This paper describes the main functionalities of an integrated framework to model the interactions between land use, climate, and hydrology along with stakeholders’ negotiation. Its novelty lies in the combination of individual-based and spatially distributed models within the Socio-Hydrology paradigm to capture the complexity and uncertainty inherent to these systems. It encompasses a land-use...

In the last decades RS and GIS technology have been increasingly used for hydrological applications. Hydrological parameters estimation is strongly related to land cover composition. This study examines the impact of different approaches to estimate land cover distribution on the prediction and the spatial pattern of surface runoff. Land cover fractions are derived at sub-pixel scale from CHRIS...

This is the first of two articles which explore the comtained using the TM map. The performance of samples selected by a combination of cover composition and conbined use of coarse and fine resolution data in land cover studies. It describes the development and evaluation of tagion index responded to the characteristics of individual tiles in terms of the selection criteria. A rigorous applian ...

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data,...

This paper proposes a two-layer Conditional Random Field model for simultaneous classification of land cover and land use. Both classification tasks are integrated into a unified graphical model, which is reasonable due to the fact that land cover and land use exhibit strong contextual dependencies. In the CRF, we distinguish a land cover layer and a land use layer. Both layers differ with resp...

two different methods of bayesian segmentation algorithm were used with different band combinations. sequential maximum a posteriori (smap) is a bayesian image segmentation algorithm which unlike the traditional maximum likelihood (ml) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately. landsat 7 etm+ data with pa...