Entropies of the Chinese Land Use/Cover Change from 1990 to 2010 at a County Level

Fan, Yong; Yu, Guangming; He, Zhongshi; Yu, Hailong; Bai, Rui; Yang, Linru; Wu, Di

doi:10.3390/e19020051

Cited by 30 publications

(31 citation statements)

References 26 publications

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“…The urban areas covered 70.35% of the total landscape in 2016, which is predicted to increase to 85.87% in 2027 ( Figure 6). The Renyi's entropy value of the predicted urban area in 2027 is expected to be 1.7, which is above the range value of 1 [35], indicating a tremendous level of US. Overall, the extent and level of US for the coming 12 years is expected to increase at an alarming rate and cause degradation of urban ecosystem services.…”

Section: Us Extent and Level Prediction For 2027mentioning

confidence: 87%

“…However, in 2003 the entropy value reached the threshold value of 0.5, indicating the initiation of dispersion, i.e., US, in Chennai [35,57]. In 2016, the entropy value was 0.9, which indicates a very high level of US in the periphery of Chennai.…”

Section: Change In Us Level In Chennaimentioning

confidence: 96%

“…Renyi's Entropy (RE) [35,51,52] was calculated to estimate the level of US between 1991 and 2016. First, we aggregated the classified LULC map into two classes, i.e., built-up and non-built-up.…”

Section: Level Of Usmentioning

confidence: 99%

“…The classified image homogenization and delineation of LULC regions were performed using the ENVI (Version 5.1) feature extraction tool [43,47]. The accuracy of the image classification was assessed using the kappa coefficient (KC) [35,36]. We compared the classified images with the ground truth data obtained from Google Earth image of 2016 and the landuse and landcover map of 2005 from the Indian Geo-platform of ISRO-Bhuvan (http://bhuvan.nrsc.gov.in) [16].…”

mentioning

confidence: 99%

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Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Padmanaban

Bhowmik

Cabral

et al. 2017

Entropy

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Urban sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, adversely affects the provision of ecosystem services. The quantification of US is thus crucial for effective urban planning and environmental management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive US triggered by the doubling of total population over the past three decades. However, the extent and level of US has not yet been quantified and a prediction for future extent of US is lacking. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10 km suburban buffer of Chennai. The level of US was then quantified using Renyi's entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi's entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services.

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Section: Us Extent and Level Prediction For 2027mentioning

confidence: 87%

Section: Change In Us Level In Chennaimentioning

confidence: 96%

Section: Level Of Usmentioning

confidence: 99%

mentioning

confidence: 99%

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Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Padmanaban

Bhowmik

Cabral

et al. 2017

Entropy

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“…If q = 0, M 0 = S represents macro state entropy; If q = 1, M 1 = H represents Shannon information entropy; if q = 2, M 2 denotes correlation entropy. As a spatial measure, Renyi entropy has been successfully applied to the studies on regional land use and urban sprawl [35,36], and the results are revealing for geographers. In fact, different types of fractal dimension can be integrated into an expression by Renyi's entropy.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Fractal Dimensions of Urban Forms through Spatial Entropy

Chen

Wang

Feng

2017

Entropy

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Abstract:The spatial patterns and processes of cities can be described with various entropy functions. However, spatial entropy always depends on the scale of measurement, and it is difficult to find a characteristic value for it. In contrast, fractal parameters can be employed to characterize scale-free phenomena and reflect the local features of random multi-scaling structure. This paper is devoted to exploring the similarities and differences between spatial entropy and fractal dimension in urban description. Drawing an analogy between cities and growing fractals, we illustrate the definitions of fractal dimension based on different entropy concepts. Three representative fractal dimensions in the multifractal dimension set, capacity dimension, information dimension, and correlation dimension, are utilized to make empirical analyses of the urban form of two Chinese cities, Beijing and Hangzhou. The results show that the entropy values vary with the measurement scale, but the fractal dimension value is stable is method and study area are fixed; if the linear size of boxes is small enough (e.g., <1/25), the linear correlation between entropy and fractal dimension is significant (based on the confidence level of 99%). Further empirical analysis indicates that fractal dimension is close to the characteristic values of spatial entropy. This suggests that the physical meaning of fractal dimension can be interpreted by the ideas from entropy and scaling and the conclusion is revealing for future spatial analysis of cities.

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Spatial analysis of cities using Renyi entropy and fractal parameters

Chen¹,

Feng²

2017

Chaos, Solitons & Fractals

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The spatial distributions of cities fall into two groups: one is the simple distribution with characteristic scale (e.g. exponential distribution), and the other is the complex distribution without characteristic scale (e.g. power-law distribution). The latter belongs to scale-free distributions, which can be modeled with fractal geometry. However, fractal dimension is not suitable for the former distribution. In contrast, spatial entropy can be used to measure any types of urban distributions. This paper is devoted to generalizing multifractal parameters by means of dual relation between Euclidean and fractal geometries. The main method is mathematical derivation and empirical analysis, and the theoretical foundation is the discovery that the normalized fractal dimension is equal to the normalized entropy. Based on this finding, a set of useful spatial indexes termed dummy multifractal parameters are defined for geographical analysis. These indexes can be employed to describe both the simple distributions and complex distributions. The dummy multifractal indexes are applied to the population density distribution of Hangzhou city, China. The calculation results reveal the feature of spatio-temporal evolution of Hangzhou's urban morphology.This study indicates that fractal dimension and spatial entropy can be combined to produce a new methodology for spatial analysis of city development.

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Entropies of the Chinese Land Use/Cover Change from 1990 to 2010 at a County Level

Cited by 30 publications

References 26 publications

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Understanding the Fractal Dimensions of Urban Forms through Spatial Entropy

Spatial analysis of cities using Renyi entropy and fractal parameters

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