Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm—A Case Study in the Qinling Mountains

Kai Cheng,Juanle Wang

doi:10.3390/f10070559

Abstract

Spectral, spatial, and temporal features play important roles in land cover classification. However, limitations still exist in the integrated application of spectral-spatial-temporal (SST) features for forest type discrimination. This paper proposes a forest type classification framework based on SST features and the random forest (RF) algorithm. The SST features were derived from time-series images using original bands, vegetation index, gray-level correlation matrix, and harmonic analysis. Random forest-recursive feature elimination (RF-RFE) was used to optimize high-dimensional and correlated feature space, and determine the optimal SST feature set. Then, the classification was carried out using an RF classifier and the optimized SST feature set. This method was applied in the Qinling Mountains using Sentinel-2 time-series images. A total of 21 SST features were obtained through the RF-RFE method, and their importance was evaluated using the Gini index. The results indicated that spectral features contribute the most to separating shrubs, spatial features are more suitable for discrimination among evergreen forest types, and temporal features are more useful for evergreen forest, deciduous forest, and shrub types. The forest type map was generated based on the optimal SST feature set and RF algorithm, and evaluated based on an agreement with the validation dataset. The results showed that this integrated method is reliable, with an overall accuracy of 86.88% and kappa coefficient of 0.86, and can support forest type sustainable management and mapping at the local scale.

Highlights

Forests play an important role in global climate regulation, hydrological cycling, soil and water conservation, biodiversity conservation, CO2 absorption, and natural disaster mitigation [1,2,3]
The results indicated that spectral features contribute the most to separating shrubs, spatial features are more suitable for discrimination among evergreen forest types, and temporal features are more useful for evergreen forest, deciduous forest, and shrub types
This paper proposed a forest type classification framework based on SST features and a random forest classifier

Summary

Introduction

Forests play an important role in global climate regulation, hydrological cycling, soil and water conservation, biodiversity conservation, CO2 absorption, and natural disaster mitigation [1,2,3]. Spatial, and temporal features derived from remote sensing play important roles in forest type classification and mapping. The application of temporal features in the identification of forest types is mostly reflected in the use of images from different periods, through the changes in phenology, to assist the classification of forest types. This is because phenology embraces very clear processes, such as coloring of leaves in deciduous temperate forests in autumn due to leaf senescence. Xia et al mapped the mangrove forest based on multi-date images [15] This method still uses the spectral difference at different times and does not take precise advantage of the temporal characteristics of the time-series images. It strongly relies on the selected image with determined time and least cloudiness as soon as possible

Methods

Results

Discussion

Conclusion