Analysis Of Spatio-temporal Evolution Research Articles

Analysis of Spatiotemporal Evolution and Driving Forces of Vegetation from 2001 to 2020: A Case Study of Shandong Province, China

As global climate change intensifies and human activities escalate, changes in vegetation cover, an important ecological indicator, hold significant implications for ecosystem protection and management. Shandong Province, a critical agricultural and economic zone in China, experiences vegetation changes that crucially affect regional climate regulation and biodiversity conservation. This study employed normalized difference vegetation index (NDVI) data, combined with climatic, topographic, and anthropogenic activity data, utilizing trend analysis methods, partial correlation analysis, and Geodetector to comprehensively analyze the spatiotemporal variations and primary driving factors of vegetation cover in Shandong Province from 2001 to 2020.The findings indicate an overall upward trend in vegetation cover, particularly in areas with concentrated human activities. Climatic factors, such as precipitation and temperature, exhibit a positive correlation with vegetation growth, while land use changes emerge as one of the key drivers influencing vegetation dynamics. Additionally, topography also impacts the spatial distribution of vegetation to a certain extent. This research provides a scientific basis for ecological protection and land management in Shandong Province and similar regions, supporting the formulation of effective vegetation restoration and ecological conservation strategies.

An Analysis Method of System Coupling and Spatio-Temporal Evolution of County New Urbanization and Logistics Industry

New urbanization in counties and the logistics industry are closely related and are essential in promoting regional economic and social development. There are specific challenges and obstacles to revealing the interaction mechanism and system state measurement between the two. This paper explains the two coupling mechanisms and constructs the evaluation index system. It proposes a new analysis method based on the coupling coordination degree model, the spatio-temporal evolution analysis, and the grey prediction model. The goal is to learn more about and fully realize the coordinated development mechanisms of the two. It then uses the Hebei province of China as an example to empirically analyze its systematic cross-sectional data from 2013 to 2022. Research findings: (1) In Hebei province, the new urbanization in counties and the logistics industry have a systematic coupling relationship. However, the logistics industry’s comprehensive development level is relatively lagging. The two systems have been at a high-level coupling stage for the last decade and maintain a high coupling status. The coupling coordination has shown significant improvement. (2) Although the geographical distribution of the coupling and coordination degree of the new urbanization in counties and logistics industry system has short-term volatility, it is still stable in the long term and presents economic-related spatial characteristics. (3) Over the next five years, the coupling coordination of 11 cities in Hebei province will steadily grow. There will be greater harmonization between the two systems. (4) From the analysis results, the evaluation index system of the coupled system constructed is scientific and reasonable. The analysis method can not only measure the system’s coupling degree, but it can also predict the development trend and analyze the spatial evolution. The technique has novelty and validity, which can be used as a reference for analyzing and making decisions about similar systems.

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons.

Analysis of Spatio-Temporal Evolution and Driving Factors of Eco-Environmental Quality during Highway Construction Based on RSEI

One essential part of transportation infrastructure is highways. The surrounding eco-environment is greatly impacted by the construction of highways. However, few studies have investigated changes in eco-environmental quality during highway construction, and the main impact areas of the construction have not been clarified. The highway from Sunit Right Banner to Huade (Inner Mongolia–Hebei border) was used as the study area. GEE was used to establish RSEI. During highway construction, Sen + M-K trend analysis, Hurst analysis, and Geodetector were employed to assess RSEI changes and driving factors. The results show the following: (1) An area of 1500 m around the highway is where the ecological impact of highway construction will be the greatest. (2) The curve of the annual mean of the RSEI from 2016 to 2021 is V-shaped. From northwest to southeast, there is an increasing trend in spatial distribution. (3) The largest environmental degradation during highway construction occurred during the first year of highway construction. (4) The factor detector results indicate that DEM, precipitation, distance from the administrative district, and FVC were the main RSEI drivers in the research region. The interaction detector’s findings show that the drivers’ combined influence on the RSEI was greater than that of their individual components. (5) Compared to the 2016–2021 trend, the proportion of future degraded areas in terms of eco-environmental quality will increase by 3.16%, while the proportion of improved areas will decrease by 2.99%.

Assessing the Drivers of Sustained Agricultural Economic Development in China: Agricultural Productivity and Poverty Reduction Efficiency

Poverty eradication is a crucial element of SDG 1. Whether the financial resources invested by the Government provide a critical impetus for deeply impoverished rural areas needs to be studied by quantitative analysis. Therefore, this study presents a theoretical analytical framework for agricultural production–government poverty reduction. It divides the poverty reduction process into two stages, agricultural production and poverty reduction, from the perspective of sustainable agricultural development. The comprehensive measurement and spatio-temporal evolution analysis of China’s agricultural production and poverty reduction efficiency are conducted using a novel dynamic two-stage DEA model, which incorporates non-expected factors. The study found that (1) China’s agricultural production and poverty reduction efficiency exhibit overall poor performance, characterized by two poles of differentiation. (2) The agricultural production efficiency score is higher in the northern region than in the southern region, while the poverty reduction efficiency score is higher in the eastern region compared to the western region. (3) The coupling and coordination between China’s production efficiency and poverty reduction efficiency are inadequate.

Detecting regional unevenness and influencing factors of ecological resilience in China

Existing research on the state transition process of ecological resilience (ER) has neglected the ultimate goal of enhancing well-being and lacks theoretical framework support. To address these issues, we have innovatively developed a three-dimensional analysis framework of “resistant, absorptive, and restorative capacity” to measure ER. We also explored the regional unevenness and factors influencing ER at the provincial level in China through spatiotemporal evolution analysis and GeoDetector models. The results indicate that: the ecological resilience of Chinese provinces ranges from 0.415 to 0.596, with all provinces falling into the risk or good areas. Furthermore, a fluctuating and increasing trend is observed. The eastern coastal provinces, such as Beijing and Shandong, comprise predominantly good areas of ecological resilience that are distributed in a band manner, while the western provinces, notably Xinjiang, comprise primarily risk areas distributed in a clustered pattern. The per capita electricity consumption, per capita GDP, and patent applications in high-tech industries are identified as the primary factors influencing the spatiotemporal heterogeneity of ecological resilience, and the interaction effects of each factor have synergistic enhancement effects. Spatial ecological resilience displays certain degrees of “nonhomogeneous” agglomeration and spatial heterogeneity. However, no apparent spatial polarization phenomenon is observed. These findings provide valuable insights for cities aiming to address ecological risks and enhance urban resilience.

Spatiotemporal evolution analysis of OpenStreetMap buildings in the Yangtze River Delta of China based on Tree-like model

OpenStreetMap (OSM) is one of the most successful and well-known projects in the field of Volunteered Geographic Information (VGI). It has not only become supplement to basic mapping services and professional surveying data, but also a popular free spatial dataset in urban analysis. However, the completeness of OSM building data in China was relatively low in the past. As a result, there is a relatively small number of studies on OSM building data in China, especially in terms of spatial patterns and evolution of OSM building data. In response to this research gap, this article proposes an OSM building data evolution model that combines gravity model and tree model. The spatial distribution pattern and evolution trend of OSM building data in the Yangtze River Delta region of China from 2014 to 2020 were systematically studied, providing insights and references for the future development of OSM building data in China. The research results indicate: (1) From 2014 to 2020, OSM building data in various cities in the Yangtze River Delta region grew rapidly, with an average annual growth rate of 30%, but there were significant spatial differences; (2) In terms of space, due to the influence of urban economy, population, and education level, OSM buildings mainly evolve and develop along large cities, forming a local radial development structure around large cities. Shanghai-Suzhou-Wuxi-Nanjing-Xuzhou/Hefei is the main evolutionary path of OSM building data in the Yangtze River Delta region; (3) In terms of time series, the gap between cities gradually decreases with the development of time, and OSM buildings gradually expand and develop from large cities to small and medium-sized cities, with spatial differences gradually narrowing. However, when OSM buildings have a certain scale, the city size will limit the development of OSM buildings to some extent.

Satellite-based ANN Identification and Spatiotemporal Evolution Analysis of Industrial Heat Sources Coupled with Temperature Characteristics

Satellite-based ANN Identification and Spatiotemporal Evolution Analysis of Industrial Heat Sources Coupled with Temperature Characteristics

Analysis of Spatiotemporal Evolution and Influencing Factors of Vegetation Net Primary Productivity in the Yellow River Basin from 2000 to 2022

The Yellow River Basin (YRB) plays a very important role in China’s economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB.

MULTI-DIMENSIONAL POVERTY IDENTIFICATION AND EVOLUTION ANALYSIS IN HEBEI PROVINCE BASED ON NIGHTTIME LIGHT REMOTE SENSING DATA

Abstract. This paper explores the use of spatiotemporal geographic information and advanced technology to effectively address the issue of poverty reduction and development. The focus is on Hebei Province, where multi-dimensional poverty identification and spatiotemporal evolution analysis are conducted using nighttime light remote sensing data. The study establishes a Multi-dimensional Poverty Index (MPI) system based on the regional average nighttime light index (ANLI) extracted from data spanning 2010, 2014, and 2018. A coupled regression model confirms the correlation between MPI and ANLI. Visualization and analysis are performed using GIS technology, Moran's I, and Getis's G* to interpret the identification results. From the experimental results, MPI established in this paper fits well with ANLI, which can be used for poverty identification and monitoring. The established multi-dimensional poverty model can identify multi-dimensional poverty counties better. However, there is a large discrepancy in the match with the traditional list of poor counties issued by the state from the perspective of absolute economic poverty. From the perspective of spatiotemporal evolution, it can be seen that the overall poverty level in Hebei Province has changed with time. Although there is aggregation among poverty areas, the aggregation is not deep. The poverty level of the traditional national-level poor counties has also been reduced, but the pattern of poverty aggregation remains unchanged. The "C-shaped" poverty belt around Beijing formed by Chengde, Zhangjiakou, Baoding and other surrounding counties in northern Hebei Province is still the focus of poverty alleviation work in the next stage.

Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media

Timely understanding of affected areas during disasters is essential for the implementation of emergency response activities. As one of the low-cost and information-rich volunteer geographic information, social media data can reflect geographic events through human behavior, which is a powerful supplementary source for fine-grained flood monitoring in urban areas. However, the value of social media data has not been fully exploited as potential location and water depth information may be embedded in both text and images. In this study, we propose a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas based on Sina Weibo. First, we construct a novel fine-grained location corpus specifically for urban flooding contexts. The corpus summarizes characteristics of address descriptions in flood-related Weibo texts, including standard address entities and spatial relationship entities, based on the named entity recognition (NER) model. Then, water depth information in texts and images is obtained based on different deep learning modules and fused at the decision level. Specifically, in text analysis module, we summarize and extract diverse descriptions of water depth, and in image analysis module, we develop a water level hierarchical mapping method. Finally, we analyze the spatio-temporal distribution characteristics and variation patterns of the extracted information to enhance situational awareness. Taking the urban flood occurred in Anhui, China as a case study, we find that the variation of flooding hotspot areas in Sina Weibo and rainfall centers show a significant spatial and temporal consistency, and the fusion of text and image-based information can facilitate dynamic perception of flood processes. The framework presented in this study provides a feasible way to implement refined situational awareness and spatio-temporal evolution analysis of urban floods at the city level in time.

A paradigm shift in Precambrian research driven by big data

The enigmatic nature of the Precambrian era poses ongoing challenges for researchers seeking to uncover its secrets. With the exponential growth of scientific data, there exists a vast repository of information through which these mysteries can be explored. However, this data is often derived from fragmented investigations of geological phenomena within specific disciplinary domains and limited spatiotemporal boundaries, resulting in untapped potential for extracting undiscovered knowledge. To address this limitation, the field of big data science provides a foundation for multidisciplinary research on the geological evolution of the Precambrian. By harnessing the power of large-scale data integration and analysis, valuable insights into this pivotal era in Earth's history can be revealed. This paper offers a comprehensive overview of big data types in geoscience and elucidates common analysis methods. Through a case study focused on the North China Craton (NCC), we demonstrate the application of conjoint analysis to a dataset comprising rock and mineral geochemistry data. Employing local singularity analysis and wavelet analysis on zircon age frequency and Hf isotopic time series, we reveal a persistent long-term periodicity of 800–500 million years since 3.5 billion years ago. This finding indicates a co-evolution between the NCC and the global supercontinent cycle dating back to the Archean period. To investigate the formation of the NCC, we employ machine learning-based crustal thickness evolution reconstruction, which highlights arc formation during subduction as the primary factor, with regional mantle activities playing a secondary role in the convergence. Combining spatio-temporal evolution analysis of magmatic intensity and εHf(t) values, we infer that the development of the NCC primarily resulted from a prolonged process of accretion targeting the Eastern Block (the primary continent nuclei) through the incorporation of diverse arc massifs. This examination of the NCC serves as an example of how data collection, processing, and utilization can enhance our understanding of formational and evolutionary processes, signaling a paradigm shift in Precambrian research driven by the integration of big data.

Statistical Measure and Spatiotemporal Evolution Analysis of Inter-provincial Finance Efficiency of Science and Technology Based on Non-expected SBM Model

The report of the 20th Party Congress pointed out that the real economy must be the core of economic development, and the financial sector, as the "backbone" of the real economy, is a key factor in realizing high-quality economic development. Science and technology finance, as a new type of bridge connecting finance and science and technology, on the one hand, accelerates the flow of capital to new technologies, and on the other hand, helps scientific and technological innovations to constantly reach a new level. Although the strength of China's science and technology financial investment has been increasing, the problems of emphasizing investment over performance and unbalanced development of science and technology finance among regions still exist. Therefore, taking the inter-provincial data of the decade 2010-2019 as the research object, based on the input-oriented three-stage SBM-DEA model containing non-expected outputs, we constructed China's S&T financial efficiency evaluation index system to measure the S&T financial efficiency of China's provinces. In order to further empirically test the geographical differentiation of China's S&T financial efficiency, based on the results of the three-stage SBM-DEA, the Dagum Gini coefficient decomposition method is used in combination with Kernel kernel and other statistical methods to reveal the characteristics of the geographical differentiation of China's S&T financial efficiency in China and the trend of its spatial and temporal evolution.

Spatiotemporal pattern evolution and influencing factors of online public opinion——Evidence from the early-stage of COVID-19 in China

With the rapid development of internet information technology, online public opinion's influence is infinitely magnified, seriously threatening social security and national governance. It is significant to clarify the spatial and temporal evolution rules of online public opinion on major epidemics and its influencing factors for the governance and guidance of online public opinion on major epidemics. In this paper, the spatiotemporal evolution analysis model of online public opinion and an analysis model of influencing factors were constructed. We selected the Baidu index and microblog crawler text data at the early stage of COVID-19 as the research objects and analyzed the evolution of online public opinion during the time period by using the optimal segmentation method, spatial autocorrelation analysis, and text analysis method. The spatiotemporal evolutionary influences and their influence are further analyzed using the geographic probe factor detection method. The results showed that the evolution of online public opinion in the early stage of the epidemic was closely related to the event's evolution and the prevention and control effect. In the time dimension, the early evolution of online public opinion has prominent periodic characteristics. In the geospatial dimension, there are significant spatial agglomeration effects and spillover effects. In the cyberspace dimension, there are significant differences in online public opinion heat, hot topics, and netizens' emotional tendencies at different stages. Furthermore, the severity of the epidemic, the number of Internet users, the number of media reports and the region's attributes jointly affect the spatial and temporal evolution pattern of online public opinions about the epidemic. The research results provide decision-making references for the government and planners to effectively manage online public opinion on emergencies and improve the government's public opinion governance capacity and level.

Extraction and Spatiotemporal Evolution Analysis of Impervious Surface and Surface Runoff in Main Urban Region of Hefei City, China

The biophysical composition index (BCI)-based linear spectral mixture model (LSMM) is used in this study to extract the impervious surface (IS), vegetation, and soil coverage of the main urban region (MUR) of Hefei City over the 2001–2021 period. In addition, the Soil Conservation Service-Curve Number (SCS-CN) model is first applied to simulate the surface runoff (SR) in the MUR of Hefei City over the past 21 years, then assessed for simulation accuracy using typical waterlogging points in the study area. On this basis, the spatiotemporal evolution of IS and SR and their relationships in the MUR of Hefei City are investigated and discussed in this study. The obtained results showed that (1) the root-mean-square error (RMSE), mean absolute error (MAE), and systematic error (SE) values of the BCI index-based LSMM are smaller than those of the LSMM, demonstrating a higher extraction accuracy of urban IS extraction of the BCI index-based LSMM. (2) The IS area of the MUR of Hefei City exhibits an increasing trend from 107.555 km2 in 2001 to 387.660 km2 in 2021. In addition, the change rate and change intensity values indicate an increasing–decreasing–increasing trend. The highest change rate and change intensity values are 24.839 km2/year and 23.094%, respectively, and were observed in the 2001–2005 period. (3) The simulated SR (165–195 mm) in the MUR of Hefei City demonstrates an increasing trend in the 2001–2021 period at a rainfall intensity value of 200 mm/d. In addition, the simulated SR amount in the central area exhibits slight changes, while that in the surrounding areas shows substantial variations. (4) The distribution of IS and SR in the MUR of Hefei City reveals strong directional variations, which are all affected by geographical conditions. The IS coverage and SR show high positive correlation coefficients in different years. (5) The present study provides primary data for effective urban planning, water resources management and regulation, and disaster prevention and mitigation in Hefei City, as well as a scientific reference for future studies on urban IS, SR, and their quantitative relationships in other regions.

Spatiotemporal of ecosystem service values response to land use/cover change based on geo-informatic Tupu – A case study in Tianjin, China

Revealing the spatiotemporal evolution of ecosystem service value (ESV) is a prerequisite for regional ecosystem management. This paper integrates the Geo-informatic Tupu method, the Markov transfer model, and the Barycenter model to propose an analytical method for the spatiotemporal response of ESV to land use/cover change(LUCC). Taking Tianjin as the empirical study area, we estimated its ESV and spatiotemporal transfer, rise and fall changes, and migration of the barycenter. The results showed that: (1) The land use composite index of Tianjin increased, and land use changes tended to level off throughout the study period. (2) From 2000 to 2020, ESV rose and then fell, with a total decrease of 8.03 billion RMB. (3) The change of Tupu is most dramatic in Xiqing, Binhai New Area, and Dongli, where economic and social growth is rapid. The barycenter of transfer Tupu shifts from centralized distribution to decentralized distribution around Dongli, and the ESV tends to increase to the south. (4) LUCC, NDVI, and DEM mainly drive the spatial heterogeneity of ESV in Tianjin. This research offers new insights into the spatiotemporal evolution analysis of ESV and could provide scientific references for regional ecological management and policy formulation.

Analysis of Spatio-Temporal Evolution of Regional Settlements under the Background of Rapid Urbanization: A Case Study in Sishui County, China

China’s urbanization has achieved rapid development in the past 20 years, with towns expanding in size and the population increasing, while rural society has also undergone dramatic changes. An in-depth study on the evolution process of rural settlements in the context of rapid urbanization is beneficial to the rational planning of villages and the promotion of green and sustainable urban development. Located in East China, Sishui County is in the transition area between three types of landforms: hills, plains, and mountains. The spatial distribution of rural settlements in the urbanization process shows obvious regional differences. To our best knowledge, research on the spatio-temporal evolution of regional settlements in Sishui County is rare. In this study, we chose Sishui County as the study area, utilized Landsat5 (TM) and Landsat8 (OLI) satellite data as remote sensing data sources, and applied Geographic Information System (GIS) spatial analysis methods, central place theory, and core–periphery theory to explore the evolution process for the pattern, scale, and structure of rural settlements in this region from 2000 to 2021 and to investigate the influencing factors. The results show that: (1) in terms of the evolution of the rural settlement pattern, its distribution shows a gradual increase in the degree of dispersion, which indicates an overall development trend in the region of Sishui County in recent years and that the trend is gradually increasing; (2) in terms of scale evolution, the degree and speed of expansion in rural settlements of Sishui County have gradually decreased, and the scale grade has gradually increased; (3) in terms of structural evolution, the hierarchy system in rural settlements of Sishui County is constantly being improved and optimized from a simple to a complex core–periphery structure. These results will provide data support for the rational planning of villages and sustainable, high-quality urban development. They will also help local governments take appropriate measures to achieve coordinated and sustainable socio-economic and environmental development in the region.

Spatiotemporal Evolution Analysis of the Chinese Railway Network Structure Based on Self-Organizing Maps

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China’s railway network.

Spatiotemporal evolution analysis of human disturbances on giant panda: A new approach to study cumulative influences with large spatial scales

The disturbance of wildlife habitats by human activities has become an important driving force in the evolution of ecological landscapes. However, human disturbances in panda habitats are highly diverse and complex. To study the changes in the temporal and spatial dynamics of human disturbances affecting panda populations, an analysis model for the evolution of human interference was designed using information entropy and statistical methods. Based on data from the third and fourth National Wild Giant Panda surveys in China, we analyzed the developmental trend, spatial distribution, and characteristics of the current state and regional distribution of human disturbances affecting panda populations. The results showed that shoot gathering, farming, and tourism were key local distribution factors, whereas hunting, road infrastructure, and logging had a more widespread impact on panda habitat. Livestock and herb gatherings, however, showed no clear distribution patterns. The influence of the main disturbance factors (logging, livestock, road infrastructure, and shoot gathering) tended to increase over time, whereas the development of the relative strength and weakness of the disturbances showed an evolutionary trend. Although logging, livestock, shoot gathering, and road infrastructure have historically been the main panda habitat disturbances, the impact of logging and shoot gathering has been diminishing over time, with road infrastructure emerging as the leading disturbance factor. Farming has shaped the distribution area of giant panda habitats over the past several hundred years. Additionally, logging has caused current fragmentation patterns for several decades. Moreover, it is estimated that road infrastructure will have a broad and profound impact in the coming decades. Our results indicate that the information entropy theory can be effectively used to analyze the temporal and spatial characteristics of human disturbances that affect wildlife. Our findings will provide a basis for ecological policies that aid the preservation of threatened panda populations and guide future research on habitat disturbance.

GRACE Satellite-Based Analysis of Spatiotemporal Evolution and Driving Factors of Groundwater Storage in the Black Soil Region of Northeast China

Clarifying the evolution pattern of groundwater storage (GWS) is crucial for exploring the amount of available water resources at a regional or basin scale. Currently, the groundwater resources of Northeast China have been extensively exploited, but only limited studies have assessed the extent of GWS depletion and its driving mechanisms. In this study, the groundwater storage anomaly (GWSA) in the black soil region of Northeast China was explored based on the Gravity Recovery and Climate Experiment (GRACE) satellite combined with the Global Land Data Assimilation System (GLDAS) hydrological model. The results show that from 2002 to 2021, the overall GWSA decreased (−0.4204 cm/a), and specifically, the average rates of decrease in Heilongjiang, Jilin, and Liaoning Provinces were −0.2786, −0.5923, and −0.6694 cm/a, respectively, with the eastern, southern, and central parts of Heilongjiang, Jilin, and Liaoning Provinces losing seriously. Especially the GWSA deficit trend can reach −0.7471 cm/a in southern Jilin Province. The GWSA deficits in the three provinces from April to September were greater than 0.40 cm/a, while the deficit values from January to March and from October to December were less than 0.40 cm/a. This study is the first to quantitatively analyze the GWSA and its influencing factors in Northeast China for 2002–2021. The results of the study help clarify the differences in the spatial and temporal distribution of groundwater resources and their driving mechanisms in the northeastern black soil regions and provide a reference for the conservation and sustainable utilization of groundwater resources in the black soil region.