Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification

Xiya Zhang,Cai Cai,Peijun Li

doi:10.3390/rs70607671

Abstract

Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) data, MODIS normalized difference vegetation index (NDVI) data, and land surface temperature (LST) data. We first analyzed how well MODIS NDVI and LST data quantify the properties of urban areas. Considering that urban area is the only class of interest, we applied the one-class support vector machine (OCSVM) to classify different combinations of the three datasets. We evaluated the effectiveness of the proposed method and compared with the locally optimized threshold method in regional urban extent mapping in China. The experimental results demonstrate that DMSP/OLS NTL data, MODIS NDVI and LST data provide different but complementary information sources to quantify the urban extent at a regional scale. The results also indicate that the OCSVM classification of the combination of all three datasets generally outperformed the locally optimized threshold method. The proposed method effectively and efficiently extracted the urban extent at a regional scale, and is applicable to other study areas.

Highlights

Urban areas occupy a small fraction of the earth’s surface, but they significantly impact ecosystems and climates at local, regional, and global scales
They found that normalized difference vegetation index (NDVI) values in urban regions are relatively low, whereas Defense Meteorological Satellite Program (DMSP)/Operational Line-scan System (OLS) night-time light (NTL) values gradually increase towards the urban core along the transect [39,70]
The training samples for the urban class selected using the DMSP/OLS NLT and MODIS NDVI data include water pixels, which will affect the classification results. These water pixels were not present when using the combination of the DMSP/OLS NLT, MODIS NDVI, and land surface temperature (LST) data

Summary

Introduction

Urban areas occupy a small fraction of the earth’s surface, but they significantly impact ecosystems and climates at local, regional, and global scales. This is because of land cover conversion, the release of anthropogenic greenhouse gases, and loss of biodiversity [1,2,3,4,5,6,7,8]. Accurate and timely information about the extent and spatial distribution of urban areas (especially at regional and global scales) is crucial and significant for a diverse range of applications. Moderate resolution images have been used for land cover mapping at continental scales [13,14,15], the time and labor required for processing and interpreting these images make it difficult to map urban areas at a large scale

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Methods

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Discussion

Conclusion