A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model

Firozjaei, Mohammad Karimi; Seddighi, Amir Saied; Argany, Meysam; Jelokhani‐Niaraki, Mohammadreza; Arsanjani, Jamal Jokar

doi:10.1016/j.cities.2019.05.001

Cited by 87 publications

(40 citation statements)

References 57 publications

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“…Land use The approach based on CNN achieved an accuracy of ≅ 98% for land use and land cover analysis [84] The proposed approach confirmed its suitability for urban planning because it had a superior performance compared to the global one [56] Living conditions Deep learning demonstrated a high potential to map areas of deprived living conditions [85] Land cover The multivariate time series algorithm showed high accuracy for rare land cover classes Forest Sentinel-2 is considered a powerful source of data for forest monitoring and mapping [52] RF was the best method to predict and map the area and volume of eucalyptus [88] is to group image pixels or sub-pixels into unlabelled classes [11]. Table 8 lists some recent examples regarding the application of unsupervised classification techniques.…”

Section: Sdg 8 (Decent Work and Economic Growth) Slaverymentioning

confidence: 77%

“…From Fig. 2, it can be depicted that EO can provide quite a large number of indicators for the SDG framework such as data on the condition of the atmosphere [49], oceans [50], crops [51], forests [52], climate [53], natural disasters [54], natural resources [55], urbanisation [56], biodiversity [57] and human conditions [58]. The two most important indicators are population distribution (I-1), and cities/infrastructure mapping (I-2) since they contribute to all the SDGs.…”

Section: Overview On Earth Observation For Sustainable Goals Developmentmentioning

confidence: 99%

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Monitoring sustainable development by means of earth observation data and machine learning: a review

2020

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This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

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Section: Sdg 8 (Decent Work and Economic Growth) Slaverymentioning

confidence: 77%

Section: Overview On Earth Observation For Sustainable Goals Developmentmentioning

confidence: 99%

Monitoring sustainable development by means of earth observation data and machine learning: a review

2020

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“…The region's population has grown by more than 20% from 1985 to 2017 [19]. The population growth has led to built-up expansion of the city and changed agricultural lands and green spaces around the city [50,51]. The results of previous studies indicate that the built-up land increased from 19% of the total area in 1985 to 36.52% in 2015.…”

Section: Study Areamentioning

confidence: 99%

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

et al. 2019

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Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

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“…It could be useful to determine an appropriate degree of risk based on urban growth in previous time periods and use it for future prediction. The Ordered Weighted Averaging (OWA) method, a class of MCDA aggregation methods, has the potential to incorporate the concept of risk into urban scenarios and geo-simulation processes (see Firozjaei, Sedighi, et al, 2019;Jelokhani-Niaraki & Malczewski, 2015c;Kiavarz & Jelokhani-Niaraki, 2017;Malczewski et al, 2003;Malczewski, 2006).…”

Section: Introductionmentioning

confidence: 99%

An urban growth simulation model based on integration of local weights and decision risk values

Firozjaei

Seddighi

Jelokhani‐Niaraki

2020

Transactions in GIS

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Monitoring and managing the rapid growth of cities, especially in developing countries, highlights the need for appropriate spatio‐temporal models to predict urban growth. The parameters affecting the spatio‐temporal analysis of urban growth play a key role in the prediction results. This study proposes an urban growth simulation model by integrating local subjective–objective weights and decision risk values (ORness) into a CA–Markov model. This involves the use of ordered weighting averaging as one of the multicriteria decision analysis methods to combine the weights and degree of risk for generating a variety of risk‐averse and/or risk‐taking urban growth prediction scenarios. The proposed model has been applied to predict the physical growth of Babol city, located in Mazandaran, Iran. The results indicate that the degrees of ORness = 0.3 and ORness = 0.9 yield better prediction results in the case of using the local and global weighting strategies, respectively. Furthermore, the overall accuracy of local and global weighting strategies at different degrees of risk was 87.6 and 86.8, respectively. This implies that the use of local subjective–objective weights leads to more accurate results than the global weights for simulating urban growth.

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A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model

Cited by 87 publications

References 57 publications

Monitoring sustainable development by means of earth observation data and machine learning: a review

Monitoring sustainable development by means of earth observation data and machine learning: a review

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

An urban growth simulation model based on integration of local weights and decision risk values

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