International Journal of Remote Sensing Applications (IJRSA)

Editor-in-Chief: Eman M. Ghoneim
Frequency: Quarterly
ISSN Online: 2226-4353
ISSN Print: 2226-4361
Paper Infomation

Analysis of Forest Cover Change Detection

Full Text(PDF, 1060KB)

Author: Eric K Forkuo, Adubofour Frimpong

Abstract: While the concepts of change detection analysis is not new, the emergence of new imaging sensors and geospatial technologies has created a need for image processing techniques that can integrate observation from a variety of different sensors and datasets to map, detect and monitor forest resources. In addition to timber, forests provide such resources as grazing land for animals, wildlife habitat, water resources and recreation areas and these are threatened constantly by both human impacts like forest fires, air pollution, clearing for agricultural uses, and illegal cutting. Farming activities, continued sand winning operations and the allocation of plots of land to prospective developers in and around the catchments of the Owabi dam pose a serious threat to the forest covers and the lifespan of the dam. The overall objective of this study is to map out and analyze the structural changes of forest cover using Landsat and ASTER imageries of the study area. A supervised classification was performed on three multi-temporal satellite imageries and a total of eight major land use and land cover (LULC) classes were identified and mapped. By using post-classification techniques, from 1986 to 2002 and 2002 to 2007 the forest cover has decreased by an amount of 2136.6 ha and 1231.56 ha respectively representing 24.7% and 14.2%. Generally, the results indicate that from 1986 to 2007, forest cover reduced by 3368.16 ha, representing 38.9%. Decrease in vegetation has been as a result of anthropogenic activities in the study area. An NDVI analysis was performed on these images and it was noted that there was no significant difference between the NDVI classification and the supervised classification of the images. Overlay of the reserved forest of the 1974 and the classified map of 2007 shows vegetation changed during 1986-2007 remarkably.

Keywords: Land Use; Land Cover; Forest Cover; Change Detection; Classification


[1] Ahmad, G. Mapping a Dry Shrub Forest for Biodiversity Conservation Planning (A case study in the Salt range of Pakistan, using Remote Sensing and GIS tools). MSc Thesis, Forest Science Division, ITC, Enschede, the Netherlands, 2001, pp. 81.

[2] Adia, S. O., and Rabiu, A. B. Change Detection of Vegetation Cover, using Multi-temporal Remote Sensing Data and GIS Techniques, 2007. Available at /environment/ffm/index.htm. Data Accessed: 4th July, 2011.

[3] Angelsen, A. The Causes of Land use and Land Cover Change: Moving beyond the Myths. Global Environmental Change11 (4), 2001, pp.261-69.

[4] Atmopawiro, V.P. Detection of Single Tree Felling in the Tropical Forest Using Optical Satellite Data and Image Classification Techniques ( a Case Study in the Labanan Concession, East Kalimantan, Indonesia), MSc Thesis, ITC, the Netherlands, Enschede, 2004, 91 pp.

[5] Bernard, A. C., and Wilkinson, G. G. Training Strategies for Neural Network Soft Classification of Remotely-Sensed Imagery. International Journal of Remote Sensing, vol. 18, issue 8, 1997, pp. 1851-1856.

[6] Bannari, A. D.; Morin, F.; Bonn, and Huete, A. R. A Review of Vegetation Indices. Remote Sensing Reviews. (13), 1995, pp. 95-120.

[7] Boakye, E.; Odai, S. N.; Adjei, K. A., and Annor, F. O. Landsat Images for Assessment of the Impact of Land Use and Land Cover Changes on the Barekese Catchment in Ghana. European Journal of Scientific Research, Vol.22 No.2, 2008, pp.269-278.

[8] Boltz, F.; Holmes, T. P.; And Cater, D. R. Economic and Environmental Impacts of Conventional and Reduced-impact Logging in Tropical South America: A Comparative Review. Forest Policy and Economics, 5(1), 2003, pp. 69-81.

[9] Congalton, R. G. Accuracy Assessment: A Critical Component of Land Cover Mapping. Gap Analysis. American Society for Photogrammetry and Remote Sensing, 1996, pp. 119 – 131.

[10] Congalton, R. G. A. Review of Assessing the Accuracy of Classification of Remotely Sensed Data, Remote Sensing of Environment, Vol. 37, 1991, pp. 35–46.

[11] Congalton, R. G. Using Spatial Autocorrelation Analysis to Explore the Error in Maps Generated from Remotely Sensed Data, Photogrammetric Engineering and Remote Sensing, 54, 1988, pp 587-592.

[12] Foody, G. On the Compensation for Chance Agreement in Image Classification Accuracy Assessment. Photogrammetric Engineering and Remote Sensing. Vol. 58, No. 10, 1992, pp. 1459-1460.

[13] Devi, M. R., and Baboo, S. S. Land Use and Land cover for one Decade in Coimbatore Dist Using Historical and Recent High Resolution Satellite Data, International Journal of Scientific and Engineering Research, Volume 3, Issue 2, 2012, pp. 1-5.

[14] Forestry Commission of Ghana. Owabi Wildlife Sanctuary, Kumasi, Ghana, 2006. Available at Date Accessed: 10 February, 2011.

[15] Forkuo, E. K. Digital Elevation Modelling Using ASTER Stereo Imagery, Journal of Environment Science and Engineering. Vol. 52, No.2, 2010, pp. 81-92.

[16] Frimpong, E. D. Owabi Dam under Threat, 2007. Available at html. Date Accessed: 12 July, 2011.

[17] Groten, S .M. E. and Ocatre, R. Monitoring the Length of the Growing Season with NOAA. International Journal of Remote Sensing, 23, 2002, pp. 2797–2815.

[18] IGBP-IHDP. Land use and land cover change implementation strategy. IGBP Report 48 and IHSP Report 10. IGBP Secretariat, Stockholm, Sweden. Pp287. International Journal of Remote Sensing 10(6), 1999, pp. 989 - 1003.

[19] Immerzeel, W. W., Quiroz, R. A., and De Jong, S. M. Understanding Precipitation Patterns and Land use Interaction in Tibet using Harmonic Analysis of SPOT VGT-S10 NDVI Time Series. International Journal of Remote Sensing, Vol. 26, No. 11, 2005, pp.2281–2296.

[20] Jensen J. R., and Cowen D. C. Remote Sensing of Urban Suburban Infrastructure and Socioeconomic Attributes, Photogrammetric Engineering and Remote Sensing, 65, 1999, pp. 611-622.

[21] Joshi, C. Invasive Banmara (Chromolaena Odorata): Spatial Detection and Prediction .MSc Thesis, ITC, Enschede, the Netherlands, 2001, pp. 53.

[22] Lambin, E. F., and Strahler, A. Remotely-sensed Indicator of Land-Cover Change for Multi-Temporal Change-vector Analysis. International Journal of Remote Sensing, Vol. 15, No.10, 1994, pp.2099-2119.

[23] Manandhar, R., Odeh, I. O. A., and Ancev, T. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data using Post-classification Enhancement. Remote Sensing, 1(3), 2009, pp. 330-344.

[24] Mas, J. F. Monitoring Land-Cover Changes: A Comparison of Change Detection Techniques. International Journal of Remote Sensing, 20(1), 1999, pp. 139 - 152.

[25] Miller, A. B.; Bryant, E. S., and Birnie, R. W. An Analysis of Land Cover Changes in the Northern Forest of New England Using Multi-temporal LANDSAT MSS Data. International Journal Remote Sensing, Vol. 19, No. 2, 1998, pp. 215-265.

[26] Miwei, L. (2009). Monitoring Ephemeral Vegetation in Poyang Lake using MODIS Remote Sensing Image. MSc Thesis, ITC, Enschede, the Netherlands.

[27] Myeong, S., Nowak, D. J., and Duggin, M. J. A Temporal Analysis of Urban Forest Carbon Storage using Remote Sensing. Remote Sensing of Environment, 101, 2006, pp. 277 – 282.

[28] Parviainen, J., and Päivenen, R. Information Needs for Biodiversity Assessment Derived from International Forestry Discussions. In: Bachmann, P., Köhl, M. and Päivinen, R. (Eds.). Assessment of Biodiversity for Improved Forest Planning, Kluwer Academic Publishers, Dordrecht, 1997, pp. 331–342.

[29] Ringrose, S., Vanderpost, C., and Maheson, W. Use of Image Processing and GIS Technique to Determine the Extent and Possible Causes of Land Management/Fenceline Induced Degradation Problems in the Okavango Area, Northern Botswana. International Journal of Remote Sensing, Vol. 18, No. 11, 1997, pp. 2337-2364.

[30] Rosenfield, G. and Fitzpatrick-Lins, K. A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering and Remote Sensing, Vol. 52, No. 2, 1986, pp. 223-227.

[31] Rosyadi, S.; Birner, R. and Zeller, M. (2005). Creating political capital to promote devolution in the forestry sector – a case study of the forest communities in Banyumas District, Central Java, Indonesia. Forest Policy and Economics, Vol.7, 1986, pp. 313-226.

[32] Singh .A. Review Article: Digital Change Detection Techniques using Remotely Sensed Data. International Journal of Remote Sensing; Vol.10, 1986, pp. 989- 1003.

[33] Teng. S.P., Chen. Y. K., Cheng. K. S. and Lo. H. C. Hypothesis-test-Based Land Cover Change Detection using Multi-temporal Satellite Images –A comparative Study, Advances in Space Research, Vol.41, No. 11, 2008,pp.1744-1754.

[34] Tuxen, K. A., Schile, L. M., Kelly, M., and Siegel, S. W. Vegetation Colonization in a Restoring Tidal Marsh: A Remote Sensing Approach, Restoration Ecology, Vol. 16, No. 2, 2008,pp. 313–323.

[35] Yang, X.; Lo, C. P. Using a Time Series of Satellite Imagery to Detect Land Use and Land Cover Changes in the Atlanta, Georgia Metropolitan Area. International Journal of Remote Sensing, Vol.23, 2002, pp.1775–1798.

[36] Zaitunah, A. Analysis of Physical Factors Affecting Single Tree Felling of Illegal Logging Using Remote Sensing and GIS (A Case Study in Labanan Concession, East Kalimantan, Indonesia). MSc thesis, ITC, the Netherlands, Enschede, 2004, 108 pp.