Trend Analysis Method Research Articles

Vegetation Quality Assessment of the Shaanxi Section of the Yellow River Basin Based on NDVI and Rain-Use Efficiency

The Yellow River Basin is a critical region for ecological environment protection and social and economic development in China. It is of great significance to study vegetation dynamics for the high-quality development of the Yellow River Basin. In this study, based on the data of NDVI and precipitation datasets in the growing season (June to September) from 2000 to 2019, we used a Sen+Mann–Kendall trend analysis and other methods to study the spatial and temporal evolution characteristics of precipitation and vegetation cover in the Shaanxi section of the Yellow River Basin and to assess the regional vegetation quality change characteristics based on estimating the rain-use efficiency (RUE). The results show the following: (1) The precipitation in the study area showed a spatial distribution pattern of more in the south and less in the north, in which Yulin City had the lowest precipitation overall, but it was an area with significant increasing precipitation. (2) The NDVI value of the Shaanxi section of the Yellow River Basin showed an overall upward trend from 2000 to 2019, with a growth rate of 0.327/10a. The vegetation cover showed the spatial characteristics of high in the south and low in the north, which showed that the vegetation growth condition was poor in the wind-sand grassland area at the southern edge of the Mu Us Sandland in the northwestern part of Yulin City and the construction areas in the Guanzhong Plain. Meanwhile, the vegetation grew well in Yan’an City and the area close to the Qinba Mountains. Moreover, the NDVI of the study area increased with the increase in precipitation. (3) The vegetation quality in the study area showed fluctuating interannual changes and a weak upward trend. More than 80% of the vegetation in the study area was in a state of improvement, and the areas with more significant improvement were mainly located in the northern part of the study area, while the vegetation was degraded in the urban and urban suburb areas in the Guanzhong Plain. The results of this study are of great practical significance for promoting the socio-economic development of the Yellow River Basin in coordination with ecological environmental protection.

Monitoring and Analysis of the Driving Forces Behind Ecological and Environmental Quality at the County Scale Based on Remote Sensing Data

Chaohu Lake, as an important freshwater lake in China, mainly relies on surface runoff for water replenishment, and the environmental quality of the surrounding towns directly impacts the environment of Chaohu Lake. Given the characteristics of rich water resources and extensive river networks in the lake area, this paper utilizes the GEE platform and selects Landsat data from 1992 to 2022, taking Feidong County, one of the lake’s inlets, as the study area. We used the water benefit-based ecological index (WBEI) to monitor and evaluate the ecological quality of the study area and employ the Sen+MK trend analysis method to analyze the spatial-temporal characteristics of ecological quality changes. To explore the driving forces behind the spatial-temporal changes in the WBEI, this study selects land use type, elevation, slope, aspect, potential evapotranspiration, annual average precipitation, annual average temperature, and five characteristic factors used in the construction of the WBEI as influencing factors. Using the geo-detector method, the study analyzes the driving forces behind the spatial-temporal changes in the WBEI in the study area. Results show that the WBEI, considering water efficiency, integrates waterbody information into regional environmental quality assessments, comprehensively reflecting the ecological environment of lakeside cities. From 1992 to 2022, the WBEI of the study region shows an increasing trend, with an improved area accounting for 1110.42 km2, or 51.21% of the total area. Among these, the significantly improved area covers 372.9789 km2 or 17.2% of the total area, while the slightly improved area covers 737.4411 km2, corresponding to 34.01% of the total area. Interaction types of influencing factors include bivariate enhancement and nonlinear enhancement, with the primary interactive factors affecting the ecological environment quality change in Feidong County being CLCD∩RVI; changes in land use and vegetation cover are the main driving forces behind the changes in ecological and environmental quality in Feidong County. From 1992 to 2022, the main land type transformations in the study area were from arable land to other land types, with a significant conversion of arable land to construction land, which is the main reason for the degradation of local ecological and environmental quality. The results of this study can provide practical references and theoretical support for ecological environment assessment, governance, and improvement in areas with abundant water resources.

Evolution of Vegetation Coverage in the Jinan Section of the Basin of the Yellow River (China), 2008–2022: Spatial Dynamics and Drivers

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth.

Reconstruction of 2‐m Air Temperature From ERA5 Reanalysis at Dome A, Antarctica

ABSTRACTIn this study, we jointly used in situ air temperature from AWS and reanalysis data from ERA5 to make the first‐ever reconstruction of a 42‐year (1978–2020) air temperature time series for Dome A, Antarctica. By analysing the impact of environmental variables, we found that the 10‐m u‐component of wind was the predominant one for air temperature bias between ERA5 and AWS, followed by total cloud cover. Air temperature deviations between ERA5 and AWS during the period of 2005–2020 were successfully reduced by applying a random forest (RF) model, decreasing the bias by 0.52°C, the RMSE by 3.16°C and the MAE by 2.77°C. We next applied the RF model to predict the 2‐m air temperature difference which was added back to correct ERA5 from 1978 to 2004. This yielded an accurate time series of air temperature from 1978 to 2020. Using the innovative trend analysis method to analyse the temperature trend of the corrected data, we found that Dome A has experienced a gradual warming of 0.10°C dec−1 over the 42‐year period. Among the seasonal temperature changes, spring showed a significant warming trend of 0.57°C dec−1, autumn and winter showed no significant warming, while summer showed a slightly cooling trend. Also, over the 42‐year analysis period, a stable oscillation period of ~28 year was observed. This cycle emerged as the dominant pattern, influencing the overall temperature evolution. The method proposed in this research, which combines machine learning with AWS to correct ERA5 air temperature data, holds the potential to address spurious changes of reanalysis data in long‐time series studies, thus improving the reliability of trend analyses.

Exploration of the Spatiotemporal Characteristics and Driving Mechanisms of Vegetation Greenness Changes in Laos

In the context of climate change, vegetation changes in Laos have attracted widespread attention, especially the profound impact of its greenness changes on ecosystems, water cycles, and climate feedback. However, our understanding of the driving factors of vegetation greenness changes in different latitudes is still limited. This study utilized EVI and climate factor data from 2001 to 2023, employing trend analysis, correlation analysis, and machine learning methods to investigate the spatiotemporal patterns of vegetation greenness changes across Laos and their responses to climate factors. Results revealed an overall increasing trend in vegetation greenness, with 75% of the area exhibiting annual increases, primarily in northern, central, and parts of the southern regions. Conversely, 24.8% of the area experienced declines, concentrated near Vientiane and certain southern regions. Seasonal trends during the wet season largely aligned with annual patterns, although reduced rainfall negatively impacted some areas. The dry season exhibited the most pronounced changes, with 70% of the area showing increased greenness, especially in northern and central regions, despite localized rainfall constraints. Minimum temperature (TMMN) emerged as the most influential factor, with importance values of 0.42 for annual changes and 0.37 for dry season changes, while precipitation impacts varied across space and time. High temperatures affected vegetation more significantly in low-latitude regions, whereas high-latitude areas relied on changes in DSR. This significant finding underscores the differential impact of climate factors on vegetation greenness across latitudes, which is crucial for understanding the complex dynamics of tropical inland ecosystems under climate change and for developing targeted conservation and adaptation strategies.

Climate change trends in the Southeastern Anatolia region of Türkiye: precipitation and drought

ABSTRACT Drought, earthquake, flood, and fire are disasters whose effects occur after a more extended period than other disasters. Meteorological drought is called the beginning of drought types. In this study, trend analyses and temporal changes in temperature, precipitation, and drought index values were carried out between 1981 and 2022 at three meteorological observation stations in the Southeastern Anatolia region of Türkiye. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, China-Z Index, and Effective Drought Index methods were used for drought analysis, while Sen's slope, Mann–Kendall, and innovative trend analysis methods were used to detect the trend in precipitation. It was determined that precipitation generally had tended to decrease, and drought increased since 1996. Although every type of dry and wet periods has occurred, normal dry periods were observed more. In the spatial distribution of drought, the inverse distance weighted method gives larger areas with more extreme drought and wet values than the Kriging method. The increase in extreme values in the region indicates that the severity of drought will increase. It has been determined that the region's water resources and agricultural activities are under pressure due to climate change and drought.

Analisis Kapasitas Produksi Menggunakan Metode Rough Cut Capacity Planning Pada CV Tahaki Multi Kreasi

CV Tahaki Multi Kreasi is a company engaged in interior, exterior and advertising. The number of requests for cabinet room products tends to fluctuate every month. In 2023, from January to December, CV Tahaki Multi Kreasi received 143 cabinet room order projects. To estimate the amount of demand, effective forecasting is needed. Forecasting uses methods namely Moving Average, Exponential Smoothing, and Trend Analysis. The most effective forecasting uses the Trend Analysis method which has the smallest forecasting error. The MAD value is 1.9005, MSE is 4.5343, and MAPE is 19.2362. The production schedule at CV Tahaki Multi Kreasi is carried out every two weeks for optimal results. Production capacity requirements are calculated using the Rough Cut Capacity Planning (RCCP) method. The RCCP method is used to determine the possibility of insufficient capacity or overload in determining production capacity.. The total capacity required in all work centers is 962 hours, while the total available capacity is 2016 hours. The analysis shows that there is no capacity shortage at each work station because the variance shows negative and the load percentage is 54.2%.

Applications of Innovative Polygon Trend Analysis and Trend Polygon Star Concept Methods for the Variability of Precipitation at Synoptic Stations in Benin (West Africa)

Climate variability poses new risks and uncertainties. In the sub-Saharan region, the impacts are already being felt and represent an additional level of obstacles for most vulnerable people, as well as a threat to sustainable development. This study analyzes the variability of precipitation in Benin using new approaches. The precipitation data used is the monthly average recorded at synoptic stations from 1970 to 2019 by the Metéo-Bénin agency. Two innovative graphical trend methods, innovative polygon trend analysis (IPTA) and trend polygon star concept (TPSC), are applied to the data. Both methods allow for the assessment of periodic characteristics of the monthly average rainfall and visually interpreting the transition trends between two consecutive months. The results show that the average monthly precipitation does not follow a regular pattern. There is also a general upward trend in precipitation for most months at the stations used. Most TPSC arrows were found in regions I and III. According to the TPSC graphs, the longest transition arrows between two consecutive months were observed in quadrant III. They were noted between the months of June and July in Cotonou, October and November in Bohicon and Save, and between September and October for the remaining stations. The results of this study are of great importance for policies regarding ongoing climate change in the agricultural, health, economic, security, and environmental sectors.

Driving Factors for Vegetation NDVI Changes in a Cold Temperate Zone: Climate, Topography, and Land Use

Exploring the spatio-temporal evolution and driving mechanism of the NDVI (Normalized Difference Vegetation Index) is important in order to understand the operating forces of the ecosystem and the response process of environmental change. We analyzed spatio-temporal vegetation changes by using the trend analysis method during 2001–2020 based on the MODIS NDVI, the meteorological data, the DEM (Digital Elevation Model) and land use types data. We quantitatively revealed the influence degree and mechanism of each detection factor and their interaction on the spatial differentiation of vegetation by using the geographical detector model. Results showed that the vegetation NDVI showed an increasing trend with an increasing rate of 0.021/10 a during 2001–2020 and mainly distributed in the northwest and southwest of the Greater Khingan Mountains. The explanatory power values of each driving factor are as follows: land use (0.384) > elevation (0.193) > slope (0.159) > annual precipitation (0.104) > aspect (0.069) > average annual temperature (0.056). The explanatory power of interaction between driving factors were relatively high, as follows: Land use ∩ Aspect (0.490) > Land use ∩ Slope (0.471) > Land use ∩ Annual precipitation (0.460) > Land use ∩ elevation (0.443) > Land use ∩ Annual temperature (0.421) > Aspect ∩ elevation (0.408). Our research was of great significance for understanding the growth law of vegetation, protecting the ecological environment, and sustainable development in cold temperate zones.

Research on Directional Elements of Two-Terminal Weak-Feed AC Systems with a Negative Sequence Control Strategy

It has become a typical scenario in power systems that renewable energy power supply is connected to an AC system through flexible DC transmission. However, since both sides of the AC line are power electronic converters, the negative sequence suppression strategy will be put into the converters at both ends during the asymmetric fault, which causes fundamental changes in the fault characteristics of the system, which is reflected in the two-terminal weak-feed characteristics, leading to the decline of traditional protection performance and affecting the safe operation of the system. Therefore, this paper presents a directional element of a double-ended weakly fed AC system with a negative sequence control strategy. Firstly, the characteristics of the negative sequence impedance under the negative sequence suppression strategy are analyzed when the AC line has asymmetric faults. Secondly, the difference in negative sequence impedance amplitude is analyzed. Finally, the direction element is constructed by the method of de-wave trend analysis The proposed scheme can realize the rapid identification of fault directions at both ends. The simulation results show that the proposed scheme is suitable for a two-terminal weak-feed AC system and can operate reliably under 300 Ω transition resistance and 20 dB noise interference.

온라인 교육사례 동향 연구를 통한 ESD와 평생교육에 대한 시사점 도출

Objectives This study used systematic literature analysis and case study trend analysis methods for empirical analysis of online education cases to derive implications for ESD and lifelong education by analyzing trends in online education cases in Korea. Methods To do so, 281 domestic academic papers of RISS (Academic Research Information Service) were collected from 2003 to 2023, and MS Excel, text mining, content analysis methods (case study design, case study type, case study type), and duplicate frequency processing were used. Results first, from 2020 to 2023, the number of studies related to online education cases exploded to a total of 231, indicating that COVID-19 has caused many changes in Korea's educational field. Second, Online education case studies were conducted most often on college students, and most universities operated an online learning management system (LMS), so researchers could easily measure the effectiveness of online education and analyze quantitative data. Third, it can be seen that more mixed and qualitative studies were conducted than practical and quantitative studies due to factors such as the specificity of online education and online environment, the responsive necessity of pandemic situations, and the analysis of various learner data. Conclusions In this study, the research trends on online education cases in Korea are analyzed by yearly publication status, published academic journals, research subjects, research methods, data collection period, data collection methods, research topics, research tools, etc. to understand the achievements so far. In addition, we aim to derive implications for ESD and lifelong education.

The Trend of Coal Mining-Disturbed CDR AVHRR NDVI (1982–2022) in a Plain Agricultural Region—A Case Study on Yongcheng Coal Mine and Its Buffers in China

The destruction of arable land caused by coal mining in coal grain compound areas is a major bottleneck restricting grain production increase. The spatiotemporal correlation between the decline in cultivated land quality and crop growth deterioration due to mining subsidence still needs to be clarified. This study employed the CDR AVHRR NDVI dataset and applied correlation and trend analysis methods to extract vegetation cover information from 1982 to 2022. It also explored the relationships between vegetation cover and temperature and precipitation. The study found the following: (1) Over the past 41 years, the NDVI in the study area showed a significant upward trend. Specifically, the average annual NDVI growth rate in the mining area was 51.85%, while the corresponding growth rates for the 10 km buffer area, 20 km buffer area, and check area (CK) were 65.91%, 65.86%, and 68.09%, respectively. The start of the growing season (SOS) for winter wheat in the mining area and control area advanced by 49 ± 1.5 days and 65 ± 1.5 days, respectively, while the length of the growing season (LOS) extended by 59 ± 1.5 days and 72 ± 1.5 days, respectively. For summer maize, the SOS advanced by 11 ± 1.5 days and 15 ± 1.5 days, respectively, and the LOS extended by 17 ± 1.5 days and 19 ± 1.5 days, respectively. The study area exhibited a significant positive correlation between the NDVI and temperature. Specifically, the correlation coefficient for the mining area was 0.6865 (p < 0.01); for the 10 km buffer zone, it was 0.5937 (p < 0.01), for the 20 km buffer zone, it was 0.6775 (p < 0.01), and for the control check area (CK), it was 0.6591 (p < 0.01). The results of this study can provide data support for the collaborative rehabilitation of and source reduction in coal grain compound areas, as well as for the restoration of damaged farmland.

Trend Analysis of Annual Precipitation in Southern Benin using Innovative Trend Analysis (ITA) Method

Trend Analysis of Annual Precipitation in Southern Benin using Innovative Trend Analysis (ITA) Method

Analysis of Downstream Sediment Transport Trends Based on In Situ Data and Numerical Simulation

This study conducted an in-depth analysis of the sediment dynamics in the lower reaches of the Changhua River and its estuary on Hainan Island. Through field collection of topographic data and sediment sampling, combined with advanced computational techniques, the study explored the transport pathways and depositional patterns of sediments. The grain size trend analysis (GSTA) method was utilized, in conjunction with the Flemming triangle diagram method, to classify the dynamic environment of the sediments. Furthermore, hydrodynamic modeling results were integrated to further analyze the transport trends of the sediments. The study revealed that the sediment types in the research area are complex, primarily consisting of gravelly sand and sandy gravel, indicating a generally coarse sedimentary environment in the region. The sediments in the lower reaches of the Changhua River generally transport towards the south and southwest (in the direction of Beili Bay). The net sediment transport directions inferred from the GSTA model are largely consistent with the Eulerian residual flow patterns, especially in the offshore area, where discrepancies are observed in the nearshore zone. The nearshore transport is influenced by the combined effects of alongshore currents, residual flows, and river inputs, while the offshore transport exhibits a shift from the northwest to southwest directions, reflecting the regional circulation patterns.

Investigation of precipitation trends in Lower Mekong Delta River Basin of Vietnam by innovative trend analysis methods

Investigation of precipitation trends in Lower Mekong Delta River Basin of Vietnam by innovative trend analysis methods

Study on the Level Measurement and Spatial Distribution of Comprehensive Competitiveness of Logistics Industry in Henan Province

This paper takes the 18 cities in Henan Province as the research object, combining their relevant data in 2022, and conducts a comparative study on the comprehensive competitiveness indicators of logistics industry in Henan Province through factor analysis method. Starting from the three dimensions of infrastructure level, economic benefits, and development scale, this paper explores the level of comprehensive competitiveness of logistics industry in Henan Province and explores the spatial distribution characteristics of comprehensive competitiveness through spatial trend analysis and natural breakpoint method. And it puts forward corresponding improvement strategies.

An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

Satellite measurement data-based assessment of spatiotemporal characteristics of ultraviolet index (UVI) over the state of Johor, Malaysia

The study examines the spatiotemporal and statistical characteristics of the daily ultraviolet index (UVI) over the Johor state of Malaysia. The datasets utilized in this study are the Ozone Monitoring Instrument (OMI)/Aura satellite’s daily UVI observations between October 2004 and March 2023. The innovative trend analysis (ITA) method is employed to identify the statistical trend of daily UVI. Basic statistics of daily, monthly, and seasonal UVI are also carried out to better characterize UVI in the study area. The daily UVI data collected for the analysis exhibit wide variability, with more than 80% of the daily UVI data falling above UVI 8, indicating the severity of UV radiation (UVR) reaching the study area. The monthly and seasonal UVI also display highly varying characteristics, as observed in the daily data. The pixel-based analysis of ITA results indicates a significant increasing and decreasing trend of daily UVI in the region, with values varying between − 9.42 × 10−6 and 3.79 × 10−5. However, for the study area as a whole, UVI shows a significant increasing trend of 4.50 × 10−5. The influence of parameters such as cloud optical thickness (COT), solar zenith angle (SZA), and O3 column on the strength of UVR reaching the study area is also investigated. Based on the results, the study area is characterized by low thickness, broken clouds, and sometimes cloudless conditions. Overall, the results show that UVI in the study area is increasing, and therefore, proper health guidelines and direct UVR exposure regulations need to be implemented to reduce serious health risks associated with UVR exposure.Graphical

Spatial and Temporal Characteristics and Driving Force Analysis of Ecological Environmental Quality in Fengfeng Mining Area with Remote Sensing Ecological Index of PM2.5 Concentration

The Fengfeng mining area is an important coal-producing area in China and crucial environmental problems have been caused by large-scale exploitation of coal mines. The spatio-temporal evolution and driving factors of the ecological environment quality in this area must be explored for promoting the transformation of coal-based cities. Based on Landsat data of the Google earth engine （GEE） platform, this study constructed a new remote sensing-based ecological index （RSEInew） for the Fengfeng mining area from 2000 to 2020. The spatial and temporal evolution of RSEInew and its driving factors were evaluated by using trend analysis and geographic detector methods. The results showed that： ① From 2000 to 2020, the RSEInew of the Fengfeng mining area presented a fluctuating increasing trend （trend = 0.002 2）, and the proportion of good and excellent ecological environmental quality showed an increasing trend, rising from 24.80% in 2000 to 65.54% in 2020. ② The change in the RSEInew grade indicated that the proportion of significant improvement （3 and 4） of ecological environment quality grade in the Fengfeng Mining area from 2000 to 2020 was 10.21%, which was mainly distributed in Hexun town and Yijing town in the northwest of the Fengfeng mining area. The proportion of significant degradation （-3 and -4） was only 1.58%, mainly scattered in Linshui town and Dashe town. ③ RSEInew values increased significantly during 2000-2020 in the area accounting for 18.29%, mainly distributed in the central and northern areas and the western fringe of the Fengfeng mining area. The significantly reduced area accounted for 9.25%, mainly concentrated in the eastern area of the Fengfeng mining area. The coefficient of variation results showed that the areas with high fluctuation of RSEInew were mainly concentrated in Pengcheng town and Linshui town in the middle and eastern Fengfeng mining area. ④ From the perspective of influencing factors, the average q value of land use type （X6） during 2000-2020 was 0.290, which was much higher than other factors. The q value of social and economic factors showed an increasing trend, indicating that the spatial distribution of ecological environment quality in this region was increasingly strongly influenced by human activities. The interaction results showed that land use change was the key factor influencing ecological environment quality in the Fengfeng mining area.

Evaluations of ground surface heat fluxes and its potential vegetation effects in the permafrost region of northeastern China

Ground surface-air temperature difference (GATD) is the primary source of sensible heat flux from the ground surface, and vegetation inevitably affects it by altering the ground surface characteristics. However, the relationship between vegetation and GATD in the permafrost region has not been well-elucidated. Using meteorological station data, National Oceanic and Atmospheric Administration Climate Data Record Normalized Difference Vegetation Index (NOAA CDR NDVI) data, and some auxiliary data, this study aims to investigate the patterns of GATD in the Northeast China permafrost region and quantify the response of GATD to vegetation changes. The study employs trend analysis, Geodetector, and correlation analysis methods. The results show that the GATD in the Northeast China permafrost region decreased from west to east and the annual mean GATD exhibited an upward from 1982 to 2020. The relationship between NDVI and GATD trend is non-linear, in areas with low vegetation, vegetation changes amplify the GATD trend, while in areas with high vegetation, vegetation changes inhibit the GATD trend. Furthermore, the rate of GATD increase is higher in the discontinuous permafrost (DP) region than in the sporadic permafrost (SP) and isolated permafrost (IP) regions, suggesting that the DP region may have experienced a faster ground surface state change process. The findings of this study will provide new insights for evaluating permafrost degradation and studying the resulting ecological environmental effects in the permafrost region.