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Land-use and cover change is an important indicator for exploring global change trends, with in-depth research on land use and its driving factors being of particular significance in forging ecologically sustainable development. The present work used the Tarim River Basin as the study area, while the land-use transfer matrix, normalized difference...
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... at an important juncture of the Silk Road Economic Belt and the China-Pakistan Economic Corridor, Hotan is gradually becoming a regional center under the new national regional-development strategy. The night-time lighting area in Hotan was significantly expanded from 2010 to 2018, and the urbanization process was accelerated, which promoted economic development (Figure 8). At the same time, the area of arable and construction land significantly increased, and the migration direction of the population and GDP centers of gravity drove notable changes in land-use structure. ...
Context 2
... at an important juncture of the Silk Road Economic Belt and the China-Pakistan Economic Corridor, Hotan is gradually becoming a regional center under the new national regional-development strategy. The night-time lighting area in Hotan was significantly expanded from 2010 to 2018, and the urbanization process was accelerated, which promoted economic development (Figure 8). At the same time, the area of arable and construction land significantly increased, and the migration direction of the population and GDP centers of gravity drove notable changes in land-use structure. ...
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... Derived from the geographic concepts of the population centre of gravity and the economic centre of gravity [51], the weighted centre-of-gravity movement analysis of land classes can be used to identify spatial patterns or movements in LULC changes within the study area [51,52], or to compare the distributions of different land categories over a specific time period [44]. According to the formula provided by Chen and colleagues [51], if a certain land class in the study area consists of i patches, the coordinates of the centre of gravity (X t for the longitude and Y t for the latitude) of this land class in year t are as follows: ...
Tourism is widely recognised as a significant economic source, especially for small-scale settlements. Nevertheless, tourism may potentially result in adverse effects on the environment. In order to mitigate adverse effects and enhance the advantages, tourism development necessitates a comprehensive planning process. In order to direct and control tourism’s development effectively, it is imperative to ensure that local spatial plans are integrated with national policies and regional strategies. The aim of this article is to investigate the role of tourism planning processes in the spatial development of tourism destinations, specifically by analysing the land-use/land-cover changes along the western coastline of Mersin, with a particular emphasis on the Kızkalesi tourism destination.
... Its application to geography began with calculating the population center of gravity, expressed through latitude and longitude coordinates. By using a migration model based on the center of gravity, we could simulate temporal changes in carbon emissions' center of gravity, providing a more intuitive representation of their spatial and temporal dynamics within the study area [46]. To investigate China's changing carbon emissions patterns, this model was used to calculate the carbon emissions' center of gravity between 2000 and 2019. ...
With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions.
... Regional disparity could lead to major factors of differentiation in different regions [40]. Natural factors, such as topography, climate, and hydrology, are the foundation of the cultivated land use morphology change [41], and they show a significant impact on the macroscale or mesoscale research area with less disturbance by human activities [42]. Socio-economic factors are the core driving force. ...
In the past 20 years, the global economy has undergone tremendous changes with rapid industrialization and urbanization. Cultivated land is an important spatial carrier for human production and life, and its use pattern also changes with socioeconomic development. Natural, economic, social, and policy factors jointly drive the cultivated land use transition (CLUT). However, the spatiotemporal pattern and evolution characteristics of the CLUT at the national scale have not yet been clarified in China. Factors that play a leading role in the transition are also unclear. To this end, this paper explores the spatiotemporal evolution characteristics of the CLUT at a national scale and analyzes the main drivers and spatial differentiation rules of the transition based on relevant data from 31 provincial units on the Chinese mainland from 2000 to 2019. The results show that: (1) The CLUT in China from 2000 to 2019 had obvious stage characteristics. (2) The coordination degree of the CLUT was enhanced overall. Areas with a higher degree of coordination presented a spatial distribution pattern of small agglomeration and large dispersion, while low-level areas were distributed in spots. (3) Different drivers had various effects on the CLUT. The topography played an inhibitory role in the transition, and its influence showed obvious differences between the east and west regions. The effect of the construction land demand index shifted from inhibition to promotion, while the effects of the gross agricultural economic output and the total power of agricultural machinery in the transition were insignificant.
... Regarding whether the impact of cooperative management on cultivated land use efficiency is positive or negative after the inclusion of ecological dimension, there are few relevant studies at present. In addition, most of the existing studies have focused on changes in land use types in a region [18,19], and researchers have not yet paid attention to the LUEE in the basin. Due to the contradiction between the highly developed agriculture and the extremely fragile ecological environment in the Tarim River Basin, studying the agricultural management patterns and farmland utilization efficiency in the basin not only enriches the research results on the relationship between cooperative management and LUEE but also contributes to the green and low-carbon transition of agriculture in the region. ...
Clarifying the relationship between cooperative management and cultivated land use eco-efficiency (LUEE) is of great significance to promoting the green and low-carbon transition of agriculture. To explore the role of cooperative management in the green and low-carbon transition of agriculture of smallholder farmers in the western Tarim River Basin, in this study, based on the field survey data of 444 farmers in 2021, the carbon emissions of cultivated land were used to measure the LUEE with the slack-based model (SBM) with undesirable outputs. Then, propensity score matching (PSM) was used to test the relationship between cooperative management and LUEE. Additionally, the mediating effect of farmers’ green development willingness (FGDW) and the moderating effect of farmers’ part-time off-farm employment (POE) on the relationship was explored. The present study hypothesized that joining cooperatives has an improving effect on the LUEE, which can be achieved by increasing FGDW, and this effect can be enhanced by farmers’ POE. The results show that: (1) The LUEE was generally low (average LUEE value: 0.2678), and there was a significant difference between farmer households (the difference between the maximum and minimum values was as high as 2.8716). (2) Cooperative management had a significant improving effect on the LUEE. The LUEE of cooperative farmers (ACF) increased by 8.6% compared with that of non-cooperative farmers (NACF). (3) Joining a cooperative could improve the LUEE by improving FGDW. (4) POE could enhance the improving effect of cooperative management on the LUEE. Overall, all three hypotheses were supported: cooperative management could achieve scale effects that small farmers cannot achieve, which had a positive effect on improving the LUEE. This study provides a new ecological perspective for the analysis of the relationship between agricultural cooperatives and LUEE and decision-making reference for the rational utilization of cultivated land in northwest China.
... Human activities, as an influencing factor, have inhibited forest regrowth by fire or grazing [70]. The socioeconomic factors that are often considered are GDP and demography factors [71][72][73] due to national development policy and urbanization expansion. At the same time, Mongolia is dominated by herding activities. ...
During the past several decades, desertification and land degradation have become more and more serious in Mongolia. The drivers of land use/cover change (LUCC), such as population dynamics and climate change, are increasingly important to local sustainability studies. They can only be properly analyzed at small scales that capture the socio-economic conditions. Several studies have been carried out to examine the pattern of LUCC in Mongolia, but they have been focused on changes in single land types at a local scale. Although some of them were carried out at the national scale, the data interval is more than 10 years. A small-scale and year-by-year dataset of LUCC in Mongolia is thus needed for comprehensive analyses. We obtained year-by-year land use/cover changes in Mongolia from 1990 to 2021 using Landsat TM/OLI data. First, we established a random forest (RF) model. Then, in order to improve the classification accuracy of the misclassification of cropland, grassland, and built and barren areas, the classification and regression trees model (CART) was introduced for post-processing. The results show that 17.6% of the land surface has changed at least once among the six land categories from 1990 to 2021. While the area of barren land has significantly increased, the grassland and forest areas have exhibited a decreasing trend in the past 32 years. The other land types do not show promising changes. To determine the driving factors of LUCC, we applied an RF feature importance ranking to environmental factors, physical factors, socioeconomic factors, and accessibility factors. The mean annual precipitation (MAP), evapotranspiration (ET), mean annual air temperature (MAAT), DEM, GDP, and distance to railway are the main driving factors that have determined the distribution and changes in land types. Interestingly, unlike the global anti-V-shaped pattern, we found that the land use/cover changes show an N-shaped trend in Mongolia. These characteristics of land use/cover change in Mongolia are primarily due to the agricultural policies and rapid urbanization. The results present comprehensive land use/cover change information for Mongolia, and they are of great significance for policy-makers to formulate a scientific sustainable development strategy and to alleviate the desertification of Mongolia.
... Nighttime light levels can reflect the degree of urbanization in an area, while nighttime light data can also monitor regional urbanization processes and expansion [60,61]. According to the results of Wang's study, the lit area of the Tarim River Basin shows an increasing trend, which also indicates the rapid urbanization in the basin [62]. With the rapid population growth and economic development, the total population increased from 7,128,500 to 11,951,700 during 1990-2020, for an increase of 67.66%. ...
Urbanization not only affects a region’s economic development but also impacts its land use structure. As the largest inland river basin in China, the Tarim River Basin has experienced rapid economic growth and urbanization in recent years, posing a serious threat to its soil and water resources and ecological sustainability. In this study, four remote sensing data products from 1990–2020 are selected to explore the distribution of land use types and their land structure changes in the basin in the context of rapid urbanization. The results show that the built-up land area increased by 2855.74 km2 during 1990–2020, mainly from the transfer of grassland and barren land. Furthermore, the migration of the center of gravity of built-up land moved from the desert to oasis areas, indicating that the urbanization process intensified during the study period. The overall trend is toward a continuous increase in arable and built-up land area and a continuous decrease in barren land. Future trends in the Tarim River Basin predict that arable land will decrease and that built-up land will continue to increase. However, the increase in built-up land will level off, mainly due to the transfer of arable land and grassland, which accounts for 37.94% and 20.40%, respectively. The migration characteristics of the center of gravity of each land type in the basin varied widely during 1990–2020, but the land structure will tend toward a gradual balance in the future. Therefore, in the context of increasing urbanization, focusing on the sustainable development of regional soil and water resources and ecology is crucial for the coordinated development of regional resources and economy.
... Due to the limited data availability in early years and the insufficient coverage of the remote sensing data, it is difficult to obtain land use data spanning a long time period. Previous studies have thus mostly focused on the spatial and temporal changes of the oasis within 10-30 years [28][29][30][31][32], excluding few studies over 30 years [27,[33][34][35][36]. ...
The variability of the natural environment and the complexity of human activities result in dynamic changes in oasis areas, which is directly related to the sustainable development of arid and semi-arid areas. In order to better balance economic development and environmentally sustainable development, based on land use data and social and economic data from 1980 to 2015, this paper analyzed the oasis change characteristics under natural and human factors in the Shiyang River Basin in Northwest China. The results indicated that the oasis in the Shiyang River Basin showed an expansion trend from 1980 to 2015, with the expansion mainly occurring in the diluvial and alluvial fan, and along the middle and lower reaches of the river. The oasis changed actively in areas at an elevation of 1350 m, with a slope of 2°, at the distance of 1500 m to rivers, and with precipitation of 120 mm and temperature of 15 °C. Furthermore, the oasis mainly evolved towards gentler slopes and lower altitudes, and migrated closer to rivers before 1992 and farther away from rivers afterwards. Population growth and economic development were the important inducers of oasis change, and government policies on agricultural benefits and ecological conservation also influenced the oasis change, especially after 2000. The evolution patterns of oasis distribution revealed in this study can provide a reference for promoting oasis ecological restoration and sustainable development.
... Landscapes are affected by external disturbances (such as earthquakes, erosion, climate change, fire, human disturbances, etc.) and internal perturbations (such as physical flow of matter and energy, community turnover, etc.) with change processes that evolve over time. Landscape dynamics are very important for environmental management and conservation; they are a vital field of sustainable development research [1]. Mountain ecosystems around the world are particularly vulnerable, especially in the plateau mountainous regions of Southwest China, due to climate change and human disturbances [2]. ...
... However, the use of optical satellite-based imagery (such as Landsat imagery) is onerous in mountainous areas due to persistent heavy cloud cover, data gaps, and topographic variations [10,11]. Thus, the development of LTS maps to analyze landscape dynamics from optical satellite-based images faces numerous challenges: (1) consecutive cloud cover may obscure information related to seasonal or inter-yearly landscape change [12]; (2) topographic influences are obvious in mountainous areas, and impose additional differences in spectral radiance within any particular land cover due to changes in surface slope angle and aspect, leading to errors that may impact landscape classification and pattern analysis [13]; and (3) little or no historical ground truth or other reference data for landscape classification samples (training or validation samples) are available [14]. Consequently, determining LTS changes across a long time span is a difficult task in cloudy mountainous areas. ...
Detailed knowledge of landscape dynamics is crucial for many applications, from resource management to ecosystem service assessments. However, identifying the spatial distribution of the landscape using optical remote sensing techniques is difficult in mountainous areas, primarily due to cloud cover and topographic relief. Our study uses stable classification samples from mountainous areas to investigate an integrated approach that addresses large volumes of cloud-cover data (with associated data gaps) and extracts landscape time series (LTS) with a high time–frequency resolution. We applied this approach to map LTS in a typical cloudy mountainous area (Erhai watershed in northwestern Yunnan, China) using dense Landsat stacks, and then we also used the classified results to investigate the spatial–temporal landscape changes in the study area at biennial intervals. The overall accuracy of the landscape classification ranged from 81.75% to 88.18%. The results showed highly dynamic processes in the landscape throughout the study period. Forest was the main land cover type, covering approximately 39.19% to 41.68% of the total study area. Alpine meadow showed fluctuating trends, with a net loss of 11.22% and an annual reduction rate of −0.4%. Shrub cover increased by 1.26%, and water bodies showed a small decrease in area, resulting in an overall net change of −0.03%. Built-up land and farmland areas continued to expand, and their annual growth rates were 1.52% and 1.06%, respectively. Bare land showed the highest loss, with a net change of 228.97 km2. In the Erhai watershed, all the landscape classes changed or transitioned into other classes, and a substantial decrease in bare land occurred. The biennial LTS maps allow us to fully understand the spatially and temporally complex change processes occurring in landscape classes; these changes would not be observable at coarse temporal intervals (e.g., 5–10 years). Our study highlights the importance of increasing the temporal resolution in landscape change studies to support sustainable land resource management strategies and integrate landscape planning for environmental conservation.
... The center of gravity is a physical concept, which represents the spatial geographical equilibrium point of a region. The center of gravity of the EQI changes at different times, and the migration of the center of gravity can reflect the spatial trajectory of EQI evolution [38]. The formula is as follows: ...
Spatio-temporal changes to the eco-environmental quality index (EQI) and determination of their spatial differentiation characteristics are important bases for land management and ecological environment protection. This study evaluates the changes in EQI and its spatial distribution characteristics with reference to the three dominant functions of land use, namely “production-ecology-living” (PEL), based on the interpretation of land use remote sensing data in 2000, 2010 and 2018. The spatial diversity of ecological environment quality and its driving factors were quantitatively analyzed by gravity center transfer, cold and hot spot analysis, and the GeoDetector model. The results showed that: (1) The transformation of land in Ningxia from 2000 to 2018 mainly manifested by the increase in industrial and mining production land (IMPL), urban living land (ULL) and rural living land (RLL), and the decrease of grassland ecological land (GEL), especially in the north of Ningxia. (2) The ecological environment quality decreased slightly during the research period, but there was an improvement trend in the north. High environment quality values were concentrated in the Liupan Mountain area in the south of Ningxia, while the low values were mainly in the desert areas of Shapotou County and Zhongning County in the west. (3) The interaction between land use intensity and topographic factors led to spatial change in EQI in the research area. Effects of land use intensity are the dominant factor, reflecting the degree of impact of human activities on natural ecosystems. Our results suggest that topographic factors and human disturbances should be fully taken into account in future land and spatial development decisions to minimize human-ecological conflicts.
... NTL has already been used to extract [36] and identify tourist attractions [37]. Ecological quality has been closely linked to tourism development; therefore, NTL data are used to study ecological changes [38]. As the accessibility of tourism POI data gradually improved, it was found that the POI of tourism factors had a significant influence on the difference in the spatial distribution of NTL [39]; the spatial coupling between the two is good. ...
Nighttime light (NTL) data have become increasingly practical and are now widely used in studies on urbanization, energy consumption, population estimation, socio-economic evaluation, etc. Based on NTL data and the basic tourism economy (TE) data from 31 provinces of China in 2019, this paper adopted a geographic concentration index, inconsistency index, spatial agglomeration coupling index, global and Local Moran’s index and geographical detector to explore the spatial relationship between NTL and TE. The results of the study were as follows. Firstly, there is a high spatial correlation between NTL and TE. Secondly, the concentration degree, as well as the concentrated distribution area of NTL and TE, are very similar, roughly showing a higher concentration in East and South-Central China. Thirdly, NTL and TE show a type of coordinated development in East and North China, and a TE surpassing NTL in Southwest and South-Central China. The spatial agglomeration coupling index is higher in North China, South-Central China and the coastal regions of East China, and relatively lower in Southwest and Northwest China. Furthermore, in the spatial agglomeration distribution of NTL and TE, there is an obvious high–high and low–low agglomeration. Finally, the geographical detector analysis showed that the driving factor of tourism economy level (TEL) also has a great influence on NTL. The spatial distribution of NTL and TE is integrated to reasonably allocate tourism resources for different areas and promote the sustainable development of NTL and TE among regions.
