Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

Abstract

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.

Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions

“…We assume that the residential building stock of Valparaíso and Viña del Mar can be fully represented in terms of the typologies originally proposed by the SARA project [67]. That project established the composition of the residential building stocks of the Andean countries based on expert elicitation to design "mapping schemes".…”

“…For each dwelling class, the authors proposed "dwelling fractions" (i.e., dwellings per building type) to obtain the number of buildings. For Valparaíso, the SARA project proposed 22 classes [67], while we reduced this to 16 (Figure 3). The assumptions considered to reduce the number of typologies were as follows:…”

Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing

“…Therefore, remote sensing measurements can be recorded using various platforms, such as unmanned aerial vehicles (UAVs), airplanes, and satellites, from which many infrastructure changes can be detected and monitored. A few examples of such remote sensing applications to infrastructure monitoring include observing infrastructure damage after natural and man-made disasters, planning long-term road consolidation, and detecting dam instabilities [6][7][8][9][10][11][12][13][14][15]. Figure 1a shows the total length (approximately 214,000 km) of the road network in Iran, including primary (freeway and highway), secondary (major and local), and rural roads.…”

Editorial: Special Issue “Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems”

“…This special issue focused on the applications of AI to environmental systems related to hazard assessment in Urban, Agriculture and Forestry. A total of ten papers were published in this special issue, with topics ranging from reviewing the current climate-smart agriculture approaches for smart village development [ 1 ] to the integration of visible and infrared thermal cameras for automated urban green infrastructure monitoring on top of moving vehicles [ 2 ]; the implementation of machine learning to classify contaminant sources for urban water networks [ 3 ]; water network contamination assessment using machine learning in the UK [ 4 ]; future landscape changes, seismic and hazard assessment tested in Tabriz, Iran assessed using satellite remote sensing [ 5 ]; AI applied to a robotic dairy farm to assess milk productivity and quality traits using meteorological and cow data [ 6 ]; AI and computer vision from visible and infrared thermal images to obtain non-invasive biometrics from sheep to assess welfare [ 7 ]; the assessment of smoke contamination and smoke taint in wines due to bushfires using a low-cost electronic nose and AI [ 8 ]; the classification of smoke contaminated grapevine berries and leaves using chemical fingerprinting and machine learning [ 9 ]; and the detection of aphid infestation in wheat plants and insect-plant physiological interactions using low-cost electronic noses, chemical fingerprinting and machine learing [ 10 ].…”

“…Similar work was conducted by Grbčić et al [ 4 ] to locate contamination sources in water networks with a combination of Artificial Neural Network (ANN) to classify pollution sources. Other types of hazard assessments in urban systems were based on a case study in Tabriz, Iran, by Mohammadi et al [ 5 ]. Using satellite remote sensing to extract land information made it possible to predict landscape changes due to seismic activity with high accuracy ranging from 94 to 96%.…”