Land-use change in upper catchments impact downstream water flows. As trees uselarge amounts of water the expansion of upstream plantations can substantially reducewater availability to downstream users. There can also be impacts on downstreamsalinity due to reduced dilution flows. In some jurisdictions afforestation requires thepurchase of water rights from downstream holders, while in others it does not,effectively handing the water rights to the upstream landholders.

The main objective of this study was to investigate the effect of forest road construction on the Soil Organic Carbon Stock (SOCS) in Ziarat Catchment. Therefore, soil samples were collected from five land use types including road construction, cultivated area, channel bank, pasture and forest land and soil organic carbon concentration and bulk density were measured in the samples and SOCS were calculated. The results showed that the forest road construction reduce SOCS.

The aim of this research is to evaluate the spatial differences of the agricultural economy of Latvia during the First Republic by using spatial autocorrelation calculations. Morans’s I coefficient of spatial autocorrelation is used to describe the spatial relations of the analyzed indicators. The obtained results show that Moran’s I for more than half of the obtained parameters is greater than 0.5, which indicates a rather high spatial autocorrelation. It was observed that variations of the spatial autocorrelation values can be explained by what affects these parameters.

and is becoming a scarce natural resource due to the burgeoning population growth and urbanization. Essentially, detecting changes in land surface is significant for understanding and assessing human impacts on the environment. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in Birjand of Iran using Landsat TM5 images between 1986 and 2010. Artificial neural network was used for classification of Landsat images.