Midekisa, A., Holl, F., Savory, D.J., Andrade-pAcheco, R., Gething, W., Bennett, A. and Sturrock, H.J.W. (2017) Mapping Land Cover Change over Continental Africa Using Landsat and Google Earth Engine Cloud Computing, (30 m), 1-15.

has been cited by the following article:

• TITLE: Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand

  AUTHORS: Uday Pimple, Dario Simonetti, Asamaporn Sitthi, Sukan Pungkul, Kumron Leadprathom, Henry Skupek, Jaturong Som-ard, Valery Gond, Sirintornthep Towprayoon

  KEYWORDS: Google Earth Engine, Landsat, Random Forest, Mangrove Forest, Land Use Land Cover Change

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.1, December 29, 2017

  ABSTRACT: Monitoring and understanding the changes in mangrove ecosystems and their surroundings are required to determine how mangrove ecosystems are constantly changing while influenced by anthropogenic, and natural drivers. Cosistency in high spatial resolution (30 m) satellite and high performance computing facilities are limiting factors to the process, with storage and analysis requirements. With this, we present the Google Earth Engine (GEE) based approach for long term mapping of mangrove forests and their surroundings. In this study, we used a GEE based approach: 1) to create atmospheric contamination free data from 1987-2017 from different Landsat satellite imagery; and 2) evaluating the random forest classifier and post classification change detection method. The obtained overall accuracy for the years 1987 and 2017 was determined to be 0.87 and 0.96, followed by a Kappa coefficient 0.80 and 0.94. The change detection results revealed a significant decrease in the agricultural area, while there was an increase in mangrove forest, shrimp/fish farm, and bareland area. The results suggest that interconversion of land use and land cover is affecting the landscape dynamics within the study area.