title: [Estimating leaf area index of black locust (Robinia pseudoacacia L.) plantations based on texture parameters of quickbird imagery].
abstract: The black locust plantations located in Weibei area were chosen as research objects and the texture parameters of different window sizes from high resolution imagery were measured. Four different techniques, including simple linear regression model, quadratic regression model, power model and exponential model, were developed to describe the relationship between the texture parameters and field measurements of LAI and to select the most effective texture parameters and window size. The results showed that the texture parameters influenced the accuracy of LAI estimation. Angular second moment and entropy index yielded better adjust r2 than the other parameters. The r2 changed with the window size. Dissimilarity and contrast index gained the largest r2 when the window size was 9x9. The r2 of the other texture parameters reduced as the window size increased and a window size of 3 x 3 was more successful than any of the others. Power equation performed poorest than the other three techniques for estimation of LAI.
authors.forename: Jing-Jing
authors.initials: JJ
authors.lastname: Zhou
authors.forename: Zhong
authors.initials: Z
authors.lastname: Zhao
authors.forename: Jin-Liang
authors.initials: JL
authors.lastname: Liu
authors.forename: Qing-Xia
authors.initials: QX
authors.lastname: Zhao
authors.forename: Jun
authors.initials: J
authors.lastname: Zhao
mesh_terms: D065928:Forests; D015233:Models, Statistical; D018515:Plant Leaves / Q000254:growth & development*; D031301:Robinia / Q000254:growth & development*; D063809:Satellite Imagery