3.1. Study area:
The study was conducted in the temperate-alpine habitats of Chopta-Tungnath (between 30°28’39"– 30°29’51" N latitude and 79°12’9" to 79°13’21" E longitude), a pasture and a trekking-pilgrimage area situated on the south-western fringe of Kedarnath wildlife sanctuary in the Garhwal Himalayas of Rudraprayag district, Uttarakhand (Figure 1A). The climate of the landscape is characterized by severe frost, diurnal to seasonal blizzards, hailstorms, and daily orographic precipitation at higher altitudes, throughout the year (Khare et al. 2010). The precipitation ensues in the form of snow, sleet-hail, rains, and showers throughout the year (Rai et al. 2012). The snowfall occurs from November to April. Snow melting in April is the major source of soil water before the monsoon. Maximum rainfall is recorded in July-August (Figure 1B). The mean monthly atmospheric temperature ranges from a maximum of 19°-37°C (May to October) to a minimum, as low as –15°C (December to February) (Figure 1B).
The area is known for the shrine of Tungnath, situated at the Tungnath ridge a major relief structure dividing the drainage of the region. The shrine is associated with alpine grassland (the Tungnath Bugyal). The Tungnath shrine lies 2 km below the Chandrashila Peak, the highest point of the landscape. The landscape is characterized by rocky outcrops having moderate to steep slopes. The topography of the area is dominated by ridges formed by exposed rocks and patches of flat temperate and alpine grasslands. The soil in the area is thin-layered, coarse-textured/ sandy loam at lower altitudes and sandy at higher altitudes, with proper drainage and acidic pH (pH 3.0-5.5) (Rai et al. 2012). The vascular plant vegetation of the study area shows stratified composition along the elevation gradient consisting of temperate mixed oak and coniferous forest at lower elevations, transitioning into the subalpine forest and ultimately culminating into alpine scrub/ grassland (Rai et al. 2012).
3.2. Remote sensing (RS) and geographical information systems (GIS) analysis:
3.2.1. Topographic studies using shuttle radar topography mission-digital elevation model (SRTM-DEM) data:
The topographic study of the Chopta-Tungnath landscape was done on shuttle radar topography mission-digital elevation model (SRTM-DEM) data. The SRTM was an international effort consisting of an 11-day mission of space shuttle Endeavour in February 2000, which mapped almost the entire earth from 56°S to 60°N with STRM payload employing interferometric synthetic-aperture radar technique and obtained a high-resolution digital topographical data of earth (Nikolakopoulos et al. 2006). SRTM-DEM data covers compiled by Consultative Group for International Agriculture Research Consortium for Spatial Information (CGIAR-CSI) covers about 80% of the globe. The SRTM 90m DEMs are with a resolution of 90m and are available for free download as 5×5-degree tiles at http://srtm.csi.cgiar.org/.
The 5×5-degree tile, SRTM, version 4, 90 m data of the study area was downloaded as Geo TIFF file from http://srtm.csi.cgiar.org/srtmdata/ (Fig.2). The data file was pre-processed for noise reduction, identification, and elimination of man-made terrain features (if any), and for estimating and eliminating the forest canopy data (Köthe et al. 2009). The SRTM Geo TIFF file was processed in Esri ArcGIS® ArcMAPTM 10.5. The study area was clipped from the regional downloaded SRTM Geo TIFF file, using the shapefile created from Google Earth Pro 7.3.2.5776 (GEPr). The processed SRTM data along with prior knowledge of the authors was used for the selection of probable sample areas. The final georeferenced and geotagged elevation map of the study area with sampling sites and landmarks was prepared using cumulative input data from SRTM-DEM, GEPr, and previous field studies (Fig.2).
3.2.2. Land use land cover (LULC) classification:
The collative use of google earth imagery and Landsat data was done to assess and prepare the land use/ land cover of the study area (Fig. 4). The Google earth land use/land cover map was prepared by visual interpretation of data based on size, shape, tone, texture, association, and relationship to other objects (Fig. 4).
To study the Landsat-based land use/ land cover (LULC) of the study area, the Landsat 8 satellite data (path 145, row-39) of 18th February 2014 and 10th June 2014 was downloaded from the Earth Explorer website (https://earthexplorer.usgs.gov/ ). The satellite data was imported, stacked and the subset of area of interest (AOI) was created using LeicaTM ERDAS IMAGINE® 9.2. The image was processed for preparing the unsupervised false-colour composite (FCC) map of the AOI using three bands (5, 4 and, 3), which was used in the field excursions for ground-truthing. For Google earth (GE) LULC studies the GE imagery of the Chopta-Tungnath landscape was downloaded using Google Earth Pro 7.3.2.5776 (GEPr). The images were imported, stacked, noise reduction, image enhancement, and georeferencing were done as pre-processing (Fig. 3). The GEPr imagery was processed for the preparation of the LULC map of AOI using 8 classification classes in LeicaTM ERDAS IMAGINE® 9.2. The preliminary LULC maps i.e., FCC image by Landsat 8 and GEPr LULC class maps were finally interpreted by supervised classification based on ground-truthing (Fig. 3).
Reconnaissance field visits/surveys were carried out in different months during 2014-2018 to establish the relationship between land use/ land cover and their tonal variation on the satellite data (Fig. 4). Ground truthing of the Landsat 8 FCC maps/ GEPr LULC class maps was done using handheld GPS (Garmin GPSMAP® 76S).
The final land use/land cover (LULC) was interpreted using digital and visual analysis of Landsat 8 satellite/ GEPr-LULC data. Supervised classification was performed with the training sites of known targets and then the spectral signatures of these sites were extrapolated to other unknown classes (Fig. 3). For this, the Gaussian maximum likelihood classification (GMLC) algorithm was used. The classifier used the training statistics to compute a probability value of whether it belongs to a particular class, which allows for the within-class spectral variance. In this image, the analyst used prior knowledge to weigh the probability function. GMLC provided the highest classification accuracies (Lillesand et al. 2015). For visual analysis elements of visual interpretation like tone, texture, shadow, was used to classify the land cover of the study area using Google Earth imagery and ground-truthing observations during field visits (Fig. 3). The LULC class area of each land use was calculated in km2 and percent. The comparative error matrix and accuracy of visual (Google Earth Pro) and digital (Landsat 8, 2014) interpretation for the LULC classes were assessed using kappa index statistics and area measurements.
3.3. The quantitative study of lichen diversity:
3.3.1 Field methods, collection curation, and identification of lichens:
Based on the SRTM-DEM and previous field visit experiences of the two authors (i.e., Himanshu Rai and Roshni Khare), four sites of the collection were selected along the bridle approach path following the increasing elevation gradient from Chopta to Chandrashila through Tungnath (Fig 4. Table 1). A circular plot of 24 m diam. was randomly selected at each site along the study landscape (Gasparyan et al. 2018; Nag et al. 2019). The lichen diversity was recorded employing a standardized probabilistic method with three 10×50 cm narrow frequency grids which were subdivided into five sampling units of 10×10 cm, laid randomly i.e., fifteen, 10×10 cm sampling units were laid in each plot (Asta et al. 2002; Scheidegger et al. 2002; Rai et al. 2012a, b; Nag et al. 2019). The lichen samples collected were air-dried and curated according to the standardized protocol (Obermayer 2002; Rai et al. 2014b).
The collected lichens were identified up to the species level at the Lichenology laboratory and herbarium (LWG) of the National Botanical Research Institute (NBRI), Lucknow, Uttar Pradesh, India using standardized morpho-anatomical examination, chemical spot tests, standardized thin-layer chromatography, and relevant literature (Awasthi 2007; Orange et al. 2010; Elix 2014; Rai et al. 2014b). The authenticated lichen samples were deposited as voucher specimens in the herbarium (LWG), NBRI.
3.4. Data analysis:
The lichen assemblage of all the four collection sites was quantitatively analyzed for frequency, regarding species richness (number of species) and growth form diversity, (Curtis and McIntosh 1950; Rai et al. 2012). The indirect gradient ordination method, principal component analysis (PCA), was used to summarise the compositional differences of lichen communities between the sites using the var-covariance matrix, employing singular value decomposition, along the RS-GIS recognized LULC classes (Gauch 1982; Ter Braak 1995; Ter Braak and Prentice 2004; Rai et al. 2012).