Abstract
Global land cover/land use product in multiple periods is pivotal to understand the complex drivers and mechanisms in global climate change, and to forecast future land use trends in sustainable development. GlobeLand30, as the world’s first high spatial resolution land cover product (83% accuracy), needs to be continually updated to meet various needs. However, many challenges - such as removing pseudo change to keep consistency of updating - remain unsolved. To deal with high temporal and spatial variability happened within built-up area class and between it and other classes, this paper presents an alternative approach that exploits domain knowledge and object-based change detection technique. The central premise of the approach is that one-class segmentation is first proceeded on both former image and current image. Then, segments of former image are labeled by using corresponding Globeland30 product. Segments of built-up area in current image are finally recognized through correlation which is established based on domain knowledge. Knowledge used in this study mainly includes area index, shape index, perimeter index, spectral similarity, 'from to' types and spatial relation. The proposed method and classification method were tested for their ability for built-up area updating in Shandong area. Results showed that the proposed method proved particularly effective for maintaining consistency of unchanged areas from former product to current one, and more than 80% changes could be identified correctly. The proposed method also provided a practical way for an economic and accurate updating of Globeland30 product.
Highlights
Global land cover/land use product in multiple periods is pivotal to understand the complex drivers and mechanisms in global climate change, and to forecast future land use trends in sustainable development (Chameides et al, 1994; Bianchin et al, 2008)
In order to evaluate the proposed method, the proposed method and classification method were tested for their ability to update built-up area
Classifiers of the maximum likelihood (ML) method was used in this study
Summary
Global land cover/land use product in multiple periods (i.e. three or more) is pivotal to understand the complex drivers and mechanisms in global climate change, and to forecast future land use trends in sustainable development (Chameides et al, 1994; Bianchin et al, 2008). Remote sensing image with different spatial resolutions has been used in land cover/land use mapping in recent decades. Taking into account of availability, coverage and rich spatial details of requirements of remotely sensed images, 1 km to 30 m spatial resolutions are usually chosen for global land cover/land use mapping and change detection (Small, 2005). Multiple sets of global land cover product have been produced, from 1 kilometres to 30 m spatial resolution(Hansen et al, 2000;Friedl et al, 2010;Bicheron et al, 2012). In 2015, a POK-based operational approach has been proposed to create a global land cover map, the GlobeLand, the world’s first high spatial resolution (30 m) land cover product with 2000 and 2010 two periods. Compared with former products, GlobeLand is one order of magnitude higher in spatial resolution. GlobeLand provides a solid research foundation for geography monitoring and other applications (Chen et al, 2015)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
