Abstract:To analyze the main variables affecting crown biomass, we measured crown biomass and a number of variables describing tree size, such as stem diameter at breast height (D), tree height (H), crown width (Cw) and crown length (Cl), from 754 sample trees of four pine species in China, specifically, Pinus massoniana, P. yunnanensis, P. tabulaeformis and P. elliottii. In addition, we developed individual tree crown biomass equations and compatible branch and needle biomass equations based on one-, two-and multi-variable models using nonlinear mixed-effects modeling and error-in-variable simultaneous equations. The results showed that: (i) crown biomass was strongly correlated to D and H, and the new combined variables (D 4 /H) and (D 2 /H) were suitable for modeling two-and multi-variable crown biomass equations, respectively; (ii) the four species were ranked as P. yunnanensis, P. massoniana, P. elliottii and P. tabulaeformis from small to large based on estimates from crown biomass equations, and the crown biomass estimate of P. tabulaeformis was about 60% greater than that of P. yunnanensis; (iii) the ratio of branch to crown biomass increased with increasing D, and the four species were ranked as P. elliottii, P. tabulaeformis, P. massoniana and P. yunnanensis from small to large by the proportion of the branch to crown biomass; (iv) the mean prediction errors (MPE's) of the OPEN ACCESSForests 2015, 6 434 crown biomass equations for the four species were less than 10%, which could meet precision needs for biomass estimation.

Based on the brightness, greenness, humidity indices after Tasseled Cap transformation, NDWI and DEM. Extracting the alpine wetland information in source regions of three Rivers. Result shows that: the humidity can be used to distinguish wetland types from non-wetland classes, brightness component is effective in grassland extracting, while bare rock and gravel land, sand land etc. could be distinguished through its high value on lightness component. Elevation and slope component could be taken as the threshold variable in distinguishing the marsh and bare rock and gravel land. It shows that RS feature index based classification method used on Alpine wetland could improve the overall accuracy by 10.71% and Kappa index by 0.1250 after comparing with the maximum likelihood method. The result indicates that, the method based on indices got from image band transformation is an effective way of alpine wetlands information remote sensing extracting.

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