The use of remote sensing and GIS for spatio-temporal analysis of the physiological state of a semi-arid forest with respect to drought years

A. Volcani, A. Karnieli, T. Svoray

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

Drought years are a very frequent phenomenon in Israel. Between the years 1994/1995 and 2001/2002, Israel experienced four (non-consecutive) years of drought. Consequently the Yatir forest, a pine forest located in the desert fringe, suffered from a notable water shortage. The aim of this research is to detect and assess seasonal/phenological changes and inter-annual changes in the forest trees with respect to the drought effect. The use of a spectral vegetation index, namely the Normalized Difference Vegetation Index (NDVI) to detect stress conditions was implemented by using eight Landsat-TM and ETM+ images. In addition, the change detection NDVI Image Differencing technique was applied for assessing seasonal and inter-annual variations in vegetation. The results indicate similarity between the photosynthetic activity and the NDVI dynamics along the growing season. Considerable NDVI decline was observed between 1995 and 2000 due to the drought events during these years, enabling assessment of the spatial and temporal effects of such a disaster. The NDVI measured from the forest trees was found to be inversely related to the age of the trees due to strong effect of soil background in the younger forest sections that are characterized by lower vegetation density. Topographic attributes such as slope orientation (aspect) were found to affect NDVI only at the year that was not under stress. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.

Original languageEnglish
Pages (from-to)239-250
Number of pages12
JournalForest Ecology and Management
Volume215
Issue number1-3
DOIs
StatePublished - 25 Aug 2005

Keywords

  • Change detection
  • Drought
  • Forest
  • Israel
  • Landsat
  • NDVI
  • Remote sensing
  • Semi-arid

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