Abstract
Long-term Land Cover Change Detection Using Multisensor and Multiresolution Remote Sensing Images: A Case Study of Chang’an University, China
Highlights
With the rapid urbanization and increasing population of China over the last few decades, major changes have taken place in the land use and land cover (LULC) patterns of China.(1) LULC change detection techniques are an important tool for detecting changes on the earth’s surface at different spatiotemporal scales.(2) LULC patterns are to a large extent determined by the natural environment and the demand for various economic activities, which have a ISSN 0914-4935 © MYU K.K. https://myukk.org/Sensors and Materials, Vol #, No # (202#)critical influence on urban development
This study mainly focuses on the usage of multisensor, multiresolution, and multitemporal remote sensing images, and the application of different combinations of change detection methods to different remote sensing images in the LULC change detection field
The experimental results showed that the direct spectral comparison method using Landsat-5 images was more effective than the post-classification change detection method using Landsat-7 images for detecting LULC changes from 1998 to 2008 on Weishui campus
Summary
With the rapid urbanization and increasing population of China over the last few decades, major changes have taken place in the land use and land cover (LULC) patterns of China.(1) LULC change detection techniques are an important tool for detecting changes on the earth’s surface at different spatiotemporal scales.(2) LULC patterns are to a large extent determined by the natural environment and the demand for various economic activities, which have a ISSN 0914-4935 © MYU K.K. https://myukk.org/Sensors and Materials, Vol #, No # (202#)critical influence on urban development. With the progress of satellite and sensor technology, remote sensing images have become the main sources of LULC change detection in the last decade.(6) On the basis of the spatial resolution of remote sensing images, change detection methods have been further explored in two directions: low- or medium-resolution image change detection and high-resolution image change detection. Many researchers have attempted to use low- or medium-resolution remote sensing images to address LULC change detection problems, such as crop species classification and urban changes. With the help of recently emerged remote sensing image processing algorithms, many LULC change detection techniques have been developed, such as image differencing,(10) spectral change vector analysis,(11) and post-classification comparison.(12) Change detection methods using low- or medium-resolution remote sensing images are the earliest, most commonly used, and most intensively studied methods. The accuracy of information extraction and classification can be greatly improved
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