Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia

Yulianto, Fajar; Fitriana, Hana Listi; Sukowati, Kusumaning Ayu Diah

doi:10.1007/s40808-019-00686-9

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…The relationship between population growth rate and built-up change has been used in several studies to indicate sprawl (Bhatta, 2009; Sudhira et al, 2004; Yulianto et al, 2020). Typically, the annual rate of built-up expansion in an area is compared with the annual rate of population growth.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Urban Sprawl and Land Value in Batangas City, Philippines

Daguio¹,

Rivera²,

Reyes

et al. 2021

Environment and Urbanization ASIA

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This article, using a combination of quantitative and qualitative approaches, illustrates that Batangas City for the period 1990–2015 was leaning towards a sprawling development. Areas where considerable sprawling has occurred were further investigated in relation to land value. The study also revealed that there is a moderate positive relationship between sprawl and land values in the city, which meant that land values have increased in areas where considerable sprawling has occurred.

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Section: Analysis and Discussionmentioning

confidence: 99%

Urban Sprawl and Land Value in Batangas City, Philippines

Daguio¹,

Rivera²,

Reyes

et al. 2021

Environment and Urbanization ASIA

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“…If the value is maximally concentrated, the value of entropy can be close to 0, while entropy near 1 indicates maximal dispersion. In most cities, built‐up land is dense near the center and sprawls with increasing distance (Du et al, 2022; Feng et al, 2019; Yulianto et al, 2020). The entropy (

H_{n}

) of the built‐up area can be calculated with the help of the following formula.

H_{n} = \sum_{i = 1}^{n} p_{i} * \log (\frac{1}{p_{i}})

Where :

p_{i}

indicates the variable proportion in i th zone (variable proportion of built‐up land in each zone or ward), n shows the total number of zones or wards

p_{i} = \frac{x_{i}}{\sum_{i = 1}^{n} x_{i}}

Where :

x_{i}

—observed built‐up land in each zone and n ‐total number of zone or wards.…”

Section: Methodsmentioning

confidence: 99%

“…If the value is maximally concentrated, the value of entropy can be close to 0, while entropy near 1 indicates maximal dispersion. In most cities, built-up land is dense near the center and sprawls with increasing distance (Du et al, 2022;Feng et al, 2019;Yulianto et al, 2020). The entropy (H n ) of the built-up area can be calculated with the help of the following formula.…”

Section: Shannon Entropy Model For Urban Settlement Land (Usl) Growthmentioning

confidence: 99%

Spatio‐temporal analysis of land use/land cover change and urban growth dynamics of Silchar City, India using very high‐resolution satellite data and the Shannon entropy model

2023

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Silchar City and its surrounding areas have witnessed massive population growth in the last few decades due to local and migrated peoples, which led to the rapid transformation of its land use/land cover (LU/LC) pattern. The spatio‐temporal very high‐resolution satellite data (VHRS) and on‐screen visual interpretation techniques were used for detailed large‐scale LU/LC analysis. The LU/LC classification of the City has been prepared for 2005, 2011, and 2018 with five primary and a further 17 sub‐classes. In the present study use was made of the Census of India reports from 1991 to 2011, and the geometrical increase method was used to evaluate the ward‐level population, population density, and land requirements for residential purposes; these have been predicted from 2021 to 2051 with every 10 years interval. The ity's wards have been classified into five zones, and the Shannon entropy model has been applied to analyze the degree of randomness and trends of urban built‐up growth. The obtained results indicate that Silchar City is compact near the center, which follows the infill built‐up growth trend. However, as the distance increased from its center, the scattering with an infill growth pattern was observed. The accuracy assessment techniques are used to validate the results of the classification. The results of this study can provide detailed information for land‐use planners, researchers, policy‐decision makers, and municipal authorities.

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“…It is generally based on the concept of entropy, which measures the level of uncertainty or randomness, and abnormality between causality and consequences (Lin 1991;Yufeng ve Fengxiang 2009). It determines the maximum and minimum impact levels, or in other words, the entropy level of factors influencing the occurrence of hazards (Yulianto et al 2020) The ratio of the high and low values of the measure affects the susceptibility level. A high value indicates a high probability situation, while a low value indicates a low probability situation (Al-Hinai ve Abdalla 2021).…”

Section: Shannon's Entropy (Se)mentioning

confidence: 99%

Erosion Susceptibility Analysis in Bingöl (Türkiye) using Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy methods

İNİK,

UTLU

2024

Preprint

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Soil erosion is one of the most important and critical processes occurring in Turkey, as in all parts of the world. It is of great importance to understand the processes that occur as soil erosion continues. The aim of this study is to determine the erosion susceptibility occurring in the Çapakçur Stream basin, one of the important erosion areas of Turkey. In the study, erosion susceptibility analysis was used using 19 conditioning factors based on 4 different methods (Shannon Entropy (SE), Logistic Regression (LR), Frequency Ratio (FR) and Weight of Evidence (WoE)), which are actively used today in erosion susceptibility analysis and determination of critical areas in terms of erosion. has been made. Model performances of the Analysis Results were evaluated based on a data set of 840 training (70%) points and 360 validation (30%) points using ROC and AUC values.. According to result of the ROC and AUC values show that Logistic regression seems to perform well on both training (AUC = 94.7%) and validating datasets (AUC = 93.5%). On the other hand, Weight of Evidence training (AUC = 93.5%) and testing datasets (AUC = 91.4%), Frequency Ratio training (AUC = 93.5%) and testing datasets (AUC = 92.4%). of the Weight of Evidence resault show that AUC and ROC values smilar to Logistic Regression result, but slightly lower than Logistic Regression. Additionally, Shannon Entropy shows that it performs lower than other methods on both training (AUC = 55.7%) and testing datasets (AUC = 56.3%). Conducting analyzes based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.

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Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia

Cited by 14 publications

References 24 publications

Urban Sprawl and Land Value in Batangas City, Philippines

Urban Sprawl and Land Value in Batangas City, Philippines

Spatio‐temporal analysis of land use/land cover change and urban growth dynamics of Silchar City, India using very high‐resolution satellite data and the Shannon entropy model

Erosion Susceptibility Analysis in Bingöl (Türkiye) using Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy methods

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