Skip to main content

Advertisement

Log in

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

  • Original Article
  • Published:
Regional Environmental Change Aims and scope Submit manuscript

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.

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

Similar content being viewed by others

References

  • Alves DS (2002) Space-time dynamics of deforestation in Brazilian Amazonia. Int J Remote Sens 23:2903–2908. doi:10.1080/01431160110096791

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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, Pretoria

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Department of Environmental Affairs and Tourism (2003) Overview State of the Environment Limpopo. DEAT, Pretoria, South Africa

  • 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 30:109–115

    Google Scholar 

  • 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

    Article  Google Scholar 

  • DWAF (2005) Pilot state of the forest report: a pilot report to test the national criteria and indicators. Department of Water Affairs and Forestry, Pretoria

  • Edwards D (1983) A broad-scale structural classification of vegetation for practical purposes. Bothalia 14:705–712

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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;2

    Article  Google Scholar 

  • Hill RA (1999) Image segmentation for tropical forest classification in Landsat TM data. Int J Remote Sens 20:1039–1044. doi:10.1080/014311699213082

    Article  Google Scholar 

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice Hall, Eaglewood Cliffs

    Google Scholar 

  • 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 69:899–906

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New York

    Google Scholar 

  • Limpopo Provincial Government (2004) Limpopo growth and development strategy 2004–2014. Limpopo Provincial Government, Polokwane

  • 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

    Article  Google Scholar 

  • Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 20:2365–2407. doi:10.1080/0143116031000139863

    Article  Google Scholar 

  • Mason MJ (2001) El Niño, climate change, and Southern African climate. Environmetrics 12:327–345. doi:10.1002/env.476

    Article  Google Scholar 

  • Mouat DA, Mahin GG, Lancaster J (1993) Remote sensing techniques in the analysis of change detection. Geocarto Int 2:39–50

    Article  Google Scholar 

  • Mucina L, Rutherford MC (eds) (2006) The Vegetation of South Africa, Lesotho and Swaziland, Strelitzia 19. SANBI, Pretoria, South Africa

  • Myers N (1988) Tropical deforestation and remote sensing. For Ecol Manag 23:215–225

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Prins E, Kikula IS (1996) Deforestation and regrowth phenology in miombo woodland—assessed by Landsat Multispectral Scanner System data. For Ecol Manage 84:263–266

    Article  Google Scholar 

  • Sánchez-Azofeifa GA, Harriss RC, Skole DL (2001) Deforestation in Costa Rica: a quantitative analysis using remote sensing imagery. Biotropica 33:378–384

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Smith RE (1991) Effects of clearfelling pines on water yield in a small Eastern Transvaal catchment, South Africa. Water SA 17:217–224

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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, Scottsville

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher Munyati.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Munyati, C., Kabanda, T.A. 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. Reg Environ Change 9, 41–56 (2009). https://doi.org/10.1007/s10113-008-0066-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10113-008-0066-4

Keywords

Navigation