Enhancing Urban Ecological risk assessment by integrating spatial modeling and machine learning for resilient environmental management in UNESCO world heritage cities

Zullyadini A. Rahaman

Enhancing Urban Ecological risk assessment by integrating spatial modeling and machine learning for resilient environmental management in UNESCO world heritage cities

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Title

Enhancing Urban Ecological risk assessment by integrating spatial modeling and machine learning for resilient environmental management in UNESCO world heritage cities

Type

Article

Main Author

Zullyadini A. Rahaman

Subject

S Agriculture (General)

ISBN

2509-9426

Language Code

eng

Bibliographic Details

Place of Production

Tanjung Malim

Publisher

Fakulti Sains Kemanusiaan

Year of publication

2024

Notes

Earth Systems and Environment

Dissertation Note

Other

Degree Type

Others

Corporate Name

Universiti Pendidikan Sultan Idris

HTTP Link

Access Web Resource

Abstract

Rapid urbanization in George Town, Malaysia, a UNESCO World Heritage city, has led to significant ecological degradation over the past three decades. This study enhances the Remote Sensing Ecological Index (RSEI) by integrating water resources as a new parameter, providing a comprehensive assessment of the city's ecological health from 1992 to 2022. Utilizing multi-temporal Landsat data, ecological assessment parameters such as land cover, soil moisture, surface temperature, and greenness patterns were analyzed. The integration of these parameters into the RSEI revealed correlations between forest cover and water body degradation, with a 54.90% and 46.94% reduction, respectively, leading to increased surface temperatures and negatively impacting soil moisture. The analysis shows that 37.64% of George Town experienced ecological degradation over three decades, with areas of excellent ecological health declining from 11.13 to 4.45%. A hybrid machine learning algorithm combining Cellular Automata and Artificial Neural Networks projected increased ecological vulnerability by 2032, with a further decrease in areas of good (12.20%) and excellent (0.25%) ecological health. Directional change analysis suggests that areas from the center to the eastern region experienced the highest levels of ecological degradation, a pattern projected to persist. The enhanced RSEI facilitates accurate ecological monitoring, guiding conservation efforts to maintain and restore ecological corridors and greenspaces within vulnerable ecosystems. This research provides an innovative, integrative methodology to support the global sustainable development agenda, advancing ecological change assessment in rapidly developing urban areas and informing urban planning for ecological resilience. © King Abdulaziz University and Springer Nature Switzerland AG 2024.

References

Abdullah SA, Hezri AA (2008) From forest landscape to agricultural landscape in the developing tropical country of Malaysia: pattern, process, and their significance on policy. Environ Manag 42:907–917. https:// doi. org/ 10. 1007/ s00267- 008- 9178-3.

Acharya U, Daigh ALM, Oduor PG (2022) Soil moisture mapping with moisture-related indices, OPTRAM, and an integrated random forest-OPTRAM algorithm from landsat 8 images. Remote Sens 14(15):3801. https:// doi. org/ 10. 3390/ rs141 53801.

Adam E, Mutanga O, Odindi J, Abdel-Rahman EM (2014) Land-use/cover classification in a heterogeneous coastal landscape using rapid eye imagery: evaluating the performance of random forest and support vector machines classifiers. Int J Remote Sens 35:3440–3458.

ADB (2013) Food security in Asia and the Pacific. Asian Development Bank. Mandaluyong City, Philippines. https:// www. adb. org/ sites/defau lt/ files/ publi cation/ 30349/ food- secur ity- asia- pacific. pdf.

Akomolafe GF, Rosazlina R (2022) Land use and land cover changes influence the land surface temperature and vegetation in Penang Island, Peninsular Malaysia. Sci Rep 12:21250. https:// doi. org/ 10.1038/ s41598- 022- 25560-0.

AlDousari AE, Kafy AA, Saha M, Fattah MA, Almulhim AI, Al Rakib A, Jahir DMA, Rahaman ZA, Bakshi A, Shahrier, M, Rahman MM (2022) Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustain Cities Soc 86:104107.

Asuero AG, Sayago A, González AG (2006) The correlation coefficient: an overview. Crit Rev Anal Chem 36(1):41–59. https://doi. org/ 10. 1080/ 10408 34050 05267 66.

Atkinson PM, Tatnall AR (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18:699–709.

Azari M, Billa L, Chan A (2022) Multi-temporal analysis of past and future land cover change in the highly urbanized state of Selangor, Malaysia. Ecol Process 11:2. https:// doi. org/ 10. 1186/s13717- 021- 00350-0.

Baig MHA, Fu Zhang L, Shuai T, Xi Tong Q (2014) Derivation of a tasselled cap transformation based on landsat 8 at-satellite reflectance. Remote Sens Lett 5(5):423–431.

Biggs J, von Fumetti S, Kelly-Quinn M (2017) The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers. Hydrobiologia 793:3–39. https:// doi. org/ 10. 1007/ s10750- 016- 3007-0.

Boori MS, Choudhary K, Paringer R, Kupriyanov A (2021) Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara. Russia J Environ Manag 285:112138.

Camargo FF, Sano EE, Almeida CM, Mura JC, Almeida T (2019) A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images. Remote Sens 11:1600.

Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250. https:// doi.org/ 10. 5194/ gmd-7- 1247- 2014.

Christen P, Del Fabbro O (2019) Cybernetical concepts for cellular automaton and artificial neural network modelling and implementation. In: 2019 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 4124–4130.

City Population (2021) George Town City in Penang State. City Population. Retrived from https:// www. cityp opula tion. de/ en/malay sia/ pulau pinang/ admin/ timur_ laut/ 07044 4__ george_ town/.

Deng YB, Chang Shan W, Miao L, Rong Chen R (2015) RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments. Int J Appl Earth Obs Geoinf 39:40–48.

Feng Q, Gong J, Liu J, Li Y (2015) Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier—the case of Yuyao, China. Remote Sens 7:12539–12562.

Firozjaei MK, Fathololoumi S, Kiavarz M, Biswas A, Homaee M, Alavipanah SK (2021) Land surface ecological status composition index (LSESCI): a novel remote sensing-based technique for modeling land surface ecological status. Ecol Ind 123:107375.

Gao YG, Li YH, Xu HQ (2022) Assessing ecological quality based on remote sensing images in Wugong Mountain. Earth Space Sci 9(3):e2021EA001918.

Gilpin W (2019) Cellular automata as convolutional neural networks. Phys Rev E 100(3):032402.

Halder S, Bose S (2024) Ecological quality assessment of five smart cities in India: a remote sensing index-based analysis. Int J Environ Sci Technol 21(4):4101–4118.

Hassan NA, Hashim Z, Hashim JH (2014) Asia Pac J Public Health 28(2 Suppl):38S-48S. https:// doi. org/ 10. 1177/ 10105 39515 592951.

Hu XS, Xu HQ (2018) A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: a case from Fuzhou City. China Ecol Indic 89:11–21.

Huang H, Chen W, Zhang Y, Qiao L, Du Y (2021) Analysis of ecological quality in Lhasa Metropolitan Area during 1990–2017 based on remote sensing and Google Earth Engine platform. J Geogr Sci 31:265–280.

Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688. https:// doi. org/ 10. 1016/j.ijfor ecast. 2006. 03. 001.

IPCC (2022) IPCC sixth assessment report: impacts, adaptation and vulnerability. Intergovernmental Panel on Climate Change. https://www. ipcc. ch/ report/ ar6/ wg2/ downl oads/ report/ IPCC_ AR6_WGII_ Summa ryVol ume. pdf.

Islam ARMT, Islam HMT, Shahid S et al (2021) Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change. J Environ Manag 289:112505 Jiang X, Guo X, Wu Y et al (2023) Ecological vulnerability assessment based on remote sensing ecological index (RSEI): a case of.

Zhongxian County, Chongqing. Front Environ Sci 10:1074376 Kafy A-A, Saha M, Faisal A-A, Rahman ZA et al (2022) Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms. Build Environ 217:109066. https:// doi. org/ 10. 1016/j. build env. 2022. 109066.

Kamaraj M, Rangarajan S (2022) Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environ Sci Pollut Res 29:86337–86348. https:// doi. org/ 10. 1007/ s11356- 021- 17904-6.

Krenker A, Bešter J, Kos A (2011) Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1–18.

Li X, Yeh AGO (2001) Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environ Plan A 33(8):1445–1462.

Malaymai (2023) Statistics Dept: Malaysia’s urbanisation rate tripled over five decades. https:// www. malay mail. com/ news/ malay sia/2022/ 12/ 23/ stati stics- dept- malay sias- urban isati on- rate- tripl edover-five- decad es/ 46783.