A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Land use classification is often the first step in land use studies and thus forms the basis for many earth science studies. In this paper, we focus on low-cost techniques for combining Landsat images with geographic information system approaches to create a land use map. In the Golestan region of Iran, we show that traditional supervised and unsupervised methods do not result in sufficiently accurate land use maps. Therefore, we evaluated a synthetic approach combining supervised and unsupervised methods with decision rules based on easily accessible ancillary data. For accuracy assessment, confusion matrices and kappa coefficients were calculated for the maps created with the supervised, unsupervised and synthetic approaches. Overall accuracy of the synthetic approach was 98.2 %, which is over the 85 % level that is considered satisfactory for planning and management purposes. This shows that integration of remote sensing data, ancillary data and decision rules provides better classification accuracy than traditional methods, without significant additional use of resource

Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction

Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Niño–Southern Oscillation (ENSO). The chapter applies an advanced statistical copula approach to model lag relationships between monthly Southern Oscillation Index (SOI), an ENSO indicator, and monthly Flood Index (FI) that can be used for flood prediction. Copula parameters were numerically derived from under a hybrid-evolution Markov chain Monte Carlo (MCMC) approach within a Bayesian framework. The empirical findings showed that monthly SOI data from Aug to Dec have a significant correlation with monthly FI that can be predicted at least four months ahead using SOI information. These advanced flood prediction models, presented in this chapter, are indeed imperative tools for civil protection and important to early warning and risk reduction systems