Linking SLEUTH Urban Growth Modeling to Multi Criteria Evaluation for a Dynamic Allocation of Sites to Landfill

Abstract. Taking timely measures for management of the natural resources requires knowledge of the dynamic environment and land use practices in the rapidly changing post-industrial world. We used the SLUETH urban growth modeling and a multi-criteria evaluation (MCE) technique to predict and allocate land available to landfill as affected by the dynamics of the urban growth. The city is Gorgan, the capital of the Golestan Province of Iran. Landsat TM and ETM+ data were used to derive past changes that had occurred in the city extent. Then we employed slope, exclusion zones, urban areas, transportation network and hillshade layer of the study area in the SLEUTH modeling method to predict town sprawl up to the year 2050. We applied weighted linear combination technique of the MCE to define areas suitable for landfill. Linking the results from the two modeling methods yielded necessary information on the available land and the corresponding location for landfill given two different scenarios of town expansion up to the year 2050. These included two scenarios for city expansion and three scenarios for waste disposal. The study proved the applicability of the modeling methods and the feasibility of linking their results. Also, we showed the usefulness of the approach to decision makers in proactively taking measures in managing the likely environment change and possibly directing it towards more sustainable outcomes. This also provided a basis for dynamic land use allocation with regards to the past, present and likely future changes

Simulating the spatiotemporal changes of forest extent for the Chehelchay watershed (Golestan province), using integrated CA-Markov model

The importance of land-use/ land-cover (LULC) as a dynamic factor and effective on environmental conditions, makes it necessary to get precise quantitative and qualitative information from it and to identify its changes through short time periods. Modeling LULC changes provides useful information for better understanding of the changes process, determining driving forces and predicting areas under change conditions. In this study, the integrated CA-Markov model was used to simulate forest cover changes in the Chehelchay Water catchment at minodasht area of Golestan province of Iran. CA-Markov is a combined Cellular Automata/Markov chain land cover prediction procedure that adds an element of spatial contiguity to the stochastic Markov chain analysis. Landsat images of 1987 and 2009 and land-use map of 2001 were used to derive forest extent maps of the Chehelchay Water catchment and characterize changes through time. To investigate the relationships between forest extent changes and some environmental and human-related factors and to produce transition suitability maps, a logistic regression analysis was applied between forest extent changes as response variable and the deriving factors as explanatory variables. Future LULC types for 2009 were then predicted using CA–Markov model, based on the land-cover changes between 1987 and 2001. In order to evaluate the modeling results, prediction for 2009 was compared with the observed 2009 land cover map. The computed accuracy coefficient indicated high efficiency of CA-Markov for simulating forest extent changes in the Chehelchay Water catchment (Kappa = 0.92). Finally, assuming current trends in LULC changes continue, forest cover map for the year 2020 was developed. The results indicate that there will be a notable decrease in forest area

A geo-statistical approach to model Asiatic cheetah, onager, gazelle and wild sheep shared niche and distribution in Turan biosphere reserve-Iran

Presence data for four mammals in the Turan Biosphere Reserve in Iran including the Asiatic cheetah ('Acinonyx jubatus venaticus'), the Persian onager ('Equus hemionus onager'), the wild sheep ('Ovis vignei'), and the gazelle ('Gazelle Bennettii') were used to analyze and model their potential interaction, facilitation, habitat coverage and niche dimensions. A geostatistical approach using the spatial autocorrelation between the locality points, and their relationship with habitat resources and characteristics with application of remotely sensed maximum enhanced vegetation index (EVI) and surface temperature, elevation, aspect, vegetation cover and soil moisture was used to predict herbivores species niche. The potential suitable habitat of herbivores along with environmental variables was used to model the predator species (cheetah) niche. The model results were tested using fivefold cross validation by area under the curve (AUC) values on set of independent testing data and were compared to more commonly used models of generalized linear model (GLM) and MaxEnt. The results show that cheetah's potential suitable habitat has 61% overlap with wild sheep, 36% with onager, and 30% with gazelle. Onager habitat has 64% overlap with gazelle and 60% the wild sheep. Wild sheep on the hand, shares only 37% of its habitat with gazelle. The most prey and predator interaction exists between cheetahs and wild sheep, while onagers provides facilitation for gazelles and wild sheep by potentially providing extra water sources. Among the implemented modeling techniques, spatial GLM showed better performance over GLM and MaxEnt. We suggest that conservation effort should focus more on maintaining the population of wild sheep and onagers to support other species in the habitat