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Dogan S, Kilicoglu C, Akinci H, Sevik H, Cetin M, Kocan N. Comprehensive risk assessment for identifying suitable residential zones in Manavgat, Mediterranean Region. EVALUATION AND PROGRAM PLANNING 2024; 106:102465. [PMID: 39032439 DOI: 10.1016/j.evalprogplan.2024.102465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 06/24/2024] [Accepted: 07/08/2024] [Indexed: 07/23/2024]
Abstract
The absence of comprehensive risk analysis in residential development within certain regions often leads to substantial human and material losses during natural disasters. The Mediterranean region, particularly susceptible to the impacts of climate change, is projected to witness an upsurge in the frequency of natural calamities like floods, landslides, and forest fires. Consequently, meticulous risk assessment during the selection of residential areas becomes paramount in this context. This study is dedicated to the evaluation of suitable residential zones in Manavgat, a pivotal location in the Mediterranean region with a progressively growing population. The findings indicate that approximately 4.26 % of the research area is deemed appropriate for residential establishment. The identification of these locations is crucial for ensuring human and material safety, as well as enhancing overall biocomfort. Moreover, this study provides a foundation for long-term planning initiatives within the region and makes a significant contribution to the international evaluation literature by demonstrating the application of integrated risk assessment methodologies in urban planning.
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Affiliation(s)
- Sedat Dogan
- Ondokuz Mayis University, Faculty of Engineering, Department of Geomatics Engineering, Samsun, Turkey
| | - Cem Kilicoglu
- Samsun University, Kavak Vocational School, Department of Architecture and Urban Planning, Samsun, Turkey
| | - Halil Akinci
- Artvin Coruh University, Faculty of Engineering, Department of Geomatics Engineering, Artvin, Turkey
| | - Hakan Sevik
- Kastamonu University, Faculty of Engineering and Architecture, Department of Environmental Engineering, Kastamonu, Turkey
| | - Mehmet Cetin
- Ondokuz Mayis University, Faculty of Architecture, Department of City and Regional Planning, Samsun, Turkey.
| | - Nurhan Kocan
- Bartın University, Faculty of Engineering, Architecture and Design, Department of Landscape Architecture, Bartin. Turkey.
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Wang X, Liu H, Sun Z, Han X. Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province. Heliyon 2024; 10:e37426. [PMID: 39296096 PMCID: PMC11409120 DOI: 10.1016/j.heliyon.2024.e37426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 09/02/2024] [Accepted: 09/03/2024] [Indexed: 09/21/2024] Open
Abstract
Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.
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Affiliation(s)
- Xiao Wang
- College of Mining and Geomatics, Hebei University of Engineering, Handan, China
| | - Haixin Liu
- College of Mining and Geomatics, Hebei University of Engineering, Handan, China
| | - Zhenyu Sun
- College of Mining and Geomatics, Hebei University of Engineering, Handan, China
| | - Xiaoqing Han
- Jizhong Energy Fengfeng Group Company Limited, Gaokai District, Handan, China
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Chen L, Yao Y, Xiang K, Dai X, Li W, Dai H, Lu K, Li W, Lu H, Zhang Y, Huang H, Wang M. Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 368:122261. [PMID: 39186853 DOI: 10.1016/j.jenvman.2024.122261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 08/13/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities.
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Affiliation(s)
- Liang Chen
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China; College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China
| | - Ying Yao
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China; College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China
| | - Keming Xiang
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China; College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China
| | - Xiaoai Dai
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China; College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China; Digital Hu Huanyong Line Research Institute, Chengdu University of Technology, Chengdu, 610059, China.
| | - Wenyu Li
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China; College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China
| | - Hang Dai
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China; College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China
| | - Ke Lu
- Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China
| | - Weile Li
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China
| | - Heng Lu
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, China
| | - Yang Zhang
- College of Geography and Planning, Chengdu University of Technology, Chengdu, 610059, China; School of Architecture, Southeast University, Nanjing, 210096, China
| | - Huan Huang
- Digital Hu Huanyong Line Research Institute, Chengdu University of Technology, Chengdu, 610059, China; College of Business, Chengdu University of Technology, Chengdu, 610059, China
| | - Meilian Wang
- Faculty of Geoscience and Engineering, Southwest Jiaotong University, Chengdu, 610059, China
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Keskin Citiroglu H. Determining climate classifications and producing climate border maps with GIS of Safranbolu district, Karabük, Türkiye. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:402. [PMID: 38546888 DOI: 10.1007/s10661-024-12562-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/23/2024] [Indexed: 04/02/2024]
Abstract
Knowing climate characteristics enables the detection of particular climate characteristics and their boundaries. This situation is essential in terms of providing sustainable use of areal resources and directing land use plans. For this reason, in this study, climate boundary maps of the Safranbolu district were created based on the need to form a basis for planning. For this purpose, measurement data of all meteorological stations in the district for the last 30 years were obtained; data were associated with the location, and the water balance of each station was calculated using the Thornthwaite climate classification method. In addition, the climate type was determined using different climate classification methods, and the results were compared. All applied methods have shown that Safranbolu has a humid climate; however, the humidity value in the north of Safranbolu is slightly higher than that in the central and southern parts. In addition, water shortage in the north of Safranbolu is observed in July-August, while water shortage in the central and southern parts is observed in July-August-September. Considering the long-term precipitation average of the Safranbolu district, the highest annual precipitation is observed in March and the lowest in August. Etp and Etr throughout the district are highest in July and lowest in January. Surplus water and surface flow occur in the months between December and May, with the highest amount of surface flow occurring in March. There is no month without rain in Safranbolu. Safranbolu, which is on the UNESCO World Heritage List, is a visiting area for local and foreign tourists because of its cultural, architectural, and historical features and geotourism potential. In addition to its current agricultural activities, the cultivation of the "Saffron" plant, which gives its name to the district, and its forest assets cause an increase in both the tourism capacity and population of the district. Considering all of these, studies on climate change risk management and water resources management in Safranbolu have been conducted.
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Cevik Degerli B, Cetin M. Evaluation of UTFVI index effect on climate change in terms of urbanization. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-27613-x. [PMID: 37211569 DOI: 10.1007/s11356-023-27613-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023]
Abstract
Urban heat island density and occurrence are closely related to land use/land cover and land surface temperature variation. The effect of UHI can be described quantitatively with the urban thermal area variance index. This study aims to evaluate the UHI effect of the city of Samsun with the UTFVI index. LST data from 2000 ETM + and 2020 OLI/TIRS Landsat images were used to analyze UHI. The results showed that the UHI effect increased in Samsun's coastline band in 20 years. As a result of the field analysis made from the UTFVI maps created, in 20 years, 84% decrease in the none slice, 104% increase in the weak slice, 10% decrease in the middle slice, 15% decrease in the strong slice, 8% increase in the stronger slice, and 179% increase in the strongest slice are observed. The slice with the most intense increase is in the strongest slice and reveals the UHI effect.
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Affiliation(s)
- Burcu Cevik Degerli
- Department of Landscape Architecture, Institute of Science, Kastamonu University, Kastamonu, Turkey.
| | - Mehmet Cetin
- Department of Landscape Architecture, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey
- Faculty of Architecture, Department of City and Regional Planning, Ondokuz Mayis University, Samsun, Turkey
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