An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data

An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data

Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (<i>mtry</i> and <i>ntrees</i&...

Full description

Bibliographic Details
Main Authors: Peiqing Lou, Bolin Fu, Hongchang He, Ying Li, Tingyuan Tang, Xingchen Lin, Donglin Fan, Ertao Gao
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
marsh vegetation mapping
random forest algorithm
parameter optimization
multidimensional datasets
variable selection
GF-1
Online Access:https://www.mdpi.com/2072-4292/12/8/1270

Internet

https://www.mdpi.com/2072-4292/12/8/1270

Similar Items