摘要
In recent decades, the problem of drying out of conifers has become a subject of significant importance due to the widespread mortality of trees caused by stem pest’s damage. Early detection of areas affected by insect outbreaks is of great relevance for preventing the further spread of pests. Forests of Belarus are largely affected by conifers dieback caused by the bark beetle. The aim of the study was to identify drying out conifers using a TripleSat satellite multispectral image of a woodland area in Belarus based on preliminary airborne measurements. Spectrometers operating in a spectral range of 400</span><span style="font-family:""> </span><span style="font-family:Verdana;">-</span><span style="font-family:""> </span><span style="font-family:Verdana;">900 nm were used in airborne measurements, resulting in distinguishing various drying out stages with an accuracy of 27</span><span style="font-family:Verdana;">%</span><span style="font-family:Verdana;"> - 74% for aerial data. In this study, a supervised classification of the TripleSat image based on the method of linear discriminant analysis (LDA) was performed. The input data for LDA algorithm is a set of remote sensing vegetation indices. Results of the study demonstrate that about 90% of the test site is at the green-attack stage that is confirmed by ground surveys of this area.
In recent decades, the problem of drying out of conifers has become a subject of significant importance due to the widespread mortality of trees caused by stem pest’s damage. Early detection of areas affected by insect outbreaks is of great relevance for preventing the further spread of pests. Forests of Belarus are largely affected by conifers dieback caused by the bark beetle. The aim of the study was to identify drying out conifers using a TripleSat satellite multispectral image of a woodland area in Belarus based on preliminary airborne measurements. Spectrometers operating in a spectral range of 400</span><span style="font-family:""> </span><span style="font-family:Verdana;">-</span><span style="font-family:""> </span><span style="font-family:Verdana;">900 nm were used in airborne measurements, resulting in distinguishing various drying out stages with an accuracy of 27</span><span style="font-family:Verdana;">%</span><span style="font-family:Verdana;"> - 74% for aerial data. In this study, a supervised classification of the TripleSat image based on the method of linear discriminant analysis (LDA) was performed. The input data for LDA algorithm is a set of remote sensing vegetation indices. Results of the study demonstrate that about 90% of the test site is at the green-attack stage that is confirmed by ground surveys of this area.
