Paper Info
Title
Mapping Forest Types In China With 10 M Resolution Based On Spectral-Spatial-Temporal Features
Abstract
The comprehensive application of spectral, spatial, and temporal (SST) features derived from remote sensing images is a significant technique for classifying and mapping forest types. Facing limitations in the availability of detailed forest type identification processes for large regions, a forest type classification framework based on SST features was developed in this study. The advantages of Sentinel-2 and Landsat series imagery were used to extract SST forest type classification features, using red-edge bands, a gray-level co-occurrence matrix, and harmonic analysis, with the assistance of the Google Earth Engine platform. Considering four representative Chinese geographic regions-middle and high latitudes, complex mountainous areas, cloudy and rainy areas, and the N-S climate transition zone-our method was proven to be effective, with overall classification accuracies > 85%. The scheme to assess the importance of SST features for forest classification in various regions was designed using the Gini criterion in the random forest algorithm and revealed that spectral features were more effective in classifying forest types with complex compositions. Temporal features were found to be favorable for identifying forest types with obvious evergreen and deciduous growth patterns, while spatial features produced better classification results for forest types with different spatial structures, such as needle- or broad-leaved forests. The findings of this study can provide a reference for feature selection in remote sensing forest type classification processes, and identifying forest types in this way could provide support for the accurate and sustainable management of forest resources.
Year
DOI
Venue
2021
10.3390/rs13050973
REMOTE SENSING
Keywords
DocType
Volume
forest type, spectral&#8211, spatial&#8211, temporal features, image composition, random forest algorithm, China
Journal
13
Issue
Citations 
PageRank 
5
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Kai Cheng13912.36
Juanle Wang21611.47
Xinrong Yan301.01