Sb 20(3)' 2019-057-069
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Silva Balcanica, 20(3)/2019 Spatial DISTRIBUTION OF HIGH-Mountain Ecosystems – Application OF Remote SENSING AND GIS: A CASE STUDY IN SOUTH-WESTERN Rila Mountains (Bulgaria) Kostadin Katrandzhiev, Svetla Bratanova-Doncheva Institute of Biodiversity and Ecosystem Research – Sofia, Bulgarian Academy of Sciences Abstract The dynamics in spatial distribution of high-mountain ecosystems (HME), located in South-Western (SW) Rila Mountains, Bulgaria, were studied for a period of around 40 years (1977 – 2018). The results were obtained related to these dynamics using satellite data in GIS environment. Normalized Difference Vegetation Index (NDVI) based on satellite data from Landsat and Sentinel 2 sensors was used for analysis and assessment of ecosystem condition. Some climate components (rainfall, solar radiation, temperature, evapotranspiration) were analysed in order to reveal existing dependency between the NDVI and meteorological parameters. To verify the obtained results, a Normalized Difference Greenness Index (NDGI) was also computed. A clear connection between the studied climate components, i.e. meteorological elements and NDVI values was found for the last decade. The results are presented as thematic maps, providing quantitative and qualitative assessment of the NDVI spatial distribution in the selected study area. Persistence of forested areas’ functionality was observed and the seasonal variation in functional condition of shrubs and grasslands was established. These variations remained seasonally cyclic and did not change significantly the functional condition of the HME vegetation in the long term. Key words: Ecosystem condition, satellite data, GIS, NDVI, NDGI Introduction High-mountain ecosystems (HME) are dynamic, sensitive and vulnerable to climate changes (Grace et al., 2002). This article aims to characterise the relation between ecosystem condition and meteorological component changes. Long-term HME spatial distribution depends on changes in their condition. In 2017, a methodology for assessment of woodland and forest ecosystems was developed which provides an indicator-based assessment guide for forest ecosystems’ condition (Kostov et al., 2017). The forest ecosystem condition also can be determined by dint of vegetation indices (VI) based on remote sensing data along with analyses of climate characteristics. Remote sensing data have proven to be a reliable approach in environmental research activities like ecosystem monitoring and risk assessment (Radeva et al., 2018; Radeva, 2018). It is important to mention that remote sensing methods does not confront field work but just the opposite, they amplify them as proven by the authors Tuominen et al. (2009) and Lawley et al. (2016) . The most applied method for vegetation condition assessment, 57 based on remote sensing data, includes computation and analysis of vegetation indices. The Normalized Difference Vegetation Index (NDVI) is the most frequently used index for vegetation condition and vitality assessment (Tucker, 1979). It shows vegetation’s capability of energy absorption and reflection, photosynthetic capacity and concentration of ecosystem’s leaf biomass (Pavlova, Nedkov, 2005). The NDVI is the most widely used index for establishing of presence or absence of vegetation. Moreover, with respect to time, the NDVI provides an estimate of gross primary production (Tucker, Sellers, 1986). The disadvantage of this index appears to be that in some cases the NDVI is not sensitive enough to detect small changes in vegetation cover (e.g. after wildfire or other catastrophic events). On the other hand, the Normalized Difference Greenness Index (NDGI) is a very sensitive index capable of detecting even small increase of vegetation growth (Nedkov, 2017a). In the current research, we offer assessment of HME condition through combining remote sensing via VIs (NDVI) and meteorological components analyses which represents innovative technic for HME research. Respectively, the objectives of the current article were computing of the NDVI based on satellite data from Landsat and Sentinel 2 sensors and using it for quantitative and qualitative assessment of a HMEs’ condition for a period of around 41 years (1977 – 2018). Further, a verification of the NDVI results via NDGI computation was performed. Additionally, analyses of some climate components (rainfall, solar radiation, temperature, evapotranspiration) were carried out in order to reveal existing dependency between the NDVI and climate parameters. Materials AND METHODS The current research focuses on HME. The study was carried out on a territory which was selected based on pre- set criteria, i.e. to encompass more than one vegetation type; to cover a territory corresponding to the definition of a high-mountain habitat, including treeline ecotone which marks the transition from closed tall forest to the alpine zone and to be easily accessible. The selected study area is located in the south-western parts of the Rila Mountains and includes parts of the lands of seven populated places (Bistritsa, Gorno and Dolno Osenovo, Bachevo, Godlevo, Dobarsko and Belitsa) and also of the Parangalitsa Reserve with an overall area of 9296 ha. The highest point of the study area is the Ezernik Peak (2485 m a.s.l.). Three main vegetation types were presented: (i) woodland and forests, (ii) shrubs and (iii) grasslands (Uzunov, Gussev, 2003). It includes three structurally and functionally interrelated zones (Fig. 1): closed tall forest, groups of trees (>3m) and a zone with isolated tree individuals which forms the transition from montane forest to the alpine zone (Korner, Paulsen, 2004). Dominating is the conifer forest vegetation, followed by grasslands and shrubs which form the physiognomy of the entire HME. In an attempt to assess the functional condition of the HME for a long time period from 1977 to 2018, a methodology was used based on remote sensing data (Katrandzhiev, 2018), such as multispectral satellite data from sensors Sentinel 2 58 Table 1. Data sources used for indices calculation № Date Sensor 1 22.08.1977 Landsat 2 23.05.1984 Landsat 3 26.07.1984 Landsat 4 27.06.1985 Landsat 5 22.05.1986 Landsat 6 03.07.1987 Landsat 7 11.07.1990 Landsat 8 02.09.1992 Landsat 9 29.06.1994 Landsat 10 28.06.2000 Landsat 11 13.06.2009 Landsat 12 15.07.2009 Landsat 13 23.09.2011 Landsat 14 09.09.2012 Landsat 15 02.05.2017 Landsat 16 26.10.2017 Sentinel 2 Fig. 1. HME_zones- (a) Scheme of the HME zone; 17 03.04.2018 Landsat (b) Aero photo image from study area (Copernicus – ESA) and Landsat with high spatial, spectral and radiometric resolution. The data used are presented in Table1. The Landsat database provides access to past time satellite images which is fundamental for our research. It allows us to go back in time and track condition changes in HME up to the present. As far as the Sentinel 2 sensor, it is a newer generation remote sensing tool which provides more qualitative images from space and offers free access to its database. The data from both sensors were used for computing both the NDVI and NDGI. Satellite data processing The most important in the data processing phase was the quality data extraction. A selection of appropriate scenes from a database containing the selected study area and covering the whole period of 41 years was collated. The satellite data were required to 59 Fig. 2. Maps of the NDVI for the period 1977- 2018 meet some criteria, the most important of which were to cover the vegetation period (from the end of March to the end of October) and to fulfill quality characteristics like clearness and cloudlessness. These were essential for further data processing, including elaboration of composite images, raster file creation of the selected research area and the NDVI processing of this raster which were applied to each of the selected satellite images or a total of 17. For these technical operations, specialised software for satellite data processing was used. But more work was necessary in order to make the NDVI data suitable for analysing and interpretation. Using another software product in GIS environment, we were able 60 Fig. 3. Distribution of correlation coefficients for the period 1977- 2018 Fig. 4. NDVI regression models for the period 2000-2018 61 Fig. 5. Spatial distribution of the NDVI for the period of 41 years (1977-2018) to convert the obtained NDVI raster file to shape file containing digital (numerical) data with the NDVI values and coordinates of each value. Thematic maps were also elaborated and presented, in order to illustrate the distribution of the values of the NDVI (for each satellite image) over the selected study area (Fig. 2). Thus, statistical processing of the data was possible. By means of the Sigma Plot 11.0 statistical tools, the NDVI data were visualized as 2D and 3D graphics, correlation graphic (Fig. 3) and regression models (Fig. 4). Based on them, results were obtained about the spatial distribution of the NDVI values (Fig. 5), correlation coefficients, etc. To ensure the objectivity of the NDVI results, the NDGI was computed for their verification, using the same satellite data. This index shows changes in the vegetation cover that have occurred over a specific time period. The NDGI can be used for determination of both negative and positive changes in vegetation functioning (Nedkov, 2017b). Analogically, using the same computer software, the NDGI was calculated and thematic maps were elaborated in order to verify the NDVI results (as shown in results section, Fig. 7). 62 Data processing of climate elements In addition to the indices data analyses, an analysis of climate characteristics, such as rainfall, evapotranspiration, solar radiation and mean temperature for the last decade (2009- 2018), was carried out for the territory of the study area based on the European climate database (Table 2). Using GIS software (in this case ArcMap 10.1), national (Bulgarian) climate database was derived from the European climate database for the period of 2009-2018. It is important to note that the derived data have to match the date of the satellite image in order to achieve objectivity of the results.