The effect of spatial resolution on the accuracy of leaf area index estimation for a forest planted in the desert transition zone

Michael Sprintsin, Arnon Karnieli, Pedro Berliner, Eyal Rotenberg, Dan Yakir, Shabtai Cohen

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

An approach is presented for determining leaf area index (LAI) of a forest located at the desert fringe by using high spatial resolution imagery and by implementing values from a moderate spatial but high temporal resolution sensor. A 4-m spatial resolution multi-spectral IKONOS image was acquired under clear sky conditions on March 25, 2004. Normalized differences vegetation index (NDVI) and a linear mixture model were applied to calculate fractional vegetation cover (FVC). LAI was calculated using a non-linear relationship to FVC and then compared with ground truth measurements made in ten 1000 m2 plots using the tracing radiation and architecture of canopies (TRAC) canopy analyzer under bright and clear sky conditions during March and April, 2004. Calculated LAI, corrected with a measured clumping index, was highly correlated with measured LAI (R2 = 0.79, p < 0.01). This approach was used to produce a 4-m resolution LAI map of the forest. The procedure was then applied to the MODIS 250-m resolution surface reflectance product, where MODIS LAI and VI products were used to calculate the extinction coefficient by inversion of the LAI-FVC relationship, and the extinction coefficient was then used to calculate LAI for moderate resolution. Histograms of resulting LAI distributions and descriptive statistics at the different spatial resolutions are compared. LAI spatial distribution at lower resolution was similar to that obtained at higher resolution and remained close to being normally distributed.

Original languageEnglish
Pages (from-to)416-428
Number of pages13
JournalRemote Sensing of Environment
Volume109
Issue number4
DOIs
StatePublished - 30 Aug 2007

Keywords

  • Dry-lands forestry
  • IKONOS
  • LAI
  • MODIS
  • NDVI
  • Remote sensing

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