A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

Ping Zhang,Ming Liu,Qiangqiang Sun,Jing Li,Danfeng Sun

doi:10.3390/rs9111126

Abstract

Recently, studies have focused more attention on surface feature extraction using thermal infrared remote sensing (TIRS) as supplementary materials. Innovatively, in this paper, using three-date (winter, early spring, and end of spring) TIRS Band 10 images of Landsat-8, we proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapidly accurate extraction of the built-up area. Four study areas, including one major experimental study area (Tangshan) and three verification areas (Minqin, Laizhou, and Yugan) in different climate zones, respectively, were used to empirically establish the overall strategy system, then we specified constrained conditions of this strategy. Moreover, we compared the NDSTI to the current built-up indices, respectively, for extracting the built-up area. The results showed that (1) the new index (NDSTI) exploited the seasonal thermal characteristic variation between the built-up area and other covers in the time series analysis, helping achieve more accurate built-up area extraction than other spectral indices; (2) this strategy could effectively realize rapid built-up area extraction with generally satisfied overall accuracy (over 80%), and was especially excellent in Tangshan and Laizhou; however, (3) it may be constrained by climate patterns and other surface characteristics, which need to be improved from the view of the results of Minqin and Yugan. In summary, the method developed in this study has the potential and advantage to extract the built-up area rapidly from the multi-seasonal thermal infrared remote sensing data. It could be an operative tool for long-term monitoring of built-up areas efficiently and for more applications of thermal infrared images in the future.

Highlights

A built-up area is generally defined as any anthropogenic materials primarily associated with human activities and habitation through the construction of buildings and structures [1], with a particular focus on urban, rural residential, and industrial land
Knowledge about the extent and pattern of built-up areas can provide the necessary information for expansion monitoring, environmental change and risk assessment, and disaster management and government decision-making [5,6] in built-up areas
Various approaches have been developed for extracting built-up areas via different types of remote sensing imagery in recent years such as polarimetric target decomposition (PTD), the correlation coefficient method, or the support vector machines (SVM) method derived from radar data [11,12,13], a textural signal characteristics measurement derived from panchromatic satellite data [14]

Summary

Introduction

A built-up area is generally defined as any anthropogenic materials primarily associated with human activities and habitation through the construction of buildings and structures [1], with a particular focus on urban, rural residential, and industrial land. Given the advantage of large and repetitive coverage from satellite imagery, remote sensing provides an excellent cost-effective and time-saving approach for mapping built-up lands [7] and for understanding built-up area sprawl over time [7,8,9,10]. Built-up areas have tended to be extracted from high spatial resolution images. Li et al [15] proposed an approach for built-up area detection from high-resolution remote sensing images in an unsupervised way. Chen et al [16] employed a data field-based method for the automated detection of built-up areas from high-resolution satellite images. Due to the high spatial and spectral heterogeneity of the built-up areas, the automatic methods usually have complex steps or limiting accuracy, leading to time consumption and instability

Methods

Results

Discussion

Conclusion