Classification of Landscape Functions Using SOFM Neural Network: A Case Study from Beijing and Its Peripheral Area

Abstract

Landscape multifunction is a hotspot in the fields of landscape ecology.In order to explore a method which can reflect both integrity and independence of landscape multifunction,this research focuses on the clustering of landscape functions,taking Beijing and its peripheral area,China,as the study area.Five landscape function intensities,material production,carbon storage,soil retention,habitat conservation,and population support,are calculated using a variety of ecological models and indices in a grid map.Then,based on the results of landscape multi-function calculation,the study area are clustered through self-organizing feature map model.The quantitative results show that different regions turned out to have different and relative unique effects on the regional priority functions.Beijing and its peripheral area can be divided into four landscape function regions: agricultural region,whose dominant function is material production;urban region,whose dominant functions are population support and carbon storage;ecological region,whose dominant functions are soil retention and habitat conservation;and transition region,which does not have dominant functions,but reflects the interaction between human and nature.The validation of the results also shows that the presented SOFM neural network model is an effective and appropriate method for cluster analysis.Clustering results based on the SOFM model exhibit significant regional heterogeneity,with notable regional differences in the four clustering types within the research area.This spatial comprehensive dataset,combined with the independence from mechanistic ecological assumptions of the SOFM network approach provides a unique opportunity to validate and assess modeling efforts.The dominant landscape functions influencing regional development differ from one area to another.Furthermore,characteristics of the landscape indices and functions vary with region.Despite its limitations and uncertainty,the application of the presented method on clustering landscapes function using the SOFM model organization in connection with high performance computers is encouraged as a very interesting and important goal for future studies.The approaches to achieve sustainable regional development were illustrated and their importance highlighted for policy makers and stakeholders.