Remote Sensing of Land Use and Land Change with Google Earth Engine II
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 364
Special Issue Editors
Interests: ecological restoration; geographic information system; unmanned aerial vehicles; land reclamation; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: urban remote sensing; land use cover change; urban heat island; vegetation phenology; urban growth modeling; nighttime light
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This is the first volume of the Special Issue “Remote Sensing of Land Use and Land Cover Change with Google Earth Engine”, which was a great success.
Information on land use and land cover change (LULCC) is critical for modeling human–earth systems, and remote sensing techniques have been established as the most cost-efficient and reliable approaches to gain this information. Over time, the development of sensors has evolved from humble cameras carried by pigeons and air balloons to advanced spaceborne sensors that are currently in use, including optical, microwave, thermal, LiDAR, and radar sensors. Although they provide rich and complementary information about the land surface from different perspectives, their integrated utilization requires expert knowledge, intensive computation, and storage capacity.
Google Earth Engine (GEE), a cloud-based remote sensing data processing platform, not only provides ready-to-use remotely sensed datasets, freeing researchers from tedious data preprocessing tasks to focus on creative tasks, but also provides powerful computational capacity, facilitating LULCC monitoring with multi-temporal and multi-sensor data. GEE enables free programmatic access to imagery from various satellites (e.g., MODIS, Landsat, and Sentinel) as well as geospatial datasets (e.g., land use data, climate, and weather data), through either a JavaScript or Python API. This Special Issue aims to showcase the application of GEE to monitor LULCC, including land cover mapping, land change analysis, and thematic mapping. This includes, but is not limited to, topics, such as forest change, urban expansion, mining impacts, coastal change, cropland, and specific crops (e.g., rice, maize, etc.), both on large and long-term scales.
Dr. Wu Xiao
Dr. Xuecao Li
Dr. Tong Qiu
Guest Editors
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Keywords
- google earth engine
- land use change
- change detection
- cloud computing for land cover/land use monitoring
- vegetation dynamics
- urban expansion and modeling
- mining exploitation impacts on land use
- coastal land use change detection
- land abandonment monitoring
- deep learning and machine learning
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