Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photography, Landsat TM and SPOT satellite imagery

Citation
Kr. Harvey et Gje. Hill, Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photography, Landsat TM and SPOT satellite imagery, INT J REMOT, 22(15), 2001, pp. 2911-2925
Citations number
46
Categorie Soggetti
Earth Sciences
Journal title
INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN journal
01431161 → ACNP
Volume
22
Issue
15
Year of publication
2001
Pages
2911 - 2925
Database
ISI
SICI code
0143-1161(20011015)22:15<2911:VMOATF>2.0.ZU;2-U
Abstract
The tropical wetland environments of northern Australia have ecological, so cial, cultural and economic values. Additionally, these areas are relativel y pristine compared to the many other wetland environments in Australia, an d around the world, that have been extensively altered by humans. However, as the remote northern coastline of Australia becomes more populated, envir onmental problems are beginning to emerge that highlight the need to manage the tropical wetland environments. Lack of information is currently consid ered to be a major factor restricting the effective management of many ecos ystems and for the expansive wetlands of the Northern Territory, this is es pecially the case, as these areas are generally remote and inaccessible. Re mote sensing is therefore an attractive technique for obtaining relevant in formation on variables such as land cover and vegetation status. In the cur rent study, Landsat TM, SPOT (XS and PANT) and large-scale, true-colour aer ial photography were evaluated for mapping the vegetation of a tropical fre shwater swamp in Australia's Top End. Extensive ground truth data were obta ined, using a helicopter survey method. Fourteen cover types were delineate d from 1:15 000 air photos (enlarged to 1:5000 in an image processing syste m) using manual interpretation techniques, with 89% accuracy. This level of detail could not be extracted from any of the satellite image data sets, w ith only three broad land-cover types identified with accuracy above 80%. T he Landsat TM and SPOT XS data provided similar results although superior a ccuracy was obtained from Landsat, where the additional spectral informatio n appeared to compensate in part for the coarser spatial resolution. Two di fferent classification algorithms produced similar results.