[An Analysis of the Spectrums between Different Canopy Structures Based on Hyperion Hyperspectral Data in a Temperate Forest of Northeast China].

Canopy is a major structural layer for vegetation to carry out ecological activities. The differences of light radiative transfer processes in canopies caused by forest canopy structure directly influence remote sensing inversion of forest canopy biochemical composition. Thus an analysis of spectral characteristics between different canopy structures contributes to improve the accuracy of remote sensing inversion of forest canopy biochemical components. Based on a Hyperion hyperspectral image in the north Slope of Changbai Mountain Nature Reserve, through FLAASH (the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction, different canopy reflectance spectra were extracted, and spectral transforms were carried out using continuum removal method and first derivative method for quantitative analysis of the spectral characteristics. A set of spectral indices were calculated, including NIR (near infrared reflectance), NDVI (normalized difference vegetation index), EVI (Enhanced Vegetation Index), NDNI (normalized difference nitrogen index), SPRI (normalized photochemical reflectance index) * NDVI and SPRI * EVI (vegetation productivity index). Combined with the broad foliar dominance index (BFDI), the relationships between the spectral indices and canopy structure composition were investigated. The characteristics of canopy structure composition impacting its spectral curve and indices were clarified in the temperate forest. The results showed that: (1) there existed significantly different spectral characteristics between different canopy structures: comparing to the spectrum of broad-leaved forest canopies, the red edge moved to the left and their slope decreased, blue edge and yellow edge features were also weakened, near-infrared reflectance decreased, normalized reflectance in visible region risen for the spectrum of conifer forest canopies; (2) the spectrum variation were controlled by BFDL The correlations between BFDI and the spectral indices were significant (P < 0.01). It was suggested the ratio of broad-leaved and conifer in canopy played an important role in variation of spectral indices. The coefficients of determination (R2) of BFDI and NDVI, EVI, SPRI * EVI, SPRI * NDVI and NDNI were 0.90, 0.83, 0.83, 0.81, 0.68 and 0.59 respectively. It was revealed that BFDI could control the variation of the canopy structure, greenness, leaf nitrogen concentration, leaf area index and productivity in temperate coniferous and broad-leaved mixed forests. Our findings were very significant foundation for accurate determination of forest type, quantitative extraction of canopy biochemical components, estimation of regional forest ecosystem productivity and other related researches.

[Estimation of light-use efficiency of China' s mid-subtropical planted coniferous forest based on flux measurements and spectral observations].

The photochemical reflectance index (PRI) calculated from spectral reflectance has universally become a proxy for the light-use efficiency (LUE), which significantly improves the LUE-based estimation of ecosystem gross primary productivity on a large scale through upscaling. In this study, we observed the vegetation spectral reflectance of a planted subtropical coniferous forest from the top of a flux tower at Qianyanzhou Station, one of the ChinaFLUX sites, in September and December 2013, and simultaneously measured CO2 flux and meteorological variables for correlation and regression analysis. Results showed that PRI had a better correlation with LUE (R2 = 0.20, P< 0.001) than that of normalized difference vegetation index (NDVI), i.e., PRI was preferred in LUE retrieval. During the whole observation period, PRI and soil water content (SWC)-based bivariate regression model correlated well with LUE (R2 = 0.29, P < 0.001 and R2 = 0.30, P < 0.01 for daytime and midday observation, respectively), but in autumn the bivariate regression model of PRI and vapor pressure deficit (VPD) had a higher correlation with LUE (R2 = 0.448, P < 0.001) for midday observation, which showed that environmental factors, i.e., SWC and VPD, had a potential in improving the LUE retrieval from PRI, but the choice of appropriate environmental factors depended on season.