Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China

Zhang, Zhonghao; Su, Shiliang; Xiao, Rui; Jiang, Diwei; Wu, Jiaping

doi:10.1016/j.apgeog.2013.09.013

Cited by 116 publications

(63 citation statements)

References 47 publications

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“…Our results indicate that land loss in Wenzhou was concentrated in areas of low elevation with gentle slope, near rivers and roads, which is consistent with previous research that show that factors such as elevation and distance are important driving forces for farmland occupation [20,[29][30][31][32]. Indeed, topography, water resources and accessibility substantially influence the spatial distribution of both construction lands and high quality farmlands.…”

Section: Characterization Of Quality and Quantity For Cultivated Landsupporting

confidence: 80%

Quality Perspective on the Dynamic Balance of Cultivated Land in Wenzhou, China

Lin

Gan

et al. 2017

Sustainability

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Cultivated land in China has faced severe challenges in recent years due to rapid urbanization. In 1997, the "cultivated land requisition-compensation balance" policy was implemented by the government to maintain the quantity and quality of cultivated land. Previous studies mainly focused on the quantity changes of cultivated land. In this study, from a quality perspective, we characterized the occupation and compensation of cultivated land in Wenzhou City utilizing remote sensing and geographic information systems during 2005-2010 and 2010-2014. Our results indicated that although the quantity balance of cultivated land has been achieved in Wenzhou, there was a trend of consuming prime cultivated land for urbanization while compensating with less productive land. It was also found that topography, water resources, and accessibility play important roles in cultivated land changes, with urbanization occurring on the eastern coastal plain where high quality lands are prevalent. Less than 60% of the gained lands were under cultivation, with the majority of reclaimed land from forests and coastal areas and located in remote regions. Therefore, we suggest that a "cultivated land protection red line" policy should be implemented to protect the best cultivated lands, while preventing random land reclamation to secure agricultural and environmental sustainability.

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Section: Characterization Of Quality and Quantity For Cultivated Landsupporting

confidence: 80%

Quality Perspective on the Dynamic Balance of Cultivated Land in Wenzhou, China

Lin

Gan

et al. 2017

Sustainability

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“…It cuts the image into equal square objects of a given size. Block squares (or fishnet squares, or chessboard squares) are commonly used to describe the landscape characteristics of the urban growth [41] and agricultural landscape patterns [42]. Chessboard segmentation can keep the spatial units in a uniform size.…”

Section: Stage 2: Landscape Metrics and Chessboard Segmentationmentioning

confidence: 99%

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

et al. 2016

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Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification.Keywords: land-use and land-cover (LULC); object-based image analysis (OBIA); landscape metrics; support vector machine (SVM); very high resolution (VHR); rural settlement

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“…Driving forces are always classified into physical factors, socioeconomic factors, accessibility factors, neighborhood factors, land use policy and urban planning [12,14,25,34,35]. Similar to previous studies [36,37], land use policy and urban planning are not accessible.…”

Section: Potential Driving Forcesmentioning

confidence: 91%

Spatiotemporal Variation of Driving Forces for Settlement Expansion in Different Types of Counties

Dong

Zhang

2015

Sustainability

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Understanding the process of settlement expansion and the spatiotemporal variation of driving forces is the foundation of rational and specific planning for sustainable development. However, little attention has been paid to the spatiotemporal differences of driving forces among different counties, especially when they are representatives of different development types. This study used Guanyun, Kunshan and Changshu as case studies, and binary logistic regression was employed. The results showed that the expansion rates of Kunshan and Changshu were 5.55 and 3.93 times higher than that of Guanyun. The combinations and relative importance of drivers varied with counties and periods. The change in the number of driving forces can be divided into three stages: increasing stage, decreasing stage, and stable stage. In the relatively developed counties, Kunshan and Changshu, the importance of population is decreased, while it remain an important factor in the less developed county, Guanyun. In addition, the effect of GDP stays the same in Kunshan while it becomes the most important factor in Changshu. The distance to the main road and the distance to town are increasingly important in Kunshan and Guanyun, and distance to town has been the only common factor in the last period, indicating the discrepancy is increased. The relative importance of distance to a lake in Kunshan and Changshu increased, reflecting the role of increasing tourism in accelerating settlement expansion.

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Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China

Cited by 116 publications

References 47 publications

Quality Perspective on the Dynamic Balance of Cultivated Land in Wenzhou, China

Quality Perspective on the Dynamic Balance of Cultivated Land in Wenzhou, China

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

Spatiotemporal Variation of Driving Forces for Settlement Expansion in Different Types of Counties

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