To investigate the application of hyperspectral remote sensing to estimate grassland biomass at the peak of the growing season, hyperspectral data were measured with an analytical spectral device (ASD) Fieldspec3 spectroradiometer, and harvested aboveground net primary productivity (ANPP) was recorded simultaneously in Hulunbeier grassland, Inner Mongolia, China. Ground spectral models were developed to estimate ANPP from the normalized difference vegetation index (NDVI) measured in the field following the same method as that of the National Aeronautic and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS-NDVI). Regression analysis was used to assess the relationship between ANPP and NDVI. Based on coefficients of determination (R 2 ) and error analysis, we determined that each vegetation type and the entire study area had unique optimal regression models. A linear equation best fit the arid steppe data, an exponential equation was best suited to wetland vegetation and power equations were optimal for meadow steppe and sand vegetation. After considering all factors, an exponential model between ANPP and NDVI (ANPP = 20.1921e 3.2154(NDVI) ; standard error (SE) = 62.50 g m -2 , R 2 = 0.7445, p < 0.001) was selected for the entire Hulunbeier grassland study area. Ground spectral models could become the foundation for yield estimation over large areas of Hulunbeier grassland.

Structure-based forest management is a scientific and easy-to-operate method for sustainable forest management. We analyzed the stand spatial structure of Larix principis-rupprechtii plantation under five reserve densities. The results indicated that with the decrease of densities after thinning, the average mingling degree and uniform angle index had an increasing tendency, but the amplitude was small. Most of the trees were in zero mix, and a few of them were in moderate, strong, and relatively strong mix; the horizontal distribution patterns were uniform or near-uniform random. The distribution of neighborhood comparison and opening degree changed with a fluctuant pattern, but thinning decreased the competitive intensities to some extent. A composite structure index (Ci) was established, based on the relative importance of the above four indicators, to evaluate the overall effect of thinning on stand structure characteristics. The findings showed that Ci increased with the increase of thinning intensity, that is, the stand spatial structure became more complex. This indicated that Ci may be a simple and rapid indicator to evaluate the overall effect of thinning on stand spatial structure within densities after thinning.

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