1.

INTRODUCTION

Wildfires are natural ecosystem processes that considerably disturb the functioning of ecosystems. Monitoring post-fire forest regrowth is crucial for receiving knowledge to help forest ecosystem recovery after fires. Remote aerospace methods and data provide effective tool in agriculture^1-3 as well as in forestry – forecasting, monitoring, mapping and restoration of burnt areas. Aerospace remote sensing methods are a high-tech tool for reliable and large-scale monitoring of recovery processes occurring in forest ecosystems after fire^4-7.

Spectral indices and reflectance values are mainly used for vegetation processes following disturbances after fire^8-10. Spectral indices generally rely on greenness measurements of red – near-infrared vegetation indices on the basis of different algebraic combinations between original spectral bands¹¹. Vegetation indices help the study of forest ecosystems disturbance, but they are not accurate enough to study the regrowth processes in forest ecosystems observed after a fire. The differences in fire damage, caused by differences in undergrowth, species diversity, and the different regenerative abilities of forest with different tree species are the main reason for this.

Disturbance Index (DI)¹² was examined to be a relatively efficient approach to detect the forest disturbance and monitor its change. The higher accuracy of the index in comparison to standard vegetation indices is based on the linear orthogonal transformation of multispectral satellite images – Tasseled Cap Transformation (TCT)^13-15 which increases the degree of identification of the three main components changing during fire – soil, vegetation and moisture¹⁶. Different sensors use various transformation matrices fixed only to them. For monitoring post-fire regrowth dynamics in this study we used transformation matrices fixed for Landsat sensors^17,18.

DI highlights the unvegetated spectral signatures associated with stand-replacing disturbance and separates them from all other forest signatures. When viewed sequentially, the time series of DI images provide a direct way to highlight pixels that changed from an average to a disturbed forest condition¹².

The Landsat data is widely used in forest monitoring^19-23, but their applicability for detailed vegetation studies is limited, especially by their spatial and spectral resolution²⁴. However, Landsat program offers a chance to study the long-term dynamics of forest ecosystems via long-term data series.

2.

STUDY AREA

Monitoring post-fire forest regrowth was performed on the territory of a burnt area near Chernyovtsi village, Bulgaria. The selection of the study area was based on several criteria: the burnt area should be large enough (> 5 ha) to allow mapping with Landsat data, the fire event should be before 2016 so there is enough time for regrowth processes to start, the presence of aerial images for visual interpretation and validation. Thus taking into account the above mentioned criteria and limitations the selected test fire is located next to Chernyovtsi village.

The Chernyovtsi test fire (450 - 510 m above sea level) is situated in the northeastern part of Rhodope Mountains, near Chernyovtsi village, 15 km from the city of Kardzhali, Bulgaria (Figure 1).

Figure 1.

Location of the study area – Chernyovtsi, Bulgaria. Coordinates: 41°43’40’’N; 25°32’07’’E. The red point in the small map shows the location of the area shown on the main image. Background image source: Aerial image, 2013.

A fire took place on October 1, 2012 and affected an area of 15 ha with mixed forests and coniferous forests. Mixed forests consist of Turkey oak (Quercus cerris L.), Hungarian oak (Quercus frainetto T e n) and Oriental hornbeam (Carpinus orientalis M i l l.) with Mediterranean elements in places with secondary origin. The native deciduous forests in the region refer to the Thracian province of the European deciduous forest area. However, because of the erosion processes in the 1950s and the expansion of bare lands, a massive afforestation with coniferous forests – Black pine (Pinus nigra Arn.), has been performed.

The area is characterized by Continental-Mediterranean climate. The soils are Chromic Cambisols. The forest ecosystems in this part of Bulgaria have been under stress in the summers during the last years due to frequent and prolonged droughts related to climate change^25,26. The topography influenced the development of the wildfire as well. However, the forest ecosystems were not entirely damaged, since the fire occurred during the winter. The cold and humid conditions during the winter, the higher moisture content in the forest ecosystems determined the lower intensity of the fire, which affected the forest ecosystems less.

3.

METHODS AND DATA

4.

RESULTS AND DISCUSSIONS

For the purpose of post-fire regrowth monitoring dDI rasters were generated on a yearly basis, indicating the areas and intensity of forest disturbance and regrowth after fire, actual at that certain year. The thematic raster classified by the intensity of recovery are compared with the values one month before the fire (2012), in quantitative values (%).

The concept of DI assumes that high TCB and low TCG and TCW values are typical for disturbed stands and DI values are high positive, while undisturbed or fully recovered stands exhibit low TCB and high TCG and TCW values resulting in low negative DI values¹⁵.

Figure 2 shows the dDI classified thematic raster for the study period – 2012-2022 on the territory of the burnt forest area. Figure 2 A) shows the post-fire disturbance map one year after the fire (2013). Two Landsat images were used – 09/03/2012 (Landsat ETM+) and 13/08/2013 (Landsat OLI) for representing the disturbance one year after the fire. The dark green color depicts areas with 0% disturbance – actually the unburnt forests. The light green color shows slightly affected forests – 0-20 %, the yellow color – forests with 20-50 % disturbance, the orange color – forests with 50-100 % disturbance and the red color – forests with 100-126 % disturbance.

Figure 2.

A) Post-fire disturbance map one year after the fire (2013) (%), Post-fire regrowth maps for B) 2014, C) 2015, D) 2016, E) 2017, F) 2018, G) 2019, H) 2020, I) 2021, G) 2022 (%).

Figure 2 B) – G) show the post-fire regrowth maps representing the disturbance and regrowth for the post-fire monitoring (2 – 10 years after the fire) compared with the values from one year before the fire, in percentages. The green, yellow and orange colors depict regrowth, while the red colors indicate for disturbance. The percentages are shown in the legends (Fig. 2).

After visual interpretation of the results obtained for post-fire disturbance and regrowth maps, some main conclusions are summarized as follows: