Abstract
This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data.
Highlights
The issue of the time series has been a highly discussed subject recently, in the field of observations of local and global change
The forests in the Low Tatras and Sumava National Parks have been severely affected by wind calamities and, subsequently, by bark beetle insects
The vegetation indices based on the use of the SWIR band show similar trends as well [46]
Summary
The issue of the time series has been a highly discussed subject recently, in the field of observations of local and global change. The time series from Landsat gives us insights from the 1980s that can be used for a variety of analyses This is the only continuous mission in the field of high-resolution data. For example, spectral unmixing methods [1], fractional methods [2], radiometric normalization and linear image regression [3,4,5,6,7,8,9,10,11,12,13] Other methods such as cross-calibration methods that combine sets of multiple data types [14] are used
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