Landscape fragmentation and habitat loss are one of the important factors resulting in biodiversity decrease and species extinction. A large amount of Eucalyptus have been introduced into Yunnan Province in China since 2003, replaced the native forest, farmland, wasteland and other ecosystems, leading to natural habitat loss and landscape fragmentation. In order to protect habitat and the biodiversity, the secondary evergreen broad-leaved forest were selected as “protection source”, land cover types, altitude, slope and soil texture were selected as resistance factors, to construct a medium-security and high-security level landscape eco-security patterns for Eucalyptus introduction regions by using the minimum cumulative resistance model and GIS technology. The patterns were composed of the eco-security pattern compositions and ecological function zones. The eco-security pattern compositions were composed of corridor, radiating routes and strategic point etc. The ecological function zones were composed of key eco-protection zone, eco-buffer zone, eco-transition zone, Eucalyptus planting zone, Eucalyptus banned zones, farming zone, living zone etc.

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

National land spatial planning is dominated by urban-agricultural-ecological functions and has become a Chinese national strategic issue. However, the three functional spaces have serious conflicts in the karst areas, causing inconsistencies in regional development and triggering poverty and a more serious situation for the ecological environment. In this study, we used the gray multi-objective dynamic programming model and the conversion of land use and its effects at small region extent model to simulate the developmental structures of future land use in the karst areas of Southwest China under a socioeconomic development scenario, an arable land protection scenario and an ecological security scenario. Finally, based on the coordination of the urban-agricultural-ecological functions, we used a functional space classification method to optimize the spatial structures of the national land space for 2035 year and to identify different functional areas. The results showed that the three scenarios with different objectives had differences in the quantities and spatial structures of land use but that the area of forestland was the largest and the area of water was the smallest in each scenario. The optimization of the national land space was divided into seven functional areas—urban space, agricultural space, ecological space, urban-agricultural space, urban-ecological space, agricultural-ecological space and urban-agricultural-ecological space. The ecological space was the largest and the urban-ecological space was the smallest among seven functional areas. The different types of functional spaces had significant differentiation characteristics in the layouts. The urban-agricultural space, urban-ecological space, agricultural-ecological space and urban-agricultural-ecological space can effectively alleviate the impacts of human activities and agricultural production activities in karst areas, promote the improvement of rocky desertification and improve the quality of the regional ecological environment. The results of this research can provide support for decisions about the balanced development of the national land space and the improvement of environmental quality in the karst areas.

Understanding the driving factors of land-use spatio-temporal change is important for the guidance of rational land-use management. Based on land-use data, household surveys and social economic data in 2000, 2005, 2010, and 2015, this study adopted the Binary Logistic Regression Model (BLRM) to analyze the driving factors of land-use spatio-temporal change in a large artificial forest area in the Ximeng County, Yunnan province, in Southwest China. Seventeen factors were used to reflect the socio-economic and natural environment conditions in the study area. The results show a land use pattern composed of forestland, dry cropland, and rubber plantation in Ximeng County. Over the past fifteen years, the area of artificial forests increased rapidly due to the “Grain for Green” policy, which has led to increases in rubber plantations, tea gardens, eucalyptus forests, etc. In contrast, the area of natural forest and dry cropland decreased due to reclamations for farming and constructions. The BLRM approach helped to identify the main driving factors of land-use spatio-temporal change, which includes land-use policies (protection of basic farmlands and natural reserves), topography (elevation and slope), accessibility (distance to the human settlements), and potential productivity (fertility and irrigation). The study revealed the relationship between land-use spatio-temporal change and its driving factors in mountainous Southwest China, providing a decision-making basis for rational land-use management and optimal allocation of land resources.