Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape

Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape

Land use and land cover (LULC) mapping can be of great help in changing land use decisions, but accurate mapping of LULC categories is challenging, especially in semi-arid areas with extensive farming systems and seasonal vegetation phenology. Machine learning algorithms are now widely used for LULC...

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Bibliographic Details
Main Author: Sa’ad Ibrahim
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Agriculture
Subjects:
land use
land cover
classification
random forest
Sentinel data
SRTM
Online Access:https://www.mdpi.com/2077-0472/13/1/98

Internet

https://www.mdpi.com/2077-0472/13/1/98

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