Abstract
The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.
Highlights
The use of remote sensing methods for the monitoring of forests represents a very widespread and traditional discipline
This distinction is important from an environmental point of view, from the perspective of decision-making processes in forestry, and in nature and landscape protection
This study proved a high potential of the multispectral satellite data for research on forest vegetation affected by bark beetle outbreaks
Summary
The use of remote sensing methods for the monitoring of forests represents a very widespread and traditional discipline. Remote sensing (RS) is used for a number of reasons, including the acquisition of compatible data on large territorial units and the possibility of using multispectral information to determine the health status of vegetation and its development [1,2]. Monitoring forest stands by remote sensing enables one to record data even from hard-to-reach places unsuitable for field research. A wide range of different types of satellite data is available, which differ in spatial, temporal and spectral resolution (e.g., Landsat, Sentinel, MODIS). All the spectral bands of WV2 and L8 OLI, except coastal and panchromatic, entered the classification. The selection of points was verified using vegetation indices and combinations of spectral bands of satellite images (mainly using WV2). The number of training points for each class ranged from 11
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