Identifying tree health using sentinel-2 images: a case study on Tortrix viridana L. infected oak trees in Western Iran

Farshad Haghighian, Saleh Yousefi*, Saskia Keesstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Forest land has a vital role in our planet ecosystem health. Forest areas are under natural and human pressure worldwide. Pests may have irreparable damages to vegetation cover; Tortrix viridana is one of the most important pests in the western forests of Iran and is mainly hosted by oak trees. In this study the performance of Sentinel-2 images to detect infected oaks by T. viridana in the Zagros forest habitat was considered. Vegetation indices (VIs) were extracted from affected and non-affected areas by T. viridana. The indices indices included normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), infrared percentage vegetation index (IPVI) and inverted red-edge chlorophyll index (IRECI) which were extracted from Sentinel-2 satellite images. The results of the present study show that VIs in affected and non-affected areas of the study site have significant differences at 99% of confidence level. In addition, the Spearman’s correlation coefficients between the VIs values in the affected and non-affected were 0.213, 0.213, 0.168 and 0.121 for IPVI, NDVI, IRECI and SAVI, respectively. This shows that Sentinel-2 images can be used to detect pests in forest areas.

Original languageEnglish
JournalGeocarto International
DOIs
Publication statusE-pub ahead of print - 29 Jan 2020

Keywords

  • Chaharmahal and Bakhtiari
  • IPVI
  • IRECI
  • NDVI
  • remote sensing
  • SAVI

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