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.
Highlights
Forests perform various functions, including soil and water conservation, carbon sequestration, air purification, and moderating the global climate
The temporal features remaining were NDVI_amp, NDVI_rmse, EVI_pha, and redE2_rmse, with the elevation indicator derived from the digital elevation model (DEM) retained
We found that topography was one of the major factors influencing forest type classification, especially in mountainous areas and areas with large elevation transitions, with forest type often affected by terrain elevation, slope, and aspect, as has been previously reported [19]
Summary
Forests perform various functions, including soil and water conservation, carbon sequestration, air purification, and moderating the global climate. The UN formally released 17 sustainable development goals in [2]; goal 15 was to ensure the sustainable development of terrestrial ecosystems, including forests—and the response to this requires improvements to the conservation and management of forest resources at an unprecedented level. Remote sensing and digital image processing enable the observation, identification, classification, and monitoring of forests at a range of spatial, temporal, and thematic scales [3]. Liu et al achieved forest type classification for Wuhan, China, using a machine learning algorithm and spectral and spatial features derived from multi-source remote sensing data [4]. Zhang et al (2010) calculated spectral features using NIR and infrared bands and identified shrub forests in higher altitude areas located in Dingri County
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
