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Land use and land cover changes in Doume Communal Forest in eastern Cameroon: implications for conservation and sustainable management

  • Jules Christian ZekengEmail author
  • Reuben Sebego
  • Wanda N. Mphinyane
  • Morati Mpalo
  • Dileswar Nayak
  • Jean Louis Fobane
  • Jean Michel Onana
  • Forbi Preasious Funwi
  • Marguerite Marie Abada Mbolo
Original Article
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Abstract

Large-scale identification of land use and land cover change in a tropical forest is a challenge to landscape designers and forest ecologists. Here, Landsat images acquired during the years 2000, 2009, and 2018 were used to assess the spatial-dynamics of land use and land cover (LULC) during the last two decades (2000–2018). A classification system composed of six classes—dense forest with (high tree density and low tree density), swampy Raphia forest, swampy flooded forest and savanna were designed as LULC for this study. A maximum likelihood classification was used to classify Landsat images into thematic areas. Elsewhere, Landsat-based LULC mapping, post classification at the per-pixel scales and self-knowledge on the land cover change processes were combined to analyze LULC change, forest loss and change trajectories in Doume Communal Forest in eastern Cameroon. The results show that half of the study area changed in 2000–2009 and that the different types of LULC changes increased and involved more diverse and characteristic trajectories in 2009–2018 compared to 2000–2009. Degradation to a dense forest with low tree density and swampy Raphia forest was dominant, and the forest was mostly lost due to trajectories that involved conversion to agroforestry systems (10%), and a lesser extent due to trajectories that involved deforestation to grasslands (7%). The trajectory analyses did thus contribute to a more comprehensive analysis of LULC change and the drivers of forest loss and, therefore, is essential to improve the sustainable management and support spatial planning of the forest.

Keywords

Geographic information systems Land use/land cover changes Land management Multi-temporal Landsat imagery Remote sensing Tropical rainforest Cameroon 

Notes

Acknowledgements

The lead author is grateful for the PhD exchange scholarship given by the Transdisciplinary Training for Resource Efficiency and Climate Change Adaptation in Africa (TRECCAFRICA II) project funded by the European Union. The research leading to these results has received financial funding from the British Ecological Society (EA17/1005) and The Rufford Foundation (Grant agreement N° 24,895-1), and field material funding from the IDEA WILD Foundation. We thank Dr Masha T. van der Sande for their comments and suggestions on the first manuscript of this paper. We are grateful to the Conservation and Sustainable Natural Ressources Management Network (CSNRM-Net) Association for their logistical and technical support during the entire study. The authors would like to thank the National Aeronautics and Space Administration (NASA), United States Geological Survey (USGS) for providing Landsat data. We would also thank Airbus Defense and Space through the project “Observation Spatiale des Forêts d’Afrique Centrale et de l’Ouest” (OSFACO) for providing SPOT 7 images. We are also grateful to Doume municipality for their logistical support during the fieldwork. Specifically, we thank the mayoress of the Doume municipality Mrs Mpans Giselle Rose and her secretary Mrs Ayinda Yannick for their administrative diligence and for providing us with field permits. We, furthermore, express our thanks to all those involved in fieldwork and data collection as well as community members of the different village of Doume council.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

40808_2019_637_MOESM1_ESM.docx (162 kb)
Supplementary material 1 (DOCX 162 kb)

References

  1. Anonymous (2015) Management plan of the Doume Communal Forest. Doume Council, Yaounde CameroonGoogle Scholar
  2. Basnet B, Vodacek A (2015) Tracking land use/land cover dynamics in cloud prone areas using moderate resolution satellite data: a case study in Central Africa remote sensing 7:6683Google Scholar
  3. Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377.  https://doi.org/10.1111/j.1461-0248.2011.01736.x Google Scholar
  4. CBD (2005) Handbook of the convention on biological diversity including its Cartagena protocol on biosafety, 3rd edn. Secretariat of the Convention on Biological Diversity, United Nations, MontrealGoogle Scholar
  5. Chitade AZ, Katiyar SK (2012) Multiresolution and multispectral data fusion using discrete wavelet transform with IRS images: Cartosat-1, IRS LISS III and LISS IV. J Indian Soc Remote Sens 40:121–128.  https://doi.org/10.1007/s12524-011-0140-0 Google Scholar
  6. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46.  https://doi.org/10.1016/0034-4257(91)90048-B Google Scholar
  7. Davies-Barnard T, Valdes PJ, Singarayer JS, Wiltshire AJ, Jones CD (2015) Quantifying the relative importance of land cover change from climate and land use in the representative concentration pathways. Glob Biogeochem Cycles 29:842–853.  https://doi.org/10.1002/2014GB004949 Google Scholar
  8. De Wasseige C, Devers D, Marcken P, Eba’a Atyi R, Nasi R, Mayaux P (2009) The forests of the Congo Basin—state of the forest 2008. Office des publications de l’Union Europeenne, BrusselsGoogle Scholar
  9. De Wasseige C, Flynn J, Louppe D, Hiol Hiol F, Mayaux P (2014) The forests of the Congo Basin—state of the forest 2013. Weyrich, NeufchâteauGoogle Scholar
  10. Dimyati MUH, Mizuno KEI, Kobayashi S, Kitamura T (1996) An analysis of land use/cover change in Indonesia. Int J Remote Sens 17:931–944.  https://doi.org/10.1080/01431169608949056 Google Scholar
  11. Ehlers M (2004) Spectral characteristics preserving image fusion based on Fourier domain filtering. In: Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV, Maspalomas, Canary Islands, Spain, 2004. Society of Photo-Optical Instrumentation Engineers (SPIE), p 13.  https://doi.org/10.1117/12.565160
  12. Ellis EC, Goldewijk KK, Siebert S, Lightman D, Ramankutty N (2010) Anthropogenic transformation of the biomes, 1700 to 2000. Glob Ecol Biogeogr 19:589–606.  https://doi.org/10.1111/j.1466-8238.2010.00540.x Google Scholar
  13. FAO (2006) Global forest resources assessment 2005: progress towards sustainable forest management Rome. United Nations Food and Agriculture Organization, RomeGoogle Scholar
  14. FAO (2011) State of the world’s forests. Food and Agriculture Organization of United Nations (FAO), RomeGoogle Scholar
  15. Foley JA et al (2005) Global consequences of land use Science 309:570–574.  https://doi.org/10.1126/science.1111772 Google Scholar
  16. Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropical deforestation. BioScience 52:143.  https://doi.org/10.1641/0006-3568(2002)052%5b0143:pcaudf%5d2.0.co;2 Google Scholar
  17. Ghilardi A et al (2016) Spatiotemporal modeling of fuelwood environmental impacts: towards improved accounting for non-renewable biomass. Environ Model Softw 82:241–254.  https://doi.org/10.1016/j.envsoft.2016.04.023 Google Scholar
  18. Gibbs HK, Ruesch AS, Achard F, Clayton MK, Holmgren P, Ramankutty N, Foley JA (2010) Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc Natl Acad Sci USA 107:16732–16737.  https://doi.org/10.1073/pnas.0910275107 Google Scholar
  19. Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A (2017) Modeling the spatio-temporal dynamics and evolution of land use and land cover (1984–2015) using remote sensing and GIS in Raya, Northern Ethiopia. Model Earth Syst Environ 3:1285–1301.  https://doi.org/10.1007/s40808-017-0375-z Google Scholar
  20. Grainger A (2008) Difficulties in tracking the long-term global trend in tropical forest area. Proc Natl Acad Sci USA 105:818–823.  https://doi.org/10.1073/pnas.0703015105
  21. Grainger A (2010) Uncertainty in the construction of global knowledge of tropical forests. Prog Phys Geogr Earth Environ 34:811–844.  https://doi.org/10.1177/0309133310387326
  22. Hansen MC, Loveland TR (2012) A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ 122:66–74.  https://doi.org/10.1016/j.rse.2011.08.024 Google Scholar
  23. Harris NL et al (2012) Baseline map of carbon emissions from deforestation in tropical regions. Science 336:1573–1576.  https://doi.org/10.1126/science.1217962 Google Scholar
  24. Kennedy RE, Townsend PA, Gross JE, Cohen WB, Bolstad P, Wang YQ, Adams P (2009) Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens Environ 113:1382–1396.  https://doi.org/10.1016/j.rse.2008.07.018 Google Scholar
  25. Kibret KS, Marohn C, Cadisch G (2016) Assessment of land use and land cover change in South Central Ethiopia during four decades based on integrated analysis of multi-temporal images and geospatial vector data. Remote Sens Appl Soc Environ 3:1–19.  https://doi.org/10.1016/j.rsase.2015.11.005 Google Scholar
  26. Kindu M, Schneider T, Döllerer M, Teketay D, Knoke T (2018) Scenario modelling of land use/land cover changes in Munessa–Shashemene landscape of the Ethiopian highlands. Sci Total Environ 622–623:534–546.  https://doi.org/10.1016/j.scitotenv.2017.11.338 Google Scholar
  27. Lambin EF (1997) Modelling and monitoring land-cover change processes in tropical regions. Prog Phys Geogr Earth Environ 21:375–393.  https://doi.org/10.1177/030913339702100303 Google Scholar
  28. Lambin EF (2000) Land-cover-change trajectories in southern Cameroon. AU Mertens Benoît Ann Assoc Am Geogr 90:467–494.  https://doi.org/10.1111/0004-5608.00205 Google Scholar
  29. Lambin EF et al (2001) The causes of land-use and land-cover change: moving beyond the myths. Glob Environ Change 11:261–269.  https://doi.org/10.1016/S0959-3780(01)00007-3 Google Scholar
  30. Le Quéré C et al (2013) The global carbon budget 1959–2011. Earth Syst Sci Data 5:165–185.  https://doi.org/10.5194/essd-5-165-2013 Google Scholar
  31. Le Quéré C et al (2016) Global carbon budget 2016. Earth Syst Sci Data 8:605–649.  https://doi.org/10.5194/essd-8-605-2016 Google Scholar
  32. Le Toan T, Quegan S, Davidson M et al (2011) The biomass mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860Google Scholar
  33. Letouzey R (1985) Phytogeographic map of Cameroon at 1: 500 000, accompanied by: Notice of the phytogeographic map of Cameroon at 1: 500 000. 4) TV: Domain of dense rainforest always green (Pages 95 to 142 with groupings nº 185 to 267). Institute of the International Vegetation Map, ToulouseGoogle Scholar
  34. Lewis SL et al (2009) Increasing carbon storage in intact African tropical forests. Nature 457:1003–1006Google Scholar
  35. Li G, Lu D, Moran E, Hetrick S (2011) Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery. Int J Remote Sens 32:8207–8230.  https://doi.org/10.1080/01431161.2010.532831 Google Scholar
  36. Lu D, Mausel P, Brondízio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2401.  https://doi.org/10.1080/0143116031000139863 Google Scholar
  37. Lu D, Batistella M, Moran E, De Miranda E (2005) A comparative study of terra ASTER, landsat TM, and SPOT HRG data for land cover classification in the Brazilian Amazon. In: WMSCI 2005—the 9th world multi-conference on systemics, cybernetics and informatics, proceedings, Orlando, Florida, USA, 10–13 Jul 2005, pp 411–416Google Scholar
  38. Lu D, Li G, Moran E, Dutra L, Batistella M (2011) A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. GISci Remote Sens 48:345–370.  https://doi.org/10.2747/1548-1603.48.3.345 Google Scholar
  39. Lu D, Hetrick S, Moran E, Li G (2012a) Application of time series landsat images to examining land-use/land-cover dynamic change. Photogramm Eng Remote Sens 78:747–755.  https://doi.org/10.14358/PERS.78.7.747
  40. Lu D, Batistella M, Li G, Moran E, Hetrick S, Freitas CdC, Dutra LV, Sant’Anna SJS (2012b) Land use/cover classification in the Brazilian Amazon using satellite images. Pesq Agropec Bras 47:1185–1208.  https://doi.org/10.1590/S0100-204x2012000900004
  41. Lu D, Li G, Moran E, Hetrick S (2013) Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon. Int J Remote Sens 34:5953–5978.  https://doi.org/10.1080/01431161.2013.802825 Google Scholar
  42. Lu D, Li G, Moran E (2014) Current situation and needs of change detection techniques. Int J Image Data Fusion 5:13–38.  https://doi.org/10.1080/19479832.2013.868372 Google Scholar
  43. Malhi Y, Gardner TA, Goldsmith GR, Silman MR, Zelazowski P (2014) Tropical forests in the anthropocene. Annu Rev Environ Resour 39:125–159.  https://doi.org/10.1146/annurev-environ-030713-155141 Google Scholar
  44. Marchese C (2015) Biodiversity hotspots: a shortcut for a more complicated concept. Glob Ecol Conserv 3:297–309.  https://doi.org/10.1016/j.gecco.2014.12.008 Google Scholar
  45. Meli Fokeng R, Meli Meli V (2015) Modeling drivers of forest cover change in the Santchou wildlife reserve, west Cameroon using remote sensing and land use dynamics degree indexes. Can J Trop Geogr 2:29–42Google Scholar
  46. Miheretu BA, Yimer AA (2017) Land use/land cover changes and their environmental implications in the Gelana sub-watershed of Northern highlands of Ethiopia. Environ Syst Res 6:7.  https://doi.org/10.1186/s40068-017-0084-7 Google Scholar
  47. Momo Solefack MC, Chabrerie O, Gallet-Moron E, Nkongmeneck B-A, Leumbe ONL, Decocq G (2012) Analysing deforestation by remote sensing coupled with structural equation models: example of the cloud forest of mount Oku (Cameroon). Acta Bot Gallica 159:451–466.  https://doi.org/10.1080/12538078.2012.750583 Google Scholar
  48. Momo Solefack MC, Njouonkouo AL, Temgoua LF, Djouda Zangmene R, Wouokoue Taffo JB, Ntoukpa M (2018) Land use/land cover change and anthropogenic causes around Koupa-Matapit gallery forest, West-Cameroon. J Geogr Geol 10:56Google Scholar
  49. Myers N (1988) Threatened biotas: “Hot spots” in tropical forests. Environmentalist 8:187–208.  https://doi.org/10.1007/bf02240252 Google Scholar
  50. Pendrill F, Persson UM (2017) Combining global land cover datasets to quantify agricultural expansion into forests in Latin America: limitations and challenges. PLoS One 12:e0181202.  https://doi.org/10.1371/journal.pone.0181202 Google Scholar
  51. Pradhan B, Lee S, Mansor S, Buchroithner M, Jamaluddin N, Khujaimah Z (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. J Appl Remote Sens 2(1):1–11.  https://doi.org/10.1117/1.3026536 Google Scholar
  52. Ramankutty N, Gibbs HK, Achard F, Defries R, Foley JA, Houghton RA (2007) Challenges to estimating carbon emissions from tropical deforestation. Glob Change Biol 13:51–66.  https://doi.org/10.1111/j.1365-2486.2006.01272.x
  53. Rawat JS, Kumar M (2015) Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt J Remote Sens Space Sci 18:77–84.  https://doi.org/10.1016/j.ejrs.2015.02.002 Google Scholar
  54. Sangermano F, Toledano J, Eastman JR (2012) Land cover change in the Bolivian Amazon and its implications for REDD+ and endemic biodiversity. Landsc Ecol 27:571–584.  https://doi.org/10.1007/s10980-012-9710-y Google Scholar
  55. Sebastian O, Sibyll S, Wolfgang L, Dieter G (2015) Three centuries of dual pressure from land use and climate change on the biosphere. Environ Res Lett 10:044011Google Scholar
  56. Singh SK, Laari PB, Mustak S, Srivastava PK, Szabó S (2017) Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India. Geocarto Int.  https://doi.org/10.1080/10106049.2017.1343390 Google Scholar
  57. Smail RA, Lewis DJ (2009) Forest-land conversion, ecosystem services, and economic issues for policy: a review. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, CorvallisGoogle Scholar
  58. Temgoua LF, Momo Solefack MC, Nguimdo Voufo V, Tagne Belibi C, Tanougong A (2018) Spatial and temporal dynamic of land-cover/land-use and carbon stocks in Eastern Cameroon: a case study of the teaching and research forest of the University of Dschang. For Sci Technol 14:181–191.  https://doi.org/10.1080/21580103.2018.1520743 Google Scholar
  59. Toh FA, Angwafo T, Ndam LM, Antoine MZ (2018) The socio-economic impact of land use and land cover change on the inhabitants of Mount Bambouto Caldera of the Western Highlands of Cameroon. Adv Remote Sens 7:25–45.  https://doi.org/10.4236/ars.2018.71003 Google Scholar
  60. World Resources Institute UNEP, United Nations Development Programme & World Bank (1998) World Resources 1998–99. Oxford University Press, New YorkGoogle Scholar
  61. Wu M, Schurgers G, Ahlström A, Rummukainen M, Miller PA, Smith B, May W (2017) Impacts of land use on climate and ecosystem productivity over the Amazon and the South American continent. Environ Res Lett 12:054016Google Scholar
  62. Zari MP (2014) Ecosystem services analysis in response to biodiversity loss caused by the built environment. Surv Perspect Integr Environ Soc 7:1–14Google Scholar
  63. Zhang Z, Zang R, Wang G, Huang X (2016) Classification of landscape types based on land cover, successional stages and plant functional groups in a species-rich forest in Hainan Island, China. Trop Conserv Sci 9:135–152.  https://doi.org/10.1177/194008291600900107 Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jules Christian Zekeng
    • 1
    • 2
    Email author
  • Reuben Sebego
    • 2
  • Wanda N. Mphinyane
    • 2
  • Morati Mpalo
    • 2
  • Dileswar Nayak
    • 3
  • Jean Louis Fobane
    • 4
  • Jean Michel Onana
    • 1
  • Forbi Preasious Funwi
    • 1
  • Marguerite Marie Abada Mbolo
    • 1
  1. 1.Department of Plant Biology, Faculty of ScienceUniversity of Yaounde IYaoundéCameroon
  2. 2.Department of Environmental Science, Faculty of ScienceUniversity of BotswanaGaboroneBotswana
  3. 3.Department of Natural Resource Management ASPEE Colleges of Horticulture and ForestryNavsari Agricultural UniversityNavsariIndia
  4. 4.Department of Biology, Higher Teachers’ Training CollegeUniversity of Yaounde IYaoundéCameroon

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