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Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETMþ) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R 2 ¼ 43%; root mean square error (RMSE) ¼ 97.4 m 3 ha -1 ) than other ETMþ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R 2 ¼ 73.4%; RMSE ¼ 170.13 ha -1 ) than other combinations of ETM þ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETMþ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETMþ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETMþ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.

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25Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANN) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used 30 test and validation areas to calculate accuracies of interpolated and extrapolated data. The results show that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, lower errors were observed with the WRB classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (seven soils in the case of ST vs. five with WRB). Training errors were below 11% for all the ANN models 35applied, while the test (interpolation error) and validation (extrapolation error) errors were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology, as soil-forming factors, should be used as ANN input data. 40

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