Hazard Reduction Research Articles

Site effect estimation for shallow engineering bedrock using microtremor HVSR method and geotechnical borehole data, Riyadh city, Saudi Arabia

The estimation of local site response and geotechnical properties of near-surface deposits becomes essential for improving the building codes especially in big cities as Riyadh. This study aims to estimate the site response parameters in the northern expansion zone of Riyadh city. Microtremor measurements were recorded at 45 sites at the study area, north of Riyadh, and the resonance frequency and amplification factor were calculated using the H/V spectral ratio approach. The microtremor data has been processed using geopsy software to get the H/V curve and get the value of the resonance frequency (fo) and amplitude (Ao). The values of the resonance frequency span from 0.79 to 45 Hz, while the amplification factor ranges from 1.22 to 8.5, indicating the presence of foundation bedrocks at various levels with varying soil thickness. Moreover, the N60 parameter was calculated to correct the SPT-N uncorrected field readings in order to estimate the shear wave velocity parameter down to 30 m depth (Vs30). The computed shear wave velocity for a depth of 30 m (Vs30) ranges from 210 to 630 m/s. Accordingly, the soil at the studied area can be divided into two site soil classes C and D according to the National Earthquake Hazard Reduction Program (NEHRP). The findings of this study should be considered as part of the development of the Saudi Building Code for Riyadh City.

Influence of Uncertainty in Vs Profiles in the Southeastern Korea On Site Response Analyses

The effect of uncertainty on the shear wave velocity (VS) profiles from the Korean soil conditions on the seismic site response analysis (SRA) was investigated by comparing the amplification factors (AFs) computed from 170 VS profiles to those from three (representative) statistically derived VS profiles (Approach 1: a constant standard deviation of VS, σln(Vs), Approach 2: a depth-dependent, σln(Vs), and Approach 3: 5% and 95% percentiles for generating lower and upper bounds). All of the VS profiles are divided into three site classes (B, C, and D) by the National Earthquake Hazards Reduction Program (NHERP) site classification system. Seven pairs of ground motion records (two horizontal components) that represent Korean bedrock conditions are entered into all of the three types of VS profiles to run the SRA with a single type of modulus reduction and damping curves. The root-mean squared difference (RMSD) of the mean and standard deviation, (μln(AF), and σln(AF)) of the AFs in natural log-scale between the three types of VS profiles is estimated, which results in Approach 3 with the smallest RMSD measure for all the site classes. Conventionally, characterizing the uncertainty of the VS profiles is typically assessed through the representative profiles of Approach 1; however, this practice suggests that the inclusion of the percentile bounds (Approach 3) can be supplemental to better describe the VS uncertainty while taking the Approach 1 as primary representative profiles.

Prediction of air quality in Sydney, Australia as a function of forest fire load and weather using Bayesian statistics.

Smoke from Hazard Reduction Burns (HRBs) and wildfires contains pollutants that are harmful to human health. This includes particulate matter less than 2.5 μm in diameter (PM2.5), which affects human cardiovascular and respiratory systems and can lead to increased hospitalisations and premature deaths. Better models are needed to predict PM2.5 levels associated with HRBs so that agencies can properly assess smoke pollution risk and balance smoke risk with the wildfire mitigation benefits of HRBs. Given this need, our aim was to develop a probabilistic model of daily PM2.5 using Bayesian regression. We focused on the region around Sydney, Australia, which regularly has hazard reduction burning, wildfires and associated smoke. We developed two regional models (mean daily and maximum daily) from observed PM2.5, weather reanalysis and satellite fire hotspot data. The models predict that the worst PM2.5 in Sydney occurs when PM2.5 was high the previous day, there is low ventilation index (i.e. the product of wind speed and planetary boundary layer height), low temperature, west to northwest winds in the Blue Mountains, an afternoon sea breeze and large areas of HRBs are being conducted, particularly to the west and north of Sydney. A major benefit of our approach is that models are fast to run, require simple inputs and Bayesian predictions convey both predicted PM2.5 and associated prediction uncertainty. Future research could include the application of similar methods to other regions, collecting more data to improve model precision and developing Bayesian PM2.5 models for wildfires.

Urban–rural disparity of social vulnerability to natural hazards in Australia

Assessing vulnerability to natural hazards is at the heart of hazard risk reduction. However, many countries such as Australia lack measuring systems to quantity vulnerability for hazard risk evaluation. Drawing on 41 indicators from multiple data sources at the finest spatial unit of the Australian census, we re-forged the Cutter’s classic vulnerability measuring framework by involving the ‘4D’ quantification of built environment (diversity, design, density and distance), and constructed the first nationwide fine-grained measures of vulnerability for urban and rural locales, respectively. Our measures of vulnerability include five themes—(1) socioeconomic status; (2) demographics and disability; (3) minority and languages; (4) housing characteristics; and (5) built environment—that were further used to assess the inequality of vulnerability to three widely affected natural hazards in Australia (wildfires, floods, and earthquakes). We found the inequality of vulnerability in the affected areas of the three hazards in eight capital cities are more significant than that of their rural counterparts. The most vulnerable areas in capital cities were peri-urban locales which must be prioritised for hazard adaptation. Our findings contribute to the risk profiling and sustainable urban–rural development in Australia, and the broad understanding of place-based risk reduction in South Hemisphere.

Simultaneous reduction of nitrogen oxides and sulfur dioxide in circulating fluidized bed combustor during oxy-coal combustion

For stable carbon capture utilization and storage, highly-pure CO2 content and low pollutant emissions must be maintained throughout oxy-coal firing operation; however, studies on combining absorbent addition and oxidant staging combustion to eliminate simultaneous nitrogen oxides (NO, N2O) and sulfur dioxide (SO2) have not been reported. Accordingly, the purpose of this study was to investigate the effects of oxidant staging and limestone addition on the reductions of NO, N2O, and SO2 emissions in an oxy-circulating fluidized bed combustor at 25% and 31% input oxygen. The operating parameters considered in this study were: oxidant compositions; corresponding flow rate ratios; primary, secondary, and tertiary oxidants (PO, SO, and TO, respectively); TO supply heights; and limestone addition. The results revealed that compared to the TO supply heights, increasing TO flow rate was more effective at reducing the nitrogen oxide emissions. Under an optimal flow rate ratio (PO:SO:TO = 70:10:20) and TO height (6.4 m), NO, N2O, and SO2 emissions were ≈7.4, 4.2 and 4.1 mg MJ−1, respectively. The increase in SO2 emission caused by the application of oxidant staging could be effectively decreased by 60.6% without impacting combustion performance via limestone injection into the combustor; effectively facilitating the production of >86 vol% CO2. These findings can serve as valuable references for future designs, and the efficient operation of oxy-coal combustion plants constructed at larger scales, thereby ensuring highly-pure CO2 content and low pollutant emissions.

Designing forest restoration projects to optimize the application of broadcast burning

Active forest restoration programs on western US national forests face multiple challenges to meet their broad ecological goals while designing projects that generate sufficient revenue to build and maintain private forest management capacity needed to expand the scale and scope of treatments. We explored ways to design projects where admixing of treatments along gradients of dry and moist mixed conifer forest types could maximize financial viability while including substantial area where broadcast burning could be applied in conjunction with other treatments. In general, we found that restoration treatments in dry forests that included density reduction thinning and broadcast burning resulted in a net projected cost ranging from $110 to $8000 per ha. By contrast, density reduction thinning in moist mixed conifer forests on more productive microsites generated significant commercial timber volume and projected revenue that ranged from $4000 to $20,000 per ha. We used spatial optimization methods to identify potential project areas that maximized revenue while meeting constraints to treat a minimum proportion of each project with broadcast burning. Multiple project area sizes were also explored to understand the effect of restoration scale on financial outcomes. We found that optimal projects in terms of generating revenue to subsidize density reduction and broadcast burning were 810 ha and contained >50% dry forest area. Larger projects and those with a higher percentage of dry forest area resulted in lower revenue, eliminating revenue when projects reached 2700 ha. Forest restoration programs can use these methods to plan and design restoration projects that are financially viable while addressing the broadcast burn backlog in dry forests that require relatively expensive fuel reduction treatments prior to re-introducing fire.

Wildfire burn severity and emissions inventory: an example implementation over California

Wildfire severity is a key indicator of both direct ecosystem impacts and indirect emissions impacts that affect air quality, climate, and public health far beyond the spatial footprint of the flames. Comprehensive, accurate inventories of severity and emissions are essential for assessing these impacts and setting appropriate fire management and health care preparedness strategies, as is the ability to project emissions for future wildfires. The frequency of large wildfires and the magnitude of their impacts have increased in recent decades, fueling concerns about decreased air quality. To improve the availability of accurate fire severity and emissions estimates, we developed the wildfire burn severity and emissions inventory (WBSE). WBSE is a retrospective spatial burn severity and emissions inventory at 30 m resolution for event-based assessment and 500 m resolution for daily emissions calculation. We applied the WBSE framework to calculate burn severity and emissions for historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California, U.S., a substantially more extended period than existing inventories. We assigned the day of burning and daily emissions for each fire during 2002–2020. The framework described here can also be applied to estimate severity for smaller wildfires and can also be used to estimate emissions for fires simulated in California for future climate and land-use scenarios. The WBSE framework implemented in R and Google Earth Engine can provide quick estimates once a desired fire perimeter is available. The framework developed here could also easily be applied to other regions with user-modified vegetation, fuel data, and emission factors.

Correlation between SPT-N and shear wave velocity (VS) and seismic site classification for Amaravati city, India

The present study proposes a set of correlations between standard penetration number (SPT-N) and shear wave velocity (VS) using nonlinear regression analysis. These correlations have been developed for different categories of soil for Amaravati city. This study area is the current de facto seat of government of the Indian state of Andhra Pradesh, India. For this study, SPT-N data has been obtained from 400 boreholes distributed across the city of area 217.23 km2. Most of the boreholes considered for this study have a boring depth of 25 m to 45 m below the recorded ground surface. A comparison between the proposed correlations with existing worldwide accepted correlations has been performed to check the authenticity of this study. In addition to this, Vs obtained from the recommended correlations have been validated with the Vs obtained from 30 microtremor tests as well as established graphical methods. Furthermore, the seismic site classification of the study area has been conducted using the developed correlation for all soils. It is observed that the major part of Amaravati city falls under class D as per the National Earthquake Hazard Reduction Program (NEHRP) provisions. The average SPT-N and VS distribution across the city has also been generated at depths of 5, 10, 15, 20, 25, and 30 m. The outcomes of the study can be further used for the design of earthquake-resistant structures, seismic hazard studies, estimation of ground movements, and generation of site-specific synthetic ground motions.

Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.

High-Performance Liquid Crystalline Polymer for Intrinsic Fire-Resistant and Flexible Triboelectric Nanogenerators.

Flammability is a great challenge in the fields of electronics. The emergence of triboelectric nanogenerators (TENGs) provides a safe way to harvest environmentalally friendly energy and convert it into more secure power sources. Especially, polymer-based TENGs significantly accelerate the practical application of self-powered flexible electronics. However, most of the existing polymeric materials for TENGs are easily flammable and melt, dripping, in a fire scenario, and cannot be reused after combustion, which greatly limits the application of TENGs under extreme conditions. Herein, a fire-resistant TENG based on all-aromatic liquid crystalline poly(aryl ether ester) (LCPAEE ) synthesized via simple and efficient one-pot melt polycondensation is reported. The highly rigid main chain of LCPAEE endows the LCP-TENG with outstanding anti-dripping, temperature- and fire-resistance. The resultant LCP-TENG exhibits excellent electrical output performance, which is attributed to the high dielectric constant (ε'= 4.8) and fibrous-structured morphology of LCPAEE . The device can maintain over 65% of open-circuit voltage even after 16 s combustion (≈520°C). Consequently, this work offers a novel strategy for tailoring the TENGs toward a secure power generator and electronics with fire hazard reduction, and potential application in firefighting, personal protection, and other extreme temperature environments.

Terraced Landscapes as NBSs for Geo-Hydrological Hazard Mitigation: Towards a Methodology for Debris and Soil Volume Estimations through a LiDAR Survey

Terraced landscapes are widely applied in many mountainous regions around the world as a result of the necessity to practice subsistence agriculture. Hence, they can be regarded as one of the most diffused anthropogenic modifications of the Earth’s surface. Different techniques have been used for their implementation leading to the artificial immobilization of debris and soil along the slopes whose surface is interrupted by a sequence of sub-horizontal and sub-vertical areas often using stone walls. In some areas of the world, such interventions are thousands of years old and their resistance to the degradation caused by the morphogenetic system can be attributed to the permeability of the stone walls as well as to their regular maintenance. In some other areas, the lack of maintenance has been the main cause for degradation processes ending with their collapse. The effects of climate change manifested through higher intensities and higher frequencies of rainfall are likely to accelerate the degradation process further by causing terraces to act as a source of debris or hyperconcentrated flow. This will in turn increase the severity of geo-hydrological hazards. The measures concerning reduction of geo-hydrological hazards are sought through identification of abandoned terraces and assessment of the potential for their sudden collapse. The present paper describes a framework for identification of abandoned terraces and estimation of the potential volume of shallow landslides that can be generated. The research conducted aims to advance the existing hazard assessment practices by combining numerical modeling with processing of high-resolution LiDAR data. A new algorithm is developed to support localization of terraces. The catchment scale approach applied to eight smaller catchments enables estimation of the total volume of soil and debris trapped along the slopes. It also generated some important quantitative data which will be used in the future risk assessment work. The work has been carried out within the EU-funded H2020 project RECONECT.

An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding

Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and resource demands. This article presents an efficient probabilistic modeling approach that combines a simplified 1D hydrodynamic model for the entire Mekong Delta with a detailed 1D/2D coupled model and demonstrates its application at Can Tho city in the Mekong Delta. Probabilistic flood-hazard maps, ranging from 0.5 to 100 year return period events, are obtained for the urban center of Can Tho city under different future scenarios taking into account the impact of climate change forcing (river flow, sea-level rise, storm surge) and land subsidence. Results obtained under present conditions show that more than 12% of the study area is inundated by the present-day 100 year return period of water level. Future projections show that, if the present rate of land subsidence continues, by 2050 (under both RCP 4.5 and RCP 8.5 climate scenarios), the 0.5 and 100 year return period flood extents will increase by around 15- and 8-fold, respectively, relative to the present-day flood extent. However, without land subsidence, the projected increases in the 0.5 and 100 year return period flood extents by 2050 (under RCP 4.5 and RCP 8.5) are limited to between a doubling to tripling of the present-day flood extent. Therefore, adaptation measures that can reduce the rate of land subsidence (e.g., limiting groundwater extraction), would substantially mitigate future flood hazards in the study area. A combination of restricted groundwater extraction and the construction of a new and more efficient urban drainage network would facilitate even further reductions in the flood hazard. The projected 15-fold increase in flood extent projected by 2050 for the twice per year (0.5 year return period) flood event implies that the “do nothing” management approach is not a feasible option for Can Tho.

Restoration and fuel hazard reduction result in equivalent reductions in crown fire behavior in dry conifer forests.

Over the past several decades, the management of historically frequent-fire forests in the western United States has received significant attention due to the linked ecological and social risks posed by the increased occurrence of large, contiguous patches of high-severity fire. As a result, efforts are underway to simultaneously reduce potential fire and fuel hazards and restore characteristics indicative of historical forest structures and ecological processes that enhance the diversity and quality of wildlife habitat across landscapes. Despite widespread agreement on the need for action, there is a perceived tension among scientists concerning silvicultural treatments that modify stands to optimally reduce potential fire behavior (fuel hazard reduction) versus those that aim to emulate historical forest structures and create structurally complex stands (restoration). In this work, we evaluated thinning treatments in the Black Hills National Forest that exemplify the extremes of a treatment continuum that ranges from fuel hazard reduction to restoration. The goal of this work was to understand how the differing three-dimensional stand structures created by these treatment approaches altered potential fire behavior. Our results indicate that restoration treatments created higher levels of vertical and horizontal structural complexity than the fuel hazard reduction treatments but resulted in similar reductions to potential crown fire behavior. There were some trade-offs identified as the restoration treatments created larger openings, which generated faster mean rates of fire spread; however, these increased spread rates did not translate to higher levels of canopy consumption. Overall, our results suggest that treatments can create vertical and horizontal complexity desired for restoration and wildlife habitat management while reducing fire hazard and that they can be used in concert with traditional fuel hazard reduction treatments to reduce landscape scale fire risk. We also provide some suggestions to land managers seeking to design and implement prescriptions that emulate historical structures and enhance forest complexity.

Fuel in Tasmanian Dry Eucalypt Forests: Prediction of Fuel Load and Fuel Hazard Rating from Fuel Age

This paper presents equations for fuel load and fuel hazard rating (FHR) models based on the time since last fire for dry eucalypt forests in eastern Tasmania. The fuel load equations predict the load of the surface/near-surface and elevated fine fuel. The FHR equations predict the surface, near-surface, combined surface and near-surface, bark, and overall FHR. The utility of the “Overall fuel hazard assessment guide” from Victoria, Australia, is assessed for Tasmanian dry eucalypt forests: we conclude that, when fuel strata components are weighted according to their influence on fire behaviour, the Victorian guide provides a rapid, robust, and effective methodology for estimating FHR. The equations in this paper will be used for operational planning and on-the-ground performing of hazard reduction burning, prediction of fire behaviour for fire risk assessments and bushfire control, and providing inputs into the new Australian Fire Danger Rating System.

Information Technology in Disaster Management

In the present era of technology it has been simpler to deal with the disaster both natural and man made. Disaster Management activities rely upon substantial volumes of precise, applicable, on-time geo information that different organization systematically make and maintain. Information Technology is changing each part of human life. Disaster management desires forceful enhancements in its sources to decrease injury and save the lifetime of individuals. The advanced methods of information technology such as web, remote sensing, satellite communication, GIS, etc. can facilitate in coming up with and implementation of disaster management. Web provides a useful platform for disaster mitigation communications. On GIS is basic as effective preparedness, communication and training tool for disaster management on the other hand Remote Sensing as a tool can very effectively contribute towards identification of hazards areas. Communication satellite have become vital for providing emergency communication and timely help into natural disaster monitoring and mitigation mechanisms is critical for hazards reduction. There should be more emphasis on development of new technologies in disaster mitigation. The disaster preparedness and awareness is the only effective method of mitigating the impact of future disaster. In this paper an attempt has been made to highlight the role of information technology in management of natural disaster in Nepal.

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

The Ross procedure in adult patients: a single-centre analysis of long-term results up to 28 years.

This study aimed to provide an in-depth insight into our single-centre experience with the Ross procedure. All adults who underwent the Ross procedure between 1991 and 2014 were included. Based on the total number of Ross procedures performed by each surgeon at our centre during this 24-year period, surgeon volume was classified as low (<25 procedures), intermediate (25-44 procedures) and high (≥45 procedures). Survival, complications and reinterventions were evaluated. A single cardiologist assessed the pulmonary autograft's function and the neoaortic root diameter by echocardiography. The outcomes of 224 patients {176 men, 48 women; mean age 37.2 [standard deviation (SD) 10.0] years} were analysed. Patients operated on by a low-volume surgeon had 7.22 times higher odds (P < 0.001) for a serious adverse event during the intraoperative or early postoperative course than patients operated on by a high-volume surgeon. Early mortality was 1.8%. Overall survival was 87.3% at 20 years. Compared with the demographically matched general population, the patients' survival was significantly lower (P = 0.002). The cumulative incidence of autograft and right ventricular outflow tract conduit reintervention was 21.5% and 5.9% at 20 years, respectively. Patients with preoperative aortic regurgitation had 6.25 times the subdistribution hazard of autograft reintervention (Bonferroni-adjusted P = 0.042) and a higher neoaortic root z-score [1.37 (SD 2.04) vs 0.17 (SD 1.81), P = 0.004] than patients with aortic stenosis. In patients with preoperative aortic regurgitation, autograft wrapping (remnant aortic wall and/or Vicryl® mesh) was associated with a 74% reduction in the subdistribution hazard of autograft reintervention (Bonferroni-adjusted P = 0.002) and with a reduced incidence of neoaortic root dilatation (P = 0.037). The Ross procedure performed by a specialized surgeon provides very satisfying long-term results. The higher risk of autograft reintervention in preoperative aortic regurgitation may be counteracted by supporting the autograft.

Missing and Restoring Meaning

Missing and Restoring Meaning

Ensuring the seismic resistance of a building using a geotechnical seismic insulating screen

This paper considers the issue related to the protection of buildings and structures against seismic influences and the prevention, exclusion, or reduction of seismic hazards. The catastrophic destruction of modern «earthquake-resistant» buildings in Turkey and Taiwan has shown that existing methods of strengthening and reinforcing structures are not perfect and require further study. Analysis of existing approaches to ensuring seismic resistance showed that seismic insulation and seismic suppression systems still do not have a scientific and technical justification for the effectiveness of their operation from the point of view of ensuring the stability of structures. The estimation-dynamic models of the «base-seismic insulation-structure» system developed to date do not always make it possible to simulate the joint work of their interaction during an earthquake and account for the transformation of the seismic impact on the structure. An alternative technique has been devised, a geotechnical seismic insulation screen, as a seismic insulation system that reduces the intensity of seismic loads on the structure and ensures its seismic resistance. In a specific example, the effectiveness of this seismic insulation system is confirmed. This seismic insulation technique in the form of damper screens is characterized by reliability and manufacturability in ensuring the seismic resistance of objects under construction. The results of computational and experimental modeling of the interaction of an earthquake-insulated structure with a ground base found that the values of axial forces and bending moments in a building with a seismic insulating screen are less than in a building without seismic insulation by 30‒40 %. The geotechnical seismic insulation screen makes it possible to advance the development of new seismic insulation techniques and determine their effectiveness. This technique will also be effective when strengthening the base and seismic insulation systems of historical monuments, protecting them against seismic and dynamic influences.

Suitability of Urban Green Belt and Green Wedges in Chittagong City, Bangladesh: An Investigation into the Sustainable Urban Environment

Green belts and green wedges promote ecological balancing and hazard reduction in urban areas. The study attempted to identify suitable locations for urban green belts and green wedges in Chittagong city (CC). The mechanisms of the study were reviewing CC Master Plan, Detailed Area Plan (DAP), field observation, key informant interviews, and small groups discussions. Normalized Difference Vegetation Index was used for the vegetation classification from 1990 to 2021 at a 10-year interval. The urban built-up area of the Chittagong Development Authority (CDA) was 18.64 sq. km in 1990 while it became 139.74 in 2021. In 1990, dense vegetation was 153.21 Sq.km but it decreased to 19.39 Sq.km in 2021, because of rapid population growth and unplanned urbanization. A highly suitable site for the green belt was found in CDA coastal area. Besides, Kalurghat industrial zone, Chittagong Export Processing Zone, Nasirabad, and Baijid area were found appropriate for urban green wedges. Most suitable (83.60 sq. km) and moderately suitable (232.6032 sq. km) areas for the urban green belt were identified and in the coastal zone maximum suitable areas’ length was 57.61 km. The study concluded by urging for land-use zoning and the implementation of the urban green belt and green wedges, and raising voices against hill cutting by urban neighbourhood communities in CC.