Composing a vegetation-stand map for the protected area of 'Radan' Nature Park

Abstract

Protected areas are one of the priorities for mapping habitats, especially forest habitats, which are dominant in most protected areas of central Serbia, such as "Radan" Nature Park. This paper presents the forest habitat mapping in the protected area of "Radan" NP and the development of vegetation-stand map of the protected area in an effort to examine the methodology of forest habitats mapping in Serbia, which presumes a long term systematic data collection. Although much has been done on the classification of habitats in Serbia, considering both botanical and the forestry approach, the practical application of this knowledge in habitat mapping is still in its infancy, with the exception of longstanding practical work on data collection for Forest Management Plans for the state-owned forests. Data on forest stands in Nature Park "Radan" collected in this manner were essential to developing the vegetation-stand map of "Radan" NP. The results of data processing and harmonization of typology and classification of state-owned forests have been presented in this paper, as well as the analysis of forest habitat types in this protected area. The paper presents the vegetation-stand maps of the state-owned forests in the protected area and of the pilot area of privately owned forests, for which detailed field data collection was necessary. These maps are intended for the management of protected areas and systematic and efficient implementation of protection measures and activities. Habitat mapping in protected areas is a prerequisite for an adequate biodiversity monitoring, as well as for management and sustainable use of natural resources of the protected area.

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

“…To evaluate the impact of drought on the forest cover loss at Mt. Kopaonik (Appendix A), we downloaded Landsat 7 (ETM+), Landsat 8 (OLI) Level 1, and Sentinel-2A/2B (MSI) Level 1C satellite imagery (from 2009 to 2022) using the U.S. Geological Survey Earth Explorer website (https://earthexplorer.usgs.gov, accessed 11 January 2024) and the Semi-Automatic Classification v.7.10.11-Matera (SCP) plugin [76] from the QGIS v.3.22.6 Białowie ża (OSGeo, Chicago, IL, US) software (Tables 1 and 2). The 2009 to 2022 time period was selected to ensure that the state of vegetation in pre-drought (2009), drought (2011 and 2012), and post-drought (2013-2022) periods when severe pest outbreaks occurred was analyzed in order to obtain a complete picture of how Norway spruce is responding to the adverse effects of climate change.…”

“…The downloaded Landsat 7 (ETM+) and Landsat 8 (OLI) MS bands, R, G, B, NIR, SWIR1, and SWIR2, including Sentinel-2 (MSI) Level-1C MS bands, B, G, R, VRE, VRE2, VRE3, NIR, NIR2, SWIR2, and SWIR3, were automatically processed using the SCP plugin by converting them from DN [Landsat] and scaled top of atmosphere (TOA) reflectance [Sentinel] into the TOA reflectance to reduce the inter-scene variability through a normalization for solar irradiance. Atmospheric correction of all images was carried out using an image-based technique called Dark Object Subtraction (DOS1) [77], as cited in [76]. Ordinary least squares regression (OLS) equations from Roy et al [78] were used to normalize the reflectance of one Landsat sensor to the other (ETM+ to OLI).…”

“…After drawing all the ROIs, they were dissolved to form two land cover macro classes. Using the Land Cover Signature (LCS) classification in the SCP plugin [76], we defined spectral thresholds for each ROI signature (a minimum value and a maximum value of each MS band), defying the spectral region of each land cover macro class. Spectral thresholds were calculated for all years separately to avoid misclassification of land cover due to inter-year variability in the vegetation spectral characteristics.…”

“…Spectral thresholds were calculated for all years separately to avoid misclassification of land cover due to inter-year variability in the vegetation spectral characteristics. Pixels that were not classified in either of the two macro classes, that is, pixels found inside overlapping regions or outside any spectral region, were classified using the Minimum Distance algorithm [76,83]. In this way, Euclidean distance was calculated between the spectral signatures of every pixel in the image and ROI spectral signatures, thus assigning each pixel to the class of the spectral signature that was closest.…”

“…The study area (Figure 1) is situated within the Kopaonik National Park (NP) in southern Serbia, which gained its current status in the year 1981 due to its biodiversity, rich flora and fauna, and great cultural and historical importance [73,74]. The park stretches across 11,969.04 ha of land in low-populated areas of municipalities Brus and Raška, mainly on mountain Kopaonik (2017 m.a.s.l) [75,76], of which 7427.24 ha is covered by forests [77]. Higher parts of the mountain are mainly covered with pure or mixed conifer stands of Norway spruce (Picea abies (L.) Karst.)…”