Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks

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.

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

“…Concerning the processing of the data with a lower temporal resolution (such as Landsat), several preprocessing steps for preparing the datasets are necessary. To obtain comparable results, it is necessary to use normalization [20,62,63], cross-calibration methods, specialized harmonizing algorithms (like LandsatLinkr [29]), or to use other methods (like LandTrendr [64,65]). However, in the case of the Sentinel-2 data, normalization methods were not necessary for processing for the TS when using the entire set of measurements with a high temporal resolution (outliers caused by inhomogeneity of the environment during the year may be filtered out-or the data may be fit).…”

“…These indices were selected on the basis of the above-mentioned publications as the most recommended remote sensing indices for assessing the health of the vegetation. The NDMI vegetation index was designed to investigate the changes in the physiology of the vegetation and so this should be a suitable indicator to detect the disturbances in the forest [20]. The combination of the NIR with the SWIR removed variations induced by the leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content.…”

“…In Central Europe, evaluations of the state and changes in forest vegetation using Landsat have been presented by many studies [13][14][15][16]. EO data have often been used in the evaluation of forests in Czechia, which have been struggling with long-term disturbances (wind calamities or the subsequent bark beetle invasions) [17][18][19][20][21].…”

“…To determine the state and the changes of the forest vegetation from the satellite images, the key information was found in the spectral properties of the vegetation species studied [22]. The suitability of the multispectral data and the vegetation indices calculated from these could differ in the monitoring of the forest vegetation and how it was proven in various studies [20,[23][24][25][26][27][28]. The unanswered question is of which real spectral differences measured during the observed years are relevant to determine the characteristics and individual phases of the forest disturbance.…”

Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)

“…For the remaining indices, spatial resolution was set at 10 m by resampling lower resolution band pixels with the Nearest Neighbour algorithm with "r.resample" function in order to lose as little resolution as possible, considered the reduced extension of sampled sites. These indices take advantage of SWIR, NIR and RED, which have been proven highly effective in the detection of storm damages [22]. The indices used in this study are reported in Table 2.…”