The availability of multi-resolution spatial data and advances in modeling techniques have given an impetus to land use land cover (LULC) change analyses. Geo-visualization of possible land uses (LU) with policy decisions is vital for formulating appropriate sustainable resource management policies. For the prudent management of natural resources, LU planning has to take environmental dimensions into account. LU dynamics helps to understand the macro background of regional population growth, economic development, social progress, and changes in the natural environment. In this study, LU transitions from 1985 to 2019 were assessed through a supervised classifier based on the Gaussian maximum likelihood estimation algorithm. Geo-visualization of landscape dynamics was implemented through a fuzzy analytical hierarchy process (AHP) with Markov cellular automata (MCA) for Karnataka state, India. It considered five policy scenarios, namely, (i) business as usual (BAU), (ii) agent-based land use transition (ALT), (iii) reserve forest protection (RFP), (iv) afforestation (AF), and (v) sustainable development plan (SDP). Prior knowledge of likely LU aids in assessing the implications of chosen policies forms a base for sustainable resource management with conservation of biological diversity. LU analyses revealed that forests in Karnataka state constituted 21% in 1985, witnessed large-scale transitions, and reduced to 15% of the geographical area in 2019. BAU depicts a likely increase in the built-up area to 11.5% from 3% (2019). The SDP scenario (with stringent policy implementation) indicates that the forest cover would remain at 11% (compared to 15% in 2019), which is the least possible loss among all considered scenarios (BAU, ALT, RFP, AF, and SDP). Modeling and visualization of landscape dynamics aids in regional LU planning as a spatial decision support system (SDSS) towards achieving sustainable development goals.
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Data used in the analyses are compiled from the field. Data is analyzed and organized in the form of table, which are presented in the manuscript. Also, synthesized data are archived at http://wgbis.ces.iisc.ernet.in/energy/water/paper/researchpaper2.html#ce. http://wgbis.ces.iisc.ernet.in/biodiversity/
Alavipanah S, Wegmann M, Qureshi S et al (2015) The role of vegetation in mitigating urban land surface temperatures: A case study of Munich, Germany during the warm season. Sustainability 7:4689–4706
Allen CD, Breshears DD, McDowell NG (2015) On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6:1–55
Axtell R (2000) Why agents?: on the varied motivations for agent computing in the social sciences
Batty M (2005) Agents, cells, and cities: new representational models for simulating multiscale urban dynamics. Environ Plan A 37:1373–1394
Batty M, Xie Y (1994) From cells to cities. Environ Plan B Plan Des 21:S31–S48
Bernard RN (1999) Using adaptive agent-based simulation models to assist planners in policy development: The case of rent control. Rutgers Univ Dep Urban Plan Policy Dev
Bharath S, Rajan KS, Ramachandra TV (2013) Land Surface Temperature Responses to Land Use Land Cover Dynamics. Geoinfor Geostat An Overv 1. https://doi.org/10.4172/2327-4581.1000112
Bharath S, Rajan KS, Ramachandra TV (2014) Status and future transition of rapid urbanizing landscape in central Western Ghats - CA based approach. ISPRS Ann Photogramm Remote Sens Spat Inf Sci II–8:69–75. https://doi.org/10.5194/isprsannals-II-8-69-2014
Bharath S, Rajan KS, Ramachandra TV (2021) Modeling Forest Landscape Dynamics. Nova Science Publishers, New York, NY (United States)
Breshears DD, Cobb NS, Rich PM et al (2005) Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci 102:15144–15148
Bunruamkaew K, Murayam Y (2011) Site suitability evaluation for ecotourism using GIS \& AHP: A case study of Surat Thani province, Thailand. Procedia-Social Behav Sci 21:269–278
Buyantuyev A, Wu J (2012) Urbanization diversifies land surface phenology in arid environments: interactions among vegetation, climatic variation, and land use pattern in the Phoenix metropolitan region, USA. Landsc Urban Plan 105:149–159
Chandan MC, Nimish G, Bharath HA (2020) Analysing spatial patterns and trend of future urban expansion using SLEUTH. Spat Inf Res 28:11–23
Clarke KC (2008) A decade of cellular urban modeling with SLEUTH: Unresolved issues and problems. Ch 3:47–60
Crooks AT (2010) Constructing and implementing an agent-based model of residential segregation through vector GIS. Int J Geogr Inf Sci 24:661–675
Crooks AT, Heppenstall AJ (2012) Introduction to agent-based modelling. Agent-based models of geographical systems. Springer, In, pp 85–105
Dadashpoor H, Panahi H (2021) Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region. Environ Dev Sustain 1–22
Eckhardt R (1987) Stan ulam, john von neumann, and the monte carlo method. Los Alamos Sci 15:131–136
Ermentrout GB, Edelstein-Keshet L (1993) Cellular automata approaches to biological modeling. J Theor Biol 160:97–133
Feizizadeh B, Blaschke T (2013) Examining urban heat island relations to land use and air pollution: Multiple endmember spectral mixture analysis for thermal remote sensing. IEEE J Sel Top Appl Earth Obs Remote Sens 6:1749–1756
Franklin S, Graesser A (1996) Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. International workshop on agent theories, architectures, and languages, In, pp 21–35
Fu X, Wang X, Yang YJ (2018) Deriving suitability factors for CA-Markov land use simulation model based on local historical data. J Environ Manage 206:10–19. https://doi.org/10.1016/J.JENVMAN.2017.10.012
Fuladlu K, Altan H (2021) Examining Land Surface Temperature Relations with Major Air Pollutant: A Remote Sensing Research in Case of Tehran. Res Sq
Guidolin M, Chen AS, Ghimire B et al (2016) A weighted cellular automata 2D inundation model for rapid flood analysis. Environ Model & Softw 84:378–394
Guzman LA, Escobar F, Peña J, Cardona R (2020) A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region. Land use policy 92:104445
Hamad R, Balzter H, Kolo K (2018) Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability 10:3421
Holland JH, Sigmund K (1995) Hidden order: how adaptation builds complexity. Nature 378:453
Itami RM (1994) Simulating spatial dynamics: cellular automata theory. Landsc Urban Plan 30:27–47
Jackson RB, Baker JS (2010) Opportunities and Constraints for Forest Climate Mitigation. Bioscience 60:698–707. https://doi.org/10.1525/bio.2010.60.9.7
Kumar N, Liu X, Narayanasamydamodaran S, Pandey KK (2021) A Systematic Review Comparing Urban Flood Management Practices in India to China’s Sponge City Program. Sustainability 13:6346
Li X, Gong P (2016) Urban growth models: progress and perspective. Sci Bull 61:1637–1650
Li X, Liu X (2006) An extended cellular automaton using case-based reasoning for simulating urban development in a large complex region. Int J Geogr Inf Sci 20:1109–1136
Macal CM, North MJ (2009) Agent-based modeling and simulation. In: Proceedings of the 2009 Winter Simulation Conference (WSC). pp 86–98
Mosadeghi R, Warnken J, Tomlinson R, Mirfenderesk H (2015) Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision-making model for urban land-use planning. Comput Environ Urban Syst 49:54–65
Pascal JP (1986) Explanatory Booklet on Forest Map of South India. Explan Bookl For Map South India Belgaum-Dharwar-Panaji , Shimoga, Mercara-Mysore 19–30
Polasky S, Nelson E, Pennington D, Johnson KA (2011) The impact of land-use change on ecosystem services, biodiversity and returns to landowners: a case study in the state of Minnesota. Environ Resour Econ 48:219–242
Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265
Ramachandra TV, Aithal BH (2016) Bengaluru’s reality: towards unlivable status with unplanned urban trajectory. Curr Sci 110:2207–2208
Ramachandra TV, Bharath S (2019a) Carbon Sequestration Potential of the Forest Ecosystems in the Western Ghats, a Global Biodiversity Hotspot. Nat Resour Res 29:2753–2771. https://doi.org/10.1007/s11053-019-09588-0
Ramachandra TV, Bharath S (2019b) Sustainable Management of Bannerghatta National Park, India, with the Insights in Land Cover Dynamics. FIIB Bus Rev 8:118–131. https://doi.org/10.1177/2319714519828462
Ramachandra TV, Bharath S (2021) Carbon Footprint of Karnataka: Accounting of Sources and Sinks. Carbon Footprint Case Studies. Springer, In, pp 53–92
Ramachandra TV, Shwetmala (2009) Emissions from India’s transport sector: Statewise synthesis. Atmos Environ 43:5510–5517. https://doi.org/10.1016/j.atmosenv.2009.07.015
Ramachandra TV, Uttam K (2009) Land surface temperature with land cover dynamics: multi-resolution, spatio-temporal data analysis of Greater Bangalore. Int J Geoinformatics 5:44
Ramachandra TV, Setturu B, Aithal BH (2012) Peri-urban to urban landscape patterns elucidation through spatial metrics. Int J Eng Res Dev 2:58–81
Ramachandra TV, Bharath S, Rajan KS, Subash Chandran MD (2017a) Modelling the forest transition in Central Western Ghats, India. Spat Inf Res 25:117–130. https://doi.org/10.1007/s41324-017-0084-8
Ramachandra TV, Bajpai V, Kulkarni G et al (2017b) Economic disparity and CO2 emissions: The domestic energy sector in Greater Bangalore, India. Renew Sustain Energy Rev 67:1331–1344
Ramachandra TV, Bharath S, Gupta N (2018) Modelling landscape dynamics with LST in protected areas of Western Ghats. Karnataka. J. Environ. Manage. 1253–1262
Ramachandra TV, Sellers J, Bharath HA, Setturu B (2019) Micro level analyses of environmentally disastrous urbanization in Bangalore. Environ Monit Assess 191. https://doi.org/10.1007/s10661-019-7693-8
Ramachandra TV, Vinay S, Bharath S et al (2020) Insights into riverscape dynamics with the hydrological, ecological and social dimensions for water sustenance. Curr Sci 113891:118
Ramachandra T V, Vinay S, Bharath S (2021) Visualisation of landscape alterations with the proposed linear projects and their impacts on the ecology. Model Earth Syst Environ 1–13
Saaty TL (1980) The analytical hierarchy process, planning, priority. Resour Alloc RWS Publ USA
Samie A, Deng X, Jia S, Chen D (2017) Scenario-based simulation on dynamics of land-use-land-cover change in Punjab Province, Pakistan. Sustainability 9:1285
Santé I, Garcia AM, Miranda D, Crecente R (2010) Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landsc Urban Plan 96:108–122
SEEA (2017) System of Environmental Economic Accounting 2012: Central Framework. International Monetary Fund
Singh AK (2003) Modelling land use land cover changes using cellular automata in a geo-spatial environment
Spencer D (2009) Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals
Torrens PM (2000) How cellular models of urban systems work (1. Theory)
Verburg PH, Soepboer W, Veldkamp A et al (2002) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manage 30:391–405
Vinay S, Bharath S, Bharath HA, Ramachandra TV (2013) Hydrologic model with landscape dynamics for drought monitoring. In: proceeding of: Joint International Workshop of ISPRS WG VIII/1 and WG IV/4 on Geospatial Data for Disaster and Risk Reduction. Hyderabad, November, pp 21–22
Vose RS, Karl TR, Easterling DR et al (2004) Impact of land-use change on climate. Nature 427:213–214
Wahyudi A, Liu Y (2013) Cellular automata for urban growth modeling: a chronological review on factors in transition rules. In: 13th International conference on computers in urban planning and urban Management (CUPUM 2013)
Yao R, Wang L, Huang X et al (2017) Temporal trends of surface urban heat islands and associated determinants in major Chinese cities. Sci Total Environ 609:742–754
Zhou D, Zhao S, Zhang L, Liu S (2016) Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities. Remote Sens Environ 176:272–281
This work is part of the international, EU-funded Natural Capital Accounting and Valuation of Ecosystem Services (NCAVES) project. The NCAVES project is carried out as a collaboration between the United Nations environment program (UNEP), United Nations Statistics Division (UNSD), the Ministry of Statistics and Programme Implementation (MoSP), Government of India and the ENVIS division, The Ministry of Environment Forests and Climate Change (MoEFCC), Government of India. We are grateful to the ENVIS Division, the Ministry of Environment, Forests and Climate Change, Government of India.
This work is part of the international, EU-funded Natural Capital Accounting and Valuation of Ecosystem Services (NCAVES) project. The NCAVES project is carried out as a collaboration between the United Nations environment program (UNEP), United Nations Statistics Division (UNSD), the Ministry of Statistics and Programme Implementation (MoSP), Government of India and the ENVIS division, The Ministry of Environment Forests and Climate Change (MoEFCC), Government of India.
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Setturu, B., Ramachandra, T.V. Modeling Landscape Dynamics of Policy Interventions in Karnataka State, India. J geovis spat anal 5, 22 (2021). https://doi.org/10.1007/s41651-021-00091-w
- Land use dynamics
- Sustainable development