Remote Sensing of Land Use and Land Change with Google Earth Engine II

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

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• 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

• Remote Sensing of Land Use and Land Change with Google Earth Engine in Remote Sensing (11 articles)