Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China

Abstract

Accurate mapping of tree species is highly desired in the management and research of plantation forests, whose ecosystem services are currently under threats. Time-series multispectral satellite images, e.g., from Landsat-8 (L8) and Sentinel-2 (S2), have been proven useful in mapping general forest types, yet we do not know quantitatively how their spectral features (e.g., red-edge) and temporal frequency of data acquisitions (e.g., 16-day vs. 5-day) contribute to plantation forest mapping to the species level. Moreover, it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China. We designed three sets of classification experiments (i.e., single-date, multi-date, and spectral-temporal) to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China. We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages. Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition (i.e., L8 time series, S2 time series, and fusion of L8 and S2) to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area. 1) S2 outperformed L8 images in all classification experiments, with or without the red edge bands (0.4%–3.4% and 0.2%–4.4% higher for overall accuracy and macro-F1, respectively); 2) NDTI (the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2) and Tasseled Cap coefficients were most important features in all the classifications, and for time-series experiments, the spectral-temporal features of red band-related vegetation indices were most useful; 3) increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2% (from 90.1% using single-date imagery to 93.3% using S2 time-series), yet similar overall accuracies were achieved using S2 time-series (93.3%) and the fusion of S2 and L8 (93.2%). This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests, the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.