Analysing the Driving Forces and Environmental Effects of Urban Expansion by Mapping the Speed and Acceleration of Built-Up Areas in China between 1978 and 2017

Wang, Lan; Jia, Yinghui; Li, Xinhu; Gong, Peng

doi:10.3390/rs12233929

Cited by 18 publications

(10 citation statements)

References 55 publications

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“…This is in line with other studies that reported the dominance of anthropogenic activities in driving LULC change [89][90][91]. Anthropogenic activities are often rapid and tend to affect large tracts of land when compared to natural processes [5,[92][93][94]. This study is novel in the sense that it yielded spatially explicit information on the extent of LULC change spread, including detecting likely pathways of the spread for the different categories investigated.…”

Section: Discussionsupporting

confidence: 88%

“…According to Seybold et al [3] and Martin et al [4], LULC changes affect climate processes, biogeochemical cycles, nutrient loads, and water resources. Tracking the spatial, temporal, and likely pathways of LULC changes is crucial for landscape management and ecosystem services assessments [5][6][7]. Owing to their complexity and role as key livelihood support systems for over 60% of the world's population, interest in how LULC changes within transboundary basins (TDBs) and its effect on natural resources distribution is of high priority [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

2022

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Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

2022

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“…If the other parameters are fixed, only the contribution of land and capital to the economy is considered, capital could stimulate the economy, and land and capital could be replaced by technology. Thus, average fixed-asset investment was selected as an indicator to evaluate land-use intensity [ 30 ].…”

Section: Framework Analyzing the Driving Factors Of Urban Land Expans...mentioning

confidence: 99%

The Driving Mechanism of Urban Land Expansion from 2005 to 2018: The Case of Yangzhou, China

Lin

2022

IJERPH

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The driving mechanism of expansion is the basis for policymaking in urban land management and control. In this study, Yangzhou city in China was used as an example. Based on the supply and demand theory of land, a framework was constructed to analyze the driving factors behind urban land expansion. Path analysis was used to determine the direct and indirect factors driving urban land expansion. The results showed the following: (1) Urban land in Yangzhou city expanded 32,831.10 hm2 from 2005 to 2018, mainly in terms of farmland. This rapid expansion arose from the contraction of rural residential areas, threatening ecological spaces such as water bodies. (2) Path analysis indicated that fixed-asset investment, the added value of secondary and tertiary industries, urban population, distance to the city center, and farmland area are the direct factors driving urban land expansion. Land finance, technology input, and transportation ability indirectly influence urban land by affecting other driving factors. (3) Increasing the level of urban land-use intensification, properly adjusting economic development goals, reasonably controlling the population, exploring the retention of construction land in city centers, and optimizing farmland and basic farmland plans need to be considered in the management and control of urban land expansion. Furthermore, to guide the orderly expansion of urban land, improving land management systems, promoting scientific and technological progress, and scientifically designing transportation land are necessary strategies.

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“…There are various metrics applied to quantify the degree of urban expansion, such as the expansion area, speed, and acceleration of built-up [3]. In this study, we defined the urban spatial-expansion index as the percent of built-up area (PB), which equals the area of urban landscape divided by the total area of study unit.…”

Section: Urban Spatial-expansion Indexmentioning

confidence: 99%

“…Urban spatial expansion is one of the most dramatic types of land use and land cover change (LUCC) and exerts irreversible human impacts on the global biosphere [1,2]. Urbanization due to urban population growth and economic development has intensified expansion of the built environment [3], resulting in the conversion of a large amount of natural landscapes into non-vegetated artificial landscapes such as roads, buildings, and other infrastructures [4]. Although the global built-up area was only 0.65% in 2010 [5], LUCC in urban areas has become one of the primary drivers of habitat loss and species extinction all around the world [6].…”

Section: Introductionmentioning

confidence: 99%

Quantitatively Assessing Ecological Stress of Urbanization on Natural Ecosystems by Using a Landscape-Adjacency Index

Lin

Jones

et al. 2021

Remote Sensing

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Urban spatial expansion poses a threat to regional ecosystems and biodiversity directly through altering the size, shape, and interconnectivity of natural landscapes. Monitoring urban spatial expansion using traditional area-based metrics from remote sensing provides a feasible way to quantify this regional ecological stress. However, variation in landscape-adjacency relationships (i.e., the adjacency between individual landscape classes) caused by urban expansion is often overlooked. In this study, a novel edge-based index (landscape-adjacency index, LAdI) was proposed based on the spatial-adjacency relationship between landscape patches to measure the regional ecological stress of urban expansion on natural landscapes. Taking the entire Yangtze River Delta Urban Agglomerations (YRD) as a study area, we applied the LAdI for individual landscape classes (Vi) and landscape level (LV) to quantitatively assess change over time in the ecological stress of YRD from 1990 to 2015 at two spatial scales: municipal scale and 5 km-grid scale. The results showed that the vulnerable zones (LV ≥ 0.6) were mainly distributed in the north of the YRD, and cultivated land was the most vulnerable natural landscape (Vi ≥ 0.6) at the 5 km-grid scale. The most vulnerable landscape at the municipal scale was cultivated land in 19 of 26 cities in each period, and that in the remaining 7 cities varied at distinct urbanization stages. We used scatter diagrams and Pearson correlation analysis to compare the edge-based LAdI with an area-based index (percent of built-up area, PB) and found that: LV and PB had a significant positive correlation at both the municipal scale and 5 km-grid scale. But there were multiple LVs with different values corresponding to one PB with the same value at the 5 km-grid scale. Both indexes could represent the degree of urban expansion; however, the edge-based metric better quantified ecological stress under different urban-sprawl patterns sharing the same percent of built-up area. As changes in land use affect both the size and edge effect among landscape patches, the area-based PB and the edge-based LAdI should be applied together when assessing the ecological stress caused by urbanization.

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Analysing the Driving Forces and Environmental Effects of Urban Expansion by Mapping the Speed and Acceleration of Built-Up Areas in China between 1978 and 2017

Cited by 18 publications

References 55 publications

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

The Driving Mechanism of Urban Land Expansion from 2005 to 2018: The Case of Yangzhou, China

Quantitatively Assessing Ecological Stress of Urbanization on Natural Ecosystems by Using a Landscape-Adjacency Index

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