Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery

Abstract

Rapid urban expansion in Vietnam presents significant management and environmental challenges. This study quantifies urban growth in the Ha Long City area using a time series of six Landsat images acquired from 2000 to 2023. To address the heterogeneity of the urban class, an object-based (OB) image analysis approach was employed. Instead of relying on spectral channels, Principal component analysis (PCA) was utilized during segmentation, followed by classification using the Random Forest (RF) algorithm. For post-classification processing, a logical filter was applied to confirm the classification results at a detailed level, using additional information from the general classification results. Unconfirmed objects were subsequently verified through visual interpretation. The accuracy of the post-classified process results of this study ranges from 83.44% to 96.64% (producer accuracy) and from 83.48% to 91.27% (user accuracy). The results show that the urban area expanded more than four times, with the most significant growth occurring between 2000 and 2005, and between 2015 and 2020. Notably, land reclamation contributed significantly to urban growth. Understanding these trends is crucial for informed urban planning and environmental management in the region.

Keywords

• Urban expansion
• Ha Long City
• Landsat image
• object-based classification
• RF
• PCA