High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-val...

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Bibliographic Details
Main Authors: Ram C. Sharma, Keitarou Hara, Ryutaro Tateishi
Format: Article
Language:English
Published: MDPI AG 2017-07-01
Series:Land
Subjects:
vegetation mapping
physiognomy
Sentinel-2
Landsat 8
MODIS
machine learning
cross-validation
Japan
Online Access:https://www.mdpi.com/2073-445X/6/3/50

Internet

https://www.mdpi.com/2073-445X/6/3/50

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