Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data

Xuanyan Dong,Yue Xu,Leping Huang,Zhongwen Hu,Kangyong Zhang,Yi Xu,Zhigang Liu,Guofeng Wu

doi:10.3390/rs12213597

Abstract

The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.

Highlights

Urban land use mapping is of great importance for urban structure optimization, resource allocation, and development planning [1]
Many studies have proved the advantage of high spatial resolution (HSR) remote sensing images in land use/cover classification and analysis [4,5,6], and spectral, textural, geometric and spatial features are frequently extracted from remote sensing HSR images to improve classification accuracies
Many studies have focused on functional classification and data fusion, seldom has research discussed how the basic units influence the mapping result

Summary

Introduction

Urban land use mapping is of great importance for urban structure optimization, resource allocation, and development planning [1]. The rapid economic and urban developments in China have generated diverse and sophisticated urban functional zones, which are reflected in urban land use patterns [2]. The effective detection and mapping of urban land use patterns are significant for formulating effective urban planning policies, and need to be resolved immediately. Traditional field investigations and interview questionnaires can produce land-use maps, but they are costly and time consuming [3]. The rapid development of cities makes the field investigation out of date of the actual land use types. Urban land use types were classified around the coastal zone by extracting landscape pattern indicators from HSR images [8]

Objectives

Methods

Discussion

Conclusion