Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

The increased open-access availability of radar and optical satellite imagery has engendered numerous land use and land cover (LULC) analyses combining these data sources. In parallel, cloud computing platforms have enabled a wider community to perform LULC classifications over long periods and larg...

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Bibliographic Details
Main Authors: Christina Anna Orieschnig, Gilles Belaud, Jean-Philippe Venot, Sylvain Massuel, Andrew Ogilvie
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:European Journal of Remote Sensing
Subjects:
cart
google earth engine
gradient tree boosting
lulc
random forest
sentinel-1 and −2
svm
Online Access:http://dx.doi.org/10.1080/22797254.2021.1948356

Internet

http://dx.doi.org/10.1080/22797254.2021.1948356

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