Land use changes induced by human activities change landscape patterns and ecological processes, threatening regional and global ecosystems. Terrain gradient and anthropogenic multi-policy regulation can have a pronounced effect on landscape components. Forecasting the changing trend of landscape ecological risk (LER) is important for national ecological security and regional sustainability. The present study assessed changes in LER in the Sichuan-Yunnan Ecological Barrier over a 20-year period using land use data from 2000, 2010, and 2020. The enhanced Markov-PLUS (patch-generating land use simulation) model was used to predict and analyze the spatial distribution pattern of LER under the following three scenarios. These were business-as-usual (BAU), urban development and construction (UDC), and ecological development priority (EDP) in 2030. The influence of terrain conditions on LER was also explored. The results showed that over the past 20 years, the LER index increased and then decreased and was dominated by medium and low risk, accounting for more than 70% of the total risk-rated area. The highest and higher risk areas for the three future scenarios have increased in spatial extent. The UDC scenario showed the largest increase of 3341.13 km² and 2684.85 km², respectively. The highest-risk level has a strong selectivity for low gradients, with high-level risks more likely to occur at low gradients. The response of ecological risk to gradient changes shows a positive correlation distribution for high-gradient areas and a negative correlation distribution for low-gradient areas. The influence of future topographic gradient changes on LER remains significant. The value of multiscale geographically weighted regression (MGWR) for identifying the spatial heterogeneity of terrain gradient and LER is highlighted. It can play an important role in the formulation of scientific solutions for LER prevention and of an ecological conservation policy for mountainous areas with complex terrain.

At present, there is obvious regional segmentation in the protection of intangible cultural heritage (ICH) in the Yangtze River Economic Belt. ArcGIS spatial analysis method and minimum cumulative resistance (MCR) models are used to investigate the distribution characteristics of ICH and the suitability of corridor construction in this region. On this basis, the strategies for heritage corridor construction are proposed. (1) The results show that the density and distribution of ICH in the Yangtze River Economic Belt have distinct regional characteristics: most of the high-density areas are in the eastern region, yet the low-density areas are mainly in the western region. (2) The suitability of heritage corridor construction decreases from west to east, and the unsuitable areas are mainly distributed in the western region. (3) According to the spatial characteristics of heritage distribution and the suitability of regional corridor construction, the ArcGIS spatial analysis method is used to determine 37 heritage corridors in the Yangtze River Economic Belt. (4) On this basis, a corridor construction method that uses linear landscape as the corridor theme and connects individual heritage points is proposed. The development strategy of heritage corridors should be in line with local conditions in different regions so as to realize regional collaborative development. Furthermore, the influence domain of heritage corridors should be further increased.

Important for promoting the integrated protection and systematic management of mountains, rivers, forests, farmlands, lakes, grasslands, and sandy areas, ecological networks form the backbone of the regional ecological security pattern. An improved morphological spatial pattern analysis coupled with a minimum cumulative resistance model (MSPA–MCR) based on multi-source data was used to study, on a provincial scale, the ecological security pattern of Ningxia, an ecologically fragile region in the upper reaches of the Yellow River in China. The results show the following: (1) A reasonable classification of ecological sources and ecological corridors is key to constructing hierarchical ecological networks. Classifying ecological sources by replacing patch areas with energy factors and identifying the importance of ecological corridors by modifying the gravity model with the energy factors proposed in this paper could improve the rationality of the hierarchical structure division of ecological networks. (2) Grassland as the substrate vegetation type is an important ecological source type in arid and semi-arid ecologically fragile areas, and forests and lake wetlands are the main ecological source types in mountainous areas and oasis areas, respectively. The study area was located in the arid–semi-arid transitional area, with a variety of ecological types, such as mountain, oasis, and desert. Therefore, the complex ecological source types of forest–grassland–wetland appear in some areas. (3) There are 45 ecological patch groups that can be classified as ecological sources in Ningxia, including 10 primary source groups. The number of primary source groups is small, and their spatial distribution is unbalanced. There are two categories of ecological corridors, the river corridor and the mountain corridor, and the network connectivity is poor. (4) The ecological network structure of Ningxia is presented as an ecological security structure consisting of one belt, three screens, three corridors, and five clusters, forming a hierarchical nested ecological network security structure system.

Wildfire is one of the main hazards affecting large areas and causes great damage all over the world, and the rapid development of the wildland-urban interface (WUI) increases the threat of wildfires that have ecological, social, and economic consequences. As one of the most widely used methods for tracking fire, remote sensing can provide valuable information about fires, but it is not always available, and needs to be supplemented by data from other sources. Social media is an emerging but underutilized data source for emergency management, contains a wealth of disaster information, and reflects the public’s real-time witness and feedback to fires. In this paper, we propose a fusion framework of multi-source data analysis, including social media data and remote sensing data, cellphone signaling data, terrain data, and meteorological data to track WUI fires. Using semantic web technology, the framework has been implemented as a Knowledge Base Service and runs on top of WUIFire ontology. WUIFire ontology represents WUI fire–related knowledge and consists of three modules: system, monitoring, and spread, and tracks wildfires happening in WUIs. It provides a basis for tracking and analyzing a WUI fire by fusing multi-source data. To showcase the utility of our approach in a real-world scenario, we take the fire in the Yaji Mountain Scenic Area, Beijing, China, in 2019 as a case study. With object information identified from remote sensing, fire situation information extracted from Weibo, and fire perimeters constructed through fire spread simulation, a knowledge graph is constructed and an analysis using a semantic query is carried out to realize situational awareness and determine countermeasures. The experimental results demonstrate the benefits of using a semantically improved multi-source data fusion framework for tracking WUI fire.

Since the beginning of the 21st century, the economic development of Tibet has been accelerating. The Yarlung Zangbo River and its two tributaries, as the region with the largest population concentration and the fastest economic development in Tibet, has been under the dual influence of global climate warming and the intensification of human social and economic activities, where a high number of land types, such as woodland, grassland, and water areas, have been transformed into other land types, and the residential area has expanded in a disorderly manner. The ability to maintain sustainable regional development has been severely damaged. To meet the requirements of different stages of social and economic development and regional social development goals, in this study, we use the Yarlung Zangbo River and its two tributaries of Tibet as an example. Based on the Markov–PLUS model and considering the natural, social, and cultural conditions of the basin, combined with the multi-landscape simulation of land use, we predict the land use situation of the Yarlung Zangbo River and its two tributaries of Tibet in 2038. We observed the following: (i) the Markov–PLUS model has a high simulation accuracy for different land types in the study area, and can sufficiently simulate the changes in different land types in the Yarlung Zangbo River and its two tributaries of Tibet; and (ii) the simulation settings of the three landscapes basically meet the different development modes and paths of the basin in the future. There were obvious differences in the structure of land use in the basin, among which there were obvious differences, especially agricultural land and water areas. Use of the Markov–PLUS model can provide data support and references for the implementation in terms of ecological scrutiny, landscape planning, and early warnings for food production consumption security and unreasonable land use, in order to achieve the sustainable development of the basin.

The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in terms of temporal discontinuity and restricted threshold selection. To effectively address these issues, in this work, an improved remote sensing method was proposed to monitor global building electrification. By integrating global land cover data, built-up area data, and annual NPP/VIIRS nighttime light images, a regional threshold method was used to identify electrified and unelectrified areas yearly, generating a global building electrification dataset for 2012–2023. Based on our analysis, we found the following: (1) The five assessment metrics of the product—Accuracy (0.9856), Precision (0.9734), Recall (0.9984), F1-score (0.9858), and Matthews Correlation Coefficient (0.9715)—all exceed 0.9, demonstrating that our method achieves high reliability in identifying electrified buildings. (2) In 2023, 91.88% of global building areas were electrified, with the unelectrified buildings being predominantly located in rural regions of developing countries. (3) Between 2012 and 2023, the global electrified building area increased by 2.4199 million km², with rural areas experiencing a faster growth rate than town areas. The annual reduction rate of unelectrified building area was 0.62%. However, to achieve universal electricity access by 2030, this rate must nearly double. (4) External factors such as the COVID-19 pandemic, extreme weather events, and armed conflicts significantly affect global electrification progress, with developing countries being particularly vulnerable. In our work, remote sensing methodologies and datasets for monitoring electrification trends were refined, and a detailed spatial representation of unelectrified areas worldwide was provided.