Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur, Malaysia

doi:10.1016/j.regsus.2024.100179

Abstract

Abstract:

Kuala Lumpur of Malaysia, as a tropical city, has experienced a notable decline in its critical urban green infrastructure (UGI) due to rapid urbanization and haphazard development. The decrease of UGI, especially natural forest and artificial forest, may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future. This study analyzed land use and land cover (LULC) and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990, 2005, and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy. LULC was categorized into six main types: natural forest, artificial forest, grassland, water body, bare ground, and built-up area. Satellite images in 1990, 2005, and 2021 showed the remarkable overall accuracy values of 91.06%, 96.67%, and 98.28%, respectively, along with the significant Kappa coefficient values of 0.8997, 0.9626, and 0.9512, respectively. Then, this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050. The results showed that natural forest decreased from 15.22% to 8.20% and artificial forest reduced from 18.51% to 15.16% during 1990-2021. Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics, increasing the risk of urban floods. However, grassland showed a significant increase from 7.80% to 24.30% during 1990-2021. Meanwhile, bare ground increased from 27.16% to 31.56% and built-up area increased from 30.45% to 39.90% during 1990-2005. In 2021, built-up area decreased to 35.10% and bare ground decreased to 13.08%, indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur. This study highlights the importance of integrating past, current, and future LULC changes to improve urban ecosystem services in the city.

Key words: Urban Green Infrastructure (UGI), Urban ecosystem services, Land use and land cover (LULC) changes, Cellular Automata and Markov Chain model, Urbanization

Jafarpour Ghalehteimouri KAMRAN, Che Ros FAIZAH, Rambat SHUIB. Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur, Malaysia[J]. Regional Sustainability, 2024, 5(4): 100179.

Figures/Tables 10

Fig. 1.

Table 1

Fig. 2.

Table 2

Table 3

Table 4

Table 5

Fig. 3.

Table 6

Fig. 4.

References 75

[1]	Abdullah, S.A., Nakagoshi, N., 2007. Forest fragmentation and its correlation to human land use change in the state of Selangor, peninsular Malaysia. For. Ecol. Manage. 241(1-3), 39-48.
[2]	Agaton, M., Setiawan, Y., Effendi, H., 2016. Land use/land cover change detection in an urban watershed: A case study of upper Citarum Watershed, West Java Province, Indonesia. Procedia Environmental Sciences. 33, 654-660.
[3]	Albert, C., Aronson, J., Fürst, C., et al., 2014. Integrating ecosystem services in landscape planning: Requirements, approaches, and impacts. Landsc. Ecol. 29, 1277-1285.
[4]	Alonso-Sanz, R., Martin, M., 2007. Elementary Cellular Automata with elementary memory rules in cells: The case of linear rules. J. Cell. Autom. 1(1), 71-87.
[5]	Assesment, M.E., 2005. Ecosystems and human well-being: Synthesis. Phys. Teach. 34(9), 534, doi: 10.1119/1.2344558.
[6]	Austin, J., Johnson, D.D., Ho, J., et al., 2021. Structured Denoising Diffusion Models in Discrete Dtate-Spaces. Cambridge: Advances in Neural Information Processing Systems, 17981-17993.
[7]	Ayompe, L.M., Schaafsma, M., Egoh, B.N., 2021. Towards sustainable palm oil production: The positive and negative impacts on ecosystem services and human wellbeing. J. Clean Prod. 278, 123914, doi: 10.1016/j.jclepro.2020.123914.
[8]	Aziz, G., Minallah, N., Saeed, A., et al., 2024. Remote sensing-based forest cover classification using machine learning. Sci Rep. 14(1), 69, doi: 10.1038/s41598-023-50863-1.
[9]	Biswas, M., Banerji, S., Mitra, D., 2020. Land-use-land-cover change detection and application of Markov model: A case study of Eastern part of Kolkata. Environ. Dev. Sustain. 22(5), 4341-4360. doi: 10.1007/s10668-019-00387-4
[10]	Campos, P.B.R., Almeida, C.M.D., Queiroz, A.P.D., 2022. Spatial dynamic models for assessing the impact of public policies: The case of Unified Educational Centers in the Periphery of São Paulo City. Land. 11(6), 922, doi: 10.3390/land11060922.
[11]	Chaudhary, S., McGregor, A., Houston, D., et al., 2015. The evolution of ecosystem services: A time series and discourse-centered analysis. Environ. Sci. Policy. 54, 25-34.
[12]	Chen, J., Li, H.Y., Luo, S.X., et al., 2024. Estimating changes in inequality of ecosystem services provided by green exposure: From a human health perspective. Sci. Total Environ. 908, 168265, doi: 10.1016/j.scitotenv.2023.168265.
[13]	Chen, L.P., Sun, Y.J., Saeed, S., 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS One. 13(7), e0200493, doi: 10.3390/su12093925.
[14]	Clerici, N., Cote-Navarro, F., Escobedo, F.J., et al., 2019. Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes. Sci. Total Environ. 685, 1181-1192.
[15]	Congalton, R.G., 1991. Review of Stan Aronoff, Geographic Information Systems: A Management Perspective. Ottawa: WDL Publications, 37-38.
[16]	Daily, G.C., Matson, P.A., 2008. Ecosystem services: From theory to implementation. Proc. Natl. Acad. Sci. U. S. A. 105(28), 9455-9456.
[17]	De Groot, R.S., Alkemade, R., Braat, L., et al., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7(3), 260-272.
[18]	Desta, H., Fetene, A., 2020. Land-use and land-cover change in Lake Ziway watershed of the Ethiopian Central Rift Valley Region and its environmental impacts. Land Use Pol. 96, 104682, doi: 10.1016/j.landusepol.2020.104682.
[19]	Dewan Bandaraya Kuala Lumpur, 2022. Planning Sector. [2024-01-27]. https://www.dbkl.gov.my/.
[20]	Dong, M., Bryan, B.A., Connor, J.D., et al., 2015. Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling. Ecosyst. Serv. 15, 63-74.
[21]	Egoh, B., Rouget, M., Reyers, B., et al., 2007. Integrating ecosystem services into conservation assessments: a review. Ecol. Econ. 63(4), 714-721.
[22]	Esmaeili, M., Abbasi-Moghadam, D., Sharifi, A., et al., 2023. ResMorCNN Model: Hyperspectral images classification using Residual-Injection Morphological Features and 3DCNN Layers. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. Remote Sens. 17, 219-243.
[23]	Estrada-Carmona, N., Hart, A.K., DeClerck, F.A.J., et al., 2014. Integrated landscape management for agriculture, rural livelihoods, and ecosystem conservation: An assessment of experience from Latin America and the Caribbean. Landsc. Urban Plan. 129, 1-11.
[24]	FAO (Food and Agriculture Organization of the United Nations), 2021. Ecosystem Services & Biodiversity (ESB). [2024-01-08]. https://www.fao.org/ecosystem-services-biodiversity/news-events/news-details/en/c/1038435/.
[25]	Fairbrass, A.J., Jones, K., McIntosh, A.L.S., et al., 2018. Green Infrastructure for London: A Review of the Evidence. London: The Engineering Exchange.
[26]	Federal Territory of Kuala Lumpur, 2024. Department of Statistics, Malaysia. [2024-01-20]. https://www.dosm.gov.my/portal-main/release-content/demographic-statistics-first-quarter-2024.
[27]	Gambo, J., Shafri, H.Z.M., Shaharum, N.S.N., et al., 2018. Monitoring and predicting land use-land cover (LULC) changes within and around Krau wildlife reserve (KWR) protected area in Malaysia using multi-temporal Landsat data. Geoplanning: Journal of Geomatics and Planning. 5(1), 17-34.
[28]	Geneletti, D., Cortinovis, C., Zardo, L., et al., 2020. Planning for ecosystem services in cities. Cham: Springer, 87.
[29]	Ghalehteimouri, K.J., Ros, F.B.C., 2020. The spatial turn in the National Physical Plan (NPP) Malaysia in compare to Germany for better criteria identification on climate change and environmental hazards issues. Climate Change. 6(21), 141-155.
[30]	Ghalehteimouri, K.J., Ros, F.C., Rambat, S., et al., 2024. Spatial and temporal water pattern change detection through the Normalized Difference Water Index (NDWI) for initial flood assessment: A case study of Kuala Lumpur 1990 and 2021. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 114(1), 178-187.
[31]	Ghalehteimouri, K.J., Shamsoddini, A., Mousavi, M.N., et al., 2022. Predicting spatial and decadal of land use and land cover change using integrated cellular automata Markov chain model based scenarios (2019-2049) Zarriné-Rūd River Basin in Iran. Environmental Challenges. 6, 100399, doi: 10.1016/j.envc.2021.100399.
[32]	Grebík, J., Pikhurko, O., Tserunyan, A., 2023. Mini-workshop: Descriptive combinatorics, local algorithms and random processes. Oberwolfach Reports. 19(1), 429-455.
[33]	Havinga, I., Marcos, D., Bogaart, P., et al., 2024. Understanding the sentiment associated with cultural ecosystem services using images and text from social media. Ecosyst. Serv. 65, 101581, doi: 10.1016/j.ecoser.2023.101581.
[34]	He, H.X., Yan, J.N., Liang, D., et al., 2024. Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sens. Environ. 305, 114101, doi: 10.1016/j.rse.2024.114101.
[35]	Hua, A.K., 2017. Application of CA-Markov model and land use/land cover changes in Malacca River Watershed, Malaysia. Appl. Ecol. Environ. Res. 15(4), 605-622.
[36]	Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., et al., 2023. Assessment of spatiotemporal characteristic of droughts using in situ and remote sensing-based drought indices. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 16, 1483-1502.
[37]	Johnson, M.J., Willsky, A.S., 2013. Bayesian nonparametric hidden semi-Markov models. J. Mach. Learn. Res. 14, 673-701.
[38]	Jordan, S.J., Hayes, S.E., Yoskowitz, D., et al., 2010. Accounting for natural resources and environmental sustainability: Linking ecosystem services to human well-being. Environmental and Resource Economics. 44(5), 1530-1536.
[39]	Kajosaari, A., Hasanzadeh, K., Fagerholm, N., 2024. Predicting context-sensitive urban green space quality to support urban green infrastructure planning. Landsc. Urban Plan. 242, 104952, doi: 10.1016/j.landurbplan.2023.104952.
[40]	Kattenborn, T., Leitloff, J., Schiefer, F., et al., 2021. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS-J. Photogramm. Remote Sens. 173, 24-49.
[41]	Keeley, M., Koburger, A., Dolowitz, D.P., et al., 2013. Perspectives on the use of green infrastructure for stormwater management in Cleveland and Milwaukee. Environ. Manage. 51(6), 1093-1108. doi: 10.1007/s00267-013-0032-x pmid: 23612718
[42]	Kopperoinen, L., Barton, D.N., Costadone, L., et al., 2022. Urban Experimental Ecosystem Accounting Pilot in Nordic cities. [2024-01-27]. https://pub.norden.org/temanord2022-557/.
[43]	Kubiszewski, I., Costanza, R., Anderson, S., et al., 2017. The future value of ecosystem services: Global scenarios and national implications. Ecosyst. Serv. 26, 289-301.
[44]	Kuenzer, C., Bluemel, A., Gebhardt, S., et al., 2011. Remote sensing of mangrove ecosystems: A review. Remote Sens. 3(5), 878-928.
[45]	Lamarque, P., Quétier, F., Lavorel, S., 2011. The diversity of the ecosystem services concept and its implications for their assessment and management. C. R. Biol. 334(5-6), 441-449.
[46]	Lu, Y.T., Wu, P.H., Ma, X.S., et al., 2019. Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata-Markov model. Environ. Monit. Assess. 191(2), 68, doi: 10.1007/s10661-019-7200-2. pmid: 30644019
[47]	Maheng, D., Pathirana, A., Zevenbergen, C., 2021. A preliminary study on the impact of landscape pattern changes due to urbanization: Case study of Jakarta, Indonesia. Land. 10(2), 218, doi: 10.3390/land10020218.
[48]	Malaysia Tourism Promotion Board, 2022. Tourism Malaysia. [2024-01-27]. https://www.tourism.gov.my/.
[49]	Masi, F., Rizzo, A., Regelsberger, M., 2018. The role of constructed wetlands in a new circular economy, resource oriented, and ecosystem services paradigm. J. Environ. Manage. 216, 275-284.
[50]	Mengist, W., Soromessa, T., Legese, G., 2020. Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps. Sci. Total Environ. 702, 134581, doi: 10.1016/j.scitotenv.2019.134581.
[51]	Michelot, T., Klappstein, N.J., Potts, J.R., et al., 2024. Understanding step selection analysis through numerical integration. Methods Ecol. Evol. 15(1), 24-35.
[52]	Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being. Washington: Island Press, 563.
[53]	Mitchell, M.G.E., Schuster, R., Jacob, A.L., et al., 2021. Identifying key ecosystem service providing areas to inform national-scale conservation planning. Environ. Res. Lett. 16(1), 014038, doi: 10.17605/OSF.IO/KTCXE.
[54]	Moharrami, M., Attarchi, S., Gloaguen, R., et al., 2024. Integration of Sentinel-1 and Sentinel-2 data for ground truth sample migration for multi-temporal land cover mapping. Remote Sens. 16(9), 1566, doi: 10.3390/rs16091566.
[55]	Monte, J., 1978. The impact of petroleum dredging on Louisiana’s coastal landscape: A plant biogeographical analysis and resource assessment of Spoil Bank Habitats in the Bayou Lafourche Delta. Louisiana: Louisiana State University and Agricultural & Mechanical College, 24-26.
[56]	Morizet-Davis, J., Vidaurre, N.M.A., Reinmuth, E., et al., 2023. Ecosystem services at the farm level—overview, synergies, trade-offs, and stakeholder analysis. Glob. Chall. 7(7), 2200225, doi: 10.1002/gch2.202200225.
[57]	Mountrakis, G., Heydari, S.S., 2023. Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits. ISPRS-J. Photogramm. Remote Sens. 200, 106-119.
[58]	Niya, A.K., Huang, J.L., Kazemzadeh-Zow, A., et al., 2019. An adding/deleting approach to improve land change modeling: A case study in Qeshm Island, Iran. Arab. J. Geosci. 12(11), 333, doi: 10.1007/s12517-019-4504-z.
[59]	Ongsomwang, S., Pattanakiat, S., Srisuwan, A., 2019. Impact of land use and land cover change on ecosystem service values: A case study of Khon Kaen City, Thailand. Environment and Natural Resources Journal. 17(4), 43-58.
[60]	Pham, V.D., Thiel, F., Frantz, D., et al., 2024. Learning the variations in annual spectral-temporal metrics to enhance the transferability of regression models for land cover fraction monitoring. Remote Sens. Environ. 308, 114206, doi: 10.1016/j.rse.2024.114206.
[61]	Piralilou, S.T., Einali, G., Ghorbanzadeh, O., et al., 2022. A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions. Remote Sens. 14(3), 672, doi: 10.3390/rs14030672.
[62]	Roy, S.K., Alam, M.T., Mojumder, P., et al., 2024. Dynamic assessment and prediction of land use alterations influence on ecosystem service value: A pathway to environmental sustainability. Environ. Sustain. Indic. 21, 100319, doi: 10.1016/j.indic.2023.100319.
[63]	Schwab, K., Vanham, P., 2021. Stakeholder Capitalism: A Global Economy that Works for Progress, People and Planet. New Jersey: John Wiley & Sons, 147-167.
[64]	Seutin, G., Klein, N.K., Ricklefs, R.E., et al., 1994. Historical biogeography of the bananaquit (Coereba flaveola) in the Caribbean region: A mitochondrial DNA assessment. Evolution. 48(4), 1041-1061. doi: 10.1111/j.1558-5646.1994.tb05292.x pmid: 28564451
[65]	Shehayeb, R., Ortlepp, R., Schanze, J., 2024. A drought and heat risk assessment framework for urban green infrastructure. Climate Resilience and Sustainability. 3(1), e63, doi: 10.1002/cli2.63.
[66]	Suryawan, I.W.K., Mulyana, R., Septiariva, I.Y., et al., 2024. Smart urbanism, citizen-centric approaches and integrated environmental services in transit-oriented development in Jakarta, Indonesia. Research in Globalization. 8, 100181, doi: 10.1016/j.resglo.2023.100181.
[67]	Talagrand, M., 2005. The Generic Chaining:Upper and Lower Bounds of Stochastic Processes. Heidelberg: Springer, 1-222.
[68]	Tan, K.C., Lim, H.S., MatJafri, M.Z., et al., 2010. Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environ. Earth Sci. 60(7), 1509-1521.
[69]	Tzoulas, K., Korpela, K., Venn, S., et al., 2007. Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landsc. Urban Plan. 81(3), 167-178.
[70]	Uddin, K., Chaudhary, S., Chettri, N., et al., 2015. The changing land cover and fragmenting forest on the roof of the World: A case study in Nepal’s Kailash Sacred Landscape. Landsc. Urban Plan. 141, 1-10.
[71]	Vali, A., Comai, S., Matteucci, M., 2020. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens. 12(15), 2495, doi: 10.3390/rs12152495.
[72]	van Veller, M.G.P., Kornet, D.J., Zandee, M., 2000. Methods in vicariance biogeography: Assessment of the implementations of assumptions 0, 1, and 2. Cladistics-Int. J. Willi Hennig Soc. 16(3), 319-345.
[73]	Wilmer, H., Taylor, J.B., Macon, D., et al., 2024. Loss of seasonal ranges reshapes transhumant adaptive capacity: Thirty-five years at the US Sheep Experiment Station. Agric. Human Values. 1-19.
[74]	Yakir, B., 1994. Optimal detection of a change in distribution when the observations form a Markov chain with a finite state space. In: Carlstein, E., Müller, H.G., Siegmund, D., (eds). . Papers from the AMS-IMS-SIAM Summer Research Conference. Michigan: Institute of Mathematical Statistics, 346-358.
[75]	Zadbagher, E., Becek, K., Berberoglu, S., 2018. Modeling land use/land cover change using remote sensing and geographic information systems: Case study of the Seyhan Basin, Turkey. Environ. Monit. Assess. 190(8), 494, doi: 10.1007/s10661-018-6877-y.

No.	Type	Description
1	Natural forest	The original forests remained in Kuala Lumpur.
2	Artificial forest	Trees that planted by human around buildings and houses.
3	Grassland	Football fields, parks, and empty lands with growing grasses.
4	Water body	Ponds, swamps, lakes, rivers, ditches, and lagoons.
5	Bare ground	Any open exposed lands without vegetation cover.
6	Built-up area	Residential areas, factories, apartments, roads, and constructions.

Built-up area	Bare ground	Water body	Grassland	Artificial forest	Natural forest
2427	6569	169	875	481	4435	Natural forest
10,364	15,739	1593	2372	9644	3019	Artificial forest
3273	4408	41	820	2026	490	Grassland
294	490	2451	84	618	116	Water body
41,206	30,282	364	3451	11,402	1718	Bare ground
66,886	28,679	47	1447	7440	325	Built-up area

Built-up area	Bare ground	Water body	Grassland	Artificial forest	Natural forest
0.1259	0.3406	0.0087	0.0454	0.2494	0.2299	Natural forest
0.2425	0.3683	0.0373	0.0555	0.2257	0.0706	Artificial forest
0.2960	0.3986	0.0037	0.0742	0.1832	0.0443	Grassland
0.0725	0.1210	0.6048	0.0207	0.1524	0.0286	Water body
0.4660	0.3425	0.0041	0.0390	0.1290	0.0194	Bare ground
0.6381	0.2736	0.0004	0.0138	0.0710	0.0031	Built-up area

Built-up area	Bare ground	Water body	Grassland	Artificial forest	Natural forest
3698	2562	500	3322	4131	8058	Natural forest
9621	4632	2468	10,071	10,301	3973	Artificial forest
15,481	7370	1691	19,225	18,687	3939	Grassland
775	778	7513	1019	965	309	Water body
13,189	5191	1237	10,694	4886	675	Bare ground
49,304	14246	2459	22,473	4803	129	Built-up area

Built-up area	Bare ground	Water body	Grassland	Artificial forest	Natural forest
0.1660	0.1150	0.0225	0.1492	0.1855	0.3618	Natural forest
0.2343	0.1128	0.0601	0.2452	0.2508	0.0968	Artificial forest
0.2332	0.1110	0.0255	0.2896	0.2815	0.0593	Grassland
0.0683	0.0685	0.6614	0.0897	0.0849	0.0272	Water body
0.3677	0.1447	0.0345	0.2981	0.1362	0.0188	Bare ground
0.5278	0.1525	0.0263	0.2406	0.0514	0.0014	Built-up area