Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several me...

We modeled surface wetness at high resolution, using a dynamic hydrology model, to predict flood and swamp water mosquito abundances. Historical meteorologic data, as well as topographic, soil, and vegetation data, were used to model surface wetness and identify potential fresh and swamp water breeding habitats in two northern New Jersey watersheds. Surface wetness was positively associated wit...

The topographic wetness index (TWI), frequently used in approximately characterizing the spatial distribution of soil moisture and surface saturation within a watershed, has been widely applied in topography-related geographical processes and hydrological models. However, it is still questionable whether the current algorithms of TWI can adequately model the spatial distribution of topographic ...

This research conducted to evaluate spatial pattern of some biological variables (mean diameter, density, richness index and biodiversity index) of forest by linear regression models using terrain analysis in the Kheiroud-Kenar forest located at the Nowshahr, north of Iran. To obtain these parameters, sampling was performed on a systematic grid (250× 400 m) in 193 plots with one ha area. Densit...

Topographic wetness index (TWI) can quantify the control of local topography on hydrological processes and indicate the spatial distribution of soil moisture and surface saturation. The formulation is TWI=ln(a/tanβ), where a is the upslope contributing area per unit contour length and β is the local gradient. Many of the existing flow-direction algorithms for computing a use global partition ex...

[1] A key component to understanding and predicting water fluxes and water quality in river basins is the spatial distribution of water-saturated areas. There is limited knowledge on spatial patterns of saturated areas, their relation to landscape characteristics and processes, and the ability of hydrological models to represent the observed spatial patterns, particularly at the large scales mo...

abstract spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring co2 and preventing soil erosion strategies within the landscape, regional, and global scale. the objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain ...