No abstract

Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the eastern regions of the Qilian Mountains as an example to explore the possibility of tree species classification with satellite-derived images. We used Sentinel-2 images to classify the study area’s major vegetation types, particularly four tree species, i.e., Sabina przewalskii (S.P.), Picea crassifolia (P.C.), Betula spp. (Betula), and Populus spp. (Populus). In addition to the spectral features, we also considered terrain and texture features in this classification. The results show that adding texture features can significantly increase the separation between tree species. The final classification result of all categories achieved an accuracy of 86.49% and a Kappa coefficient of 0.83. For trees, the classification accuracy was 90.31%, and their producer’s accuracy (PA) and user’s (UA) were all higher than 84.97%. We found that altitude, slope, and aspect all affected the spatial distribution of these four tree species in our study area. This study confirms the potential of Sentinel-2 images for the fine classification of tree species. Moreover, this can help monitor ecosystem biological diversity and provide references for inventory estimation.

No abstract

Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas.