Skip to main content

Advertisement

Log in

Modeling Landscape Dynamics of Policy Interventions in Karnataka State, India

  • Published:
Journal of Geovisualization and Spatial Analysis Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

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/

References

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Batty M, Xie Y (1994) From cells to cities. Environ Plan B Plan Des 21:S31–S48

    Google Scholar 

  • 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)

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Chandan MC, Nimish G, Bharath HA (2020) Analysing spatial patterns and trend of future urban expansion using SLEUTH. Spat Inf Res 28:11–23

    Google Scholar 

  • Clarke KC (2008) A decade of cellular urban modeling with SLEUTH: Unresolved issues and problems. Ch 3:47–60

    Google Scholar 

  • Crooks AT (2010) Constructing and implementing an agent-based model of residential segregation through vector GIS. Int J Geogr Inf Sci 24:661–675

    Google Scholar 

  • Crooks AT, Heppenstall AJ (2012) Introduction to agent-based modelling. Agent-based models of geographical systems. Springer, In, pp 85–105

    Google Scholar 

  • 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

    Google Scholar 

  • Ermentrout GB, Edelstein-Keshet L (1993) Cellular automata approaches to biological modeling. J Theor Biol 160:97–133

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Holland JH, Sigmund K (1995) Hidden order: how adaptation builds complexity. Nature 378:453

    Google Scholar 

  • Itami RM (1994) Simulating spatial dynamics: cellular automata theory. Landsc Urban Plan 30:27–47

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Li X, Gong P (2016) Urban growth models: progress and perspective. Sci Bull 61:1637–1650

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265

    Google Scholar 

  • Ramachandra TV, Aithal BH (2016) Bengaluru’s reality: towards unlivable status with unplanned urban trajectory. Curr Sci 110:2207–2208

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ramachandra TV, Bharath S (2021) Carbon Footprint of Karnataka: Accounting of Sources and Sinks. Carbon Footprint Case Studies. Springer, In, pp 53–92

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Vose RS, Karl TR, Easterling DR et al (2004) Impact of land-use change on climate. Nature 427:213–214

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

Download references

Acknowledgements

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.

Funding

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.

Author information

Authors and Affiliations

Authors

Contributions

Ramachandra T V: Concept Design, field data collection, data analysis and interpretation of data; revising the article critically for important intellectual content; final editing

Bharath Setturu: Design of the experiment, field data collection and analysis, interpretation of data, manuscript writing

Corresponding author

Correspondence to T V Ramachandra.

Ethics declarations

Ethics Approval

The research does not involve either humans, animals, or tissues.

Consent for Publication

The publication is based on the original research and has not been submitted elsewhere for publication or web hosting.

Conflict of Interest

The authors declare no competing interests.

Permission to Carry Out Fieldwork

Our research is commissioned by the Ministry of Environment and Forests (ENVIS Division), Government of India, and hence no further permission is required as the field work was carried out in in the non-restricted areas / protected areas.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Annexure 1

Annexure 1

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41651-021-00091-w

Keywords

Navigation