Cropland abandonment is spreading from developed countries to developing countries such as China. Cropland abandonment in China commonly occurs in mountainous areas due to their specific natural and geographical conditions. However, due to the lack of dependable monitoring methods via medium-high-resolution remote sensing images, the scale of abandoned cropland in many mountainous areas of China is unclear, and the mechanisms driving cropland abandonment have not been clearly identified. To overcome these limitations, we took Zhong County of Chongqing in China as an example, and used Landsat 8 OLI_TIRS remote sensing image data to develop a method for mapping abandoned cropland in mountainous areas based on annual land use change monitoring. At the same time, the ridge regression method was adopted to analyze the factors influencing cropland abandonment. These analyses showed that the cropland abandonment rate in Zhong County of Chongqing was as high as 7.86%, while the overall accuracy of identifying abandoned cropland was as high as 90.82%. Among the social and economic factors that affect cropland abandonment, the rural population, economic development, and livestock husbandry development were the most important ones. At the land parcel scale, large-scale cropland abandonment occurred in areas at elevations above 650 m or with slopes of more than 15°.

Cultivated land pressure is often used to reflect the shortage of cultivated land resources. By using the methods of the Cultivated Land Pressure Index, coefficient of variation and cold-hot spot analysis, this paper analyzes the spatial-temporal differentiation pattern and dynamic change processes of cultivated land pressure in the counties of the Pearl River-Xijiang Economic Zone from 2008 to 2017, and measures the factors which influence cultivated land pressure by using Principal Component Analysis. The results show that the cultivated land pressure in the Pearl River-Xijiang Economic Zone has been in a “high pressure” state, and the Cultivated Land Pressure Index has been rising continuously from 2008 to 2017. The coefficient of variation of the Cultivated Land Pressure Index in the Pearl River-Xijiang Economic Zone and various prefecture-level cities is fluctuating and rising, which indicates that the overall spatial differences in the cultivated land pressure in this region are expanding and polarization is obvious. In addition, the area where the municipal district of the provincial capital city is located is the core area of urban development and also the area with the greatest cultivated land pressure. The spatial pattern of cultivated land pressure cold-hot spots in the Pearl River-Xijiang Economic Zone is obviously heterogeneous, in which the Pearl River Economic Zone is the main hot spot gathering area and the Xijiang Economic Zone is the main cold spot gathering area. Farmers' income, investment in fixed assets, GDP, population and other socio-economic factors are the main factors driving the changes in the cultivated land pressure in the Pearl River-Xijiang Economic Zone. Furthermore, farming production factors, such as the multiple cropping index and grain yield per unit area, will also have an important impact on the changes in the cultivated land pressure.

Total nitrogen (TN), total phosphorus (TP), total potassium (TK), and soil organic matter (OM) can significantly affect forest growth. However, these soil properties are spatially heterogeneously distributed, complicating the prescription of forest management strategies. Thus, it is imperative to obtain an in-depth understanding of the spatial distribution of soil properties. In this study, soils were sampled at 181 locations in the Tropical Forest Research Center in the southwestern Guangxi Zhuang Autonomous Region in southern China. We investigated the spatial variability of soil OM, TN, TP, and TK using geostatistical analysis. The nugget to sill ratio indicated a strong spatial dependence of soil TN and a moderate spatial dependence of soil OM, TP, and TK, suggesting that TN was primarily controlled by intrinsic factors (e.g., soil texture, parent material, vegetation type, and topography), whereas soil OM, TP, and TK were controlled by intrinsic and extrinsic factors (e.g., cultivation practices, fertilization, and planting systems). Based on the spatial variability determined by the geostatistical analysis, we performed ordinary kriging to create thematic maps of soil TN, TP, TK, and OM. Model validation indicated that the thematic maps were reliable to inform forest management.