Abstract
Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010–2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.
Highlights
Land cover change (LCC) refers to the dynamics of biophysical materials covering the earth surface [1], and it has emerged as a fundamental component of sustainability research [2], including ecological economics, climate change, etc. [3,4]
The result shows that an accuracy of 89.20% is achieved with land cover regions generated by points of interest (POIs), indicating that POIs are reliable for rapid LCC detection
To generate land cover regions of 2016, POIs were classified into different land cover classes based on their textual information
Summary
Land cover change (LCC) refers to the dynamics of biophysical materials covering the earth surface [1], and it has emerged as a fundamental component of sustainability research [2], including ecological economics, climate change, etc. [3,4]. CGI is usually available through geoweb-based tagging systems to facilitate LCC detection, where geo-tagged data are encouraged to be uploaded by volunteers via mobile devices, such as OpenStreetMap (OSM) [11,12,13,14] It has been considered as training data of remotely sensed images [15], or provided socioeconomic features in urban regions detection with the combination of satellite data in rural regions [16]. A recent research study proposed a method of generating up-to-date maps using textual information from OSM [17,18] Since these kinds of data were provided by volunteers, textual information is often insufficient and incomplete for rapid LCC detection
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