Land Use Differentiation in Typical Main Grain Producing Areas along Yellow River Based on Time Series Processes

Abstract

By exploring the laws of land use changes in the main grain producing areas, the abnormal land use behaviors can be identified and the allocation of grain production can be optimized. However, current research primarily focuses on patterns rather than processes, and simulations rather than quantitative analyses, resulting in insufficient data. Multi-period land use sequence data was used to quantitatively reveal the characteristics of land use change processes in the main grain producing areas along the Yellow River in Henan Province since 1980. Time-series processes, time-space grid modeling, cumulative dynamic degree models, spatial point pattern analyses, and other methods were employed to deepen understanding of the internal laws and multi-dimensional expressions of land use change. The results showed that since 1980, there was significant temporal and spatial heterogeneity in land use along the Yellow River. The area of cultivated land was decreased slowly, urban construction land was expanded, rural residential areas were grown slowly, the land for water areas was firstly decreased and then increased, and the forest land was increased first and then decreased. The land development activities along the Yellow River and around the county built-up areas were intensified and become more frequent. The time sequence process was greatly affected by the distance from roads and rivers, but the impact types and scope were different. The land dynamic change types Ⅰ, Ⅱ, and Ⅲ, located close to the linear elements, were in the aggregation distribution state. With the expansion of the buffer zone, the aggregation degree of type Ⅰ was firstly increased and then decreased, while types Ⅱ and Ⅲ continued to be weaken. The temporal process was greatly affected by the spatial scale. The stronger the change was, the weaker the influence of the spatial scale was. All types were in a discrete state at 1km spatial scale, but the aggregation was increased gradually with the increase of spatial scale. The time series process research method based on cumulative dynamic degree could better reveal the information of long-time series land use change, thus deepening the understanding of land use changes.

Keywords: land use changes, remote sensing, cumulative dynamic degree, sequential process, grain production