Understanding the role of illicit transactions in land-change dynamics


Anthropogenic land use has irrevocably transformed the natural systems on which humankind relies. Advances in remote sensing have led to an improved understanding of where, why and how social and economic processes drive globally important land-use changes, from deforestation to urbanization. The role of illicit activities, however, is often absent in land change analysis. The paucity of data on unrecorded, intentionally hidden transactions makes them difficult to incorporate into spatially specific analyses of land change. We present a conceptual framework of illicit land transactions and a two-pronged approach using remotely sensed data to spatially link illicit activities to land uses.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Adding illicit transactions into an action arena for land use (following refs. 6,12).
Fig. 2: Two approaches to address illicit land transactions involving remote sensing data.


  1. 1.

    Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: the Great Acceleration. Anthr. Rev. 2, 81–98 (2015).

  2. 2.

    Global Environment Outlook — GEO-6: Summary for Policymakers (UNEP, 2019).

  3. 3.

    Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science- Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).

  4. 4.

    IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse gas fluxes in Terrestrial Ecosystems (IPCC, 2019).

  5. 5.

    Zhou, B. B., Wu, J. & Anderies, J. M. Sustainable landscapes and landscape sustainability: a tale of two concepts. Landsc. Urban Plan. 189, 274–284 (2019).

  6. 6.

    Turner II, B. L., Lambin, E. F. & Reenberg, A. The emergence of land change science for global environmental change and sustainability. Proc. Natl Acad. Sci. USA 104, 20666–20671 (2007).

  7. 7.

    Chowdhury, R. R. & Turner, B. L. II The parallel trajectories and increasing integration of landscape ecology and land system science. J. Land Use Sci. 14, 135–154 (2019).

  8. 8.

    Meyfroidt, P. et al. Middle-range theories of land system change. Glob. Environ. Chang. 53, 52–67 (2018).

  9. 9.

    Verburg, P. H. et al. Land system science and sustainable development of the earth system: a global land project perspective. Anthropocene 12, 29–41 (2015).

  10. 10.

    Pongratz, J. et al. Models meet data: challenges and opportunities in implementing land management in Earth system models. Glob. Change Biol. 24, 1470–1487 (2018).

  11. 11.

    Verburg, P. H. et al. Beyond land cover change: towards a new generation of land use models. Curr. Opin. Environ. Sustain. 38, 77–85 (2019).

  12. 12.

    Ostrom, E. Background on the institutional analysis and development framework. Policy Stud. J. 39, 7–27 (2011).

  13. 13.

    Uslaner, E. M. Corruption, Inequality, and the Rule of Law: The Bulging Pocket Makes the Easy Life (Cambridge Univ. Press, 2008).

  14. 14.

    Buruma, Y. Dutch tolerance: on drugs, prostitution, and euthanasia. Crime Justice 73, 73–114 (2007).

  15. 15.

    Lawson, S. et al. Consumer Goods and Deforestation: An Analysis of the Extent and Nature of Illegality in Forest Conversion for Agriculture and Timber Plantations (Forest Trends, 2014).

  16. 16.

    Burgess, R., Hansen, M. & Olken, B. A, Potapov, P. & Sieber, S. The political economy of deforestation in the Tropics. Q. J. Econ. 2001, 1–48 (2012).

  17. 17.

    Pailler, S. Re-election incentives and deforestation cycles in the Brazilian Amazon. J. Environ. Econ. Manag. 88, 345–365 (2018).

  18. 18.

    Weinstein, L. Mumbai’s development mafias: globalization, organized crime and land development. Int. J. Urban Reg. Res. 32, 22–39 (2008).

  19. 19.

    Jaafar, H. H. & Woertz, E. Agriculture as a funding source of ISIS: a GIS and remote sensing analysis. Food Pol. 64, 14–25 (2016).

  20. 20.

    Mcsweeney, K. et al. Drug policy as conservation policy: narco-deforestation. Science 343, 489–490 (2014).

  21. 21.

    Wolford, W., Borras, S. M., Hall, R., Scoones, I. & White, B. Governing global land deals: the role of the state in the rush for land. Dev. Change 44, 189–210 (2013).

  22. 22.

    Galaz, V. et al. Tax havens and global environmental degradation. Nat. Ecol. Evol. 2, 1352–1357 (2018).

  23. 23.

    Gore, M. L. et al. Transnational environmental crime threatens sustainable development. Nat. Sustain. 2, 784–786 (2019).

  24. 24.

    North, D. C. Institutions, Institutional Change and Economic Performance (Cambridge Univ. Press, 1990).

  25. 25.

    Satija, N., Collier, K. & Shaw, A. Everyone knew houston’s reservoirs would flood — except for the people who bought homes inside them. The Texas Tribune (October 2017).

  26. 26.

    Chiodelli, F. The illicit side of urban development: corruption and organised crime in the field of urban planning. Urban Stud. 56, 1611–1627 (2019).

  27. 27.

    Andreas, P. International politics and the illicit global economy. Perspect. Polit. 13, 782–788 (2015).

  28. 28.

    Armantier, O. & Boly, A. A controlled field experiment on corruption. Eur. Econ. Rev. 55, 1072–1082 (2011).

  29. 29.

    Fredriksson, P. G. & Neumayer, E. Corruption and climate change policies: do the bad old days matter? Environ. Resour. Econ. 63, 451–469 (2016).

  30. 30.

    della Porta, D. & Vannucci, A. in The New Institutional Economics of Corruption (eds Lambsdorff, J. G. et al.) Ch. 9 (Routledge, 2005).

  31. 31.

    O’Malley, P. in The SAGE Handbook of Criminological Theory (eds McLaughlin, E. & Newburn, T.) 319–336 (SAGE, 2010).

  32. 32.

    Karstedt, S. in SAGE Handbook of Criminological Theory (eds McLaughlin, E. & Newburn, T.) 337–359 (SAGE, 2010).

  33. 33.

    Carter, N. H. et al. A conceptual framework for understanding illegal killing of large carnivores. AmBio 46, 251–264 (2017).

  34. 34.

    Gibbs, C., Gore, M. L., McGarrell, E. F. & Rivers, L. Introducing conservation criminology towards interdisciplinary scholarship on environmental crimes and risks. Br. J. Criminol. 50, 124–144 (2010).

  35. 35.

    Roy, A. Urban informality: toward an epistemology of planning. J. Am. Plan. Assoc. 71, 147–158 (2005).

  36. 36.

    Gregson, N. & Crang, M. Illicit economies: customary illegality, moral economies and circulation. Trans. Inst. Br. Geogr. 42, 206–219 (2017).

  37. 37.

    Gore, M. L., Ratsimbazafy, J. & Lute, M. L. Rethinking corruption in conservation crime: insights from Madagascar. Conserv. Lett. 6, 430–438 (2013).

  38. 38.

    Ahmed, I., Ayeb-Karlsson, S., van der Geest, K., Huq, S. & Jordan, J. C. Climate change, environmental stress and loss of livelihoods can push people towards illegal activities: a case study from coastal Bangladesh. Clim. Dev. 11, 907–917 (2019).

  39. 39.

    Avelino, F. & Rotmans, J. Power in transition: an interdisciplinary framework to study power in relation to structural change. Eur. J. Soc. Theory 12, 543–569 (2009).

  40. 40.

    Basu, G. Concealment, corruption, and evasion: a transaction cost and case analysis of illicit supply chain activity. J. Transp. Secur. 7, 209–226 (2014).

  41. 41.

    Grandia, L. Road mapping: megaprojects and land grabs in the Northern Guatemalan lowlands. Dev. Change 44, 233–259 (2013).

  42. 42.

    Hausermann, H. et al. Land-grabbing, land-use transformation and social differentiation: deconstructing ‘small-scale’ in Ghana’s recent gold rush. World Dev. 108, 103–114 (2018).

  43. 43.

    Devine, J., Wrathall, D., Currit, N., Tellman, B. & Langarica, Y. Narco-cattle ranching in political forests. Antipode https://doi.org/10.1111/anti.12469 (2018).

  44. 44.

    Holland, A. C. Forbearance. Am. Polit. Sci. Rev. 110, 232–246 (2016).

  45. 45.

    Watts, J. Madagascar’s vanilla wars: prized spice drives death and deforestation. The Guardian (31 March 2018).

  46. 46.

    Nolte, C. Identifying challenges to enforcement in protected areas: empirical insights from 15 Colombian parks. Oryx 50, 317–322 (2016).

  47. 47.

    Hall, T. Geographies of the illicit: globalization and organized crime. Prog. Hum. Geogr. 37, 366–385 (2012).

  48. 48.

    Geoghegan, J. in People and Pixels: Linking Remote Sensing and Social Science 51–69 (National Academies, 1998).

  49. 49.

    Sánchez-Cuervo, A. M., Aide, T. M., Clark, M. L. & Etter, A. Land cover change in Colombia: surprising forest recovery trends between 2001 and 2010. PLoS ONE 7, e43943 (2012).

  50. 50.

    Dávalos, L. M. et al. Forests and drugs: coca-driven deforestation in tropical biodiversity hotspots. Environ. Sci. Technol. 45, 1219–1277 (2011).

  51. 51.

    Allison, P. Fixed Effects Regression Models 7–27 (SAGE, 2009).

  52. 52.

    Bell, A. & Jones, K. Explaining fixed effects: random effects modeling of time-series cross-sectional and panel data. Polit. Sci. Res. Methods 3, 133–153 (2015).

  53. 53.

    Siriwat, P. & Nijman, V. Using online media-sourced seizure data to assess the illegal wildlife trade in Siamese rosewood. Environ. Conserv. 45, 419–424 (2018).

  54. 54.

    Blackman, A., Corral, L., Lima, E. S. & Asner, G. P. Titling indigenous communities protects forests in the Peruvian Amazon. Proc. Natl Acad. Sci. USA 114, 4123–4128 (2017).

  55. 55.

    Wright, G. D., Andersson, K. P., Gibson, C. C. & Evans, T. P. Decentralization can help reduce deforestation when user groups engage with local government. Proc. Natl Acad. Sci. USA 113, 14958–14963 (2016).

  56. 56.

    McSweeney, K., Wrathall, D. J., Nielsen, E. A. & Pearson, Z. Grounding traffic: the cocaine commodity chain and land grabbing in eastern Honduras. Geoforum 95, 122–132 (2018).

  57. 57.

    Sesnie, S. et al. A spatio-temporal analysis of forest cover loss related to cocain trafficking in Central America. Environ. Res. Lett. 12, 054015 (2017).

  58. 58.

    Gupta, M., Gao, J., Aggarwal, C. C. & Han, J. Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26, 2250–2267 (2014).

  59. 59.

    Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114, 106–115 (2010).

  60. 60.

    Wooditch, A. & Weisburd, D. Using space–time analysis to evaluate criminal justice programs: an application to stop-question-frisk practices. J. Quant. Criminol. 32, 191–213 (2016).

  61. 61.

    Groeneveld, J. et al. Theoretical foundations of human decision-making in agent-based land use models — a review. Environ. Model. Softw. 87, 39–48 (2017).

  62. 62.

    Brown, D. G. et al. Advancing Land Change Modeling (National Academies, 2014).

  63. 63.

    Magliocca, N. et al. Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system. Proc. Natl Acad. Sci. USA 116, 7784–7792 (2019).

  64. 64.

    Sikor, T. & To, P. X. Illegal logging in Vietnam: Lam Tac (forest hijackers) in practice and talk. Soc. Nat. Resour. 24, 688–701 (2011).

  65. 65.

    West, J. & Bhattacharya, M. Intelligent financial fraud detection: a comprehensive review. Comput. Secur. 57, 47–66 (2016).

  66. 66.

    Grupos de Poder en Petén: Territorio, Política y Negocios 208 (InSight-Crime, 2011).

  67. 67.

    How Corrupt Elections Fuel the Sell-Off of Indonesia’s Natural Resources (The Gecko Project, Mongabay, 2018).

  68. 68.

    Armenteras, D., Espelta, J. M., Rodríguez, N. & Retana, J. Deforestation dynamics and drivers in different forest types in Latin America: three decades of studies (1980–2010). Glob. Environ. Chang. 46, 139–147 (2017).

  69. 69.

    McSweeney, K., Richani, N., Pearson, Z., Devine, J. & Wrathall, D. J. Why do narcos invest in rural land? J. Lat. Am. Geogr. 16, 3–29 (2017).

  70. 70.

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

  71. 71.

    Tellman, E. Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America (Arizona State Univ., 2019).

  72. 72.

    Liu, Y. et al. Social sensing: a new approach to understanding our socioeconomic environments. Ann. Assoc. Am. Geogr. 105, 512–530 (2015).

  73. 73.

    Reporters Without Borders 2018 Report (Reporters without Borders, 2018).

  74. 74.

    Neimark, B. Address the roots of environmental crime. Science 364, 139 (2019).

  75. 75.

    Kitchin, R. The real-time city? Big data and smart urbanism. GeoJournal 79, 1–14 (2014).

  76. 76.

    Turner, B. L. & Robbins, P. Land-change science and political ecology: similarities, differences, and implications for sustainability science. Annu. Rev. Environ. Resour. 33, 295–316 (2008).

  77. 77.

    Toth, A. G. & Mitchell, O. A qualitative examination of the effects of international counter-drug interdictions. Int. J. Drug Policy 55, 70–76 (2018).

  78. 78.

    Rege, A. Not biting the dust: using a tripartite model of organized crime to examine India’s Sand Mafia. Int. J. Comp. Appl. Crim. Justice 40, 101–121 (2016).

  79. 79.

    Magliocca, N. R., Khuc, Q., Van, Ellicott, E. A. & de Bremond, A. Archetypical pathways of direct and indirect land-use change caused by Cambodia’s economic land concessions. Ecol. Soc. 24, 25 (2019).

  80. 80.

    Aguilar, A. G. Peri-urbanization, illegal settlements and environmental impact in Mexico City. Cities 25, 133–145 (2008).

Download references


We thank K. Benessaiah, D. Wrathall, K. McSweeney, S. Sesnie, J. Sullivan, A. Endsley, A. Agrawal, V. Galaz, J. T Erbaugh, and H. Eakin, who provided comments on this manuscript. We also thank the participants of the 2017 AAG sessions on Clandestine Land Transactions, whose research inspired this piece and is cited within. An earlier version of this Perspective was published as a panel contribution to the Population–Environment Research Network Cyberseminar, ‘People and Pixels Revisited’ (20–27 February 2018) (https://populationenvironmentresearch.org/cyberseminars/10516). Funding was provided by the National Science Foundation Doctoral Dissertation Research Improvement (grant no. 1657773) and the National Science Foundation Early-Concept Grants for Exploratory Research Project ISN (grant no. 1837698).

Author information

B.T. and B.L.T. II conceived of the original idea, N.R.M. contributed to substantial reframing and conceptual figures, and all authors wrote and commented on the paper.

Correspondence to Beth Tellman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tellman, B., Magliocca, N.R., Turner, B.L. et al. Understanding the role of illicit transactions in land-change dynamics. Nat Sustain 3, 175–181 (2020). https://doi.org/10.1038/s41893-019-0457-1

Download citation