Improving Land Cover Change Detection and Classification With BRDF Correction and Spatial Feature Extraction Using Landsat Time Series: A Case of Urbanization in Tianjin, China

Abstract

As one of the important coastal cities in China, Tianjin has been urbanized dramatically over the past 40 years, and the urbanization rate has been up to 83.15% by 2018. In this study, we used the continuous change detection and classification algorithm to comprehensively understand the urban expansion processes in Tianjin based on the Landsat time series from 1985 to 2018 with 30-m resolution. Specially, we applied the c-factor approach with the Ross Thick-LiSparse-R model to correct the bidirectional reflectance distribution function (BRDF) effect for each Landsat image and calculated a spatial line density feature for improving the change detection and the classification. Based on the study in Tianjin, we found that BRDF correction can substantially improve the change detection (9.00% higher overall accuracy) and classification (1.08% higher overall accuracy); and the line density is also beneficial to classification (0.48% higher overall accuracy), especially for impervious surface (1.70% less commission errors and 1.49 % less omission errors). By analyzing the imperious surface change processes, we observed that Tianjin has undergone rapid urban expansion in the past decades, and the urban area was mainly transformed from cropland around the central area before 2005 and later from the coast.