HR: 09:30h
AN: B41E-05    [Abstracts]
TI: Green leaf phenology at Landsat resolution: scaling from the plot to satellite
AU: * Fisher, J I
EM: Jeremy_Fisher@brown.edu
AF: Brown University, Department of Geological Sciences, Brown Univeristy Box 1846, Providence, RI 02912 United States
AU: Mustard, J F
EM: John_Mustard@brown.edu
AF: Brown University, Department of Geological Sciences, Brown Univeristy Box 1846, Providence, RI 02912 United States
AU: Vadeboncour, M
EM: Matthew_Vadeboncour@brown.edu
AF: Brown University, Center for Environmental Studies, Brown University Box 1943, Providence, RI 02912 United States
AB: Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In particular, while most phenological patterns and trends derived from satellites appear realistic and coherent, they may not reflect spatial and temporal patterns at the plot level. An obvious explanation is the drastic scale difference from plot-level to most satellite observations. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r2 = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (<500 m). These dramatic gradients, of a similar magnitude to differences in leaf-onset from MD to MA, occur in of low-relief (<40 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. For example, our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. In preliminary work, we validated the Landsat-derived metrics with similar analyses of MODIS and AVHRR, and demonstrate that aggregating diverse local phenologies into coarse grids may convolute interpretations. Despite these complications, the platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows us to effectively scale between plot and satellite phenological observations.
UR: http://porter.geo.brown.edu/~jfisher/phenology
DE: 0438 Diel, seasonal, and annual cycles (4227)
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0466 Modeling
DE: 0476 Plant ecology (1851)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: Fall Meeting 2005