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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (4): 580-601    DOI: 10.1007/s40333-024-0097-9
Research article     
Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact
TANG Xiaoyan1,2, FENG Yongjiu1,2,*(), LEI Zhenkun1,2, CHEN Shurui1,2, WANG Jiafeng1,2, WANG Rong1,2, TANG Panli1,2, WANG Mian1,2, JIN Yanmin1,2, TONG Xiaohua1,2
1College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
2Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai 200092, China
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Abstract  

Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern, and drought directly impacts urban site selection, growth processes, and future scenarios. Spatial simulation and projection based on cellular automata (CA) models is important to achieve sustainable urban development in arid areas. We developed a new CA model using bat algorithm (BA) named bat algorithm-probability-of-occurrence-cellular automata (BA-POO-CA) model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas, located in Xinjiang Uygur Autonomous Region, China. We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data, and finally projected its urban scenarios in 2030. The results showed that BA-POO-CA model yielded overall accuracy of 97.70% and figure-of-merits (FOMs) of 35.50% in 2010, and 97.70% and 26.70% in 2020, respectively. The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs, with increases of 5.50% in 2010 and 7.90% in 2020 than the model excluding drought intensity factor. This suggested that the urban growth of Urumqi City was affected by drought, and therefore taking drought intensity factor into account would contribute to simulation accuracy. The BA-POO-CA model including drought intensity factor was used to project two possible scenarios (i.e., business-as-usual (BAU) scenario and ecological scenario) in 2030. In the BAU scenario, the urban growth dominated mainly in urban fringe areas, especially in the northern part of Toutunhe District, Xinshi District, and Midong District. Using exceptional and extreme drought areas as a spatial constraint, the urban growth was mainly concentrated in the "main urban areas-Changji-Hutubi" corridor urban pattern in the ecological scenario. The results of this research can help to adjust urban planning and development policies. Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.

Key words： bat algorithm      cellular automata (CA)      probability-of-occurrence      drought intensity      algorithm-probability-of-occurrence-cellular automata (BA-POO-CA) model      arid areas     
Received: 13 October 2023      Published: 30 April 2024
Corresponding Authors: *FENG Yongjiu (E-mail: yjfeng@tongji.edu.cn)
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TANG Xiaoyan
FENG Yongjiu
LEI Zhenkun
CHEN Shurui
WANG Jiafeng
WANG Rong
TANG Panli
WANG Mian
JIN Yanmin
TONG Xiaohua
Cite this article:

TANG Xiaoyan, FENG Yongjiu, LEI Zhenkun, CHEN Shurui, WANG Jiafeng, WANG Rong, TANG Panli, WANG Mian, JIN Yanmin, TONG Xiaohua. Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact. Journal of Arid Land, 2024, 16(4): 580-601.

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http://jal.xjegi.com/10.1007/s40333-024-0097-9     OR     http://jal.xjegi.com/Y2024/V16/I4/580

Fig. 1 Spatial distribution of drought intensity in the study area (a), and satellite image map showing the administrative region of the study area (b). Note that satellite image map was downloaded from World Imagery Wayback (https://livingatlas.arcgis.com/wayback).
Category Name Resolution (m) Year Data source
Topographic factor DEM 30 2015 http://www.gscloud.cn
D_expressway 30 2020 http://www.openstreetmap.org
Locational factor D_city 30 2020 http://www.openstreetmap.org
D_town 30 2020 http://www.openstreetmap.org
Environmental factor Drought intensity 500 2020 Tang et al. (2023)
NDVI 500 2020 http://www.search.earthdata.nasa.gov
Soil moisture 500 2020 http://www.scihub.copernicus.eu
Socioeconomic factor PPP 100 2015 http://www.worldpop.org
GDP 1000 2015 http://www.ngdc.noaa.gov
Table 1 Description of the selected factors to construct cellular automata (CA) model for analyzing urban growth in the study area
Fig. 2 Spatial distribution of normalized factors selected to simulate the urban growth of study area. (a), DEM (digital elevation model); (b), D_expressway (Euclidean distance to expressway); (c), D_city (Euclidean distance to city center); (d), D_town (Euclidean distance to town center); (e), drought intensity; (f), NDVI (normalized difference vegetation index); (g), soil moisture; (h), PPP (population per pixel); (i), GDP (gross domestic product).
Fig. 3 Workflow diagram of bat algorithm-probability-of-occurrence-cellular automata (BA-POO-CA) models for simulating urban growth dynamic in arid areas
Fig. 4 Urban pattern of the study area in 2000 (a), 2010 (b), and 2020 (c) and urban growth pattern during 2000-2020 (d), as well as the growth of urban land area from 2000 to 2020 (e)
Controlling parameter Value Description Reference
OptimType Minimum Minimization of the objective function Li et al. (2022c)
RangeVar Upper bound [1, 0, 0, 0, 0, 0, 1, 0, 1, 3]
Lower bound [0, -16, -3, -16, -7, -14, 0, -3, 0, 0]		 The upper bound (maximum) and lower bound (minimum) values of variables, respectively Feng and Tong (2018)
NumPopulation 200 Number of miniature bats Feng and Tong (2018)
MaxIter 5000 The maximum number of iterations Feng and Tong (2018)
MaxFrequency 0.1 The maximum value of frequency Yang (2011)
MinFrequency -0.1 The maximum value of frequency Yang (2011)
Gama 1 Adjustment parameter for increasing pulse rate Yang (2011)
Convergence tolerance 1.00×10-6 Difference in acceptable function values Feng and Tong (2018)
AlphaBA 0.1 Adjustment parameter for decreasing loudness Yang (2011)
Constraints for Scenario-II Exceptional and extreme drought areas were set as restricted development areas. Constraints of ecological scenario -
Table 2 Control parameters of bat algorithm (BA) heuristic for retrieving CA transformation rules
Fig. 5 Convergence process of BA for optimizing BA-POO-CA models excluding (a) and including (b) drought intensity factor in the study area
Fig. 6 POO maps (a and b), values of CA parameter (c and d), and rank of factor contribution (e and f) of BA-POO-CA model excluding and including drought intensity factor
Fig. 7 Simulated urban growth pattern by BA-POO-CA model excluding (a and b) and including drought intensity factor (c and d) in 2010 and 2020
Fig. 8 Assessment results of urban growth pattern simulated by BA-POO-CA model excluding (a and c) and including (b and d) drought intensity factor in 2010 and 2020. Hit represents the urban growth area for both actual and simulation pattern; miss represents the actual urban growth area but simulated non-urban area; false alarm represents the actual non-urban area but simulated urban growth area; and correct rejection represents non-urban area for both actual and simulation pattern.
Year Type of BA-POO-CA model Overall accuracy (%) Kappa
coefficient		 Percentage
of hit (%)		 Percentage of actual urban land growth area (%) FOMs
(%)		 FOMs
increase (%)
2010 BA-POO-CA model excluding drought intensity factor 97.70 0.865 0.70 1.80 35.50 5.50
2010 BA-POO-CA model including drought intensity factor 97.80 0.871 1.00 1.80 41.00
2020 BA-POO-CA model excluding drought intensity factor 97.70 0.895 0.50 1.60 26.70 7.90
2020 BA-POO-CA model including drought intensity factor 97.80 0.896 0.80 1.60 34.60
Table 3 Accuracy of simulation pattern produced by BA-POO-CA model
Urban pattern Year Landscape-level metric
NP LPI (%) PAFRAC CONTAG (%) DIVISION SPLIT SHDI SHEI
Actual pattern 2010 43,264 92.40 1.439 83.10 0.145 1.169 0.272 0.247
2020 49,198 90.60 1.433 80.50 0.178 1.217 0.315 0.286
BA-POO-CA model excluding drought intensity factor 2010 32,465 93.40 1.348 85.20 0.127 1.146 0.251 0.229
BA-POO-CA model including drought intensity factor 2010 29,576 92.80 1.377 84.40 0.138 1.160 0.271 0.247
BA-POO-CA model excluding drought intensity factor 2020 39,114 91.80 1.379 82.50 0.156 1.184 0.291 0.265
BA-POO-CA model including drought intensity factor 2020 36,679 91.40 1.398 82.00 0.164 1.197 0.306 0.278
Table 4 Results of landscape-level metrics of observed and simulated urban patterns
Fig. 9 Urban pattern of the study area in 2030 projected by BA-POO-CA model. (a), scenario I (BAU scenario); (b), scenario II (ecological scenario).
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