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Land Use Competition pp 91–109Cite as

The Role of Maps in Capturing Distal Drivers of Deforestation and Degradation: A Case Study in Central Mozambique

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Part of the book series: Human-Environment Interactions ((HUEN,volume 6))

Abstract

While remote sensing images provide unprecedented abundance of earth observation data from both optical and radar perspectives, on a satellite or airborne platform, from plot to global scale, identifying and modeling drivers of land cover and land use change remains a huge challenge. This challenge is aggravated by the merging of distal drivers, which play an increasingly important and complex role in altering local land cover and land use change. Distal drivers, in the form of global markets, NGOs, international governments, and institutions, significantly contribute to shaping the landscape of central African states—the focus of this chapter—by controlling capital, information, knowledge flow, and international development initiatives. In this chapter, I will explore the applications and constraints of Remote Sensing (RS) and Geographic Information System (GIS) in capturing distal drivers of deforestation and forest degradation in the Beira corridor, Manica, Mozambique. I investigate how this analysis fits into the conceptual and methodological framework of distal drivers. In Beira corridor, it is primarily the scarcity and uneven distribution of forest resources shaping land use competition in woodland areas. This competition is intensified by dramatic population increases, demands from remote markets, and conflicts of interest between local government and international organizations to preserve or develop certain forests and not others. Remote sensing images can capture the patterns of land use change as ‘balanced’ results of different land uses within a geographic area with time, which reflects the respective outcomes of competition between these different land use activity types. Examples explain drivers can be connected to their spatial patterns by combining regional biomass change maps derived from optical and radar remote sensing imagery, and knowledge of local deforestation and degradation processes. Moreover, we discuss the biases and uncertainties that result from processing RS images, as well as interpolating land use maps from RS images. I conclude with a brief discussion of the biases and uncertainties that may affect our perception of land use change, and how maps and their attributes themselves may feedback into land use competition.

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Notes

  1. 1.

    Optical imagery showed a different trend than radar imagery, which might due to the characteristics of the two sensors, differences in forest and forest loss events, time of year when the image is captured, and various other factors.

References

  • Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data, Vol. 964. US Government Printing Office.

    Google Scholar 

  • BAGC. (2014). Beira agricultural growth corridor.

    Google Scholar 

  • Best, R. H. (1968). Competition for land between rural and urban uses. Land use and resources: Studies in applied geography. A memorial study to Sir Dudley Stamp, 89–100.

    Google Scholar 

  • Bourguignon, H. (2006). Enhancing the role of forests in the socio-economic development of forested African countries. International Journal of Forestry Review, 8(1), 126–129. doi:10.1505/ifor.8.1.126.

    Article  Google Scholar 

  • Bradley, P., & Dewees, P. (1993). Indigenous woodlands, agricultural production and household economy in the communal areas. World Bank Technical Paper, 63–63.

    Google Scholar 

  • Burrough, P. A., & McDonnell, R. A. (1998). Principles of GIS. London: Oxford University Press.

    Google Scholar 

  • Campbell, B. M. (1996). The Miombo in transition: Woodlands and welfare in Africa. Cifor.

    Google Scholar 

  • Campbell, J. B. (2002). Introduction to remote sensing. Boca Raton: CRC Press.

    Google Scholar 

  • Cavendish, W. (2000). Empirical regularities in the poverty-environment relationship of rural households: Evidence from Zimbabwe. World Development, 28(11), 1979–2003.

    Article  Google Scholar 

  • Committee on Needs and Research Requirements for Land Change Modeling GSC. (2013). Board on Earth Sciences and Resources, Division on Earth and Life Studies. Advancing land change modeling: Opportunities and research requirements.

    Google Scholar 

  • Dewees, P. A., Campbell, B. M., Katerere, Y., Sitoe, A., Cunningham, A. B., Angelsen, A., & Wunder, S. (2010). Managing the Miombo woodlands of southern Africa: policies, incentives and options for the rural poor. Journal of Natural Resources Policy Research, 2(1), 57–73.

    Article  Google Scholar 

  • ESA. (2012). BIOMASS, an Earth Explorer to observe forest biomass. Report for Mission Selection: Biomass.

    Google Scholar 

  • EUEI. (2012). Mozambique Biomass Energy Strategy.

    Google Scholar 

  • FAO. (2005). Global forest resources assessment 2005.

    Google Scholar 

  • FAO. (2010a). Global forest land-use change 1990–2005. Global Forest Resources Assessment.

    Google Scholar 

  • FAO. (2010b). Global forest resources assessment 2010.

    Google Scholar 

  • FAO. (2010c). Global forest resources assessment 2010. Country Report Mozambique.

    Google Scholar 

  • FAO. (2013). Analysis of incentives and disincentives for maize in Mozambique.

    Google Scholar 

  • Friis, C., Nielsen, J. Ø., Otero, I., Haberl, H., Niewöhner, J., & Hostert, P. (2015). From teleconnection to telecoupling: Taking stock of an emerging framework in land system science. Journal of Land Use Science, 1–23. doi:10.1080/1747423X.2015.1096423

    Google Scholar 

  • Garrett, R. D., Lambin, E. F., & Naylor, R. L. (2013). Land institutions and supply chain configurations as determinants of soybean planted area and yields in Brazil. Land Use Policy, 31, 385–396. doi:10.1016/j.landusepol.2012.08.002.

    Article  Google Scholar 

  • Geist, H. J., & Lambin, E. F. (2002). Proximate causes and underlying driving forces of tropical deforestation. BioScience, 52(2), 143–150. doi:10.1641/0006-3568(2002)052[0143:Pcaudf]2.0.Co;2.

    Article  Google Scholar 

  • Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., et al. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850–853. doi:10.1126/science.1244693.

    Article  CAS  Google Scholar 

  • Herd, A. R. C. (2007). Exploring the socio-economic role of charcoal and the potential for sustainable production in the Chicale Regulado. Mozambique: University of Edinburgh.

    Google Scholar 

  • Houghton, R. A., Boone, R. D., Melillo, J. M., Palm, C. A., Woodwell, G. M., Myers, N., et al. (1985). Net flux of carbon-dioxide from tropical forests in 1980. Nature, 316(6029), 617–620. doi:10.1038/316617a0.

    Article  CAS  Google Scholar 

  • Huang, W. L., Sun, G. Q., Dubayah, R., Cook, B., Montesano, P., Ni, W. J., & Zhang, Z. Y. (2013). Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment, 134, 319–332. doi:10.1016/j.rse.2013.03.017.

    Article  Google Scholar 

  • IPCC. (2006). 2006 IPCC guidelines for national greenhouse gas inventories.

    Google Scholar 

  • JAXA. (2014). New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007–2010)—version 1. http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.html

  • Kennedy, R. E., Andrefouet, S., Cohen, W. B., Gomez, C., Griffiths, P., Hais, M., et al. (2014). Bringing an ecological view of change to landsat-based remote sensing. Frontiers in Ecology and the Environment, 12(6), 339–346. doi:10.1890/130066.

    Article  Google Scholar 

  • Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford: Oxford University Press.

    Google Scholar 

  • Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30. doi:10.1641/0006-3568(2002)052[0019:Lrsfes]2.0.Co;2.

    Article  Google Scholar 

  • Lund, H. G. (2012). Definitions of forest, deforestation, afforestation, and reforestation.

    Google Scholar 

  • Mitchard, E., Saatchi, S., Lewis, S., Feldpausch, T., Woodhouse, I., Sonké, B., et al. (2011). Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest–savanna boundary region of central Africa using multi-temporal L-band radar backscatter. Remote Sensing of Environment, 115(11), 2861–2873.

    Article  Google Scholar 

  • Mitchard, E. T., Saatchi, S. S., Baccini, A., Asner, G. P., Goetz, S. J., Harris, N., & Brown, S. (2013). Uncertainty in the spatial distribution of tropical forest biomass: A comparison of pan-tropical maps. Carbon Balance and Management.

    Google Scholar 

  • Mitchard, E. T. A., Saatchi, S. S., White, L. J. T., Abernethy, K. A., Jeffery, K. J., Lewis, S. L., et al. (2012). Mapping tropical forest biomass with radar and spaceborne LiDAR in Lope National Park, Gabon: Overcoming problems of high biomass and persistent cloud. Biogeosciences, 9(1), 179–191. doi:10.5194/bg-9-179-2012.

    Article  Google Scholar 

  • NASA. (2014). Global ecosystem dynamics investigation lidar (GEDI). http://science.nasa.gov/missions/gedi/. Accessed January 3, 2015.

  • Olander, L. P., Gibbs, H. K., Steininger, M., Swenson, J. J., & Murray, B. C. (2008). Reference scenarios for deforestation and forest degradation in support of REDD: A review of data and methods. Environmental Research Letters, 3(2), 025011.

    Article  Google Scholar 

  • Rueda, X., & Lambin, E. F. (2013). Linking globalization to local land uses: How eco-consumers and gourmands are changing the Colombian coffee landscapes. World Development, 41, 286–301. doi:10.1016/j.worlddev.2012.05.018.

    Article  Google Scholar 

  • Ryan, C. M., Berry, N. J., & Joshi, N. (2014). Quantifying the causes of deforestation and degradation and creating transparent REDD+ baselines: A method and case study from central Mozambique. Applied Geography, 53, 45–54.

    Article  Google Scholar 

  • Sagan, C., Toon, O. B., & Pollack, J. B. (1979). Anthropogenic albedo changes and the earths climate. Science, 206(4425), 1363–1368. doi:10.1126/science.206.4425.1363.

    Article  CAS  Google Scholar 

  • Seto, K. C., Reenberg, A., Boone, C. G., Fragkias, M., Haase, D., Langanke, T., et al. (2012). Urban land teleconnections and sustainability. Proceedings of the National Academy of Sciences, 109(20), 7687–7692.

    Article  CAS  Google Scholar 

  • Shackleton, S., Shackleton, C., Netshiluvhi, T., Geach, B., Balance, A., & Fairbanks, D. (2002). Use patterns and value of savanna resources in three rural villages in South Africa. Economic Botany, 56(2), 130–146.

    Article  Google Scholar 

  • Simard, M., Pinto, N., Fisher, J. B., & Baccini, A. (2011). Mapping forest canopy height globally with spaceborne lidar. Journal of Geophysical Research: Biogeosciences, 116. doi:Artn G04021. doi:10.1029/2011jg001708

  • Snyder, J. H. (1966). A new program for agricultural land use stabilization: The California Land Conservation Act of 1965. Land Economics, 29–41.

    Google Scholar 

  • Stedham, B. (2012). Forest carbon stocks in Malawi 2007–10. An interim report to LTS International, Vol. 1. University of Edinburgh.

    Google Scholar 

  • Stone, T. A., Houghton, R. A., Melillo, J. M., & Woodwell, G. M. (1983). Deforestation in the Amazon Basin measured by satellite—A release of CO2 to the atmosphere. Biological Bulletin, 165(2), 511.

    Google Scholar 

  • Sun, G. Q., Ranson, K. J., Guo, Z., Zhang, Z., Montesano, P., & Kimes, D. (2011). Forest biomass mapping from lidar and radar synergies. Remote Sensing of Environment, 115(11), 2906–2916. doi:10.1016/J.Rse.2011.03.021

    Google Scholar 

  • UNFCCC. (2001). Land use, land-use change and forestry decision 11/CP.7.

    Google Scholar 

  • UNFCCC. (2006). Reducing emissions from deforestation in developing countries.

    Google Scholar 

  • UNFCCC. (2014a). Key decisions relevant for reducing emissions from deforestation and forest degradation in developing countries (REDD+).

    Google Scholar 

  • UNFCCC. (2014b). Reporting and accounting of LULUCF activities under the Kyoto Protocol. http://unfccc.int/methods/lulucf/items/4129.php

  • USGU. (2014). Landsat project description. http://landsat.usgs.gov/about_project_descriptions.php

  • Wibberley, G. P. (1959). Agriculture and urban growth, a study of the competition for rural land.

    Google Scholar 

  • Woodhouse, I. H., Mitchard, E. T. A., Brolly, M., Maniatis, D., & Ryan, C. M. (2012). CORRESPONDENCE: Radar backscatter is not a ‘direct measure’ of forest biomass. Nature Climate Change, 2(8), 556–557.

    Article  Google Scholar 

  • Woodwell, G. M., Hobbie, J. E., Houghton, R. A., Melillo, J. M., Moore, B., Peterson, B. J., & Shaver, G. R. (1983). Global deforestation—Contribution to atmospheric carbon-dioxide. Science, 222(4628), 1081–1086. doi:10.1126/science.222.4628.1081.

    Article  CAS  Google Scholar 

  • Woodwell, G. M., Houghton, R. A., Stone, T. A., & Park, A. B. (1986). Changes in the area of forests in Rondônia, Amazon Basin, measured by satellite imagery. The changing carbon cycle (pp. 242–257). Berlin: Springer.

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Geosciences, University of Edinburgh, Edinburgh, Scotland, UK

    Yaqing Gou

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  1. Yaqing Gou
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Correspondence to Yaqing Gou .

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Editors and Affiliations

  1. Inst. of European Ethnology & Integrativ, Humboldt-Universität zu Berlin, Berlin, Berlin, Germany

    Jörg Niewöhner

  2. Governance and Sustainability Lab, Trier University, Berlin, Germany

    Antje Bruns

  3. Dept. of Geography & IRI THESys, Humboldt-Universität zu Berlin, Berlin, Berlin, Germany

    Patrick Hostert

  4. Dept. of Geography & IRI THESys, Humboldt-Universität zu Berlin, Berlin, Berlin, Germany

    Tobias Krueger

  5. Dept. of Geography & IRI THESys, Humboldt-Universität zu Berlin, Berlin, Germany

    Jonas Ø. Nielsen

  6. Institute of Social Ecology, Alpen-Adria University, Vienna, Austria

    Helmut Haberl

  7. Institute of Social Ecology, Alpen-Adria University, Vienna, Austria

    Christian Lauk

  8. Institute of Social Ecology, Alpen-Adria University, Vienna, Austria

    Juliana Lutz

  9. Halle (Saale) & IRI THESys, Leibniz Ins. for Agri. Devel. Trans. Eco, Berlin, Germany

    Daniel Müller

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Gou, Y. (2016). The Role of Maps in Capturing Distal Drivers of Deforestation and Degradation: A Case Study in Central Mozambique. In: Niewöhner, J., et al. Land Use Competition. Human-Environment Interactions, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-33628-2_6

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