Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing

The present study explores the possibility of using Landsat imagery for mapping tropical forest types with relevance to forest ecosystem services. The central part in the classification process is the use of multi-date image data and pre-classification image smoothing. The study argues that multi-date imagery contains information on phenological and canopy structural properties, and shows how the use of multi-date imagery has a significant impact on classification accuracy. Furthermore, the study shows the value of applying small kernel smoothing filters to reduce in-class spectral variability and enhance between-class spectral separability. Making use of these approaches and a maximum likelihood algorithm, six tropical forest types were classified with an overall accuracy of 90.94%, and with individual forest classes mapped with accuracies above 75.19% (user's accuracy) and above 74.17% (producer's accuracy).

[1]  D. Skole,et al.  Landsat pathfinder project advances deforestation mapping , 1994 .

[2]  P. Thenkabail,et al.  Characterization of the alternative to slash-and-burn benchmark research area representing the Congolese rainforests of Africa using near-real-time SPOT HRV data , 1999 .

[3]  Gordon Brent Ingram,et al.  Deforestation in Vietnam , 2000 .

[4]  Ashbindu Singh,et al.  Spectral separability of tropical forest cover classes , 1987 .

[5]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[6]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[7]  Giles M. Foody,et al.  Separability of tropical rain-forest types in the Tambopata-Candamo Reserved Zone, Peru , 1994 .

[8]  R. Hill.  Image segmentation for humid tropical forest classification in Landsat TM data , 1999 .

[9]  Tran Duc Vien,et al.  The social and environmental dimensions of changes in land use in the Ca River Basin, Vietnam. , 2001 .

[10]  C. Tøttrup.  Deforestation in the upper Ca river basin in North Central Vietnam: a remote sensing and GIS perspective. , 2002 .

[11]  Fabio Maselli,et al.  Use of multitemporal information to improve classification performance of TM scenes in complex terrain , 1991 .

[12]  W. Cohen,et al.  Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery , 2000 .

[13]  I. Douglas,et al.  Hydrological investigations of forest disturbance and land cover impacts in South-East Asia: a review. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[14]  Giles M. Foody,et al.  An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration , 1996 .

[15]  R. Lucas,et al.  Identifying terrestrial carbon sinks: Classification of successional stages in regenerating tropical forest from Landsat TM data , 1996 .

[16]  R. Lucas,et al.  Characterizing tropical secondary forests using multi-temporal Landsat sensor imagery , 1993 .

[17]  Frédéric Achard,et al.  Tropical forest mapping from coarse spatial resolution satellite data: Production and accuracy assessment issues , 2001 .

[18]  Jeffrey D. Colby,et al.  Land cover classification using Landsat TM imagery in the tropical highlands : the influence of anisotropic reflectance , 1998 .

[19]  Giles M. Foody,et al.  Estimation of tropical forest extent and regenerative stage using remotely sensed data , 1994 .