The seasonally flooded forest and upland sites in the Kakadu National Park, near Jabiru, Northern Territories, Australia were the site of extensive field measurements, bird community observations and airborne remote sensing during an initial NASA/Jet Propulsion Lab AIRSAR campaign in 1993, a field visit in 1994 and combined remote sensing and field activities during the PACRIM I Project in fall 1996. The overarching purpose of these studies was to use remote sensing technology as a way to extend intensive avian biodiversity and census field observations, as well as structural vegetation measurements from a limited survey area to the regional scale. During these two visits to the Kakadu area, field measurements were made within the dominant forest types in this region, primarily mixed Eucalyptus sp. woodlands, and open- and closed-forest sites dominated by Melaleuca sp. across a range of dry to perennially-flooded sites. Bird community measurements showed vegetation structure is needed to understand habitat relationships. A major vegetation difference between the two years was an increase of 2-3 times in leaf area index at comparison sites from 1994 to 1996. The greatest LAI at any site was 2.52 in the wet Melaleuca site near Munmalary in 1994.
To test the synergy between optical and microwave remote sensing data sets for vegetation analysis, a comparison was carried out between the results of vegetation land cover classification using multitemporal landsat thematic mapper (TM) alone, and then in conjunction with a Canadian airborne C-band synthetic aperture radar (SAR) image gathered as part of the South American Radar Experiment (SAREX'92). These data sets cover the Tapajos National Forest area of the Brazilian Amazon (Para State). Occurring within the area are many land use and cover types, including extensive tracts of undistributed humid tropical forest, large pastures, small scale agriculture, abandoned plantations and secondary forest growth on old agricultural fields. The addition of radar backscatter and texture information (HH and VV polarizations) to optical data sets significantly increased the separability of classes. For instance, VV backscatter was much higher in areas of permanent agriculture versus those of smaller rotational fields. However, the complexity of the radar backscatter information requires sophisticated analytical capabilities that are only now in development. The synergistic use of active and passive sensors holds a broad promise of solving some of the analytical needs for the global change and carbon modeling communities that cannot be solved with optical data without intensive field validation and/or extensive multitemporal data sets.
Conference Committee Involvement (1)
Image Processing and Pattern Recognition in Remote Sensing
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