[Sensitivity of spring phenology to elevation in Qinling Mountains, China]. / 秦岭山区植被春季物候的海拔敏感性.

Vegetation phenology, a regular and periodic phenomenon in nature, is an important indicator for natural environment, especially climate change. The study of spatiotemporal variations of vegetation phenology is of great significance for monitoring the changes of terrestrial vegetation. In this study, the Savitzky-Golay (S-G) filtering method was used to reconstruct time-series MODIS enhanced vegetation index (EVI) data in the Qinling Mountains from 2001 to 2018. The dynamic threshold method was used to extract the spring phenological parameter (start of growth season, SOS). The correlation between multi-year mean SOS and interannual variation with altitude and slope was analyzed. The results showed that SOS was delayed by 1.82 d with every 100 m increase in altitude in the Qinling Mountains. The interannual change trends of SOS mainly concentrated in 0-5 d·(10 a)-1. The pixels with delaying trend were mainly distributed in low-altitude regions, with the delaying degree being gradually decreased with the elevation. The interannual change trend of SOS in high-altitude regions was more complex than that in lower-altitude regions. The multi-year average SOS in the northern slope was approximately 2.9 d earlier than that of the southern slope, whereas the southern slope had a more significant advancing trend. The interannual change trends of SOS in both north and south slopes showed a delaying trend in low-altitude, with little difference between north and south slopes. The advancing trend in middle and high altitude was significantly different.

[A new spectral similarity measure based on multiple features integration].

Spectral characterization and feature selection is the key to spectral similarity measure which is the basis of both quantitative analysis and accurate object identification for hyperspectral remote sensing. However, spectral similarity measures using only one spectral feature are usually ambiguous in their distinction of similarity. Multiple spectral features integration is needed for objective spectral discrimination. We present a new spectral similarity measure, Spectral Pan-similarity Measure (SPM), based on geometric distance, correlation coefficient and relative entropy. Spectral Pan-similarity Measure objectively quantifies differences between spectra in three spectral features, the vector magnitude, spectral curve shape and spectral information content. The performance of different spectral similarity measures is compared using USGS Mineral Spectral Library and real (i.e., Operational Modular Imaging Spectrometer, OMIS) hyperspectral image. The experimental results demonstrate that the new spectral similarity measure is more effective than the spectral similarity measure taking into account only one or two features both in spectral discriminatory power and spectral identification uncertainty.