Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

The spatiotemporal evolution of rural landscape patterns in Chinese metropolises under rapid urbanization

Understanding the evolution of rural landscapes in metropolises during rapid urbanization is crucial for formulating policies to protect the rural ecological environment. In this study, remote sensing and geographical information system data, as well as applied landscape index analysis, are used to examine the spatiotemporal evolution of rural landscape patterns in the Beijing-Tianjin region of China, which has experienced rapid urbanization. The relationships between land use/land cover changes and changes in rural landscape patterns are explored. The results revealed significant spatial differences in the rural landscapes in the Beijing-Tianjin region; farmland and forestland were the main types of landscapes, creating a "mountain-field-sea" natural landscape pattern. The conversion of rural landscapes in the Beijing-Tianjin region involved mainly the conversion of farmland to urban areas, with few exchanges between other landscape types. The urban areas in the Beijing-Tianjin region increased by 3% per decade; farmland decreased at the same rate. Additionally, the rural landscape patterns in the Beijing-Tianjin region were dominated by fragmentation, dispersion, and heterogeneity and moved from complex to regular. Water bodies displayed the most fragmented natural landscape; their number of patches increased by 36%, though their network characteristics were maintained. Forestland was the most concentrated natural landscape. In this study, theoretical support and a scientific reference for the optimization of rural landscape patterns and the improvement in rural living environments in rapidly urbanizing areas are provided.

Characteristics and formation mechanism of Land use conflicts in northern Anhui: A Case study of Funan county

The rapid development of global urbanization and industrialization not only promotes a significant improvement in the level of socio-economic development, but also exacerbates the complexity and vulnerability of regional land resource utilization, resulting in frequent land use conflicts and seriously constraining the sustainable development of regional socio-economic and ecological environment. Taking Funan County as an example, based on interpretation data of Landsat TM/ETM remote sensing image data from 1980 to 2020, this paper analyses the temporal and spatial evolution characteristics of land use conflict in Funan County from 1980 to 2020 using the ArcGIS spatial analysis method, land use conflict measurement model, geographically weighted regression and geographical detector and then deeply analyses the main factors affecting land use conflict in Funan County and its driving mechanisms. In descending order, land use types undergoing the most change include cultivated land, urban and rural construction land, grassland, forestland and water area. The results of land use change are mainly the occupation of cultivated land by construction land, water area and forestland. Overall land use conflict in Funan County is serious with approximately 80 % of land use in the county in conflict, the severe land use conflict is mostly concentrated in urban and township built-up areas, and there is an increase trend year by year. Land use conflict is the result of multiple factors. Policy, economic development, and the social population and natural environment are the key driving factors behind land use conflict, which have a significant impact on the direction, location, scale and rate of land use transfer.Accurately identifying regional land use changes and conflicts and exploring the driving mechanism behind land use conflicts are of great significance for achieving the sustainable development of regional social economies and ecological environments.

Long-term detection and spatiotemporal variation analysis of open-surface water bodies in the Yellow River Basin from 1986 to 2020

Accurately investigating long-term information about open-surface water bodies can contribute to water resource protection and management. However, due to the limits of big-data calculations for remote sensing, there has been no specific study on the long-term changes in the water bodies in the Yellow River Basin. Thus, in this study, we developed a new combined extraction rule to build an entire annual-scale open-surface water body dataset for 1986-2020 with excellent effectiveness in eliminating the interference of shadows in the Yellow River Basin using all of the available Landsat images. For the first time, the spatial distribution, change trends, conversion processes, and the heterogeneity of the surface water bodies in the Yellow River Basin were analyzed comprehensively to the best of our knowledge. The extraction results had an overall accuracy of 99.70 % and a kappa coefficient of 0.90, which were validated using 34,073 verification points selected on high-resolution Google Earth images and random Landsat images. The total area of water bodies initially decreased (1986-2000) and then increased (2001-2020); however, only the size of the permanent water bodies increased in most areas, while the size of most of the seasonal water bodies decreased. In regions with human-made water bodies, the non-water areas were substantially converted to seasonal and permanent water bodies; however, in areas with natural water bodies, many permanent and seasonal water bodies were gradually converted to non-water areas. Thus, most of the increases in the water bodies occurred in the form of artificial lakes and reservoirs, while most of the decreases in the water body area occurred in natural wetlands and lakes. The areas of both the permanent and seasonal water bodies were positively correlated with precipitation, but only the area of the seasonal water bodies was negatively correlated with temperature.