Soil Surface Salinity Prediction Using ASTER Data: Comparing Statistical and Geostatistical Models

This study was conducted to evaluate the performance of univariate spatial (ordinary kriging- OK), hybrid/multivariate geostatistical methods (regression-kriging- RK, Co-kriging- CK) with multivariate linear regression (MLR) in incorporation with ASTER data in order to predict the spatial variability of surface soil salinity in an arid area in northern Iran. The primary attributes were obtained from grid soil sampling with nested-systematic pattern of 169 samples and the secondary information extracted from spectral data of ASTER satellite images. The principal component analysis, NDVI and some suitable ratioing bands were applied to generate new arithmetic bands. According to validation based RMSE and ME calculated by a validation data set, the predictions for soil salinity were found to be the best and varied in the following order: RK ASTERmultivariate > REG ASTERmultivariate > Co-kriging ASTER> kriging. Overall, this comparative study demonstrated that RK approach was a better predicator than other selected methods to predict spatial variability of soil salinity. The overall results confirmed that using ancillary variables such as remotely sensed data, the accuracy of spatial prediction can further improved

Forest Variable Estimations Using TanDEM-X Data in Hyrcanian Forests

The objective of this study was to estimate forest variables using TanDEM-X interferometric synthetic aperture radar (InSAR) data acquired over the Shastkalate forest of Gorgan in northern Iran. Inventory variables, including diameter at breast height, tree height (Lorey’s mean tree height), basal area and volume, were collected from 112 circular sample plots with a size of 0.1 ha. Interferometric phase height and coherence were computed from TanDEM-X data. Stepwise multiple linear regression was used to develop models describing the relationship between field-derived forest variables and the InSAR based statistical metrics. The validation was carried out using the leave-one-out cross-validation method. The estimation accuracy results in terms of relative RMSE for Lorey’s mean tree height, basal area and volume were 11.0, 34.1, and 37.8%, respectively, whereas the corresponding figures for R2adj were 32.0, 10.0, and 16.0%, respectively. The results were good enough for estimating forest variables and it was concluded that TanDEM-X could be used in Hyrcanian hardwood forests