Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/57592
Title: Detection of Heterobasidion Root Rot on Pinus brutia Ten. Using Different Vegetation Indices Generated from Sentinel-2 A Satellite Imagery
Authors: Çınar, Tunahan
Beram, R. Ceyda
Aydın, Abdurrahim
Akyol, Sultan
Tashigul, Nurzhan
Lehtijarvi, H. Tugba
Woodward, Steve
Keywords: Remote sensing
Sentinel-2A
Heterobasidion root rot
Decay fungi
Forest stand management
Powdery Mildew
Winter-Wheat
Annosum
Infection
Accuracy
Disease
Forests
Model
Band
Publisher: Springer
Abstract: The genus Heterobasidion includes some of the most destructive pathogens of conifers in the Northern hemisphere. Heterobasidion root rot leads to loss of root function and visible symptoms in the crowns of most Pinus spp., including Turkish red pine (P. brutia). Infected pines will eventually die. Wind-thrown trees with decayed roots or open gaps in the stand often indicate the presence of Heterobasidion root rot. Satellite imagery has recently been utilized regularly to detect damaged areas in order to apply early management procedures to pests or diseases in forests, reducing spread within an affected site and to other places. In the work described here, Sentinel-2 A satellite imagery was tested for detecting Heterobasidion root rot in P. brutia regeneration in an area in south-western Turkiye, using different vegetation indices. Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Plant Senescence Reflectance Index (PSRI) indices were calculated from Sentinel-2 A satellite images in the Google Earth Engine (GEE) platform to detect disease. Calculated indices as synthetic band were added to the Sentinel-2 A satellite image on the GEE platform. Images with the added bands were classified using Random Forest (RF) before evaluation using the Kappa Coefficient and Overall Accuracy. Based on a statistical analysis, NDRE was the most useful index for detecting the disease with an overall accuracy of 89% and a Kappa Coefficient of 0.84, followed by NDVI and PSRI, respectively. After evaluation of General Accuracy and Kappa Coefficient, disease incidence in the area was determined (affected hectares), based on the indices. NDRE detected 7.21 affected hectares, NDVI 7.9 hectares and PSRI 6.49 hectares in a total of 67.8 hectares. Sentinel-2 A bands, which allow the measurement of various land and vegetation health parameters, the effect of bands on RF classification was determined according to the indices used. The most important band for classification of NDRE and NDVI was the B2 (BLUE) band of Sentinel-2 A, and the most important band with PSRI was the B5 (RED EDGE) band. Based on these bands, the best wavelengths for detecting H. annosum diseased areas were in the range 492.4-740.5 nm in Sentinel-2 A. The system enabled the detection of differences in crown deterioration and also wind-thrown trees with decayed roots or open gaps in the stand. This study is the first to show that Sentinel-2 A satellite imagery can be applied successfully for the detection of Heterobasidion root rot on P. brutia.
URI: https://doi.org/10.1007/s12524-024-01914-1
https://hdl.handle.net/11499/57592
ISSN: 0255-660X
0974-3006
Appears in Collections:Fen Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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