M. Sgrenzaoli : Tropical forest mapping at regional scale

  News
  Newsroom
  Archive
  Calendar
  2012
  2011
  2010
  2009
  2008
  2007
  2006
  2005
  2004
  2003
  2002
  2001
  2000
  1999
  News
  RSS
  Calendar
  Open days
  Courses
  Congresses and symposia
  PhD-graduations and speeches

25 Feb 2004 16:00
Unit: Wageningen University
Location: Aula (gebouw 362), Gen. Foulkesweg 1, Wageningen
Promotor: prof.dr.ir. R.A. Feddes (Soil physics, ecohydrology and groundwater management)
Co Promotor: Dr.ir. D.H. Hoekman, Dr. G.D. De Grandi

 The work described in this thesis concerns the estimation of tropical forest vegetation cover in the Amazon region using a continental scale high resolution (100 m) radar mosaic as data source. The radar mosaic was compiled by the Jet Propulsion Laboratory (NASA JPL) using approximately 2500 JERS-1 L-band scenes acquired in the context of the Global Rain Forest Mapping project by the National Agency for Space Development of Japan (NASDA).
A novel classification scheme was developed for this purpose. The underpinning method is based on a wavelet signal decomposition/reconstruction technique. In the wavelet reconstruction algorithm, an adaptive wavelet coefficient threshold is introduced to distinguish wavelet maxima related to the transition between classes from maxima related to textural within-class variation.
Two image-labeling techniques are tested and compared: i) a region-growing algorithm and ii) a per-pixel two-stage hybrid classifier.
The large data volume problem was tackled by developing a special purpose processing chain that works on partially overlapping tiles extracted from the mosaic. Quantitative validation and error analysis of the classifiers’ performance and their generalization capability to regional scale are carried out using, as reference, maps derived from Landsat Thematic Mapper. A first result of the validation process is that the wavelet classifier provides a classification accuracy of 87% in forest/non-forest mapping. The analysis by site reveals that class degraded-forest is the major source of classification errors. The discrepancy between TM maps and SAR maps increases with increasing landscape spatial fragmentation.
A test on relative performances between the wavelet-based region growing segmentation technique and a conventional clustering technique (ISODATA) shows that the wavelet-based technique provides better accuracy and is capable of generalizing over the entire data set.
The issue of detecting the degraded-forest class - generally ignored by Amazonian deforestation mapping programs - is tackled using data acquired by both optical and SAR instruments. For optical data, a three-stage classification procedure is developed for detecting degraded forest classes in Landsat TM images. For SAR data, a multi-temporal speckle filtering technique is used to improve the signal to noise ratio. Forest degradation, characterized by small isolated and elongated bare soil regions regularly distributed in forest areas, is visually detectable in the filtered imagery.
Starting from the consideration that the discrepancy between TM maps and SAR maps increases with the landscape spatial fragmentation an inductive learning methodology, capable of correcting SAR regional-scale maps using local classification estimates at a higher resolution, is tested.
Finally some ideas and projects are put forward which are meant to be working hypotheses for future actions and practical approaches to reduce the pressure over the tropical forest ecosystem.
Print this activity