A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping

Abstract

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks.

Quantifying Forest Cover Loss as a Response to Drought and Dieback of Norway Spruce and Evaluating Sensitivity of Various Vegetation Indices Using Remote Sensing

“…The selection of VIs was based on their sensitivity in detecting various vegetation properties. For example, Typical VIs are well known for assessing photosynthetic activity, forest health status, and detecting forest stressors such as pest outbreaks [43,55,59,[97][98][99][100]. On the other hand, Water VIs primarily provide a quantitative measure of water content in various tree species, early detection of water stress, and assessment of drought impacts on forested areas [48,55,56,61,[100][101][102][103].…”

Correction: Miletić et al. Quantifying Forest Cover Loss as a Response to Drought and Dieback of Norway Spruce and Evaluating Sensitivity of Various Vegetation Indices Using Remote Sensing. Forests 2024, 15, 662

“…In Table 3, we would like to update the references in Column number 5. Thus, Table 3 will be updated from the following: [39] Disease Water Stress Index DSWI NIR−GREEN SWIR1+RED [54] Normalised Multi-band Drought Index NMDI NIR−(SWIR1−SWIR2) NIR+(SWIR1−SWIR2) [51] Typical VIs Normalized Difference Vegetation Index NDVI NIR−RED NIR+RED [105] Enhanced Vegetation Index EVI To the following: [39] Disease Water Stress Index DSWI NIR−GREEN SWIR1+RED [58] Normalised Multi-band Drought Index NMDI NIR−(SWIR1−SWIR2) NIR+(SWIR1−SWIR2) [55] Typical VIs Normalized Difference Vegetation Index NDVI NIR−RED NIR+RED [108] Enhanced Vegetation Index EVI 2.5*(NIR−RED) NIR+6*RED−7.5*BLUE+1 [109] Soil-Adjusted Vegetation Index SAVI NIR−RED (NIR+RED+1)*1.5 [110] Transformed Vegetation Index TVI 2 NIR−RED (NIR+RED)+0.5 [46] TC components In Section 4, Subsections 4.1-4.4, all the references were updated from the following:…”

“…Therefore, the non-invasive approach and efficiency of VIs in forest health monitoring, which is made possible using high temporal frequency and spatially explicit satellite data, can provide insights into current and future forest health status over largescale forested areas. For example, various VIs have been applied in forest health monitoring, and the most common ones are the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Transformed Vegetation Index (TVI), the Normalized Difference Moisture Index (NDMI), the Disease Water Stress Index (DSWI), Tasseled Cap Wetness (TCW), and Tasseled Cap Greenness (TCG) [38,39,[45][46][47][48]50,[53][54][55][56]. Despite their high accuracy, other conventional methods require constant, time-consuming, and cost-ineffective monitoring service, thus indicating the utter importance and innovativeness of remote sensing-produced VIs in monitoring forest health status over large-scale forested areas.…”

“…To the following: By utilizing the spectral reflectance characteristics of plants, gathered via various imaging techniques, and combining reflectance from specific spectral wavelengths (bands) [53], VIs make the large-scale analysis of forest vegetation inexpensive and reliable. As proven by many studies [43,45,[54][55][56][57], VIs have also been found to be very sensitive in forecasting conifer health status, as they can signal drought stress and pest outbreaks, facilitating timely interventions in forest management and thus reducing the adverse effects of such disturbances. Therefore, the non-invasive approach and efficiency of VIs in forest health monitoring, which is made possible using high temporal frequency and spatially explicit satellite data, can provide insights into current and future forest health status over largescale forested areas.…”

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

“…Huo, Persson and Lindberg [32] demonstrated that the Ratio Drought Index (RDI) and Normalised Difference Water Index (NDWI) using SWIR bands showed more prominent differences between healthy and stressed samples (green-attack). Candotti, et al [47] found that the vegetation indices that used the Red-Edge (B06 and B07), SWIR (B12), and Red (B04) bands were more appropriate for effectively differentiating between healthy and stressed (red-attack) trees. Similar to this study, NDVI was the most reliable index in the Bryk, et al [48] study, using a thresholding mask for deadwood, and might be helpful for spruce forest monitoring by Landsat8 imagery on a landscape scale.…”