Time-series land cover mapping and urban expansion analysis using OpenStreetMap data and remote sensing big data: A case study of Guangdong-Hong Kong-Macao Greater Bay Area, China

Ding Q., Shao Z., Huang X., Altan O., Hu B.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, cilt.113, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 113
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.jag.2022.103001
  • Dergi Adı: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Environment Index, Geobase, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: OpenStreetMap, Remote sensing big data, Sample optimization and transfer, Spatio-temporal consistency modification, Annual land cover mapping, Urban expansion, GOOGLE EARTH ENGINE, IMPERVIOUS SURFACE, GREEN SPACE, URBANIZATION, CLASSIFICATION, DYNAMICS, MAP, CLIMATE, IMAGES, INDEX
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Accurate land cover mapping provides important scientific support for ecological environment protection and sustainable urban development. However, given the high expense of acquiring training samples and the difficulty of fully utilizing remote sensing big data, large-scale time-series land cover mapping remains to be a challenge. To address the issue, we proposed a novel time-series large-scale mapping approach that obtains high-quality training samples from OpenStreetMap volunteered data and transfers them for land cover mapping in histori-cal years. Relying on the data archived on the Google Earth Engine platform, we constructed a discriminating feature set that contains spectrum, texture, and backscatter coefficient, among others. Taking the Guangdong -Hong Kong-Macao Greater Bay Area as a case area, the annual land cover maps after spatio-temporal consis-tency modification from 1986 to 2021 were obtained. The validation samples proved that the derived land cover classification results have high accuracy. We verified the reliability and superiority of the proposed approach by comparing our results with existing land cover products. The proposed mapping approach owns great trans-ferability, and the mapping results provide a valid decision-making basis for urban planning.