Forest health and vitality: the detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital multispectral imagery

  • R Ismail Department of Geography and Environmental Studies, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa
  • O Mutanga Department of Geography and Environmental Studies, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa
  • U Bob Department of Geography and Environmental Studies, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa
Keywords: digital multispectral imagery, remote sensing, <i>Sirex noctilio</i>, vegetation indices

Abstract

The Eurasian woodwasp, Sirex noctilio, causes considerable tree mortality in commercial pine plantations in southern KwaZulu-Natal, South Africa. Broad-scale visual assessments of infestation provided by forest managers are currently used to measure forest health and vitality. The effectiveness of visual assessments is questionable because they are qualitative, subjective and dependent on the skill of the surveyor. Remote sensing technology provides a synoptic view of the canopy and thus offers an alternative to the conventional methods of monitoring forest health and vitality. In this study, high resolution(0.5 X 0.5m) digital multispectral imagery (DMSI) was acquired over commercial Pinus patula trees of varying age classes, which had been ground assessed and ranked on an individual tree crown basis using a severity scale. The severity scale was based on a hierarchy of decline symptoms that are visibly apparent on the infested tree and are represented in this study as the green, red and grey stages. A series of ratio- and linear-based vegetation indices were then calculated and compared to the different crown condition classes as determined by severity scale. Of the vegetation indices derived from the high-resolution DMSI, significant differences between the pre-visual (healthy and green stages) and visual (red and grey stages) crown condition classes were obtained. Canonical variate analysis further revealed that greater discriminatory power between the different crown condition classes is obtained when using the normalised difference vegetation index NDVI). Overall the study demonstrated the potential benefit of using high-resolution DMSI to discriminate between healthy trees and trees that were in the visual stage of infestation.

Southern Hemisphere Forestry Journal 2007, 69(1): 39–47
Published
2007-09-06
Section
Articles

Journal Identifiers


eISSN: 2070-2639
print ISSN: 2070-2620