Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

Abstract

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin.

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

“…Hence, different interactions between the sensor backscatter and the structure of the canopy of landscape patches can provide novel information on ecological structure. Nevertheless, many recent studies [36][37][38][39][40] noted the evident advantages of using ML methods in image processing. For instance, ref.…”

Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

“…In recent years, with advancements in wireless communication technology, satellite communication has gained widespread application in both military and civilian sectors due to its abundant spectrum resources, wide coverage, and freedom from geographical constraints [1,2]. However, the continuous development of communication frequency bands, the emergence of new modulation techniques, and the proliferation of unidentified systems and proprietary protocols have resulted in an increasing number of signals.…”

Monitoring the Distribution of Mangrove Area using Synthetic Aperture Radar (SAR) and Optic Remote Sensing Data Fusion based on Deep Learning in Kotabaru Regency, Indonesia

“…Remote sensing analyses are less expensive in labor and time than field surveys and aerial photography, and they can be readily expanded to larger scales 16) . Remote Sensing techniques are widely used to monitor the environment and natural resources over extended periods 17,18) , for purposes such as land cover classification 19,20) , urban planning 21,22) , traffic monitoring 23,24) , and land cover change detection 25,26) . In many tropical regions, remote sensing is crucial to coastal monitoring because detailed estimates of changes in forest cover are necessary due to the effects these changes have on the environment and on sustainable development 27) .…”