Land use∖cover change and influencing factors inside the urban development boundary of different level cities: A case study in Hubei Province, China

Zhang, Hongwei; Wang, Zhanqi; Chai, Ji

doi:10.1016/j.heliyon.2022.e10408

Cited by 8 publications

(3 citation statements)

References 43 publications

(52 reference statements)

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“…Cao proposed a water-energy-carbon spatial optimization strategy for land use in urban agglomerations based on cities in the middle reaches of the Yangtze River [16]. At the city level, Zhang analyzed the factors influencing land use changes [17]. Ke established a hybrid network framework and revealed the role of different types of land in the low-carbon development of megacities [18].…”

Section: Impact Of Land Use Changes On Spatiotemporal Patterns Of Car...mentioning

confidence: 99%

Optimizing Land Use for Carbon Neutrality: Integrating Photovoltaic Development in Lingbao, Henan Province

Xie,

Deng,

et al. 2024

Land

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This study aims to examine the impact of land use variations on carbon emissions by incorporating the development of photovoltaics as a scenario. To meet this end, we investigate the carbon emissions fluctuations resulting from different development scenarios: natural development, low-carbon strategies, and widespread adoption of photovoltaic technology. We identify important influencing factors related to these changes and utilize multi-objective optimization and the PLUS model to simulate land use patterns in Lingbao City projected for 2035, with a focus on achieving carbon neutrality. Through multiple scenarios, we analyze differences in carbon emissions, economic benefits, ecological impacts, and land use allocations. Our findings demonstrate that the photovoltaic scenario leads to a substantial 3500-ton reduction in carbon emissions and boosts overall benefits by RMB 85 million compared to the low-carbon scenario. This highlights the significant role of photovoltaic systems inefficient land utilization, meeting carbon emission targets, and generating economic gains. This research explores the relationship between land use alterations and carbon emissions, aiming to achieve ambitious carbon reduction objectives by integrating photovoltaic applications across diverse land types. It provides fresh perspectives for examining urban land utilization and strategies to reduce carbon emissions.

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Section: Impact Of Land Use Changes On Spatiotemporal Patterns Of Car...mentioning

confidence: 99%

Optimizing Land Use for Carbon Neutrality: Integrating Photovoltaic Development in Lingbao, Henan Province

Xie,

Deng,

et al. 2024

Land

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“…The land use transfer matrix is a two-dimensional matrix constructed based on the relationship between land use changes in the study area at different times [28]. It can reflect in detail the dynamic information of the interconversion of the area of various land types in the study area at the beginning of the study period and at the end of the study period, and can not only indicate the area data of various land types in the study area at a certain point of time but also reveal the information of the area transfer out of various land types at the beginning of the period and the area transfer in of various land types at the end of the period [29].…”

Section: Land Use Transfer Matrix Modelmentioning

confidence: 99%

Evolution of Landscape Ecological Risk and Identification of Critical Areas in the Yellow River Source Area Based on LUCC

Song

Zhao

2023

Sustainability

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A reasonable evaluation of the ecological risk status of the landscape in the Yellow River source area is of practical significance for optimizing the regional landscape pattern and maintaining ecosystem function. To explore the regional heterogeneity of ecological risk in the watershed landscape, a landscape ecological risk evaluation model is constructed to evaluate the ecological risk status of the watershed for 20 years, and correlation analysis is used to further reveal the characteristics of the relationship between ecological risk and land use. The results show that the rapid expansion of urbanization and the increasing intensity of land development and use has caused significant changes in the Yellow River source area ecological environment and various land use types. The area of grassland decreased the most, by a total of 6160.04 km2, while the area of unused land increased the most, by a total of 2930.27 km2. A total of 12,453.11 km2 of land in the Yellow River source area was transformed, accounting for 9.52% of the total area. The most significant area of grassland was transferred out, accounting for 49.47% of the transferred area. During the study period, the proportion of area in the low-risk zone decreased from 54.75% to 36.35%, the proportion of area in the medium-low-risk zone increased from 21.75% to 31.74%, and the proportion of area in the medium-high-risk and high-risk zones increased from 10.63% to 14.38%. The high-risk areas are mainly located in areas with fragmented landscapes and are vulnerable to human activities. The mean ecological risk values in the study area show an increasing trend, and the spatial distribution shows a hierarchical distribution of “lower around the center and higher in the center”. The global Moran’s I index is higher than 0.68, which indicates that the ecological risk values have a significant positive correlation in space, the area of cold spots of ecological risk varies significantly, and the spatial pattern fluctuates frequently, while the spatial distribution of hot spots is relatively stable. Therefore, the landscape ecological risk in the Yellow River source area is rising, but the different risk levels and their spatial aggregation patterns and cold and hot spot areas continue to transform, which requires continuous planning of the landscape pattern to enhance the safety and stability of the regional ecosystem.

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“…In recent years, machine learning algorithms have been widely used in object recognition and remote sensing information extraction due to their powerful adaptive and self-learning parallel information processing capabilities [ [14] , [15] , [16] ]. Among them, the artificial neural network, decision tree, support vector machine, and random forest (RF) algorithm show good classification effect, and have received increasing attention from researchers [ [17] , [18] , [19] , [20] ].…”

Section: Introductionmentioning

confidence: 99%

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China

Chen,

Yang,

Jiang

et al. 2023

Heliyon

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Land use∖cover change and influencing factors inside the urban development boundary of different level cities: A case study in Hubei Province, China

Cited by 8 publications

References 43 publications

Optimizing Land Use for Carbon Neutrality: Integrating Photovoltaic Development in Lingbao, Henan Province

Optimizing Land Use for Carbon Neutrality: Integrating Photovoltaic Development in Lingbao, Henan Province

Evolution of Landscape Ecological Risk and Identification of Critical Areas in the Yellow River Source Area Based on LUCC

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China

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