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Regional Environmental Change

, Volume 9, Issue 1, pp 41–56 | Cite as

Using multitemporal Landsat TM imagery to establish land use pressure induced trends in forest and woodland cover in sections of the Soutpansberg Mountains of Venda region, Limpopo Province, South Africa

  • Christopher MunyatiEmail author
  • Tibanganyuka A. Kabanda
Original Article

Abstract

Globally, tropical forests are being perturbed by human activity. Tropical vegetation constitutes some of the largest terrestrial carbon stocks against the build up of greenhouse gases. In this paper, a local-scale case study utilising remote sensing methodology in estimating forest loss is presented, for a section of tropical South Africa’s Soutpansberg Mountains where land use pressure threatens some of the last remaining indigenous forests. Landsat TM images from October 1990, August 2000 and September 2006 were used, together with municipality level demographic data. Hybrid image classification techniques distinguished forest cover on the images, which were classified into vegetation density categories. About 20% of forest and woodland cover was lost in the 16-year analysis period, mainly due to pine and eucalyptus plantation and residential housing expansions. The local-scale key drivers behind the deforestation are examined.

Keywords

Deforestation Vegetation Remote sensing South Africa 

Notes

Acknowledgments

This research was facilitated by a Cooperation Fund grant from the Council for Scientific and Industrial Research (CSIR), for collaborative research with the University of Venda.

References

  1. Alves DS (2002) Space-time dynamics of deforestation in Brazilian Amazonia. Int J Remote Sens 23:2903–2908. doi: 10.1080/01431160110096791 CrossRefGoogle Scholar
  2. Archard F, Eva HD, Stibig H-J, Mayaux P, Gallego J, Richards T et al (2002) Determination of deforestation rates of the world’s humid tropical forests. Science 297:999–1002. doi: 10.1126/science.1070656 CrossRefGoogle Scholar
  3. Binns JA, Illgner PM, Nel EL (2001) Water shortage, deforestation and development: South Africa’s working for water programme. Land Degrad Dev 12:341–355. doi: 10.1002/ldr.455 CrossRefGoogle Scholar
  4. Brandt JS, Townsend PA (2006) Land use–land cover conversion, regeneration and degradation in the high elevation Bolivian Andes. Landscape Ecol 21:607–623. doi: 10.1007/s10980-005-4120-z CrossRefGoogle Scholar
  5. Buchanan GM, Butchart SHM, Dutson G, Pilgrim JD, Steininger MK, Bishop DK et al (2008) Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds. Biol Conserv 141:56–66. doi: 10.1016/j.biocon.2007.08.023 CrossRefGoogle Scholar
  6. Bucini G, Hanan NP (2007) A continental-scale analysis of tree cover in African savannas. Glob Ecol Biogeogr 16:593–605. doi: 10.1111/j.1466-8238.2007.00325.x CrossRefGoogle Scholar
  7. Butt MJ, Everard DA, Geldenhuys CJ (1994) The distribution and composition of vegetation types in the Soutpansberg–Blouberg mountain complex. Report FOR DEA-814. Environmental Conservation Research Programme, Department of Environmental Affairs and Tourism, PretoriaGoogle Scholar
  8. Chavez PS (1988) An improved dark object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479. doi: 10.1016/0034-4257(88)90019-3 CrossRefGoogle Scholar
  9. Clark PE, Seyfried MS, Harris B (2001) Intermountain plant community classification using Landsat TM and SPOT HRV data. J Range Manage 54:152–160. doi: 10.2307/4003176 CrossRefGoogle Scholar
  10. Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565–1596. doi: 10.1080/0143116031000101675 CrossRefGoogle Scholar
  11. DeFries R, Achard F, Brown S, Herold M, Murdiyarso D, Schlamadinger B et al (2007) Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environ Sci Policy 10:385–394. doi: 10.1016/j.envsci.2007.01.010 CrossRefGoogle Scholar
  12. Department of Environmental Affairs and Tourism (2003) Overview State of the Environment Limpopo. DEAT, Pretoria, South AfricaGoogle Scholar
  13. Dezso Z, Bartholy J, Pongracz R, Barcza Z (2005) Analysis of land-use/land-cover change in the Carpathian region based on remote sensing techniques. Phys Chem Earth 30:109–115Google Scholar
  14. Du Plessis MA (2000) The effects of fuelwood removal on the diversity of some cavity-using birds and mammals in South Africa. Biol Conserv 74:77–82. doi: 10.1016/0006-3207(95)00016-W CrossRefGoogle Scholar
  15. DWAF (2005) Pilot state of the forest report: a pilot report to test the national criteria and indicators. Department of Water Affairs and Forestry, PretoriaGoogle Scholar
  16. Edwards D (1983) A broad-scale structural classification of vegetation for practical purposes. Bothalia 14:705–712Google Scholar
  17. Foody GM, Palubinskas G, Lucas RM, Curran PJ, Honzak M (1996) Identifying terrestrial carbon sinks: classification of successional stages in regenerating tropical forest form Landsat TM data. Remote Sens Environ 55:205–216. doi: 10.1016/S0034-4257(95)00196-4 CrossRefGoogle Scholar
  18. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH et al (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302. doi: 10.1016/S0034-4257(02)00078-0 CrossRefGoogle Scholar
  19. Grouzis M, Akpo LE (1997) Influence of tree cover on herbaceous above-and below-ground phytomass in the Sahelian zone of Senegal. J Arid Environ 35:285–296. doi: 10.1006/jare.1995.0138 CrossRefGoogle Scholar
  20. Hansen MC, DeFries RS, Townshend JRG, Marufu L, Sohlberg R (2002) Development of a MODIS tree cover validation data set for Western Province, Zambia. Remote Sens Environ 83:320–335. doi: 10.1016/S0034-4257(02)00080-9 CrossRefGoogle Scholar
  21. Hansen MC, DeFries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact 10:1–15. doi:10.1175/1087-3562(2003)007<0001:GPTCAA>2.0.CO;2CrossRefGoogle Scholar
  22. Hill RA (1999) Image segmentation for tropical forest classification in Landsat TM data. Int J Remote Sens 20:1039–1044. doi: 10.1080/014311699213082 CrossRefGoogle Scholar
  23. Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice Hall, Eaglewood CliffsGoogle Scholar
  24. Kogan F, Gitelson A, Zakarin E, Spivak L, Leved L (2003) AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: calibration and validation. Photogramm Eng Remote Sens 69:899–906Google Scholar
  25. Lambin EF (1999) Monitoring forest degradation in tropical regions by remote sensing: some methodological issues. Glob Ecol Biogeogr 8:191–198. doi: 10.1046/j.1365-2699.1999.00123.x CrossRefGoogle Scholar
  26. Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New YorkGoogle Scholar
  27. Limpopo Provincial Government (2004) Limpopo growth and development strategy 2004–2014. Limpopo Provincial Government, PolokwaneGoogle Scholar
  28. Lu D, Mausel P, Brondizio E, Moran E (2002) Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. Int J Remote Sens 23:2651–2671. doi: 10.1080/01431160110109642 CrossRefGoogle Scholar
  29. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 20:2365–2407. doi: 10.1080/0143116031000139863 CrossRefGoogle Scholar
  30. Mason MJ (2001) El Niño, climate change, and Southern African climate. Environmetrics 12:327–345. doi: 10.1002/env.476 CrossRefGoogle Scholar
  31. Mouat DA, Mahin GG, Lancaster J (1993) Remote sensing techniques in the analysis of change detection. Geocarto Int 2:39–50CrossRefGoogle Scholar
  32. Mucina L, Rutherford MC (eds) (2006) The Vegetation of South Africa, Lesotho and Swaziland, Strelitzia 19. SANBI, Pretoria, South AfricaGoogle Scholar
  33. Myers N (1988) Tropical deforestation and remote sensing. For Ecol Manag 23:215–225CrossRefGoogle Scholar
  34. Patenaude G, Milne R, Dawson TP (2005) Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocal. Environ Sci Policy 8:161–178. doi: 10.1016/j.envsci.2004.12.010 CrossRefGoogle Scholar
  35. Phua M-H, Tsuyuki S, Furuya N, Lee JS (2008) Detecting deforestation with a spectral change detection approach using multitemporal landsat data: a case study of Kinabalu Park, Sabah, Malaysia. J Environ Manage 88:784–795. doi: 10.1016/j.jenvman.2007.04.011 CrossRefGoogle Scholar
  36. Prins E, Kikula IS (1996) Deforestation and regrowth phenology in miombo woodland—assessed by Landsat Multispectral Scanner System data. For Ecol Manage 84:263–266CrossRefGoogle Scholar
  37. Sánchez-Azofeifa GA, Harriss RC, Skole DL (2001) Deforestation in Costa Rica: a quantitative analysis using remote sensing imagery. Biotropica 33:378–384Google Scholar
  38. Serra P, Pons X, Sauri D (2003) Post-classification change detection with data from different sensors: some accuracy considerations. Int J Remote Sens 24:3311–3340. doi: 10.1080/714110283 CrossRefGoogle Scholar
  39. Shimabukuro YE, Batista GT, Mello EMK, Moreira JC, Duarte V (1998) Using shade fraction image fragmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon Region. Int J Remote Sens 19:535–541. doi: 10.1080/014311698216152 CrossRefGoogle Scholar
  40. Smith RE (1991) Effects of clearfelling pines on water yield in a small Eastern Transvaal catchment, South Africa. Water SA 17:217–224Google Scholar
  41. Stoms DM, Estes JE (1993) A remote sensing agenda for mapping and monitoring biodiversity. Int J Remote Sens 14:1839–1860. doi: 10.1080/01431169308954007 CrossRefGoogle Scholar
  42. Thompson M (2004) Differences in the extent and transformation of South Africa’s woodland biome as determined from two national databases. In: Lawes MJ, Eeley HAC, Shackleton CM, Geach BGS (eds) Indigenous forests and woodlands in South Africa: policy, people and practice. University of KwaZulu Natal Press, ScottsvilleGoogle Scholar
  43. Tottrup C (2004) Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing. Int J Remote Sens 25:717–730. doi: 10.1080/01431160310001598926 CrossRefGoogle Scholar
  44. Vågen T-G (2006) Remote sensing of complex land use change trajectories—a case study from the highlands of Madagascar. Agric Ecosyst Environ 115:219–228. doi: 10.1016/j.agee.2006.01.007 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Council for Scientific and Industrial Research, Natural Resources and the Environment UnitEcosystems Earth Observation Research GroupPretoriaSouth Africa
  2. 2.Department of Geography and GIS, School of Environmental SciencesUniversity of VendaThohoyandouSouth Africa

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