Analysis and Planning of Ecological Networks Based on Kernel Density Estimations for the Beijing-Tianjin-Hebei Region in Northern China

Sustainability

Chao Zhang¹  Jianyu Yang¹  Yahui Lv¹  Pengshan Li¹  Dehai Zhu¹  Wenju Yun² 

[1] College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;Key Laboratory for Agricultural Land Quality Monitoring and Control, Ministry of Land and Resources, Beijing 100035, China;

关键词: ecological networks;    kernel density estimation;    eco-environment quality;    landscape planning;    Beijing-Tianjin-Hebei region;   

DOI  :  10.3390/su8111094

来源: DOAJ

【 摘 要 】

With the continued social and economic development of northern China, landscape fragmentation has placed increasing pressure on the ecological system of the Beijing-Tianjin-Hebei (BTH) region. To maintain the integrity of ecological processes under the influence of human activities, we must maintain effective connections between habitats and limit the impact of ecological isolation. In this paper, landscape elements were identified based on a kernel density estimation, including forests, grasslands, orchards and wetlands. The spatial configuration of ecological networks was analysed by the integrated density index, and a natural breaks classification was performed for the landscape type data and the results of the landscape spatial distribution analysis. The results showed that forest and grassland are the primary constituents of the core areas and act as buffer zones for the region’s ecological network. Rivers, as linear patches, and orchards, as stepping stones, form the main body of the ecological corridors, and isolated elements are distributed mainly in the plain area. Orchards have transition effects. Wetlands act as connections between different landscapes in the region. Based on these results, we make suggestions for the protection and planning of ecological networks. This study can also provide guidance for the coordinated development of the BTH region.

【 授权许可】

Unknown