a b s t r a c tModern planning theories encourage approaches that consider all stakeholders with a variety of discourse values to avoid political and manipulative decisions. In the last decade, application of quantitative approaches such as multi-criteria decision making techniques in land suitability procedures has increased, which allows handling heterogeneous data. The majority of these applications mainly used decision-making techniques to rank the priority of predefined management options or planning scenarios. The presented study, however, shows how spatial decision-making can be used not only to rank the priority of options and performing scenario analysis, but also to provide insight into the spatial extent of the alternatives. This is particularly helpful in situation where political transitions in regard to urban planning policies leave local decision-makers with considerable room for discretion. To achieve this, the study compares the results of two quantitative techniques (analytical hierarchy procedure (AHP) and Fuzzy AHP) in defining the extent of land-use zones at a large scale urban planning scenario. The presented approach also adds a new dimension to the comparative analysis of applying these techniques in urban planning by considering the scale and purpose of the decision-making. The result demonstrates that in the early stage of the planning process, when identifying development options as a focal point is required, simplified methods can be sufficient. In this situation, selecting more sophisticated techniques will not necessarily generate different outcomes. However, when planning requires identifying the spatial extent of the preferred development area, considering the intersection area suggested by both methods will be ideal.

Multi-criteria decision making techniques have become increasingly widespread in strategic environmental decision making. In Australia these techniques are used to integrate both conservation and development aspects of natural resource use. MCDM can also evaluate the effects of uncertainties at each stage of the decision making process and examine the sensitivity of results to the inputs. This paper reviews the potential uncertainties in environmental management decision making procedures and explores how uncertainty analysis in the framework of MCDM can address some of these uncertainties. It then examines the application of MCDM in 16 Australian case studies to determine how uncertainty has been addressed in practice. Results demonstrate that appropriate use of MCDM can address uncertainties associated with decision makers' preferences and from using different techniques (epistemic uncertainty). Results also highlighted the need for incorporating visualizing techniques such as GIS and simulation algorithms (e.g. Monte Carlo Simulations) to examine the effects of uncertainty on the spatial pattern of the outcomes. This approach also presents promising ways to gain an understanding of the effects of some

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Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC) in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized difference vegetation index, Fisher supervised classification and object-based image classification methods. Accuracy assessment results showed that the hybrid classification method produced maps with total accuracy up to 84 percent with kappa statistic value 0.81. Results of this study showed that the proposed classification method worked better with OLI sensor than with TM. Although OLI has a higher radiometric resolution than TM, the produced LULC map using TM is almost accurate like OLI, which is because of LULC definitions and image classification methods used.