Changes In Spatial Patterns Research Articles

Predicting the distribution pattern changes of dye plant habitats caused by climate change

Climate change has accelerated the habitat loss and fragmentation of wildlife. Dye plants of “Fengxiang dyeing” are important indigenous natural resources for traditional printing and dyeing craft in southwest China, is of practical and cultural importance for dozens of ethnic minorities. However, lack of the spatial distribution information of these plants has hampered holistic and efficient conservation management measures. We analyzed the potentially suitable areas of four dye plants (Liquidambar formosana, Strobilanthes cusia, Persicaria tinctoria and Indigofera tinctoria) necessary for “Fengxiang dyeing” based on their geographical distribution sites under different climatic situations using the maximum entropy (MaxEnt) model. The results showed that temperature, precipitation and elevation were the most important factors affecting the suitable geographical areas of the four dye plants. Under the current climate conditions, the overlapping suitable habitat areas of the four plants were mainly in the four southern provinces of China, including Guizhou, Guangxi, Guangdong and Hainan. L. formosana was used as the base plant for combination with the other three plants under the two future climate scenarios (SSP126 and SSP585), and the overlapping suitable habitat areas of the obtained seven combination patterns were considered suitable for potential craft development. Five patterns showed an increase, while two patterns showed a decreasing trend with the increasing carbon emission. The prediction results showed that the overlapping suitable habitat center of the four plants will gradually move to the northeast, indicating that the overlapping suitable habitat area and craft distribution area will be changed. These results provide the basis for understanding the spatial distribution pattern changes of dye plants caused by climate change and establishing measures for protecting and developing printing and dyeing craft.

Global expansion of tropical cyclone precipitation footprint

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

Coupling graph neural networks and travel mode choice for human mobility prediction

Travel mode serves as the link for communication among people, between people and objects, and between people and places. The human mobility is closely related to travel mode. Scientifically predicting human mobility can help alleviate traffic congestion and provide flexible travel choices. However, in current predictions, only the travel demand or human mobility is taken into account, while the influence of residents on the travel mode choice is neglected. Therefore, taking Wuhan City as an example, this paper proposes a new method for predicting human mobility by employing graph neural network techniques and travel mode choice behavior. The prediction model established in this study utilizes network analysis to measure the accessibility time and road network distance of traffic travel mode. The graph neural network method is employed to capture the dynamic temporal and spatial relationships underlying human mobility. Furthermore, the performance of the prediction model is evaluated. The results indicate that at a fine spatial scale, the new method can more accurately reveal the spatial patterns of changes in human mobility, significantly improving the accuracy of predicting human mobility.

Age-related decline in thickness and surface area in the cortical surface and hippocampus: lifespan trajectories and decade-by-decade analyses.

Previous studies on age-related changes in cortical and hippocampal morphology were not designed or able to reveal the complex spatial patterns of changes across the lifespan. To this end, the current study examined these changes in a decade-by-decade manner by comparing consecutive age decades at the vertex-wise level. Additionally, the lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled and plotted out to provide an overview of their age-related changes. Using two lifespan datasets (Ntotal = 1378; 18 ≤ age ≤ 100), vertex-wise thickness and surface area measurements were extracted from the cortical and unfolded hippocampal surfaces and analyzed using whole-brain/hippocampus vertex-wise analyses. Lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled with generalized additive models for location, scale, and shape. These models revealed fairly linear declines in both cortical measures and inverted U-shaped trajectories for both hippocampal measures. Across the different age decades, the sizes and locations of cortical thinning clusters were highly variable across the age decades. No significant clusters of cortical surface area changes were observed across the age decades. Significant clusters of hippocampal surface area and thickness reduction were not observed until the 70s. Generally, the agreement between datasets on the hippocampal findings was much higher than those of the cortical surface. These findings revealed important nuances in the age-related changes of cortical and hippocampal morphology and cautioned against using lifespan trajectories to infer decade-by-decade changes in the cortical surface and the hippocampus.

Integration of Watershed eco-physical health through Algorithmic game theory and supervised machine learning

The eco-physical health assessment of watersheds is crucial for sustainable water resource management and ecosystem services. This study quantifies the eco-physical health of the Talar watershed in Iran using the geometric mean method (GMM), game-theoretic algorithm (GTA), and machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Simple Linear Regression (SLR), and K-Nearest Neighbor (KNN) for distributed and semi-distributed monitoring. The results show that the RF algorithm performed better than other models, as indicated by MAE, MSE, RMSE, and AUC statistics with values of 0.032, 0.003, 0.058, and 0.940, respectively. The watershed health index prioritization with different approaches showed that the pattern of watershed health changes positively from upstream to downstream. Based on the watershed health prioritization using GMM, it can be said that sub-watersheds Int6 and Int5 are the healthiest sub-watersheds in the studied watershed, with values of 0.93 and 0.90, respectively. Based on the watershed prioritization using the GTA approach, it can also be said that sub-watersheds Int6, Int5, and Int01 are the healthiest ones. In the case of the RF algorithm, the average values of the pixels in each sub-watershed showed that sub-watersheds Int6 and Int01 were recognized as the healthiest sub-watersheds with values of 0.91 and 0.88, respectively. Int6 consistently emerged as the healthiest sub-watershed across all methods, attributed to high TWI and NDVI values and low slope, DEM, erosion, and CN values. In general, it can be said that the watershed health index in the studied catchment fully followed the factors affecting the catchment's health and that the spatial patterns of change of this index were consistent with the physiographic and hydroclimatic conditions in all three semi-distributed and distributed approaches. The study's implications underline the importance of multi-criteria and multi-algorithm approaches in accurately assessing and managing watershed health for sustainable development.

Population Change and Residential Segregation in Italian Small Areas, 2011–2021: An Analysis With New Spatial Units

This article explores spatial patterns of demographic change and residential segregation in Italy over the past decade, a topic which has not been studied in detail in the literature. Population ageing and migration are unfolding rapidly in a number of European countries, generating tensions and challenges at local level. Aggregate regional or national statistics can conceal significant variations at local level, which are of considerable interest and relevance. This is particularly the case in Italy, where spatial heterogeneity and regional disparities are marked. The analysis presented in this paper uses a new source of data derived from large public archives, which permits comparisons to be made at local level with the 2011 census of population. In this way, it is possible to map out and to analyse demographic trends at a fine level of spatial definition. In order to exploit the potential of these data, the authors use a new set of spatial units which were derived by applying automatic rezoning procedures. These output areas are well suited to the study of the age structure of local populations, population change, and migration in a uniform way across the entire national territory, as the empirical results confirm.

Monitoring Long-Term Land Cover Change in Central Yakutia Using Sparse Time Series Landsat Data

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Assessment of American Bullfrog (Lithobates catesbeianus) spreading in the Republic of Korea using rule learning of elementary cellular automata

The spread of American Bullfrog has a significant impact on the surrounding ecosystem. It is important to study the mechanisms of their spreading so that proper mitigation can be applied when needed. This study analyzes data from national surveys on bullfrog distribution. We divided the data into 25 regional clusters. To assess the spread within each cluster, we constructed temporal sequences of spatial distribution using the agglomerative clustering method. We employed Elementary Cellular Automata (ECA) to identify rules governing the changes in spatial patterns. Each cell in the ECA grid represents either the presence or absence of bullfrogs based on observations. For each cluster, we counted the number of presence location in the sequence to quantify spreading intensity. We used a Convolutional Neural Network (CNN) to learn the ECA rules and predict future spreading intensity by estimating the expected number of presence locations over 400 simulated generations. We incorporated environmental factors by obtaining habitat suitability maps using Maxent. We multiplied spreading intensity by habitat suitability to create an overall assessment of bullfrog invasion risk. We estimated the relative spreading assessment and classified it into four categories: rapidly spreading, slowly spreading, stable populations, and declining populations.

Subtropical mangroves poleward shift to the Yangtze Estuary under different carbon emission scenarios

Mangroves are crucial plant communities in the coastal intertidal zones, providing various ecological services and supporting biodiversity. To identify the primary factors affecting the poleward shift of subtropical mangroves and the northernmost extent of subtropical mangroves under various carbon emission scenarios, the MaxEnt model was utilized with presence data of subtropical mangrove and environmental data under different carbon emission scenarios. Kandelia obovata, Avicennia marina, and Aegiceras corniculatum in China were included for modeling, utilizing 22 bioclimate variables to predict mangrove distributions, and kernel density analysis was further employed to identify conservation and afforestation hotspots for mangroves. The results indicated that the number of days below 10 °C per year is the major climatic factor shaping the distribution of subtropical native mangroves, while precipitation exerts a relatively lower contribution. Under the low and medium emissions scenarios (SSP126 and SSP245), the northern boundary of the distribution of K. obovata remains stable, while A. marina and A. corniculatum are projected to shift northward to Fuzhou, Fujian Province (26.1°N) from 24.9°N by 2100. Under higher emissions scenarios (SSP370 and SSP585), the suitable habitat of K. obovata is expected to shift north to the mouth of the Yangtze River (31.9°N) from 26.8°N, and A. marina and A. corniculatum are projected to move northward to the coastal area of Ningde, Fujian Province (26.8°N) by 2100. In conclusion, with the increase in carbon emissions, there is a significant northward migration observed in subtropical native mangroves (p < 0.01), and suitable areas for restoration in China have been identified, with the northernmost region located in Zhejiang Province. The spatial and temporal patterns of range change predicted in this study provide valuable information for conservation and afforestation strategies for these ecologically important species.

Study of ecosystem service functions in typical receiving areas of the South-to-North Water Diversion Central Route based on a set of long time series.

Hebi is located in the northern part of China's Henan Province and is a typical receiving area for China's South-to-North Water Diversion Project. The assessment of habitat quality and water yield over a long time series is important for evaluating the stability of ecosystem services in Hebi and other receiving areas and for maintaining ecological security and promoting sustainable development. This paper aims to evaluate and dynamically analyse habitat quality and water yield in Hebi, and analyses the characteristics of changes in spatial and temporal patterns of land cover types, habitat quality and water yield in Hebi over the past 20 years, using 2000, 2005, 2010, 2015 and 2020 as horizontal years. The results indicate that: (1) During the study period, the overall land use type in Hebi City has been constantly changing, with the most significant conversion from arable land to other land types; combined with its landscape pattern index, Hebi City has a general characteristic of significant landscape fragmentation and complexity in land use. (2) Habitat quality in Hebi shows an overall trend towards better development, with water availability decreasing and then increasing; the zoning of ecosystem services in Hebi is divided into three classes: superior, good and general, with the area covered by the superior and general classes expanding year by year. (3) Correlation analysis by SPSS software shows that the correlation between habitat quality and landscape pattern index is greater than the correlation between habitat quality and climate change. Additionally, the correlation between water availability and climate change is greater than the correlation between water availability and landscape pattern index.

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Constructed from a dataset capturing the seasonal and annual water body distribution of the lower Qarqan River in the Taitma Lake area from 2017 to 2022, and combined with the meteorological and hydraulic engineering data, the spatial and temporal change patterns of the Taitma Lake watershed area were determined. Analyses were conducted using Planetscope (PS) satellite images and a deep learning model. The results revealed the following: ① Deep learning-based water body extraction provides significantly greater accuracy than the conventional water body index approach. With an impressive accuracy of up to 96.0%, UPerNet was found to provide the most effective extraction results among the three convolutional neural networks (U-Net, DeeplabV3+, and UPerNet) used for semantic segmentation; ② Between 2017 and 2022, Taitma Lake’s water area experienced a rapid decrease, with the distribution of water predominantly shifting towards the east–west direction more than the north–south. The shifts between 2017 and 2020 and between 2020 and 2022 were clearly discernible, with the latter stage (2020–2022) being more significant than the former (2017–2020); ③ According to observations, Taitma Lake’s changing water area has been primarily influenced by human activity over the last six years. Based on the research findings of this paper, it was observed that this study provides a valuable scientific basis for water resource allocation aiming to balance the development of water resources in the middle and upper reaches of the Tarim and Qarqan Rivers, as well as for the ecological protection of the downstream Taitma Lake.

An uncertain future change in aridity over the tropics

An ensemble of climate models from phase six of the Coupled Model Intercomparison Project shows that temperature and potential evapotranspiration (PET) are projected to increase globally towards the end of the 21st century. However, climate models show a spatially heterogeneous change in precipitation over the tropics. Consequently, future changes in aridity (a measure of water availability) are complex and location-dependent. We assess future changes in aridity using three climate models and several single-forcing experiments. Near-term (2021–2040) changes in aridity are small, and we focus instead on its long-term (2081–2100) changes. We show that the increase in greenhouse gases (GHG) primarily explains the spatial pattern, magnitude and ensemble spread of the long-term future changes in aridity. On this timescale, the effects of changes in emissions of anthropogenic aerosols are moderate compared to the effects of increases in atmospheric GHG concentrations. Model diversity in the responses to GHG concentration is large over northern Africa and North and South America. We suggest the large uncertainty is due to differences between models in simulating the effects of an increase in GHG concentrations on surface air temperature over the North Atlantic Ocean, on the interhemispheric temperature gradient, and on PET over North and South America.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Multiple sources forcing driving changes in spatial patterns of coastal wetland vegetation

Multiple sources forcing driving changes in spatial patterns of coastal wetland vegetation

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management. Changes in visitor temporal and spatial patterns and their associated determinants pre- and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed. Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression (GWR) model. Results highlight that COVID-19 significantly changed park visitation patterns. Visitors tended to explore more remote areas peri-pandemic. The GWR model also indicated distance to nearby trails was a significant influence on visitor density. Our results indicate that the pandemic influenced tourism temporal and spatial imbalance. This research presents a novel approach using combined social media big data which can be extended to the field of tourism management, and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Does settlement land expansion necessarily induce the decrease of cultivated land? Differences in national scale and local counties of China

With the extensive industrialization and urbanization taking place in China during the recent decades, land use throughout the country has experienced profound changes influenced not only by the demand for population growth and living standard improvement but also by the constraints of series of land use policies. However, whether the conflict between the expansion of settlement land (SL) and the loss of cultivated land (CL) have been resolved at the national scale or transferred between the local regions remains unclear. Based on yearly ESA CCI land use and land cover products from 1992 to 2020, the long-term trends of quantity and spatial pattern of SL expansion and CL change in China from national and local views were investigated using trend statistic methods, and finally a comprehensive zoning framework was proposed to recognize the trade-off and synergies relationships between SL expansion and CL change. There are a continuous expansion of SL with global linear trends showing three breakpoints in 2000, 2005, and 2012, and a fluctuation decline of CL presented with four breakpoints in 1997, 2002, 2006, and 2013. Aggregation and dispersion tendencies with linear characteristics of SL expansion and CL change were found with breakpoints in 2001, 2008, 2012, and 2016 and breakpoints in 2001 and 2010, respectively. A spotty spatial pattern of SL was shown spatially coincident with urban agglomerations in China while the planar continuous characteristic was found for CL. Local counties were classified into five tradeoff and synergies zones (TSZs), where general synergies (G-S) and decoupling (D) of SL expansion and CL change were rare cases and the different change in quantity and trend of SL expansion and CL change in local counties was concealed by the national trend. A few scattered counties were belonging to G-S and D TSZs, while most of the counties in the central-east and western China were in General-Tradeoff (G-T) and Superior-Tradeoff (S-T) TSZs. Counties in south and north China with higher percentages of CL were more prevalent in Superior-Synergy (S–S) TSZ. Our findings explicated the complex relationships between SL expansion and CL change of China at the national scale and in local counties, which pointed out the differences of unified land use management activities across scales and could provide insights for future policy-making and management measures of land use to both ensure the national food security and promote regional sustainable development more synchronously.

Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

The study of base flow is essential for sustainable water management. By understanding the dynamics of base flow, policymakers can make informed decisions to ensure the long-term health and availability of water resources. This study was conducted with the aim of modeling the spatial changes of Base Flow Generation Potential (BFGP) using integrated Machine Learning Algorithms (MLAs) including Decision Tree Regression (DTR), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF), Simple Linear Regression (SLR), Support Vector Machine (SVM) and Support Vector Regression (SVR) and optimal Multi-Criteria Decision Making (MCDM) methods including Best-Worst Method (BWM) and Fallback bargaining in the Cheshmeh-Kileh watershed, Iran. BFGP conditioning factors were selected for the Sub-Watershed (SW) and weighed using MCDM (BWM and Fallback bargaining algorithm) methods. BFGP maps of five classes were generated and analyzed. MCDM methods were also integrated with ML algorithms for optimum performance evaluation of the integrated unit. Based on the results, in the integrated model of BWM and Fallback bargaining algorithm with ML algorithms, SVR and RF algorithms was selected as the optimal model, respectively. In BWM in the Sehezar River, the upstream had the highest BFGP. The pattern of spatial changes based on the Fallback bargaining algorithm in the upstream of Sehezar River was similar to BWM. The combined approach of RF-GTA with the factors of minimum temperature (Tn), NDSI and elevation showed the highest correlation. It was observed that that the Fallback bargaining algorithm and BWM of the MCDM exhibited similarity in output of regarding the spatial change patterns of the BFGP. Also, Fallback bargaining exhibited a 67% similarity with the integrated RF-GTA in BFGP prioritization of the SWs, while BWM performed poorly. This suggests that the Fallback bargaining algorithm was more effective than BWM of the MCDM methods for SWs BFGP prioritization. The general summary indicated that the combined use of MCDM and MLAs were valuable tools in BF studies and helped in better understanding the complex hydrological processes. The findings of our research may be of help to Iran Water Resources Research Company using hydrological data arising from base flow studies to support their analysis. In conclusion, understanding the BF characteristics of SWs allows watershed managers to effectively prioritize conservation and restoration efforts aimed at protecting and enhancing the natural flow regime.

Incorporating physical constraints in a deep learning framework for short-term daily prediction of sea ice concentration

Grasping the rapidly changing sea ice patterns has become the key to Arctic navigation. Starting from meteorology and oceanography, this study combines the empirical orthogonal function (EOF) analysis with convolutional long short-term memory (ConvLSTM) model to realize short-term sea ice prediction. To further improve the effectiveness and predictability of the ConvLSTM model, we adopt the EOF analysis to incorporate the spatial and temporal patterns of sea ice changes into the ConvLSTM model in three ways, namely, model input factors, model internal network structure, and model loss function. Through the mean absolute error (MAE), anomaly correlation coefficient (ACC), and anomaly metrics, we find that embedding the EOF physical constraints into the model's internal structure fully employs the spatial pattern and time series information, affording an optimal prediction effect. Additionally, the impact of the weight proportion between the physical constraint and the base loss and the effect of combining the three ways are discussed. The experimental results reveal that tuning the weight proportion in the loss function can improve the model's training effect. However, multiple combinations of the same physical law make it difficult to introduce new effective information.

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China.

In this paper, a daily gridded observation data across China from 1961 to 2022 were used to calculate daily potential evapotranspiration (PET). The observed variables included daily temperature, sunshine hours, average wind speed, and average relative humidity. PET was determined using the Penman-Monteith method recommended by the Food and Agriculture Organization (FAO). The long-term trend of PET was investigated in six regions of China during different seasons. To further compressed the influence of various meteorological factors on the PET trend, the contribution of each meteorological element to the long-term trend of PET was analyzed. The results indicate the following: (1) PET reaches its peak during summer which values from 145 to 640 mm, while it is lowest during winter from 21 to 244 mm. (2) The spatial patterns of PET trend changes are relatively similar across the four seasons, characterized by a decrease in the eastern regions and an increase in the western regions. The reduction is most significant during the summer and the range of trend is from -2.04 to 1.48 mm/day, while the increase becomes more pronounced in the winter which trend is from -0.34 to 0.53 mm/day. (3) The contribution of factors varies significantly across different regions. In spring and autumn, RH and U have little difference in contribution from other factors. But tsun is varies different from regions, the contribution value is largest in the northwest and smallest in the northeast. However, during summer, tsun become the most significant contributor in the YZ and SE regions, while in winter, Tm emerges as the most significant contributor to the PET trend in all six subregions. In SW, the contribution from U2 is the smallest in all seasons, with RH and Tm being the two crucial factors determining the PET trend in this region.

Analysis of Vegetation Environmental Stress and the Lag Effect in Countries along the “Six Economic Corridors”

Climate conditions have a significant impact on the growth of vegetation in terrestrial ecosystems, and the response of vegetation to climate shows different lag effects with the change in spatial pattern and category of the ecosystem. Exploring the interaction mechanism between climate and vegetation growth is helpful to promote the sustainable development of the regional ecological environment. Using normalized vegetation index (NDVI) and meteorological data, based on univariate linear regression and partial correlation analysis, this study explores the temporal and spatial pattern and change trend of vegetation cover in regions and node cities along the “six economic corridors”, and analyzes the environmental stress of vegetation growth and the lag effect of climate response. This study shows that there are great differences in the overall vegetation coverage along the “six economic corridors”. The vegetation coverage in Southeast Asia is the best and that in central and West Asia is the worst. The vegetation coverage in the study area shows an improvement trend, accounting for 39.6% of the total area. There are significant differences in the lag effect of vegetation response and the main climate factors affecting vegetation growth, which is related to the diversity of vegetation and climate characteristics. In this study, we selected regions along the “six economic corridors” that exhibit large latitude and altitude gradients, diverse climate types, and significant seasonal changes and spatial differences in climate conditions as our research areas. Additionally, we considered the impact of different regions and various types of vegetation on their response to climate change. This is of great significance for gaining a deeper understanding of the response mechanism of global climate change and vegetation ecology. Furthermore, our research can provide valuable information to support the ecological environment protection of different typical vegetation against extreme climates, ultimately contributing to the sustainable development of “the Belt and Road”.