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Keywords:

  • Climate;
  • deciduousness;
  • environmental gradients;
  • geology;
  • Panama;
  • phenology;
  • remote sensing

ABSTRACT

Aim  Dry season deciduousness affects intra- and inter-annual patterns of carbon, water and energy balance in seasonal tropical forests. Because it is affected by rainfall, temperature and solar radiation, deciduousness may be an indicator of the response of vegetation to climate change. Better understanding of how spatial patterns of deciduousness are affected by climate and other environmental gradients will improve the ability to predict responses to climate change. This study develops remote sensing methods for quantifying tropical forest deciduousness and examines the relationship between deciduousness and environmental factors in semi-deciduous tropical forest.

Location  Central Panama.

Methods  I applied spectral mixture analysis (SMA) and the normalized difference vegetation index (NDVI) to Landsat images to predict deciduousness which was ground-truthed with field observations of the percentage of overstorey deciduous trees. Using predicted deciduousness from SMA, patterns of deciduousness at three spatial scales were analysed. I determined how deciduousness varied spatially with rainfall and geological substrate.

Results  Both SMA and NDVI had strong correlations (r > 0.9) with field observations of deciduousness. On a landscape scale, deciduousness increased as rainfall decreased, but geological substrate altered this relationship. On some geological substrates, deciduousness was much greater than expected for a given rainfall total or showed a slight but significant increase with rainfall. At an intermediate spatial scale, there were highly deciduous patches from 3 to 250 ha in size embedded in non-deciduous forest, which may have resulted from topography, soil variation or past land use.

Main conclusions  Dry season deciduousness can be accurately quantified using satellite images indicating that remote sensing can be a valuable tool for detecting change and understanding ecosystem processes in tropical forests from landscape to regional scales.