Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Su, Shiliang; Xiao, Rui; Zhang, Yuan

doi:10.1016/j.apgeog.2011.06.005

Cited by 186 publications

(86 citation statements)

References 39 publications

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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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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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“…The lack of independence among local estimates may led to the failure in valid inferences for the local estimates. In addition, when the number of sample is quite small, the estimated local coefficients can be ineffective or invalid (Su et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…where and are the spatial position of location j, acts as intercept, and is the local estimated coefficient for explanatory variables (Su et al, 2012).…”

Section: Gwr Model Extends the Ols (Ordinary Regression Squares)mentioning

confidence: 99%

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A Comparative Analysis of Landslide Susceptibility Assessment by Using Global and Spatial Regression Methods in Inje Area, Korea

Park¹

2015

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Landslides are major natural geological hazards that result in a large amount of property damage each year, with both direct and indirect costs. Many researchers have produced landslide susceptibility maps using various techniques over the last few decades. This paper presents the landslide susceptibility results from the geographically weighted regression model using remote sensing and geographic information system data for landslide susceptibility in the Inje area of South Korea. Landslide locations were identified from aerial photographs. The eleven landslide-related factors were calculated and extracted from the spatial database and used to analyze landslide susceptibility. Compared with the global logistic regression model, the Akaike Information Criteria was improved by 109.12, the adjusted R-squared was improved from 0.165 to 0.304, and the Moran's I index of this analysis was improved from 0.4258 to 0.0553. The comparisons of susceptibility obtained from the models show that geographically weighted regression has higher predictive performance. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…In other words, spatially heterogeneous structure can be represented by just one coefficient by a global model, whereas a local model estimates a set of parameters containing independent variable or variables for each spatial unit. During the past five years a considerable amount of literature has been published on GWR in a variety of fields: the regional spillover effect (Rasekhi et al, 2013), land use and water quality (Tu, 2011), agriculture (Su et al, 2012), cancer risks (Gilbert and Chakraborty, 2011), traffic levels (Selby and Kockelman, 2013), migration (Lehtonen and Tykkyläinen, 2010) and grain production (Yang et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Analysis of changes in grain production on fruit and vegetable cultivation areas in Turkey through geographically weighted regression

Göçer¹

2014

Sci. Res. Essays

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The main purpose of this paper is to address the question of whether and how farmers have tried to increase agricultural productivity after the regulatory role of governments on the agricultural industry has been reduced due to financial crises. The relationship between agricultural sowing area shifts (grain to fruit, grain to vegetables, vegetables to fruit) were explored by using geographically weighted regression model. This model showed how the decrease of grain sowing areas led to an increase of fruit and vegetable sowing area in Turkey. It was demonstrated that farmers tended to choose more productive agricultural crops in order to address agricultural sustainability problems. Geographical information system data sets regarding the years between 2000 and 2010 were obtained from 923 districts. The results of the analysis showed a strong relationship between changes in grain, vegetable and fruit areas. According to geographically weighted regression, the variation of local coefficients ranged from -0.62 to 0.34. Because of the productivity factors, grain and vegetable areas have been replaced by fruit production. However, Turkey will be faced with food security problems in the future due to the decrease of grain cultivation. The increasing opportunity for irrigation has had a profoundly important role in shifting from grain to vegetable and fruit production.

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Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Cited by 186 publications

References 39 publications

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

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

A Comparative Analysis of Landslide Susceptibility Assessment by Using Global and Spatial Regression Methods in Inje Area, Korea

Analysis of changes in grain production on fruit and vegetable cultivation areas in Turkey through geographically weighted regression

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