Assessment of pine forest damage by blight based on Landsat TM data and correlation with environmental factors

Abstract

Landsat TM images were obtained of blight damage to a Japanese red pine forest in the western part of Hiroshima Prefecture, Japan, using a spectral vegetation index; that is, the ratio of the digital number (relative reflectance on the ground surface) of TM Band 4 to Band 3 observed in May 1987, which decreased with the increase in the canopy cover of damaged pine trees measured in the field. The TM images suggested that the areas of damaged forest were concentrated in or near cities, industrial areas and expressways. The correlation between forest damage and environmental factors (air pollution and urban development) around the pine forest was therefore analysed by overlaying the blight damage with the proportion of developed areas obtained from TM data or mesh data of air pollution. The results of analysis indicated a significant correlation between forest damage and environmental factors, and showed that these two environmental factors made nearly equal contributions to the blight damage in the pine forest. This suggests that urban development and air pollution may affect the physiology of pine trees and promote blight by reducing the resistance of trees to the pinewood nematode.

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

“…Remote sensing technology has become an important technical means for identifying forest diseases and insect pests [3,4]. The spectral characteristics of infected plants constitute the main basis for identifying forest pests via remote sensing technology [5].…”

Use of satellite and field information in a forest damage survey of eastern Finnish Lapland in 1993

“…The correlation between the indices and net annual volume change was more than 0.8. Nakane and Kimura (1992) observed that the simple ratio index (NIR/ Red) decreased with increasing pine blight damage on Japanese red pine. In Canada, Hudak et al (1993) used discriminant analysis to separate defoliation classes of balsam fir on the basis of the spectral channels.…”

Monitoring Wheat Stripe Rust Using Remote Sensing Technologies in China

“…Generally, either crop plant pests, fruit tree pests, forest pests or grassland pests that could cause discoloration or deformation of the leaves, or produce residues on the leaf surfaces, could be monitored using remote sensing technologies [4]. There have been many reports about remote sensing monitoring of plant diseases in the world [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]. The reports about remote sensing monitoring of wheat stripe rust caused by Puccinia striiformis f. sp.…”