Geographica Pannonica

Monitoring and predicting land use/land cover dynamics in Djelfa city, Algeria, using Google Earth Engine and a Multi Layer Perceptron Markov Chain Model

2024-04-01T11:27:31+02:00

Understanding the historical and projected changes in land use and land cover (LULC) in Djelfa city is crucial for sustainable land management, considering both natural and human influences. This study employs Landsat images from the Google Earth Engine and the support vector machine (SVM) technique for LULC classification in 1990, 2005, and 2020, achieving over 90% accuracy and kappa coefficients above 88%. The Land Change Modeler (LCM) was used for detecting changes and predicting future LULC patterns, with Markov Chain (MC) and Multi Layer Perceptron (MLP) techniques applied for 2035 projections, showing an average accuracy of 83.96%. Key findings indicate a substantial urban expansion in Djelfa city, from 924.09 hectares in 1990 to 2742.30 hectares in 2020, with a projected increase leading to 1.6% of nonurban areas transitioning to urban by 2035. There has been significant growth in steppe areas, while forested, agricultural, and barren lands have seen annual declines. Projections suggest continued degradation of bare land and a slight reduction in steppe areas by 2035. These insights underscore the need for reinforced policies and measures to enhance land management practices within the region to cater to its evolving landscape and promote sustainable development.

Characterisation of Hungary's regional tourism and economic performance between 2004 and 2022 in the light of EU funding

2024-04-01T11:27:32+02:00

The objective of the study is to show the regional differences in Hungary in terms of economic determination and tourism performance. The overdominance of Budapest can be identified in most socio-economic indicators. The consequence of the capital's "hydrocephalus" is that Hungary's peripheral regions have developed serious economic challenges, and reducing regional disparities in these areas is key. From a tourism perspective in particular, the capital's hydrocephalus is also an opportunity, as the spill-over effect can increase the popularity of other destinations in the country. The Balaton and Western Transdanubia regions are the main beneficiaries of this effect. In addition to the analysis of regional disparities, the study also looks at the impact on tourism of the crisis periods caused by the 2008 global economic crisis and the pandemic that unfolded in 2020-2021, which led to a historic low in the tourism sector, notably the pandemic, by analysing longer time series data. The balance between international and domestic tourism is key to the resilience of tourism to the crisis. Multi-directional tourism can reduce exposure to external factors and contribute to the stability of the tourism industry.

NDVI and NDBI Indexes as Indicators of the Creation of Urban Heat Islands in the Sarajevo Basin

2024-04-01T11:27:32+02:00

Remote sensing plays a vital role in analyzing urban changes. In this regard, various datasets collected from satellites today serve as a foundation for decision-makers and urban planners. This study compares the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) as indicators for the creation of surface heat islands. Using Landsat 8 OLI/TIRS C2 L2 images, spatial correlations between land surface temperature (LST) were examined for August 2013, 2019 and 2023. Urban heat islands (UHI) are a contemporary phenomenon and increasingly common in large urban areas compared to surrounding, less populated areas. With the advancement in remote sensing, it is possible to adequately determine the spatial differentiation and prevalence of urban heat islands (UHI). The study is based on Landsat 8 satellite image sets for the Sarajevo basin in August 2013, 2019 and 2023, which were used to analyze LST, NDVI, and NDBI indices. This work indicates a relationship between LST and NDVI but varies depending on the analyzed year. Normalized Difference Built-up Index (NDBI) serves as a suitable indicator for surface UHI effects and can be used as an indicator to assess its spatial distribution within a larger urban environment.

Which Psychological Characteristics Make a Good Geography Teacher in High School?

2024-04-01T11:27:32+02:00

This study seeks to examine what traits, “myths” and skills pupils will attribute to good geography teachers, and whether their assessments are influenced by gender, age, grade and satisfaction with a teacher. The sample consists of 150 high school pupils in Serbia. The survey consisted of four parts: socio-demographic characteristics, Big Five Inventory, good teacher myths, and good geography teacher skills. The results showed that pupils believe that good geography teachers have to be impartial, friendly and conscientious in the first place. Also, 13 high school teachers were also interviewed. The data are somewhat in line with previous research, but also indicate pupils’ specific expectations of their geography teachers and teachers’ awareness that they are not just ordinary teachers.

Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan

2024-04-01T11:27:32+02:00

This study utilizes satellite-based rainfall CHIRPS to evaluate GCMs-CMIP6 models over Sudan from 1985 to 2014. Overall, the GCMs of BCC-CSM2-MR, CAMS-CSM1-0, CESM2, EC-Earth3-Veg, GFDL-ESM4, MIROC-ES2L, and NorESM2-MM are well reproduced in the unimodal pattern of June to September (JJAS), and hence employed to calculate Multi-Model Ensemble (MME). Then, we examine the capability of the GCMs and MME in replicating the precipitation patterns on annual and seasonal scales over Sudan using numerous ranking metrics, including Pearson Correlation Coefficient (CC), Standard Deviation (SD), Taylor Skill Score (TSS), Mean Absolute Error (MAE), absolute bias (BIAS), and, normalized mean root square error (RMSD). The results show that the MME has the lowest bias and slightly overestimates rainfall over most parts of our study domain, whilst, others (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, FGOALS-f3-L, FGOALS-g3) consistently overestimate rainfall in referring to CHIRPS data, respectively, but FIO-ESM-2-0 underestimates bias value. Moreover, MIROC-ES2L and NorESM2-MM demonstrate better performance than the other models. Finally, we employed a bias correction (BC) technique, namely Delta BC, to adjust the GCMs model products through the annual and monsoon seasons. The applied bias correction technique revealed remarkable improvement in the GCMs against the observations, with an improvement of 0 – 18% over the original. However, MME and MIROC-ES2L show better performance after correction than other models.

Assessing Pedestrian Thermal Comfort to Improve Walkability in the Urban Tropical Environment of Nagpur City

2024-04-01T11:27:32+02:00

Walking can be an efficient and sustainable mode of transportation for "last mile" connectivity. However, the willingness to walk largely depends on the availability of infrastructure, safety, and comfort. Improving thermal comfort on streets connected to transit stations is crucial for encouraging walking and public transit use. This study assesses seasonal and spatiotemporal variations in pedestrian thermal comfort (PTC) on an N-S-oriented street in Nagpur (India). Thermal walk surveys simultaneously monitored environmental conditions and human thermal perception (thermal sensation vote- TSV). The findings revealed that urban geometry significantly influences PTC and TSV, and the level of influence varied spatiotemporally in both seasons. This study shows the relationship between urban street geometry, microclimate, and PTC, emphasizing the necessity of a multidimensional assessment approach.