Nighttime light (NTL) images have been broadly applied to extract urban built-up areas in recent years. However, the typical NTL images provided by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) have the drawbacks of low resolution and blooming effect, which bring difficulty for the application of them in urban built-up area extraction. Therefore, this paper proposes the POI (point of interest) and LST (land surface temperature) adjusted NTL urban index (PLANUI) to extract the urban built-up areas with high accuracy. PLANUI is the first urban index to integrate POI and NTL for urban built-up area extraction. In this paper, NPP/VIIRS and Luojia 1-01 images were introduced as the original NTL data and the vegetation adjusted NTL urban index (VANUI) was selected as the comparison item. The threshold method was utilized to extract urban built-up areas from these data. The results show that: (1) Based on the comparison with the reference data, the PLANUI can make up the shortcoming of low resolution and the blooming effect of NTL effectively. (2) Compared with the VANUI, the PLANUI can significantly improve the accuracy of the urban built-up areas extracted and characterize urban features. (3) According to the results based on NPP/VIIRS and Luojia 1-01 images, the PLANUI has extensive applicability, both for regions with different degrees of economic development and NTL data with different resolutions. PLANUI can enhance the features of urban built-up areas with social sensing data and natural remote sensing data, which helps to weaken the NTL blooming effect and improve the extraction accuracy. PLANUI can provide an effective approach for urban built-up area extraction, which plays a certain guiding role for the study of urban structure, urban expansion, and urban planning and governance.

Since the implementation of the great western development strategy in 2000, the ecological environment in the western region of China has been significantly improved. In order to explore the temporal and spatial characteristics of vegetation coverage in the western region, this paper adopted the method of Maximum Value Composite (MVC) to obtain the mean Normalized Difference Vegetation Index (NDVI) of vegetation on the basis of the Moderate-resolution Imaging Spector audiometer (MODIS) data of 2000/2005/2010/2015/2018. Thereafter, the spatio-temporal differentiation characteristics of vegetation in western China were analyzed. The results show that: (1) According to the time characteristics of vegetation coverage in the western region, the average annual NDVI value of vegetation coverage in the growing season in the western region fluctuated between 0.12 and 0.15, among which that of 2000 to 2010 fluctuated more greatly but did not show obvious change trend. (2) Based on Sen trend and Mann-Kendall test analysis, the area of vegetation coverage improvement in the western region from 2000 to 2018 was larger than that of significant vegetation degradation. (3) From the perspective of global autocorrelation coefficient, Moran’s I values were all positive from 2000 to 2018, which indicates that the vegetation coverage in the west showed strong positive autocorrelation in each period. According to the average value and coefficient of variation of vegetation coverage, the vegetation coverage was lower in 2000, its internal variation was smaller, and the vegetation coverage increased with time. According to the local spatial autocorrelation analysis, the vegetation coverage levels in different regions varied greatly. (4) The standard deviation ellipse method was used to study the spatial distribution and directional transformation of vegetation. It makes the result more intuitive, and the three levels of gravity center shift, direction shift, and angle shift were considered: the vegetation growth condition in the spatial aggregation area improved in 2015; the standard deviation ellipses in 2000 and 2018 overlapped and shifted eastward, which indicates that the vegetation coverage conditions in the two years were similar and got ameliorated.

A high coupling coordination degree of urban land use efficiency promotes sustainable regional economic development. In this study, land use efficiency coupling coordination degrees were calculated for 36 mining cities of western China, with a focus on economic, social, and ecological benefits for land use efficiency. Four years (2000, 2005, 2010, and 2015) of data were selected. A land use efficiency index system was generated and the improved entropy method was used to calculate the index weights of land use efficiency for each year. The spatial distributions of the coupling coordination degree were assessed by the ArcGIS spatial analysis tool. Spatial correlation analysis was conducted for the coupling coordination degree. The following conclusions could be drawn: (1) According to the composite index results, urban land use efficiency could be divided into three stages and showed several different time patterns in mining cities of western China; (2) analysis of the spatial and temporal distributions of the land use efficiency coupling coordination degree identified a low level of coupling coordination and reluctant coupling coordination. An obvious core-periphery and gradual trickle-down trend was observed; (3) the land use efficiency of western mining cities presents negative and positive spatial autocorrelation characteristics. Shizuishan city, Ordos city, Jinchang city, and Wuhai city have significant aggregation types. Therefore, the western mining cities were subjected to different complex time and space characteristics.

The foundation of study on regional environmental carrying capacity is the detection of vegetation changes. A case of Northeast China, we, with the support of normalized difference vegetation index (NDVI) of MOD13A3 (MOD13A3-NDVI), use a three-dimensional vegetation cover model (3DFVC) to acquire vegetation cover from 2000 to 2021. Vegetation trends are then monitored by the spatio-temporal analysis models including the empirical orthogonal function (EOF), the Sen’s slope (Sen), the Mann-Kendall test (MK) and the Hurst index (Hurst). Additionally, we, through the multi-scale geographically weighted regression model (MGWR), explore the spatial heterogeneity of vegetation response to its influencing factors. On the basis of this, it is by introducing the structural equation model (SEM) that we figure out the mechanisms of vegetation response to climate and human activity. The main results are as follows: (1) Compared with the dimidiate pixel model (FVC), 3DFVC, to some extent, weaken the influence of terrain on vegetation cover extraction with a good applicability. (2) From 2000 to 2021, the average annual vegetation cover has a fluctuating upward trend (0.03·22a${}^{-1}$, p < 0.05), and spatially vegetation cover is lower in the west and higher in the east with a strong climatic zoning feature. In general, vegetation cover is relatively stable, only 7.08% of the vegetation area with a trend of significant change. (3) In terms of EOF (EOF${}_1$+EOF${}_2$), EOF${}_1$ has a strong spatial heterogeneity but EOF${}_2$ has a strong temporal heterogeneity. As for the Hurst index, its mean value, with an anti-persistence feature, is 0.451, illustrating that vegetation is at some risk of degradation in future. (4) MGWR is slightly better than GWR. Vegetation growth is more influenced by the climate (precipitation and temperature) or human activity and less by the terrain or soil. Besides, precipitation plays a leading role on vegetation growth, while temperature plays a moderating role on vegetation growth. What is more, precipitation, on different temperature conditions, shows a different effect on vegetation growth.

Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing soil quality and achieving precision agriculture. The cropping system is an important factor that affects the soil carbon cycle in farmlands, and different agricultural managements under different cropping systems lead to spatial heterogeneity of SOC. However, current research often ignores differences in the main controlling factors of SOC under different cropping systems, especially when the cropping pattern is complex, which is not conducive to farmland zoning management. This study aims to (i) obtain the spatial distribution map of six cropping systems by using multi-phase HJ-CCD satellite images; (ii) explore the stratified heterogeneous relationship between SOC and environmental variables under different cropping systems by using the Cubist model; and (iii) predict the spatial map of SOC. The Xiantao, Tianmen, and Qianjiang cities, which are the core agricultural areas of the Jianghan Plain, were selected as the study area. Results showed that the SOC content in rice–wheat rotation was the highest among the six cropping systems. The Cubist model outperformed random forest, ordinary kriging, and multiple linear regression in SOC mapping. The results of the Cubist model showed that cropping system, climate, soil attributes, and vegetation index were important influencing factors of SOC in farmlands. The main controlling factors of SOC under different cropping systems were different. Specifically, summer crop types had a greater influence on spatial variations in SOC than winter crops. Paddy–upland rotation was more affected by river distance and NDVI, while upland–upland rotation was more affected by irrigation-related factors. This work highlights the differentiated main controlling factors of SOC under different cropping systems and provides data support for farmland zoning management. The Cubist model can improve the prediction accuracy of SOC under complex cropping systems.