Using the Remote Sensing Method to Simulate the Land Change in the Year 2030

Degerli, Burcu; Çetin, Mehmet

doi:10.24925/turjaf.v10i12.2453-2466.5555

Cited by 39 publications

(4 citation statements)

References 29 publications

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“…Promoting the UCI effect is key in developing sustainable, resilient, and adaptive urban ecosystems (Kong et al, 2014a;Yu et al, 2021;Çevik Degȇrli and Çetin, 2022). Blue-green spaces in cities serve as efficient heat regulation mechanisms, improving the urban thermal environment and thereby mitigating the urban heat island effect.…”

Section: Ethics Statementmentioning

confidence: 99%

Nature-based Solutions for Urban Resilience and Human Health

2024

Frontiers Research Topics

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Climate change and rapid urbanization have significant impacts on biodiversity and ecosystem functions and services. Nature-based solutions (NBS) is an action to work with and enhance nature to solve social challenges, and NBS is an "umbrella concept" for other mature nature-based approaches. Blue-green spaces (BGS) can provide a wide range of ecosystem services, including mitigation of urban heat island effects, reduction of flooding, mitigation of air pollution, and provision of recreational spaces, thereby promoting physical and mental health. Hence, NBSs can serve as cost-effective climate mitigation and adaptation tool that contribute to additional co-benefits for ecosystem health and human well-being. Environmentalists, epidemiologists, ecologists, urban planners, and policymakers have paid more attention to NBSs for urban resilience and human health. In this Research Topic, we hope to discuss these topics: (1) ecological exposure and health benefits; (2) climate adaptation and human health promotion possibilities by NBSs; (3) methodological and theoretical approaches as well as technologies of NBSs corresponding to urban resilience; (4) underlying pathways and potential mechanisms of NBSs in improving human health; and (5) policies and management for planning and design of the successful implementation of NBSs in relation to urban resilience and human health. This Research Topic focuses on, but is not restricted to the following issues: • Nature-based interventions for climate adaptation. • Ecological exposure and physical and psychological health outcomes. • Climate adaption environmental policies and management.

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Section: Ethics Statementmentioning

confidence: 99%

Nature-based Solutions for Urban Resilience and Human Health

2024

Frontiers Research Topics

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“…Previous studies have considered elements such as climatic factors (precipitation [21] and temperature [22]) and elevation [23], or climatic factors and nighttime lighting [24], ignoring the systematic effects of soil type [25], land cover type [26,27], slope [28], aspect [29], and population density [30] on vegetation growth. It also focuses on qualitative discussions and fails to quantitatively separate the impacts of climate change, topographic conditions, land cover, and human activities on vegetation change; there have not been enough studies conducted on the sustainability and future trend of vegetation development in Hubei Province.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

Chen,

Pan,

et al. 2024

Forests

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Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation.

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“…In this term, geospatial technology and remote sensing methodology are crucial for analyzing LULC change detection [7]. Remote sensing (RS) is used to detect changes in the impact of reservoirs on LULC change using supervised classification with a maximum probability algorithm to produce thematic maps [8,9].…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Pattern Changes Model in Small Island of Aceh Province, Indonesia: Implications for Sustainable Spatial Development

Azwar,

Achmad,

Mahidin

et al. 2024

Pol. J. Environ. Stud.

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Developing models for land use and land cover (LULC) and monitoring changes through predictive scenarios is crucial for supporting urban development strategies and improving our understanding of urban dynamics. Analysis of urban growth patterns based on LULC data from remote sensing using Geographic Information System (GIS) and Remote Sensing (RS) provides valuable insights into LULC changes. The CA-Markov model was used to predict LULC changes based on maps for 2012 and 2023, derived from satellite imagery using the maximum likelihood method, with an accuracy of 93% and 94% for each map. Analysis of urban growth patterns in Sabang City from 2013 to 2021 shows that the expansion of the built-up area is mainly driven by the conversion of bareland around the city center, with a 67% expansion pattern, 1% infilling pattern, and 16% outlying pattern. In Scenario 1, the growth of the built-up area in the city center is not significant, while in Scenario 2, the built-up area is projected to increase by 32 hectares to 742.6428 hectares by 2032. The urban growth pattern aligns better with Scenario 2, which emphasizes land conservation for forests and water bodies to preserve the highest carbon reserves from LULC changes.

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Using the Remote Sensing Method to Simulate the Land Change in the Year 2030

Cited by 39 publications

References 29 publications

Nature-based Solutions for Urban Resilience and Human Health

Nature-based Solutions for Urban Resilience and Human Health

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

Urban Growth Pattern Changes Model in Small Island of Aceh Province, Indonesia: Implications for Sustainable Spatial Development

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