Assessment of Land - Use Change Effects on Future Beekeeping Suitability Via CA-Markov Prediction Model

In this study, landuse changes in the Muğla province were determined and future Land Use Cover Change (LUCC) maps were predicted. Because Muğla accounts for 90% of pine honey production in the world, the study area has vital importance for the Turkish (also for other countries) beekeeping sector and this importance reveals the necessity of both monitoring and predicting the LUCC of Muğla in future. This study demonstrates a combined CA-Markov land use change model and beekeeping suitability analysis via Multi-Criteria Decision Analysis (MCDA) to predict the future of beekeeping suitability in Muğla in the Geographical Information Systems (GIS) platform. 2006 and 2012 LUCC maps were used to predict the 2018 LUCC, and transition probabilities between land cover classes were analyzed. A recent 2018 LUCC map was used to demonstrate accuracy analysis of the predicted 2018 LUCC map. Considering the 0.96 Kappa accuracy, a good fit was determined and the CA-Markov model was used to predict the 2025, 2030, 2040 and 2050 LUCC maps. Moreover, using the Analytical Hierarchy Process (AHP), beekeeping suitability assessment was generated. The results indicate that there will be a considerable increase in the urban areas and decrease in grasslands in the future. Related to this, the suitable areas will be decreased by 50 km2 and non-suitable areas will be increased by 76 km2 from 2018 to 2050. The study simulated the beekeeping suitability to guide beekeepers and local authorities towards a better understanding of the reasons for decreasing suitability and developing urgent land use management systems.

Quantifying the Effect of Land Use Change Model Coupling

Land-use change (LUC) is a complex process that is difficult to project. Model collaboration, an aggregate term for model harmonization, comparison and/or coupling, intends to combine the strengths of different models to improve LUC projections. Several model collaborations have been performed, but to the authors’ knowledge, the effect of coupling has not been evaluated quantitatively. Therefore, for a case study of Brazil, we harmonized and coupled the partial equilibrium model GLOBIOM-Brazil and the demand-driven spatially explicit model PLUC, and then compared the coupled-model projections with those by GLOBIOM-Brazil individually. The largest differences between projections occurred in Mato Grosso and Pará, frontiers of agricultural expansion. In addition, we validated both projections for Mato Grosso using land-use maps from remote sensing images. The coupled model clearly outperformed GLOBIOM-Brazil. Reductions in the root mean squared error (RMSE) for LUC dynamics ranged from 31% to 80% and for total land use, from 10% to 57%. Only for pasture, the coupled model performed worse in total land use (RMSE 9% higher). Reasons for a better performance of the coupled model were considered to be, inter alia, the initial map, more spatially explicit information about drivers, and the path-dependence effect in the allocation through the cellular-automata approach of PLUC.

What are the merits of endogenising land-use change dynamics into model-based climate adaptation planning?

Integrated assessment models often treat land-use change as an external driving force. In reality, land-use is influenced by environmental conditions. This paper explores the merits of endogenising land-use change, i.e. making the land-use change a dynamic internal process, in models used for supporting climate adaptation planning. For this purpose, we extend the Waas model, a hypothetical case study used before for testing new model-based climate adaptation approaches. We use a utility-based land-use change model for endogenising the land-use dynamics, evaluate its implications, and identify the conditions under which it becomes important. We find that endogenising land-use dynamics changes the performance of the policies, allows for assessing policies that affect land-use, and widens the outcomes of interest that can be considered. The relevancy of endogenising land-use dynamics depends on (i) the expected severity of future climate change, (ii) the society’s sensitivity to climate events, and (iii) the types of policy options that decision makers want to evaluate. Ignoring the interaction between the environment and the society (in this case land-use) can result in both under- and overestimation of the impacts of adaptation and might limit the adaptation options that are considered.

Simulating land use change in the southern part of Warsaw Metropolitan Area with application of Dyna-CLUE model

The present research covers the issue of prediction of changes of the land use appearing due to progressing urban sprawl process in Piaseczno county, located in the Warsaw Metropolitan Area. The Dyna-CLUE land use change model was applied, assuming a variety of scenarios pertaining to the direction of change of land use within a time prospect of 20 and 35 years. The scenarios pertaining to the expected land use changes are developed with consideration given to the expected population changes. When implementing the Dyna-CLUE model, 8 factors having impact on the land use patterns within the research area are considered. As a result of the modelling, maps demonstrating the expected land use patterns have been created, making it possible to indicate municipalities and areas within which there is the highest probability of emergence of new buildings.