Land use/cover change detection and urban sprawl analysis in Bandar Abbas city, Iran

Dynamics of streamflow predictability and memory in response to hydrological extremes: insights from the Bandar Abbas watershed

Hydrological extremes, worsened by climate change, disrupt river flow, threatening water resources in arid regions. This study analyzed data from 17 hydrometric stations (1981-2017) across two periods to assess drought impacts on flow behavior in the Bandar Abbas watershed, Iran. Using the Stream Drought Index, noise variance, Lyapunov exponent (LE), Hurst coefficient (H), approximate entropy (ApEn), and the Mann-Kendall trend test, we identified significant changes. Drought severity increased, with noise variance in drought patterns rising by up to 3475% in central zones and dropping by 70% elsewhere, signaling more frequent and intense dry spells. River flow declined at 12 stations, with the Mann-Kendall test confirming negative trends, reducing water availability by up to 150% in volume at northern sites. LE decreased by 1165% across most stations, indicating drought lowered flow sensitivity to initial conditions, while H fell by 50%, weakening long-term flow memory. Meanwhile, ApEn surged by 354%, reflecting increased randomness and reduced predictability, particularly in northern areas. These shifts strain water availability for ecosystems and agriculture, disrupt aquatic habitats, and challenge management strategies reliant on stable flow. This multi-tool approach, novel in this context, merges chaos, memory, and randomness analyses to clarify drought's effects. Focused on Bandar Abbas, the findings offer insights for arid regions globally, providing a framework for adaptive water management to address scarcity and unpredictability in river systems under climate stress.

Potentially toxic elements (PTEs) in coastal sediments of Bandar Abbas city, North of Persian Gulf: An ecological risk assessment

The concentration of potentially toxic elements (PTEs; Lead (Pb), Nickel (Ni), and Cadmium (Cd) Pb, Ni, and Cd), using flame atomic absorption spectrometry (FAAS) was measured in fifty surface coastal sediment samples collected from 5 points coastal sediment of Bandar Abbas city, Iran besides the potential ecological risk index (RI) estimated the environmental health risk. The rank order of PTEs was Pb (52.090 ± 4.113 mg/kg dry weight) > Ni (34.940 ± 8.344 mg/kg dry weight) > Cd (2.944 ± 0.013 mg/kg dry weight). RI due to PTEs in sediments for A, B, C, D, and E points were 187.655, 190.542, 191.079, 189.496, and 192.053, respectively. RI for sampling points A to E was at moderate risk (150 ≤ RI < 300). Therefore, it is recommended to carry out control programs to reduce the amount of PTEs in the coastal sediment of the Persian Gulf.

Coastal Flooding Risk Assessment Using a GIS-Based Spatial Multi-Criteria Decision Analysis Approach

Coastal areas are expected to be at a higher risk of flooding when climate change-induced sea-level rise (SLR) is combined with episodic rises in sea level. Flood susceptibility mapping (FSM), mostly based on statistical and machine learning methods, has been widely employed to mitigate flood risk; however, they neglect exposure and vulnerability assessment as the key components of flood risk. Flood risk assessment is often conducted by quantitative methods (e.g., probabilistic). Such assessment uses analytical and empirical techniques to construct the physical vulnerability curves of elements at risk, but the role of people’s capacity, depending on social vulnerability, remains limited. To address this gap, this study developed a semiquantitative method, based on the spatial multi-criteria decision analysis (SMCDA). The model combines two representative concentration pathway (RCP) scenarios: RCP 2.6 and RCP 8.5, and factors triggering coastal flooding in Bandar Abbas, Iran. It also employs an analytical hierarchy process (AHP) model to weight indicators of hazard, exposure, and social vulnerability components. Under the most extreme flooding scenario, 14.8% of flooded areas were identified as high and very high risk, mostly located in eastern, western, and partly in the middle of the City. The results of this study can be employed by decision-makers to apply appropriate risk reduction strategies in high-risk flooding zones.

A Spatiotemporal Assessment of Land Use and Land Cover Changes in Peri-Urban Areas: A Case Study of Arshaly District, Kazakhstan

In this study, the spatiotemporal dynamics of land use and land cover (LULC) were evaluated in the peri-urban area of the Arshaly district, which borders the capital of the Republic of Kazakhstan. Landsat multispectral images were used to study the changes in LULC. The analysis of LULC dynamics was carried out using supervised classification with a multi-temporal interval (1998, 2008, and 2018). During the study period, noticeable changes occurred in LULC. There was an increase in the area of arable land and forests and a reduction in the pastures. There was a sharp increase in the built-up area; that is, there was an intensification of land use through an increase in the share of arable land as well as the transformation of agricultural land for development. However, in general, the influence of urban sprawl in this peri-urban area has so far been accompanied by only a slight focus on its sustainable development.

Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk

Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences the accuracy of urban object detection, and (2) the urban structure, which influences the morphological recognition and characterization of built-up areas. In this study, high repetitiveness remote sensing Sentinel-2A and SPOT 6 high-resolution satellite images were combined to characterize and detect urban built-up areas over the city of Yakutsk. High temporal resolution of Sentinel-2A allows land use change detection and metric spatial resolution of SPOT 6 allows the characterization of built-up areas’ socioeconomic functions and uses.

Differences in Urban Built-Up Land Expansion in Zhengzhou and Changsha, China: An Approach Based on Different Geographical Features

The disorderly expansion of urban built-up land is a global issue. It is of great significance to guide urban land use scientifically through the analysis of geographical features to identify the mechanisms that underlie differences in urban built-up land expansion. We selected Changsha and Zhengzhou in China, whose built-up areas during the initial period of study had different natural geographical features, but similar human geographical features, and systematically explored the development and evolution characteristics of the natural and human geographical features from 1990 to 2010 using a landscape metrics analysis and an urban built-up land intensive use analysis. We found that (1) although human beings have a strong ability to transform nature, they have to rely on the natural endowment of the land to develop the cities and, thus, have formed different landscape patterns and levels of urban built-up land intensive use; (2) in places where the natural geographical features are more restrictive, land-use policy-makers are more cautious in their decision-making, which more closely links the land-use policies and human geographical features, thereby simultaneously increasing the degree of intensive built-up land use and reducing the number of problems that arise from urban built-up land expansion. This research can provide a reference for the development of policies for urban built-up land use in Changsha and Zhengzhou. It also can provide ideas for how to implement different built-up land management policies for other cities with different natural and human geographical features.

Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran

The study of changes and destruction rate in the previous years as well as the possibility of prediction of these changes in the following years has a key role in optimal planning, controlling, and restricting non-normative changes in the future. This research was approached to detecting land use/cover changes (1985-2007) and to forecast the changes in the future (2021) use of multitemporal satellite imagery in semi-arid area in western Iran. A supervised classification of multilayer perceptron (MLP) was applied for detecting land use changes. The study area was classified into five classes, those of forest, rangeland, agriculture, residential, and barren lands. The change detection analysis indicated a decreasing trend in forest cover by 30.42%, while other land uses were increased during 1985 to 2007. The land use changes were predicted using Markov chain model for 2021. The model was calibrated by comparing the simulated map with the real detected classes of land cover in 2007. Then, for further model processing, an acceptable accuracy at 83% was achieved between them. Finally, land use changes were predicted by using transition matrix derived from calibrated approach. The findings of this study demonstrate a rapid change in land use/cover for the coming years. Transforming the forest into other land uses especially rangeland and cropland is the main land cover changes in the future. Therefore, the planning of protection and restoration of forest cover should be an essential program for decision-makers in the study area.