A review of approaches to land use changes modeling

Noszczyk, Tomasz

doi:10.1080/10807039.2018.1468994

Cited by 107 publications

(62 citation statements)

References 120 publications

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“…The logistic regression model is a statistical analysis tool for classifying variables and can be used to determine the intensity of independent variables that affect the probability of occurrence of dependent variables [38]. This model has been widely used in studies on the driving forces of urban expansion and its simulation [7,9].…”

Section: Modified Logistic Regression Modelmentioning

confidence: 99%

“…Data sampling is a key step in logistic regression analysis and requires the same amount of observational data valued as 1 and 0 because an unequal sampling rate would not affect the explanatory variables on the coefficient estimates in the regression model, but would affect the constant value of the model [38]. Stratified random sampling was used to select sample points to eliminate the subjectivity of data sampling as much as possible.…”

Section: Data Sampling For Logistic Regression Analysismentioning

confidence: 99%

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Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

et al. 2019

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Urban development policies and planning schemes are essential drivers of urban expansion in the contemporary world. However, they are usually investigated by qualitative analysis and it is difficult to use them in spatial analysis models. Within the advancement of technology regarding the geostatistical dataset, this study uses a field strength model to quantify policy-oriented factors and designs a modified logistic regression model to analyze the main drivers of urban expansion by selecting natural environment, socioeconomic development, and especially policy-oriented variables. Wuhan City in central China is taken as an example: the modified model is applied and compared with the classical model, and the driving mechanism of urban expansion in Wuhan from 2006 to 2013 is determined through spatial analysis. The results show that the urban system planning in combination with various anthropologic and environmental factors can be comprehensively quantified and described by the urban field strength. The methodological innovation of the classical logistic regression model is tested by statistical and spatial analysis methods, and the results verify that the modified regression model can be used more accurately to investigate the driving mechanism of urban expansion in the past and simulate the spatial pattern of urban evolution in the future.

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Section: Modified Logistic Regression Modelmentioning

confidence: 99%

Section: Data Sampling For Logistic Regression Analysismentioning

confidence: 99%

Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

et al. 2019

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“…However, as land use change alters landscapes and vegetation, there are knock-on effects for ecosystem functions and ecosystem services (ES) [3]. Moreover, climate system changes occur in response to greenhouse gas (GHG) fluxes, which relate closely to land use decisions [4,5]. Bateman et al [4] indicated that land use change not only affect agricultural production, but also are simultaneously linked to greenhouse gas emissions, sustainable tourism development, preservation of green space and biodiversity.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Public’s Preferences toward Sustainable Planning under Climate and Land Use Change in Forest Parks

et al. 2019

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Balancing the goals of sustainable planning under climate and land use change (CLUC) with ecosystem service functions is a huge challenge for the management and programming of protected areas today. We construct a new evaluation framework towards the perspectives of sustainable land management based on the choice experiment (CE) model, and apply it to investigate the public's preferences for the forest parks in Taiwan. This study found that implementing organic farming, increasing species populations, increasing the acreage of secondary forest area, and developing an integrated framework for ecotourism would best satisfy the public's preferences for sustainable land use management. Second, we identify that the heterogeneity of the public's preferences for forest park management varies depending on whether individuals are (1) members of environmental groups, (2) agricultural landowners, and (3) residents of the municipality. Third, we find integrated land use programs generate the highest welfare values among scenarios comprising different financial attributes.

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“…Since the 1990s, with the wide application and rapid development of computer technology, numerous land cover information extraction methods based on machine learning and classification have been developed, such as the iterative self-organizing data analysis techniques algorithm (ISODATA), maximum likelihood classification (MLC), classification and regression trees (CART), random forest (RF), back propagation (BP), and multi-scale segmentation object-oriented classification [12][13][14][15]. In recent years, with the development of large-scale image data and computer artificial intelligence technology, artificial neural networks (ANNs) have the ability to learn and mimic complex phenomena and have the advantage of being able to merge data from various sources in one classification, so they are widely used in land use modeling [16]. As an important branch of ANNs, deep convolutional neural networks (DCNNs) are being widely used in image discrimination and target recognition technology [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China

Zhang

et al. 2018

Remote Sensing

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Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.

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A review of approaches to land use changes modeling

Cited by 107 publications

References 120 publications

Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

Evaluating the Public’s Preferences toward Sustainable Planning under Climate and Land Use Change in Forest Parks

A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China

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