Search

Understanding the evolutionary process of land use and the landscape pattern of the Yellow River Delta wetland is an important prerequisite for promoting its sustainable development. In this study, we combined Landsat-5/7/8 data with the random forest algorithm to map the land cover/land use from 2002 to 2021 in the Yellow River Delta wetland and evaluated and analyzed them. The result demonstrated that: (1) the classification method used is promising, with an average OA and Kappa coefficient of 90.5% and 0.891, respectively; (2) the built-up area of Dongying District and some townships has been expanded since 2008; wheat fields are in Guangrao County, while paddy fields are in the northern part of Xicheng in Dongying District, in the east of the built-up area of Hekou District, and in the highstandard farmland project area; tamarisk shrubs are in the northeastern part, and suaeda meadows near the tidal flat; tidal flats, breeding aquatics, and saltern are in the eastern and northern parts; cropland accounted for the largest proportion, but smaller food cropland during 2002-2016; (3) the decreased woodland in the study area was converted to cropland, and the unused land was converted to waters, impervious surfaces, and cropland; (4) the landscape fragmentation, shape complexity, and aggregation were all reduced firstly and then increased with 2008 as the turning point. The research on mapping and landscape pattern evolution analysis in the Yellow River Delta wetland can provide references for environmental protection and urban and rural development. [ABSTRACT FROM AUTHOR]

This study presents the case of China's Jiangsu Province. The spatial-temporal pattern evolution of different manufacturing sectors is discussed using spatial analysis technology (spatial autocorrelation and standard deviation ellipses). The Granger test is used to analyze the relationship between the change in the manufacturing industry spatial agglomeration and regional economic differences. The following conclusions are drawn: 1) The spatial agglomeration trend of most manufacturing sectors is weakening. Much of the manufacturing sector, like the rubber and plastic product industries, has been transferred from southern to northern Jiangsu. 2) From the scale, only a minority of these enterprises possess substantial registered capital. The capital injection scale of more manufacturing enterprises is insignificant. At the same time, manufacturing companies with substantial financial resources are increasingly inclined to choose less-concentrated areas when choosing new investment areas. 3) The reduction of regional economic differences is considered to be the Granger-cause for the decline of the spatial agglomeration degree of the manufacturing industry in Jiangsu Province. Analyzing the spatiotemporal pattern of the manufacturing industry in Jiangsu Province will provide specific policy reference values for the manufacturing industry and economic development of Jiangsu province. In addition, for companies of different sizes, the findings of this paper also provide valuable references on how they can choose suitable investment locations according to their size in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Abstract The spatial differences of efficiency and development level of regional tourism are evident, and the dynamic grasp of their spatiotemporal evolution characteristics and coupling coordination relationship is important to promote high-quality sustainable tourism development. Herein, we measured the tourism development level of 31 provincial units in China during 2000–2020, introduced the data envelopment analysis-based Malmquist productivity index to determine tourism efficiency, used exploratory spatiotemporal data analysis methods to explore the spatiotemporal dynamic characteristics of regional tourism differences and spatial structure, constructed a coupled coordination model of tourism efficiency and development level and analyzed their coupling excellence and synergistic consistency. The results were as follows: (1) The spatial differences of comprehensive tourism efficiency from 31 provinces were evident, and the average situation showed high and low distribution characteristics in the east and west, respectively. The interannual changes showed a fluctuating downward trend, with the scale efficiency playing a supporting role for comprehensive efficiency and technical efficiency playing an influencing and restraining role. (2) Less fluctuation existed in the local spatial structure of tourism efficiency and development level; the direction of dependence was more stable, tourism development level was slightly more volatile, and the spatial dependency direction changes were similar. The tourism efficiency of local structure competition posture was stronger than that of collaboration, with the tourism development level collaboration integration being stronger. (3) The local spatial structure of tourism development level was more stable, the relative position of provincial units was more difficult to change, tourism efficiency of local spatial structure was unstable, and provincial units exhibited a greater possibility of change. (4) Tourism efficiency and the scale of the overall coupling degree and coupling coordination gradually improved, with similar spatiotemporal values. The overall coupling degree and coupling coordination degree of tourism efficiency and development level gradually improved, with similar spatiotemporal heterogeneity and volatility of local evolution. The area with a higher value coupling coordination degree spread slower and more widely.

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya’an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations.

Breakthrough innovation and incremental innovation have different impacts on economic development. For regional development, it is important to find a balance in dual innovation, which entails effective coordination of allocating innovation resources and managing risks. However, little attention has been given to the spatial relationship and differentiation mechanisms between breakthrough innovation and incremental innovation. Therefore, our research takes China’s three major urban agglomerations as examples, aiming to explore the spatial–temporal pattern evolution, influencing factors, spatial relationship, and spatial organizational patterns of breakthrough innovation and incremental innovation from 2000 to 2021. The research found that the spatial distribution of urban dual innovation is affected by the law of distance decay, and the spatial distribution of incremental innovation is more polycentric than that of breakthrough innovation. In terms of the differentiation mechanism, breakthrough innovation is more affected by the innovation atmosphere, while incremental innovation is more likely to be affected by the economic foundation and built environment. Our research effectively supplements the shortcomings in the spatial relationship research of breakthrough innovation and incremental innovation and provides references for formulating innovation policies.

Cotton is an important agricultural crop and strategic resource. China is currently the country with the largest global cotton production and consumption, but few studies have systematically analyzed the dynamic relationship between factor price change (FPC) and cotton production pattern evolution (CPPE). Based on provincial panel data from the main cotton planting areas from 1985 to 2021, this paper used spatial econometric models to empirically analyze the impact of FPC including labor price, production material cost, and mechanical cost on CPPE from the perspective of mechanical substitution difficulty. The findings are that (1) FPC significantly affected CPPE, specifically the rise of labor price induced the demand for mechanical substitution, resulting in a significant cotton agglomeration effect; (2) spatial econometric analysis found that FPC had a significant spatial spillover effect on CPPE in adjacent regions; and (3) the transfer analysis found that higher mechanical substitution difficulty exerted an inhibitory effect on cotton planting, leading to a gradual shift of the main cotton planting areas from the Yangtze River Basin and Yellow River Basin with high labor prices to the Northwest Inland region with lower labor prices. Updating the production technology and improving the efficiency of cotton specialization are effective strategies. The results are valuable for policy making related to the development of China’s cotton industry.

Warming weather has led to melting sea ice, and increasingly complex global geopolitics has drawn more countries’ attention to the Arctic. The Arctic Northeast Passage, as an emerging route connecting Eurasia, has seen a sharp increase in vessel activity. The period from 2015 to 2020, being a stable and undisturbed data period, is of significant theoretical importance for exploring the natural development of the Arctic Northeast Passage. The study found that the research period can be divided into three stages: from 2015 to 2017, the number of vessels grew slowly. In 2018 and 2019, the number of vessels and vessel activities saw significant growth, but an unexpected reverse growth occurred in 2020. Different types of vessels have unique activity characteristics and evolutionary patterns, influenced by the Arctic’s unique geographical environment, abundant natural resources, deepening Sino-Russian cooperation, and increasing global trade supply and demand. The results of this study aim to provide policymakers with analysis based on the initial development stage of the route, offering data support for future policy formulation, route planning, and research on the navigation safety of vessels on the Arctic Northeast Passage.

The capacity for urban innovation is a significant symbol of contemporary urban development. In order to promote sustainable urban innovation, it is crucial to match and optimize innovation spaces, actors, and their behavioral needs. Based on the data from patent inventions, which are commonly used to represent urban innovation, in this study, we investigated the formation mechanism of Guangzhou’s innovation pattern and its characteristics from 1990 to 2020 using Geographic Information System (GIS) technology. The results indicated that Guangzhou’s innovation spaces developed a center-radiation structure of “two districts and seven cores”. We investigated the path dependence of spaces, actors, and behavioral needs by examining the interaction between the innovation space layout and behavioral needs. The findings provide theoretical support for the city’s sustainable development in terms of innovation in the future.

The study of spatio-temporal evolution characteristics and factors affecting the coordinated development of population and green economy (CD_PGE) in Shandong province, China, has significant decision-making implications for promoting high-quality and sustainable regional development. Based on 2001 to 2020 panel data for each city and economic zone in Shandong province, this paper constructs an evaluation model for the CD_PGE systems. Using growth elasticity models, geographic concentration models, kernel density estimation models, spatial autocorrelation, analysis of population and regional green economy development in Shandong Province from the perspective of spatial agglomeration coupling, spatial and temporal coupling coordination patterns, and evolutionary characteristics. In addition, we use the fixed effect models and panel quantile models to empirically test the effects of coordinated demographic and green economy development. The results show that: (1) In terms of demo-graphic and economic development characteristics, Shandong's demographic and green economy development trends are good, but there are still many challenges. (2) According to the time series evolution and spatial distribution characteristics, the degree of CD_PGE in Shandong Province is on the rise, and the level of spatial distribution is distinct. (3) From the spatio-temporal dynamical grid evolution of the degree of CD_PGE, the CD_PGE is characterized by significant spatial clustering, but with large regional differences. (4) From an impact factor perspective, both market mechanisms and government intervention have a significant impact on the degree of CD_PGE, but the direction and extent of the impact vary.

Since the inception of China’s reform and opening-up policy, the rapidly advancing process of urbanization and the primacy accorded to urban development policies have imparted increasingly profound ramifications on rural domains. Nonetheless, antecedent research has predominantly fixated on urban sprawl, overlooking the spatial metamorphosis of rural settlements and the prospective developmental trajectories within the policy paradigm. Consequently, this inquiry endeavors to scrutinize the evolution of the spatial configuration of rural settlements in She County from the advent of reform and opening-up (1980–2020) utilizing remote sensing data. In tandem, through scenario delineation and the utilization of the CLUE-S model, it aspires to prognosticate the evolving trends in the spatial arrangements of rural settlements in She County by 2035. The empirical findings divulge that (1) The temporal progression of rural settlement spatial configurations in She County over the preceding four decades can be delineated into two discernible phases. From 1980 to 2000, alterations in the number, extent, and spatial morphological attributes of rural settlements remained circumscribed. While the count of rural settlements registered a diminution (by 3), the aggregate extent experienced a marginal augmentation (by 8.45%), concomitant with a gradual gravitation towards regular boundaries, manifesting a stochastic distribution throughout the investigation expanse. Conversely, from 2000 to 2020, the quantity and extent of rural settlements in She County underwent a precipitous augmentation (92 and 36.37%, respectively), characterized by irregular peripheries. (2) The CLUE-S model achieved an overall precision of 0.929, underscoring its applicability in emulating fluctuations in rural settlements. (3) Within the new-type urbanization scenario, the cumulative expanse of rural settlements witnessed a decline of 35.36% compared to the natural development scenario, marked by substantial conversions into grassland and urban land usage. Furthermore, orchestrated planning and directive measures have propelled the consolidation of rural settlements in She County, engendering a more equitable and standardized layout. Under the aegis of the ecological conservation scenario, the total rural settlement area recorded a 0.38% reduction vis-à-vis the natural development scenario, primarily entailing competitive coexistence with arable land, grassland, and urban land usage in spatial terms.

Featuring complex and fractured terrain, the Loess hilly and gully regions suffer poor grain production capacity. The behavior of farmers, the major users of agricultural production space, significantly influences the agricultural production space. Hence, it is essential to explore the evolution rules of the agricultural production space under the influence of farmer behavior and reveal the influencing mechanism of agricultural production space change, which will facilitate the promotion of ecological protection and high-quality development of the Yellow River Basin. Relying on six-stage remote sensing images of the Yanhe River Basin from 1995 to 2018, this study utilized a land use dynamic index, transfer matrix and landscape pattern index to analyze the spatial pattern evolution of agricultural production in the Yanhe River Basin. Furthermore, the geographic detector model was applied to quantitatively analyze the influencing factors of the spatial pattern evolution of agricultural production. The results demonstrated the following: (1) From 1995 to 2018, the overall area of cultivated land in the Yanhe River Basin decreased by 927.02 km2, with a change degree of 21.07%. The spatial structure of agricultural production changed, mainly transferring the cultivated land to woodland and grassland. (2) The spatial form of agricultural production has changed from fragmentation to regularity, and the complexity of the production space shows a trend of first increasing and then decreasing. (3) The evolution of the spatial pattern of agricultural production was affected by multiple factors of farmer behavior, where significant interactive enhancement effects existed. Specifically, labor input was the dominant factor affecting the overall scale of the production space, with an influence value of 0.202; fertilization input and cultivated land transfer were the key factors affecting the spatial distribution of production, with influence values of 0.264 and 0.242, respectively; income level and social interaction were the base factors affecting the spatial form of production, with influence values of 0.558 and 0.438, respectively. The research results provide scientific support for the improvement of agricultural production quality and the spatial evolution mechanism of agricultural production in the Yanhe River Basin.

Based on panel data on tourism and related industries for 30 Chinese provinces from 2008 to 2019, this paper uses tourism consumption stripping coefficients, spatial autocorrelation models and spatial lag models to analyze the differences in carbon productivity in China's tourism industry over time and space and the factors influencing them. The research shows:(1) From 2008 to 2019, the overall carbon productivity of China's tourism industry has shown an increasing trend. However, the spatial distribution is uneven, generally showing the spatial pattern of high in the east and low in the west, and there is a trend of expansion to the middle and west. (2) There is a strong positive spatial autocorrelation in the carbon productivity of China's tourism industry, and the agglomeration in the local space is stronger than the heterogeneity. The "hot spot" areas (H-H type) are mainly adjacent to the eastern coastal areas, while the "cold spot" areas (L-L type) are mainly located in the central and western regions. (3) The degree of economic development, technological innovation and market size have a significant negative effect on carbon productivity in tourism, and government intervention has a non-significant negative effect and industry structure has a non-significant positive effect. Finally, we propose countermeasures to improve the carbon efficiency of China's tourism industry. [ABSTRACT FROM AUTHOR]

【Objective】There is a large population and strong demand for vegetables in Guangdong-Hong KongMacao Greater Bay Area(hereinafter called "the Greater Bay Area"). The evolution of vegetable production pattern and its influencing factors are studied in order to provide references for the spatial layout optimization and high-quality development of vegetable industry in the Greater Bay Area.【Method】The global autocorrelation indexes of vegetable gross outputs of each year were calculated to judge the regional correlation of vegetable production by using the statistical data at counties of the Greater Bay Area from 2010 to 2020 and the administrative map of the same year. The aggregation area of vegetable production and its spatial pattern evolution in counties were analyzed by local autocorrelation index. Spatial regression model was used to analyze the socio-economic factors affecting vegetable production under spatial correlation.【Result】From 2012 to 2020, the global autocorrelation index passes the test and the value is greater than 0, indicating that the vegetable production at county level forms a positive spatial correlation pattern, and the vegetable production has a certain degree of aggregation in space and increases with inter-annual changes. Through local spatial autocorrelation test and analysis, it is found that vegetable production areas such as Longmen county, Boluo county and Huicheng district shows a high-high aggregation pattern, and gradually forms an aggregation pattern in the northeast of the Greater Bay Area over time. The high-low and low-high spatial patterns of vegetable production are gradually affected by the positive correlation of adjacent areas, and finally forms the same pattern with the surrounding areas. Through the analysis of spatial regression model, it is found that the main factors affecting vegetable production are land, economic level, labor force and agricultural technology input in 2012. After 2016, the main influencing factor is land, while the influence of other factors is not obvious or significant, indicating that the expansion of vegetable planting scale is the first factor of yield increase.【Conclusion】There is a positive spatial correlation in vegetable production in Guangdong-Hong Kong-Macao Greater Bay Area, and gradually forms an industrial core area. In the future, it is necessary to increase investment in agricultural science and technology in the core areas of vegetable production such as Boluo, Longmen, Huicheng and Conghua, to further improve the modernization level of production, continuously drive the surrounding areas to improve the comprehensive industrial benefits, and promote the high-quality development of the vegetable industry.

【Objective】 Exploring the evolution trend and influencing factors of the spatiotemporal distribution of tea production in China, can provide a reference basis for timely adjusting the spatial layout of the tea industry, achieving optimal allocation of regional resources, and promoting high-quality development of China’s tea industry. 【Method】 Based on the Panel data of tea production in 19 tea producing provinces and cities in China from 1990 to 2020,this paper used the barycenter model, spatial autocorrelation and gray correlation to explore the evolution law of the spatial distribution of tea production in China and the trajectory of barycenter movement, and clarify the main influencing factors. 【Result】 In terms of production layout, China’s tea production had developed rapidly, with the Southwest Tea Area and Jiangnan Tea Area occupying more than 3/4 of the national tea planting area and nearly 70% of the tea production, becoming the two core production areas in the country. In terms of center of gravity migration, the planting area and yield of Chinese tea had cumulatively shifted 256. 19 km and 267. 15 km to the southwest, respectively. The production center of Chinese tea had shown an overall trend of shifting to the southwest, and the speed of expansion towards the west was greater than that towards the south. In terms of industrial agglomeration, the Moran’s I index of China’s tea production had shown an increasing trend year by year, and the Chinese tea industry had shown a significant geographical spatial agglomeration phenomenon; The LISA map of Chinese tea gardens showed an increase in ‘high-high’ clustering areas, while both ‘low-low’ clustering areas and ‘low-high’ clustering areas had decreased. The spatial spillover effect and polarization phenomenon of Chinese tea production were significant, forming tea production clustering areas in Yunnan, Sichuan, Guizhou, Hunan and other provinces, and playing a driving role in tea production in neighboring provinces. In terms of influencing factors, the top three factors that had an impact on China’s tea production were agricultural added value, total power of agricultural machinery, and tea planting area. The primary industry employment had the smallest impact on China’s tea production. 【Conclusion】 Reasonably transforming the industrial development model, accelerating the cultivation of advantageous brands, and efficiently promoting the development of industrial clusters are important ways to improve the quality and efficiency of the national tea industry. [ABSTRACT FROM AUTHOR]

Astigmatic unitary transformations allow for the adiabatic connections of all feasible states of paraxial Gaussian beams on the same modal sphere, i.e., Hermite-Laguerre-Gaussian (HLG) modes. Here, we present a comprehensive investigation into the unitary modal evolution of complex structured Gaussian beams, comprised of HLG modes from disparate modal spheres, via astigmatic transformation. The non-synchronized higher-order geometric phases in cyclic transformations originate a Talbot-effect-like modal evolution in the superposition state of these HLG modes, resulting in pattern variations and revivals in transformations with specific geodesic loops. Using Ince-Gaussian modes as an illustrative example, we systematically analyze and experimentally corroborate the beamforming mechanism behind the pattern evolution. Our results outline a generic modal conversion theory of structured Gaussian beams via astigmatic unitary transformation, offering a new approach for shaping spatial modal structure. These findings may inspire a wide variety of applications based on structured light.

Rural settlement is an important part of studying the relationship between humans and land; it is highly significant in revealing the evolution, driving mechanism and reconstruction scheme of rural settlement pattern. In this paper, Jiangning District, a rapidly urbanized area, was selected as a typical case. Using remote sensing image data, the landscape pattern index, the rank-scale law, the local hot spot-detection model, and the geographical-detector were comprehensively used to analyze the rural settlements pattern evolution and driving mechanism in the rapidly urbanized areas. The results are as follows: (1) From 2010 to 2020, the number of rural settlements showed a trend of large-scale reduction, and the settlements scale system was relatively uniform in Jiangning. The settlements scale had the autocorrelation characteristics of spatial agglomeration, and the local hotspot agglomeration pattern was significant. (2) The spatial distribution of rural settlements in Jiangning showed an “agglomeration” pattern, and the settlements density showed a “multi-core” distribution characteristic. (3) The pattern of rural settlements in Jiangning was shaped by natural environmental factors such as topography, water system and cultivated land resources; economic social factors such as agricultural population, per capita GDP, distance from town, and policy and system were the leading factors that promoted the settlements’ pattern evolution in Jiangning, and the interaction between the factors could enhance the interpretation of the settlements’ pattern evolution. The research can provide a reference for optimizing the spatial layout of settlements in rapidly urbanized areas.

The mainstream basin of the Tarim River serves as a vital ecological security barrier that prevents the merging and expansion of deserts and an important strategic corridor directly linking Qinghai and Xinjiang. With society’s development and climate change, ecological issues such as river interruption, vegetation degradation, and land desertification in the basin have notably intensified, and the ecological security is facing a critical test. Exploring the characteristics of landscape changes and their driving factors within the basin is crucial in improving the ecological environment system’s management. Based on land use data from 1980 to 2020, this study analyzed the characteristics of the spatiotemporal changes and pattern evolution of the landscape through a landscape transfer matrix and landscape pattern indices. It further revealed the impact factors of the landscape pattern through canonical correspondence analysis. The results showed that (1) in 1980–2020, the areas of desert, forest, farmland, and settlement landscapes increased, while the area of grassland landscape decreased, and the water landscape showed an “increasing–decreasing–recovery” pattern. The landscape transition types mainly included the transition from grassland to desert; mutual transitions among farmland, grassland, and forests; mutual transitions between water and grassland; and the transition from farmland to settlements. (2) The overall landscape pattern demonstrated increased fragmentation, shape complexity, and evenness with decreased aggregation. Furthermore, different landscapes exhibited distinct characteristics of landscape pattern changes; for instance, grassland landscape showed severe fragmentation, while desert landscape displayed the strongest dominance. (3) The landscape pattern was a result of the combined impact of natural and human factors, with the soil thickness (SOT), road density (ROD), annual actual evapotranspiration (AAE), population density (POD), and mean annual temperature (MAT) exhibiting significant influences. Specifically, the settlement and farmland landscapes were mainly influenced by the mean annual relative humidity (MAH), POD, GDP density (GDP), and distance to artificial water (DAW); the forest, grassland, and water landscapes were mainly influenced by the SOT, soil organic matter content (SOM), AAE, ROD, elevation (ELE), MAT, slope (SLP), and distance to natural water (DNW); and the desert landscape was mainly influenced by the DAW, DNW, SLP, AAE, SOT, SOM, and ROD. These findings can provide a scientific reference for landscape management and restoration, as well as sustainable social and economic development, in the mainstream basin of the Tarim River.

With the rapid development of the economy and society in recent years, the ecological environment has deteriorated significantly. The governments at all levels, departments, and relevant scholars have begun to pay attention to urban ecological construction and research on urban disaster prevention and mitigation. The concept of resilience has gained widespread attention in this context. However, at present, urban ecological resilience research is still in its infancy, and there are many qualitative but few quantitative studies in terms of assessment methods. This study explores the complex dynamic evolution mechanism of urban ecosystem resilience based on the three resilience characteristics and selects Nanchang, an important ecological barrier in the middle and lower reaches of the Yangtze River and a model area for integrated management of the Great Lakes basin, as an assessment target. This study establishes a quantitative measurement and evaluation framework for urban ecological resilience in three dimensions (resistance, adaptation, and vitality) and conducts a comprehensive evaluation of urban ecosystem resilience. The results show that the trend of land-use change from 1990 to 2020 is mainly characterized by the continuous decrease in arable land and ecological land (forestland, grassland, and water) and the continuous increase in construction land, among which the land-use dynamic attitude of construction land is the largest, reaching 5.33% from 1990 to 2005. During the study period, the overall ecosystem resilience of Nanchang showed a gradual decline and was always at a moderate-to-low level, with a long-term distribution pattern of “low in the middle and high in the surrounding areas” and clear spatial heterogeneity. This study helps clarify the security status of the regional ecosystem and provides a reference for exploring the complex dynamic evolution mechanism of ecological resilience.

Using the land use data set from 2000 to 2020 as the data source, GIS technology was used to explore the characteristics of land use change in Zhaotong city from three aspects: land use transfer matrix, standard deviation ellipse and center of gravity transfer model, and landscape pattern index was used to analyze the landscape pattern evolution from the landscape level and landscape type level. The results show that: (1) The main land types in Zhaotong are forest land, cultivated land and grassland, the growth rate of construction land is the largest, the standard deviation ellipse of grassland is the least obvious, and the distribution of center of gravity of cultivated land, forest land and grassland is relatively stable. (2) From the landscape level, the number of patches increased, the degree of connectivity decreased, the largest patch distribution dominance weakened, the degree of fragmentation increased, the connectivity decreased, the cohesion decreased and the complexity increased. (3) From the landscape type level, the landscape type with the largest patch number, patch density and edge density is cultivated land, the landscape type with the largest patch index and aggregation index is woodland, the landscape type with the largest mean patch fractal dimension is construction land, and the landscape type with the largest interspersion & juxtaposition index is water. [ABSTRACT FROM AUTHOR]