characteristics of block vitality by measuring the density of active population gatherings. It explores the spatiotemporal heterogeneity of block vitality based on a geographically and temporally weighted autoregressive model (GTWAR), considering built environment features such as POI density, functional mix, and road network density. Additionally, it investigates their interactions using a geographic detector and compares differences between weekdays and weekends across five daily periods. Key findings include:(1) The overall urban vitality in the region exhibits a spatial pattern of “one core, two centers, multiple sub-centers, and multiple clusters” and undergoes spatiotemporal dynamic changes across five periods, characterized by ‘dispersion, agglomeration, deep agglomeration, and dispersion.” (2) The marginal effects of POI density, functional mix, and road network density on block vitality exhibit instability in both spatial and temporal dimensions.(3)Interaction effects between two factors exert a more significant impact on block vitality than the effects of individual factors, with interaction types including dual-factor enhancement and nonlinear enhancement.(4)Differences exist in the spatiotemporal characteristics of block vitality, built environment influences, and their interactions between weekdays and weekends.

Spatiotemporal Dynamic Change and the Driving Mechanism of Desertification in the Yellow River Basin

Spatiotemporal Dynamic Change and the Driving Mechanism of Desertification in the Yellow River Basin

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km2, accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km2, accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region’s natural ecosystem.

Use of Space-Time Cube Model and Spatiotemporal Hot Spot Analyses in Fisheries—A Case Study of Tuna Purse Seine

Katsuwonus pelamis, or skipjack, is a vital resource in purse seine fishing across the Central and Western Pacific. Identifying skipjack distribution hotspots and coldspots is crucial for effective resource management, but the dynamic nature of fish behavior means these spots are not constant. We used Chinese fishing logbook data from 2010 to 2019 to analyze skipjack resource hotspots and coldspots in a space-time cube. The study revealed 13 spatiotemporal patterns in skipjack Catch per Unit Effort (CPUE). Hotspots (36.53%) were concentrated in the central area, predominantly showing oscillating hotspots (21.25%). The significant effect of the eastern oscillating hotspot continues to be enhanced and extends to the east. Coldspots constituted 63.47% of the distribution, mainly represented by intensifying coldspots (25.07%). The no-pattern-detected type (10.53%) is distributed between coldspots and hotspots. The fishing grounds exhibited longitudinal oscillations of 3°–6° and latitudinal oscillations of 1°–2°. The spatial autocorrelation of cold and hot spot distribution was strong, and the spatiotemporal dynamic changes in skipjack resources were closely related to the El Niño-Southern Oscillation (ENSO) phenomenon. Notably, during 2011–2016, hotspots exhibited an eastward expansion trend, which continued from 2017–2019 due to the influence of fishery management measures, such as the Vessel Day Scheme (VDS) system.

What Should Be Learned from the Dynamic Evolution of Cropping Patterns in the Black Soil Region of Northeast China? A Case Study of Wangkui County, Heilongjiang Province

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in Northeast China and analyze their spatio-temporal dynamic change. Using the geo-information Tupu methods and transfer land matrix, this study identified the cropping patterns and their spatio-temporal change based on remote sensing data for three periods, namely 2002–2005, 2010–2013, and 2018–2021. The main results revealed that the maize continuous, mixed cropping, maize-soybean rotation, and soybean continuous cropping patterns were the main cropping patterns in Wangkui County, with the total area of the four patterns accounting for 95.28%, 94.66%, and 81.69%, respectively, in the three periods. Against the backdrop of global climate warming, the cropping patterns of continuous maize and soybean and the mixed cropping pattern in Wangkui County exhibited a trend towards evolving into a maize-soybean rotation in the northern region. Moreover, the maize-soybean rotation further evolved into a mixed cropping system of maize and soybean in the north. Furthermore, the spatio-temporal evolution of cropping patterns was significantly driven by natural and social factors. Specifically, natural factors influenced the spatio-temporal patterns of variation in cropping patterns, while social factors contributed to the transformation of farmers’ cropping decision-making behavior. Accordingly, new insights, institutional policies, and solid solutions, such as exploring and understanding farmers’ behavior regarding crop rotation practices and mitigating the natural and climatic factors for improving food security, are urgent in the black soil region of China.

Accuracy assessment of land cover products in China from 2000 to 2020

The accuracy assessment of land cover data is of significant value to accurately monitor and objectively reproduce spatio-temporal dynamic changes to land surface landscapes. In this study, the interpretation and applicability of CCI, MCD, and CGLS long time-series land cover data products for China were evaluated via consistency analysis and a confusion matrix system using NLUD-C periodic products as reference data. The results showed that CGLS had the highest overall accuracy, Kappa coefficient, and area consistency in the continuous time-series evaluation, followed by MCD, whereas CCI had the worst performance. For the accuracy assessment of subdivided land cover types, the three products could accurately describe the distribution of forest land in China with a high recognition level, but their recognition ability for water body and construction land was poor. Among the other types, CCI could better identify cropland, MCD for grassland, and CGLS for unused land. Based on these evaluation results and characteristics of the data products, we developed suitable selection schemes for users with different requirements.

Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City

The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals.