Understanding land carbon stock dynamics is essential for sustainable land use and ecological conservation amid rapid urbanisation. This study investigates how land use changes contribute to carbon sequestration, offering insights to support China's carbon peaking (2030) and carbon neutrality (2060) goals. Using high-resolution land use data (30 m) from 2000 to 2020 for the Chengdu Plain region, derived via Google Earth Engine and Random Forest classification, the Patch-generating Land Use Simulation (PLUS) model was applied to predict land use changes under four scenarios: natural development scenario (NDS), ecological protection scenario (EPS), cultivated land preservation scenario (CLDS), and economic development scenario (EDS) for 2030 and 2060. Carbon stock dynamics were quantified using the InVEST model, while the Optimised Parameter Geographical Detector (OPQD) model identified key drivers and their interactions. Between 2000 and 2020, cropland decreased by 4.14% while construction land increased by 4.15%, reflecting rapid urban expansion. Scenario simulations predict further cropland loss (2.80%-7.44%) and substantial construction land growth (26.89%-39.95%) by 2060, with forest and grassland recovery only under conservation scenarios. Carbon stock declined by 5.1%-5.5%, with the EPS and CLDS scenarios mitigating losses, while the NDS and EDS scenarios caused significant declines. Anthropogenic factors, such as urbanisation and economic growth, had a greater impact (> 15%) on carbon stock than natural factors (< 4%), with their interactions exhibiting nonlinear enhancement effects.This study underscores the benefits of conservation strategies and provides actionable insights for climate change mitigation, carbon trading, and sustainable urban planning. Further exploration of additional factors and predictive refinements will enhance regional ecological conservation efforts.

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction.

Understanding the drivers of changes in vegetation net primary productivity (NPP) is critical for comprehending ecosystem dynamics and their ability to respond to environmental shifts. However, the complexity and nonlinear variations of NPP across the Tibetan Plateau, along with spatial and temporal inconsistencies, present significant analytical challenges. This study leverages the Google Earth Engine (GEE) platform and applies non-parametric trend analysis methods, such as the Sen slope estimator, Mann-Kendall test, coefficient of variation, and Hurst exponent, to investigate NPP trends from 2001 to 2021. The Optimal Parameters-based Geographical Detector (OPGD) model was employed to assess the combined effects of natural factors and human activities on NPP's spatial distribution and variability, identifying key drivers and their optimal ranges for promoting NPP growth. Results revealed nonlinear fluctuations in NPP during the study period, ranging from 184.06 to 208.53 gC m-2.a-1, with an average annual growth rate of 1.16 gC m-2.a-1. Significant spatial differences were observed, with higher NPP in the grasslands and forests of the southeast, while lower productivity was found in the alpine deserts of the northwest. Over 55% of the study area showed an increasing trend in NPP, with 28.14% experiencing significant growth (p < 0.05). The study further indicated that natural factors such as elevation, solar radiation, and mean annual temperature were major determinants of NPP fluctuations, while human activities (e.g., distance, population density, and land use) also played a crucial role in shaping NPP patterns. The significant interaction between natural factors and human activities demonstrates synergistic enhancement and non-linear effects, highlighting the complexity of multi-factor drivers influencing NPP changes. The key promoting factors and their optimal ranges identified provide a foundation for understanding the impact of natural and human activities on NPP variation, offering scientific support for ecosystem management and sustainable development on the Tibetan Plateau.

The terrain complexity of mountain cities often limits the rational layout and agglomeration distribution of newly added urban land. To this end, this paper proposes an innovative urban growth boundary optimization model —— Pareto Frontier theory and Ball-Tree algorithm optimized model (SP-BT). By treating newly added urban land as agents, the model dynamically optimizes the land patch locations based on the two goals of urban landscape compactness degree and suitability for urban construction. The SP-BT model is unique in that its optimal search and update process always unfolds along the Pareto front, achieving a balance between urban land agglomeration and suitability. In addition, compared with Suitability Optimum Evaluation Model (SOE), Artificial Neural Network in Cellular Automata (ANN-CA), and Ant Colony Optimization Model (ACO), the model has simplified parameter setting, fast evolution speed, flexible output results, and excellent defragmentation effect, especially suitable for mountain cities under complex terrain conditions. In the empirical study taking Bazhong city, China as an example: (1) SP-BT model significantly increased the mean size of urban landscape patch from 8.86 to 22.74–95.88, and the decrease of urban total suitability was controlled at 3.14%. (2) The SP-BT model excels in handling urban planning in mountainous cities, particularly in reducing landscape fragmentation, making it suitable for practical urban planning in mountainous environments. In general, the model proposed in this paper provides an efficient and flexible solution to the problem of new land use optimization under the complex terrain conditions of mountainous cities, which has important practical application value and can provide planners with more scientific and practical decision support tools.

Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) From 2000 to 2019, the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration. (2) Carbon emissions exhibited significant spatial clustering, with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration. (3) Factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions. (4) The spatial heterogeneity of each influencing factor was evident.

Accurately identifying ecological problem areas is crucial to prevent the expansion and exacerbation of ecological issues. However, existing methods for ecological security assessment and ecological problem identification struggle to precisely delineate ecological problem areas. This study comprehensively utilized a projection pursuit model optimized by simulated annealing, an obstacle degree model, and parameter-optimized geo-detectors to integrate and define ecological problem areas in the Chengdu Plain region. The research findings indicate: (1) The projection pursuit model optimized by simulated annealing increased the verification accuracy of ecological security assessment to 79.48%. (2) After approximately 2000 iterations, the model achieved the optimal balance between time cost and efficiency in precision enhancement. (3) The Chengdu Plain region, on the whole, is in a relatively secure ecological state. The ecological security level is closely related to topography, economic conditions, and urban-rural disparities. (4) The primary ecological issue in this area is environmental pollution, while ecological degradation is mainly associated with new urban development and urban expansion. (5) Relevant authorities should pay special attention to pollution emissions in urban built-up areas and take preventive measures against potential ecological degradation caused by new urban development.

The research on vegetation changes plays a crucial role in the assessment of ecosystem health, monitoring environmental changes, providing early warnings for natural disasters, and supporting decision-making for sustainable development. However, understanding the nonstationary characteristics of drivers affecting vegetation change remains challenging. This study used Enhanced Vegetation Index (EVI) data obtained through Google Earth Engine (GEE), Theil-Sen, and Mann-Kendall methods to analyze the spatial-temporal patterns and trends of vegetation changes in Sichuan, western China from 2000 to 2020. The Geographical and Temporal Weighted Regression (GTWR) method was applied to deal with spatial and temporal nonstationarity simultaneously. Results showed that vegetation cover in Sichuan was good overall, with medium and high vegetation covering more than 78% of the area. About 72.75% of the area showed an increasing trend in vegetation cover, and areas with extremely significant and significant EVI growth (p < 0.01 and 0.01 & LE; p < 0.05) accounted for 23.94% of the total area. The areas with significant increases in vegetation EVI were mainly distributed in northeast, east, southeast, central, and southwest in Sichuan, while the areas with significant decreases were mainly distributed in the central Sichuan plain urban agglomeration and western Sichuan plateau. GTWR addressed the nonstationary effect of the temporal dimension on the drivers of natural and human activities, with a fitted R2 of 0.846. The study identified climate, terrain, and human activities as the primary driving factors behind vegetation EVI fluctuations. Annual average temperature and precipitation, human activities, and slope had a positive impact on vegetation EVI changes, while solar radiation and aspect had a negative inhibitory effect. The effects of climate, terrain, and human activities on EVI changes exhibited significant spatial heterogeneity and clustering, resulting in either positive promotion or negative inhibition. This study provides an additional methodology to solve the nonstationary problem of vegetation change trends and their response mechanisms. The revealed changes in vegetation EVI and the spatiotemporal heterogeneity characteristics of their driving factors are important for fragile ecosystems to adapt to and mitigate the effects of natural changes and human activities. Revealing the variations in vegetation EVI and their underlying drivers can showcase diverse characteristics across regions and time periods, the presence of spatiotemporal heterogeneity holds great significance in

国家实施天然林资源保护工程与退耕还林等生态建设工程，为构筑长江上游生态屏障、促进长江流域经济可持续发展做出了突出贡献；评估退耕还林等生态工程实施后植被恢复成效及影响因素是促进区域植被恢复优化与生态环境改善的关键一步。基于MODIS MOD13Q1数据，应用Theil Sen斜率与Mann-Kendall趋势检验、“基线”评价方法、时空地理加权回归模型等量化不同时间尺度的植被时空变化、恢复成效和恢复机制。结果表明（1）植被覆盖状况良好，截止2019年底，四川省91%的区域植被出现增长，四川盆地东北部、四川省南部地区以及东南部乌蒙山、川西北高原地区植被覆盖较高；成都市内以及周围市区植被覆盖率较低。（2）植被恢复成效时空差异显著，占全省面积98.68%的区域植被恢复成效明显，高值区面积占比71.47%,集中于除成都平原外的四川省绝大部分区域。（3）气候变化对植被变化的影响以不显著为主，气温、降水对四川省植被恢复影响微弱，海拔和＞35°坡度面积比等地理环境因子则以弱抑制作用为主。（4）在相对平稳的气候背景下，人均财政支出、耕地面积与人均GDP所代表的社会经济因素是植被恢复成效改善的重要影响因素。研究揭示的植被恢复效果及关键气候、地理环境和社会经济因子，可为植被恢复政策的优化提供一定理论支撑。

Investigating the driving mechanisms behind fluctuations in vegetation net primary productivity (NPP) has the potential to enhance our comprehension of ecosystem dynamics and their response to environmental changes. However, identifying the nonlinear and spatiotemporal heterogeneity of factors contributing to NPP variation remains a challenge. This research employed the Theil-Sen trend, Hurst index, and nonparametric Mann-Kendall test methodologies through the utilization of Google Earth Engine (GEE) to detect monotonic trends in NPP, distinguishing between upward and downward trends. The influence of individual factors and their interactive effects on NPP changes were quantified using the optimized parameter-based geographical detector (OPGD) model. The findings revealed a general upward trend in NPP, exhibiting an average growth rate of around 85.06 gC m−2.a-1. Nevertheless, these rates of growth were not uniform, leading to noteworthy fluctuations spanning from 447.44 to 543.69 gC m−2.a-1. The eastern and central regions showcased relatively elevated NPP, whereas the western and southern regions demonstrated comparatively reduced levels. Roughly 51.66% of the areas displayed a rising trend in NPP, with 44.81% of the entire area indicating a noteworthy increase (p < 0.01), including both a substantial increase (p < 0.01) and a moderate increase (0.01 ≤ p < 0.05). The areas witnessing heightened NPP were predominantly situated in the northeastern, southeastern, northwestern sections, and the southwestern portion of Sichuan. The fluctuations in NPP trends displayed mild persistence or slight antipersistence traits, with regions where 0 < H < 0.5 constituting 80.26% of the overall area. Natural factors (such as elevation, mean annual temperature, NDVI, and topographic relief) along with human influences (changes in land use type) were identified as effectively accounting for the fluctuations in NPP. These factors demonstrated interactive effects on NPP, with the synergistic effect resulting in nonlinear enhancement and bilinear enhancement effects. The interaction between these two factors strengthened the influence of each individual factor. Identifying the optimal characteristics or ranges of these factors can facilitate ecological conservation and vegetation restoration. These factors exhibited interactive effects on NPP, with the synergistic effect resulting in nonlinear enhancement and bilinear enhancement effects. The interplay between these two factors heightened the influence of each separate element. Determining the optimal attributes or ranges of these factors can contribute to the facilitation of ecological preservation and the restoration of vegetation.

Vegetation cover is a crucial indicator of biodiversity and ecological processes, but there are still uncertainties about the factors driving changes in vegetation. In this study, we conducted a comprehensive analysis of vegetation cover changes in Sichuan Province from 2000 to 2020 using Formation Vegetation Cover (FVC) derived from MODIS13Q1 data. Our results revealed a consistent increase in vegetation FVC, rising from 0.506 to 0.624 over the 21-year period, with an annual growth rate of 0.0028. The turning point in this growth occurred in 2006. Of significance, the expansion of vegetation covered a substantial portion, accounting for 84.76%, while the decrease constituted 13%. Elevation proved to be an effective explanatory factor, with a coefficient of 0.417, indicating its role in explaining vegetation cover changes. It is important to note that FVC trends and averages exhibited distinct patterns concerning elevation, land use, population density, topography, and soil type, while their correlation with meteorological factors was relatively weak. Concurrently, the increase in construction and urban development had a negative impact on vegetation cover.

We estimated the population density and quantified its characteristics using remote sensing, census data, and Geographic Information System (GIS). The interactive influence of these factors on population density was quantified based on geographic detectors to identify the differentiation mechanisms in the Chengdu metropolitan area of China. We identified the key factors that contribute to population density growth. The models used to simulate population density had the highest R2 values (>0.899). Population density tended to increase with time, with a multicentre spatial agglomeration pattern; the centre of gravity of the spatial distribution tended to move from the southeast to the northwest. Industry proportions, Normalised Difference Vegetation Index (NDVI), land use, distance to urban centers or construction land, and GDP per capita can satisfactorily explain population density changes. The combined impact of these elements on population density variation exhibited mutual and non-linear strengthening, with the mutual effect of the two elements intensifying the impact of each individual element. Our study identified the key driving forces that contribute to the differentiation of population density, which can provide valuable support for the development of effective regional and targeted population planning guidelines.

The purpose of land ecological security (LES) assessment is to evaluate the influence of land use and human activities on the land ecosystem. Its ultimate objective is to offer decision-making assistance and direction for safeguarding and rejuvenating the well-being and effectiveness of the land ecosystem. However, it is important to note that there are still significant uncertainties associated with current land ecological safety assessments. This paper presents a comprehensive evaluation model that combines the strengths of subjective and objective weighting methods. The model is built upon an index system developed using the Pressure-State-Response (PSR) framework. To verify the level of LES, theThe results of classifying the total ecosystem service valueTotal Ecosystem Service Value are utilized to verify the level of LES. Furthermore, spatial distribution patterns of regional land ecological safety levels are analyzed using statistical techniques, such as Moran’s I, Mann–Whitney U-test, and Kruskal–Wallis H-test. The findings indicate that: (1) theThe evaluation model developed in this paper achieves a validation accuracy of 75.55%, indicating that it provides a more accurate reflection of the level of land ecological safety in the region; (2) The ecological security index is generally safe, with a mean value in the moderate safety range. It experienced a turning point in 2010, showing initial deterioration followed by improvement, mainly due to the transition between unsafe and relatively safe zones. (3) The level of economic development, topography, and urban-–rural structure are significant factors influencing the spatial concentration of LES in the region, ultimately shaping the spatial pattern of LES in the Chengdu Plain region.

Understanding urban sprawl and its drivers is crucial for sustainable urban development. Most studies on Chinese urbanization have focused on coastal areas, paying little attention to urban centers in western China. This study examines urban expansion based on the Google Earth Engine (GEE), remotely sensed image, urban expansion model, and analysis of buffer and quadrant location in the Geographic Information System (GIS). Additionally, driving forces of urban expansion are examined based on the principle component analysis (PCA). Results indicate that urban land area increased more than 5.60 times, reaching 124,723 ha, an increase of over 400 % during 1990–2020. The urban expansion rate and intensity significantly increased and exhibited spatio-temporal heterogeneity. We identified that urban spatial expansion patterns changed from patch filling to patch border expansion, and urban expansion direction was mainly in the southern, northeastern, southwestern, and northwestern regions, extending along the traffic corridor, ring road, and adjacent cities. We suggest that economic development, population, and urbanization have become the driving factors of urban expansion. The GEE provides a new geographic processing algorithm based on massive image datasets, facilitating remote sensing processing. The results revealed that Chengdu is following trends witnessed in coastal cities of China; however, the significance of various drivers of urban expansion in these cities differs from that of the eastern cities. This study will help formulate policies for better urban land management and sustainable land development.

Quantifying the influence of factors on changes in fractional vegetation cover (FVC) is critical for assessing regional environmental changes and consequent ecological protection. However, accurately identifying the factors responsible for vegetation changes remains a challenge. This study focuses on the Wumeng Mountain Area, China (WM), where the ecological environment is extremely fragile and the social economy underdeveloped. Using the enhanced vegetation index to calculate FVC, Sen's slope trend analysis, Mann-Kendall test with the trend-free prewhitening procedure, Pettitt change-point test, and Hurst exponent, we analyzed the spatiotemporal variations in vegetation from 2000 to 2019 and projected future variations. The geographical detector model was used to analyze the spatial differentiation driving mechanism of changes in vegetation cover in the WM. We observed that the spatiotemporal variation of vegetation in the WM was significant between 2000 and 2019. The areas of the WM with extremely significant growth and significant growth accounted for 32.57% (p < 0.01) and 15.28% (0.01 < p < 0.05), respectively. The mutation years of the significantly changed vegetation were concentrated between 2007 and 2011. However, 36.09% of vegetation growth exhibited strong unsustainable characteristics and based on the past 20 years, a potential decreasing trend that has great uncertainty in the future. The geographical detector model indicated that temperature and soil type were the primary driving forces for spatial differentiation of vegetation changes in the WM, with q values of 0.131 and 0.101, respectively. Interactions between climate, topography, and human activities promote vegetation growth in a nonlinear fashion

Several studies have examined the role of changes in the natural and anthropogenic factors of soil heavy metals; however, the causes of spatial heterogeneity of soil heavy metals remains to be understood. To study heavy metals in soil, we collected a total of 134 soil samples from diverse land-use types; whereby we measured the concentrations of six metals (Cr, Ni, Cu, Zn, As, and Pb). Herein, we used Kriging interpolation methods in a geographic information system (GIS) and geo-accumulation index to map the distribution and evaluate the pollution of heavy metals in soil. We applied geographic detector models, a new spatial statistical method, to determine how the dominant factors and interactions led to spatial heterogeneity of heavy metal variations in soil, in the northern Chengdu Plain in western China. The results indicated that the overall pollution of heavy metals in soil was relatively light, with Cr, Ni, Cu, Zn, As, and Pb in soil reaching 5.37%, 22.15%, 22.82%, 17.45%, 4.70%, and 27.52%, respectively. We observed that heavy metals in soil demonstrated significant spatial heterogeneity, with high Cr and Ni contents in the central region and low in the south and north; Cu was high and widely distributed in the western parts, exceeding the background value; Zn was high in the north and west, and low in the east, covering a wide area with significantly high contents; As was high in the west and central parts, and low in the north, south, and east; Pb was high in the north and central parts, and low in the south. The results revealed that the dominant factors affecting the spatial heterogeneity of Cr in soil were moisture content, available phosphate, and distance to factory; the explanatory power was 31.52%, 25.77%, and 10.71%, respectively. Moisture content, organic matter, and available phosphate most significantly influenced Cu and Pb in soil, with explanatory powers of 13.11%, 8.20%, 3.21%, 10.55%, 10.49%, and 11.87%, respectively. The available phosphate, moisture content, and soil type can explain the spatial heterogeneity of Zn in soil, with explanatory powers of 31.52%, 2 5.77%, and 10.71%, respectively; whereas the dominant factors affecting the spatial heterogeneity of As in soil were soil type, slope, and geomorphic type, with explanatory powers of 16.11%, 6.68%, and 6.40%, respectively. Moisture content, soil type, and GDP had the greatest influence on Ni in soil, with explanatory powers of 1.4%, 0.77%, and 0.62%, respectively. This study suggests that the interactions among impact factors mutually and non-linearly enhance the impact of a single factor on the spatial heterogeneity of soil heavy metals. Our research highlights that the geodetector method is an effective approach to disentangle the complicated driving factors and reveal the optimum characteristics and ranges of each factor, further contributing to our understanding of the spatial heterogeneity of soil heavy metals and the driving mechanisms. Our results are useful for providing theoretical contributions and practical references to accelerate the formulation of land pollution management policies.

[目的]川西北高原藏区是贫困人口聚集区和脱贫攻坚的主战场,开展贫困化地域分异机制研究巩固脱贫成果与乡村振兴具有重要的理论意义,然而对贫困化地域分异机制仍然难以准确理解.[方法]文章应用地理探测器、空间自相关和GIS空间分析等模型,探究川西北高原藏区贫困化分异的主导因素,揭示影响因素对农村贫困化地域分异和贫困发生率变化的交互影响,确定影响贫困变化的各主要因子适宜特征.[结果](1)农村贫困化的空间分布存在三大热点区域和三小热点区域;高—高聚集(HH)、低—低聚集(LL)、低—高聚集(LH)异常值区域.(2)GDP密度、年降水量、湿润指数、人均耕地、人口密度、总辐射等6个因子是农村贫困化地域分异的主导因子.(3)农村贫困化分异机制差异显著,存在自然制约型、交通制约型、经济制约型和社会制约型等4种类型.(4)各因子之间呈相互增强和非线性增强关系,两种因子的交互作用增强单因子对贫困化的影响,研究揭示的影响贫困发生率各主要因子最适宜特征,有助于更好地理解不同维度因素对贫困发生率影响及其驱动机制,为巩固扶贫成果提供参考依据.[结论]川西北高原藏区农村贫困化地域分布是多种因素共同作用的结果,进入后扶贫时代,应当对热点地区、主导因素和不同类型贫困村进行精准施策,加快基础设施建设,发展特色农牧业和旅游业,构建多种产业模式.

岷江上游地区承载着不可替代的资源支撑、生态服务与环境调节功能,是国家生态红线的水土流失敏感主控区域,也是国家生存与发展重要的自然基础.目前,该区域生态和经济社会过程在空间上的叠加,不仅影响到资源、环境与生态等功能的发挥,而且对岷江流域乃至长江上游的生态安全和区域的可持续发展均构成严重威胁.认识和理解岷江上游植被NDVI时空变化及其地形响应机制,分析植被变化最佳地形位,可以为该地区生态环境建设提供方向指引与科学参考.综合运用遥感与GIS技术对岷江上游植被NDVI时空变化进行分析,并通过空间叠加分析详细探讨了植被NDVI时空变化对海拔、坡度、坡向与地形起伏度等地形因子的响应,结合不同植被变化类型的分布指数进一步明确了不同地形因子背景下植被变化地域分异规律,得出了不同植被变化类型最佳地形位.结果表明:(1)2000～2020年间,岷江上游地区植被覆盖良好且格局稳定,主要沿河谷地带扩散状分布约有26.62％的区域植被显著增长,仅有1.21％的区域植被显著退化;(2)植被变化趋势类型在高程、坡度、坡向与地形起伏度等不同地形位下具有显著不同的分布格局,整体而言不同地形位下面积占比波动较小,分布指数各异;(3)植被显著退化区域的最佳地形位分别为受人类活动影响的"优势"地带与受自然条件制约的"劣势"地带,植被显著增长区域的最佳地形位主要分布在海拔相对较低,而坡度、起伏度较大且为阴坡的人类聚落辐射区域.

The land use degree reflects the land use due to natural factors and human activities, and thus, its spatial differentiation analysis and the study of factors influencing it is of great importance. Although the mechanisms by which changes in factors affect land use have been extensively studied, the impact of factor variations on spatial differentiation of land use degree remains poorly understood. Therefore, in this study, we applied geographical detector, a new tool of spatial statistics, and used spatial autocorrelation and GIS spatial analyses to study the interactive effects of factors on land use degree and their changes in Sichuan, western China and identified the most appropriate characteristics and scope of factors. The land use degree showed an increasing trend. The geographical differentiation of land use was significant, with a high land use degree in the Chengdu Plain and its surrounding areas in the east, a low land use degree in the plateau area of western Sichuan and a significant aggregated distribution. Topographic relief, elevation, annual average temperature, geomorphic type, ≥10°C accumulated temperature, and other factors provided a good explanation for the variability in the land use degree. There were interactive effects of factors that influenced the land use degree. The synergistic effects of factors exhibited mutual and non-linear enhancement relationships, and the interaction of the factors enhanced the influence of individual factors. The most appropriate characteristics and scope of the main factors revealed by our study will contribute to a better understanding of the influences of factors on the changes in land use and their driving mechanisms.

Carbon emissions from urban areas are pivotal in meeting emission reduction targets. While carbon emissions in the coastal areas of urban China have been clarified, those for central cities in the west remain limited, despite rapid economic development likely raising them. Here, we developed a model to estimate carbon emissions from land under construction in urban areas of Chengdu, China, using remote sensing data and carbon emission statistics. The spatial distribution of carbon emissions was quantified from spatial analyses using a geographic information system (GIS). The feasibility of the proposed model for estimating urban carbon emissions was supported by the high R2 for estimated and statistical carbon emissions (>0.87) and the F-test (F-statistic >112.59). During 2000-2015, carbon emissions from construction land increased substantially (by 19.09 Mg C), with emissions increasing from core cities and beyond. Carbon emissions clearly differed among construction areas in Chengdu's districts, adjacent counties, and outer counties. The results of this study could help guide the development of policies and plans for managing carbon emissions, emission density, and urban sustainability.

The influence of factors on vegetation changes in different regions is still largely unknown. We applied the geographic detector, a new spatial statistical method, to study the interactive effects of factors on the spatial patterns of normalised vegetation index (NDVI) changes and determine the optimal characteristics of key impact factors beneficial to vegetation growth. Our results show that from 2000 to 2015, the vegetation cover for the upper reaches of the Minjiang River, western China was in good condition. Furthermore, more than 80% of the areas had NDVI values ranging from 0.6 to 0.8 and NDVI > 0.8, and the spatial-temporal changes of vegetation cover were significant. The vegetation cover changes showed a significant transformation in the regions with NDVI > 0.6. Our study uniquely illustrated that elevation, annual average temperature and soil type can explain vegetation changes quite well. We propose that interactive effects exist among impact factors on vegetation NDVI, and the synergistic effects of the impact factors show mutual and nonlinear enhancements. The interactions among impact factors significantly enhance the impact of a single factor on vegetation changes. The most suitable characteristics of the main impact factors that promote vegetation growth were revealed by this study and will help improve our understanding of factors that impact NDVI and its driving mechanisms. Our findings suggest that the established favourable value range or the most suitable characteristics of impact factors will help management plans to intervene and promote vegetation change for vegetation restoration and alleviate environmental degradation.