[Advances in the studies on topographic effects of vegetation indices.] / æ¤è¢«æŒ‡æ•°çš„åœ°å½¢æ•ˆåº”ç ”ç©¶è¿›å±•.

Vegetation index is a key indicator for qualitative and quantitative assessment of green vegetation, which has been widely used in vegetation monitoring. Forests are often distributed in mountainous areas with complex topography, which is one of the main factors of accurate retrieval of forest vegetation information. Here, we analyzed the topographic effects on canopy reflectance using a geometric optical model. The responses of complete ratio vegetation indices [simple ratio index (SR), normalized difference vegetation index (NDVI) and moisture adjusted vegetation index (MAVI)], incomplete ratio vegetation indices [enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI)], non-ratio vegetation indices [reduced simple ratio (RSR), modified normalized difference vegetation index (MNDVI), greenness vegetation index (GVI)] and, topography adjusted vegetation index [topography adjusted vegetation index (TAVI)] to topography were discussed in detail, with the aim to provide reference for selecting vegetation index in complex terrain mountainous area. The shortcomings of current literatures about the topographic effects on vegetation indices were analyzed and the future research directions were prospected.

[Retrieval of leaf net photosynthetic rate of moso bamboo forests using hyperspectral remote sen-sing based on wavelet transform].

This study focused on retrieval of net photosynthetic rate (Pn) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of Pn retrieved by the ideal high frequency wavelet vegetation index ( VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVIw), simple ratio vegetation index of wavelet (SRw) and difference vegetation index of wavelet (Dw) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with Pn, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between Pn estimated by multivariate linear model constructed by the ideal wavelet VI and the measured Pn, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the Pn of moso bamboo more in detail, and the overall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of Pn of moso bamboo forest using hyper spectral remotely sensed data.

[Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter]. / 双集合卡尔曼滤波LAI同化结合BEPS模型的竹林生态系统碳通量模拟.

LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R2 of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R2 of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.