WORK UNDERTAKEN OUTPUT Year 2018-2019 PROJECT ACTIVITIES Qtr 1 Qtr 2 Qtr 3 Qtr 4

Separate Bank Separate Accounts of PI and CO-PIs are Account Opening  opened and the funds are allocated to and Fund respective PIs. Allocation

Recruitment of   Recruitment of project staffs are over. Project Staffs

Procurement of  Fieldspec4 Spectroradiometer is purchased. field instruments such as FieldSpec Spectroradiometer

Field Sampling  The Field sampling for Kashmir was scheduled. It needs to be specified here that enough data is not there due to the fact that first sampling was cancelled due to Pulwama attack and second sampling plan was curtailed to just a few hours due to the advisory issued by Government of owing to the scrapping of article 370.

Literature Survey     An extensive literature review on the of the Himalayan species as well as on the methodologies on Species as well as finding the biophysical/biochemical the study of the properties of the species has been carried Methodologies of out. Plant biophysical/biochemical the retrieval of parameter retrieval and related algorithms vegetation are also surveyed. properties. The details are given in Annexure I

Lab Setup   The setup of Automated Radiative Transfer Models Operator (ARTMO) is over which will be used for the retrieval of biophysical and biochemical parameters using the hyperspectral data. The details of the lab setup are given in Annexure II.

The formation of   The identification of the rare, endangered Spectral Libraries. medicinal and other economically important plant species is carried out. The Creation of the spectral library of the plant species have been started and data on medicinal or high value plants were digitized along with photographs of respective species whenever available. The digital spectral library is prepared for more than 100 species by recording the reflectance these species. Among many techniques studied, Spectral Angle Mapper was found suitable for species detection and monitoring. The details of the libraries are given in Annexure III.

A detailed study    Apart from the identification of the is conducted in rare, endangered medicinal and other relation to economically important plant species, a pedological and detailed study is conducted in relation to climatic pedological and climatic conditions. NDVI. conditions. LST, LAT and WRF models were applied for a duration of 15 years w.e.f. 2001 till 2015 in Dachigam and Pindari regions.  Landsat 5,8 and MOD11A2 are used as a data source for NDVI, LST, LAI and WRF modeling.  The detailed analysis of NDVI, LST, LAI and WRF models is explained in section 7.  Spatial distribution of plant species for a proxy site is conducted as represented in Figure 25. After data collection, we are planning to make a spatial distribution of plant species in complete Himalayan range.

ANNEXURE I

Literature Review An extensive literature review on the species as well as on the methodologies on finding the biophysical/biochemical properties of the species has been carried out which indicates that the Himalayan ecosystem is affluent in plant species diversity.

Available species in the complete Himalayan Range (Table 1).

Table 1: Medicinal Plants in the complete Himalayan Range Name Height Type Location Remarks (  Altitude  Blooming Season  Medicinal Value) Nepata laevigata 1-3 feet Herb Pangi, Himachal 2500 mts Nepata leucophylla 2-3 feet Herb Kumaon, benth Himalayan, Mussoorie, dhanolti Nepata 2-3 feet Herb Kazork longibracteata spanganak, lang benth Sea- Kangri, thukje, pang, debring (hemis National Park), stakna Nyctanthes arbor 2-3 feet Small tree and South India, trisitis L. (Parijat) deciduous shrub Mumbai, Delhi, near Allahabad Ocimum basilicum 2-3 feet Herb All over India L. (basil) Organium vulgare 0.6 ft-2.6ft Perennial Herb Badhangari, Alt. 1500 – 2800 m L. Chamoli (Himalayan Aeradev, Almora Marjorum/ Kamedi Devi, oregano) Bageshwar Patal Bhuvneshwar, Pithoragarh Liti, Bageshwar Shama, Bageshwar Badhangari, Chamoli Dronagiri, Almora Purara, Bageshwar Shama, Bageshwar Vitex negundo L. 10-20 ft tall Shrubs or small Bulekha trees Uttaranchal, Dehradun Zanthoxylum Small Plants Pithoragarh, In evergreen forests, armatum DC. Lohaghat, dana between 90-1000 m. Thuja Orientalis More than 20 m/ Shimla 15 m (49ft) by 5 m L. / Platycladus 10 to 200 feet (16ft) Orientalis Thymus linearis Shrub Hemis national benth. park, pang, sangtha, burwa, Nanda Devi National Park, Martuli Pancholi, Badrinath, jaspa, Zanskar, Balta (away from 20 km from Dachigam national Park) Thymus 40 cm Perennial Shrub Dachigam Serphyllum L. National Park, Chandanwadi (near Dachigam), Mussoorie (UK) Valeriana 40- 50 cm Perennial Herb Dachigam national 1500-4000 m hardwickii Wall park, palchan, Marh (Himachal), Nanda Devi national park and Dehradun 15-65 cm tall Perennial Herb Burwa (near Manali), Valeriana Mussoorie, Jawarna jatamansi Jones (Himachal), forest Range near Mussoorie Rhododendron 2-3 ft Alpine Shrub Chitkul (3600 m) anthopogon in Kinnaur district, Great Himalayan National Park (3500 m) in Kullu district, Tungnath, at an altitude of approx. 3600 m

Senecio Rufinervis 3 ft tall Woody Herb In the Himalayas 1800-3000 m DC in Uttaranchal and Nepal. Skimmia 4 ft Aromatic erect or Harinagar, Nartola anquetilia Tayl. creeping shrub (District Nainital) Skimmia lauerola 4 ft Kedarnath, 4000 m (DC) Agasthamani, Ransi Mountain, Shimla 3–6 feet (1–1.83 Herb with biennial Govind pashu m). tubers vihar national Selinum nuifolium park, Dachigam national park Salisb.

30-70 Cm Perennial Herb Jim Corbett, tanda Deshral, Zanskar Senecio Nudicaulis (Hemis National Park) buch 50-70 Cm Herb occurs throughout India, in dry Solnum situations as a weed along the Xanthcarpum roadsides and wastelands Satcys sericea - - - wall.ex benth Tanacetum erect hairy Kedarnath wildlife longifolium wall. herbaceous plant sanctuary, Tanacetum gracile 30–60 cm tall Small plant Near Hemis July to September Hook. National Park, Ladakh Himalaya, Ganglas, Ladakh on the way to Nubra valley (altitude 3500 m a.s.l.) Tanacetum Stems of 18-33 Perennial Herb Malari in the September-October nubigenum Wall Cm of Uttar Pradesh, Perovskia 100 Cm Perennial Shey, phey Dry stream beds on abrontanoides subshrub (mountains) near mountain Kar. Hemis National Park, Dargoo, Skurbhuchan, Dah, Damkhar Persea duthiei 25 m Large trees Barmdeo 1500–3200 m. (king) Kosterm/ (Bhabar), Kumaon Machilus duthiei Persea up to 25 m tall; Trees Jaunsar-Bawar is a 2100 mts gamblei(king Ex hilly region, 85 Hook f. ) Kosterm/ km from Machilus gambeli Mussoorie, Kalatope, Chamba, Dehradun, Rishikesh Phoebe lanceolate 8 m tall. Trees Dehradun, (Nees) Nees Lacheen, Siliguri, (lauraceae) Nagaland Pinus roxburghii 4-15(-20) m tall Trees Dehradun, Sarg. Lohaghat, Pithoragarh, Almohra, Kausani, Dwarahat, Gwaldam, Salyana, Uttarkashi, Mussorie Piper betle L. perennial creeper Bihar, Bengal, (Piperaceae) Orissa, Tamilnadu and Karnataka. Pleurospernum 10-30 ft tall Tree Niti, Chamoli angelicoides (Wall (Uttarakhand), ex. DC) Benth ex. North-Western C.B. Clarke Himalaya, India Cedrus deodara / 50 m high Tree Kashmir to Deodar Garhwal at altitude ranges from 1210 to 3050 m Abies himalayensis 50 m Tall tree /Pinus Pindrow /Pinus Spectabilis /var Pindrow Justicia Adhoatoda 1.2-6 m tall perennial shrub Dehradun, Linn Haldwani, Agave Americana 25–30 ft (8–9 m) tall. Centella Asiatica perennial, creeper medicinal herb Eastern Himalaya Temperate and tropical Linn herb, attains swampy areas in many height up to 15cm regions of the world. (6inches) Threatened at IUCN Mangifera indica Large tree to 150 tree Jim Corbett, Elevation: 980 to 4,200 ft (45 m) growing Rudraprayag, ft (300-1,300 m). wild in the eastern zirkapur Himalayas, Nepal, (Chandigarh) and the Andaman Islands. Holarrhena height up to 13 m A small deciduous antidysenterica tree. Leaves: Carissa spinarum upto 4m. spiny shrub Tanda desharal, Gurugram Acorus calamus semiaquatic, Linn. perennial, aromatic herb with creeping rhizomes Arnebia benthamii Height of 60 Cm Perennial Plant Wallich Arisaema Max 2m Scrub and Alpine Pahalgam Valley, tortuosum Meadows in the Kashmir Himalaya. Himalayas Perennial Calotropis 1-4 m tall. shrub procera/ Madar 30 to 100 cm tall annual herb Artemisia annua Artimisia 1-4 m height Aromatic Shrub Nilagirica Arster flaccidus 3-30 cm tall Perennial Herb Hemis national park, balthi, Kelong, Kishwar throughout Kashmir region Centipedia minima 1,200 metres Prostrate glabrous herbs Cichorium intybus 1-4 feet tall Bushy Perennial Munsayri (Nanda Herb Devi national park), garbyang Erigeron asteroids 15-25(45) m tall evergreen tree Shimla, Dehradun, IUCN endangered roxb. 40-60(90) cm Bharmour (near W. Himalaya at 1800- dhauladar range, 300 m on limestone Dhamashala) substrates Rhododendron 40 to 50 ft evergreen shrub or Dehradun, Shimla, elevations of 4500 to arboretum tree up to 14 m in small tree with a Hemis National 10,500 ft height & 2.4 m in showy display of Park, Nainital, girth bright red flowers Mukteshwar Near Almohra, Mussorie Emblica Officinalis Bahuinia variegata 20-40 ft tall Orchid Tree Haridwar, Dehradun Quercus 25 m Evergreen Tree Garhwal leucotrichophora Himalaya- Cam. Chandrabadani, Chaurangikhal, Ghuttu, Khirsoo and Mussoorie Aesculus indica. 20-30 m tall Chestnut is a tall Dehradun, 3,000 m above sea level deciduous Haldwani, spreading shady Ranikhet, Nanda tree. Devi National park, Nanital, Dungari, ghes, Dhoru Shadab (Srinagar) Albizia lebbek 25 m high Deciduous trees Dehradun, haldawani, Kapurthala, Nakudar Ficus carica 15-30 ft Small Tree Kalpa,

Ficus religiosa 25 m high Deciduous trees Haridwar Rishikesh, Nanital, Dehradun, Bageshwar, Rudrayag, Haldwani Myrica esculanta 6 - 8 m in height small tree Haldwani, Shimla, Buch Ham. patal Bhuvneshwar, Pithoragarh, Chamoli, Gwaldham, Shimla Rheum australe 1ft long Branched Clusters Nanada Devi national Park, Zanskar zumdo Prunus persica up to 15 m tall Trees, Uttarkashi, Dehradun

Murraya koenigii 3-5 m tall deciduous Chandigarh, aromatic shrub Haridwar, Dehradun, Tanda Deshral Sapindus mukrossi 25 m Tall Tree Dehradun, Haldawani, Pittoragarh Taxus baccata 30 m tall under-story tree Found in the Flowering occurs from Linn. temperate March to May and Himalayas at the seeds ripen between elevation of 6000- August and November 11000 ft. of the same year. Vulnerable

Cinnamomum Leaves  1,500–2,500 m glanduliferum false camphor tree  March to May (Wall.) or Nepal camphor  Leaves are used as Meisn. tree a stimulant, (Lauraceae) carminative, and to treat coughs and colds Cinnamomum Leaves, root  1,000–2,000 m Tamala  Jan – March (Buch. -Ham.) T.  tejpatta leaves Nees and to treat gastric Nees (Lauraceae) problems Juglans regia L. Bark, leaves  June-July (Juglandaceae)  walnut Leaf essential oil from Kashmir, antibacterial Juniperus indica Leaves, berries  2600-5200 m Bertol.  leaves and berries (Cupressaceae) are used to treat fevers, coughs, skin diseases Murraya koenigii Leaves, bark,  1500 m (L.) twigs  April-may Spreng. (Rutaceae)  sweet neem, Kadi Patta anthelmintic and in blood disorders Eucalyptus Leaf, bark and, oil Bluegum globules Labill. Lyptus (Myrtaceae) Melia azedarach Leaves barks and  In the spring L. fruits  Chinaberry tree Daikan Bark and fruit (Meliaceae) extract are used to kill parasitic roundworms. In Manipur, leaves and flowers are used as a poultice in nervous headache. Leaves, bark, and fruit are insect repellent. Seed-oil is used in rheumatism. Wood- extract is used in asthma. Pistacia khinjuk Leaves, petiole, Antimicrobial Socks. and branches Kakarsingi (Anacardiaceae) Pterocarpus .  1,200 m marsupium Roxb Gum leaf and September-October. Bijesar (Fabaceae) flower  Beeja patta the wood and bark of the tree are known for anti- inflammation property and their anti-diabetic activity Shorea robusta Leaves and bark Sal Roxb. ex. Sal (Dipterocarpaceae) Taxus baccata Leaves and bark anti-cancer agents auct. (Non-L.) Thuner (Taxaceae) Pistacia The whole plant its galls have been integerrima Stews (leaf, bark, root, utilized for the and galls) treatment of cough, asthma, dysentery, liver disorders and for snake bite. Flacourtia indica root, Leaves, Kancu Skin diseases, Fruits Poisonous biting, Jaundice Ginkgo biloba leaves treatment for blood disorders and memory problems, enhancement of cardiovascular function and to improve eye health

The Pindari Basin is very rich in terms of forest resources and diversity. From the valley region to the highly elevated alpine meadows, locally known as Kharakor Bugyal, the rich diversity in plants is found. In the mid slopes, Chir(pine) is common, while in the upper reaches, temperate coniferous forests, mainly Banj(oak), Tilonj (Quercesdilitata) and Devadar(Cedrusdeodara), are found extensively. Altitude wise distribution of the forest is given in Table 2. Table 2: Forest Diversity Based on Altitude in Pindari Belt/Altitude Geographical Area Main Species Valley regions/below 1000 m Along the valley of Pindar River Eucalyptus, Dendrocalamus sp Middle altitude 1000 m to 1600 m The slope of the various streams Pine dominate such as Kaver Gadhera, Ming (blue pine and Gadhera, Pranmati and Atagarh chir forest), Kaver Gadhera, Ming Gadhera, and Pranmati Gadhera Temperate zone 1600 m to 2000 m Watershed regions, Love-Kush Deodar forests tope, Kanpur Garhi, Khankhrakhet, (Cedrusdeodara), Shubhtal-Chhaltal, Kurur-Kwarad oak forests and Sol-Dungri (Quercus species), fir (Abiespindrow) & spruce (Picea smithiana), ringal (bamboo) forests (Dendrocalamus spp.) Alpine Meadows/between 2600 m to the Snow line Bedni Bugyal, and Shail Dominated by Samunder herbs Ringal (bamboo) 3000-4000m Alpine Meadows Osmunda-tussock grass, tussock grass, tussock grass-forb, tussock grass- Sedge, Sedge- forb, Sedge, early successional Rumaxnepalensis

The following group had maximum tree density 1. Quercus floribunda, 2. Rhododendron arboreum

The minimum tree density is found below 1. Abies pindrow 2. Betula utilis.

The dominant shrub species is available as 1. Myrsine Africana

The rare tree species are found as 1. Pinus wallichiana 2. Betula utilis 3. Tsuga dumosa 4. Taxus baccata 5. Cedrus deodara Plant biophysical/biochemical parameter retrieval and related algorithms The technical specifications of LAI estimation based on statistical models are listed in Table 3. The evolution of RT models is represented as a flow chart in Figure 1. This section in tabular form categorizes the approaches used for LAI estimation. The approaches are described into three types: - statistical models, physical models, and hybrid models. The conceptual framework for iterative optimization is given in Figure 2 while the conceptual framework for inversion based on LUT, NN, Bayesian expert systems or support vector regression (SVR), CR, Canopy reflectance is given in Figure 3. The hybrid inversion model is given in Figure 4 and a model for LAI estimation using the signal to noise ratio, partial least squares, and the lookup table is given in Figure 5. Table 3. Technical Specifications of Representative LAI Estimation Studies Based on Statistical Models

Figure 1 The Evolution of RT Models. A representative version of scattering by arbitrarily inclined leaves (SAIL) model. B, a series of canopy reflectance models developed from the N-K model. N- K, Nilson-Kuusk model; MSRM, multispectral canopy reflectance model; ACRM, a two-layer canopy reflectance model.

Figure 2 Conceptual Framework of Iterative Optimization Inversion. CR, Canopy Reflectance.

Figure 3 Conceptual Framework for Inversion Based On LUT, NN, Bayesian Expert Systems or SVR CR, Canopy Reflectance.

Figure 4 Conceptual Framework for Hybrid Inversion.

Figure 5 A conceptual framework for LAI estimation. SNR, signal-to-noise ratio; PLS, partial least squares; LUT, look-up table.

ANNEXURE II

Fieldspec4 Spectroradiometer This started with the E tendering of the Fieldspec4 Spectroradiometer. A detailed calibration has been performed with the help of engineers coming from the vendor. The spectral resolution of the spectrometer is represented in Figure 6.

350 – 2500 nm

350 – 1000 nm 1000 – 1800 nm 1800 – 2500 nm

UV/VNIR SWIR1 SWIR2

UV/VNIR Scanning SWIR1 Scanning SWIR2

Figure 6 Spectral Resolution of the spectroradiometer

After setting up the Spectroradiometer, calibration needs to be done through the software. Reliability of the collected spectrometry data depends mainly upon accurate calibration of the instruments used. To achieve this, accurate wavelength calibration is necessary which is achieved in the lab in the following manner. The UV/VNIR detector array and in-house setup are combined to provide a simple linear relationship between wavelength and the channel number. We have achieved well-characterized emission lines from a monochromator which was set to emit at 50 nm intervals, spread throughout the region from the 350-1000nm wavelength. This was plotted against the responding channel numbers and the first wavelength is extrapolated from a linear regression fit of the data. The final equation is a simple linear formula in the form: wavelength = lamstart + (lamstep)(channelnumber) The SWIR scanning spectrometers, covering the range from 1000 - 2500 nm, use much the same calibration principles, with two major differences: 1. Through the use of a monochromator as only emission source, and 2. Two-third order polynomials are calculated for each SWIR detector, to account for both forward and backward scans of the gratings. This amounts to the calculation of eight constants for each detector. We then check the SWIR wavelength calibrations with well-defined absorption features in a material such as Mylar or Polystyrene. 10-inch x 10-inch calibrated White Spectralon Panel or 3.62" Round Uncalibrated White Spectralon Panel is used for white reference. Model Setup-ARTMO for biochemical/biophysical modelling: - The proposed research work would retrieve biophysical and biochemical parameters of economically important medicinal plant species of Pindari Region in Kumaun West Himalaya and Dachigam National Park in the Kashmir Himalaya using forward and inward toolboxes of Radiative transfer model namely Automated Radiative Transfer Models Operator (ARTMO). Remote Sensing data used for the study is hyperspectral airborne AVIRIS data and Spectroradiometer acquired ground hyperspectral data. ARTMO Graphic User Interface (GUI) is a freely available software package that consists of many tools that are imperative for running and inverting a suite of plant RTMs, both at the leaf and at the canopy level. It provides consistent and intuitive user interaction, thereby streamlining model setup, running, storing and spectra output plotting for any kind of optical sensor operating in the visible, near-infrared and shortwave infrared range (400-2500 nm). Fundamentally, ARTMO allows the execution of the following: i. Configuration and running of leaf and canopy RTMs, independently or combined, in an intuitive way through various GUIs with input options to insert single values, value ranges, or imported external datasets. ii. Simulation and storage of a massive quantity of spectral output based on a look-up table (LUT) approach in a relational database. iii. Plotting groups of simulated spectra in the same plotting window with color gradients as a function of input parameters. iv. Exporting simulated spectra and associated meta-data to a text file for further processing. v. Analysis and application of retrieval techniques for generation of biophysical parameters maps from remote sensing imagery. vi. Radiative Transfer Models: Dorsiventral Leaf Model (DLM), LIBERTY, Fluspect-B, INFORM, SCOPE Modules Tools: Sensor tool, Spectral resampling tool, Global Sensitivity Analysis (GSA), Emulator toolbox, Scene Generator Module. vii. Apps: Retrieval toolboxes: MLRA toolbox, Spectral Indices toolbox, LUT-based inversion toolbox. Following snapshots (Figure 7 to Figure 11) provides the stepwise procedures followed while running the ARTMO tool.

Figure 7 Input parameters for PROSPECT (Allen's generalized “plate model”)

Figure 8 Input parameters for SAIL (Scattering by Arbitrary Inclined Leaves)

Figure 9 Forward running of Prospect

Figure 10 Forward running of SAIL

Figure 11 Inversion using LUT

Spectrum Discrimination

A quadrate sampling of 20 m by 20 m plot size is performed on a proxy site. Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) campaign of this year data will be used.

Underlying Methods and Library Creation Procedure

The digital spectral library is possible by recording the reflectance of important species. Among many techniques explored, Spectral Angle Mapper is found suitable for species detection and monitoring.

Weather Research and Forecasting Model setup:

The Weather Research and Forecasting (WRF) model is a quantitative weather prediction model developed for atmospheric research as well as operational forecasting needs. The WRF is a non-hydrostatic, mesoscale model, consist of many physics packages and dynamical cores which can be easily executed with the similar operating code for optimizing performance (Xu and Powell 2012). Because of high computing and performance, the WRF model is configured for both research and forecasting applications (Skamarock and Klemp 2008). WRF model can be used for various applications such as assimilation of meteorological datasets, air quality modeling, downscaling, ocean coupling (Mohan and Sati 2016), real-time NWP, forecasting, parameterization (Skamarock and Klemp 2008).

The WRF model can be used in a broad range of applications across scales ranging from meters to thousands of kilometres (Schwartz et al. 2009; Srivastava et al. 2015a; Srivastava et al. 2015b). The downscaling of meteorological variables is performed using the WRF model with domain fixed over the Indian region, and the meteorological information of the Himalayan region is retrieved using the software -Grid Analysis and Display System (GrADS). The WRF modelling system is fully compressible, non-hydrostatic Euler equations following the philosophy of Ooyama (1990), cast in flux conservation form, using mass (hydrostatic pressure) vertical coordinate and state-of-the-art atmospheric simulation system that is portable and efficient on available parallel computing platforms. The WRF model is useful in both operational forecasting as well as in atmospheric research. The working framework of WRF model includes the pre-processing of meteorological data under WRF Preprocessing System (WPS), the output of WPS as an input to the forecast model (WRF-ARW). The schematic of the WRF modelling system provided in figure 12. The detailed description of model governing equations, physics and dynamics are presented in Skamarock et al. (2005).

Figure 12 Framework of the WRF model

ANNEXURE III

Hydro-Climatology of the study area is found in the upper reaches of Kumaon Himalaya and situated at an elevation 5200 m. to the southeast of Nanda Devi, Nanda Kot (28° 43' 55" to 30° 30' 12" N and 78° 44' 30" to 80° 45' E), between altitudes 1500-3000 m. Dachigam National park is located in the Zabarwan Range of the western Himalayas (34.1372° N, 75.0378° E). The variation in altitude is vast, ranging from 5500 ft to 14000 ft above mean sea level. Due to this vast variation, the park is demarcated into an uneven region. The terrain ranges from gently sloping grasslands to sharp rocky outcrops and cliffs. It is represented in Figure 13.

Indian Himalayan Region

Nepal

Pindari Region in Kumaun West Himalaya

Dachigam National Park (DNP), in the Kashmir Himalaya

Figure 13 Himalayan Terrain

Normalized difference vegetation index (NDVI) for Dachigam A long-term (2001-2015) Climatology of NDVI is derived using Landsat 5 and 8 datasets for the vigor of vegetation estimation of Dal catchment. In the present study, 30 m spatial and 32-day composite temporal product of Landsat 5 and 8 was used for climatology derivation. NDVI is a compilation product of visible and near-infrared bands, which ranges from -1.0 to 1.0. The generated map of Dal catchment NDVI ranged between -0.14 and 0.45. North and Central part of catchment had highest NDVI (0.3 to 0.45) whereas area underwater body (Dal Lake) had negative NDVI (Figure 14). The NDVI between 0.07 to 0.16 shows built up and barren land and NDVI above 0.16 shows the area under vegetation.

Figure 14 NDVI-climatology for the multi-year data over Dal catchment

Land Surface Temperature (LST) for Dachigam Land Surface Temperature (LST) Climatology has been derived using version 6 Moderate Resolution Imaging Spectroradiometer (MOD11A2) product. The MOD11A2 product provides 8-day LST with 1000 m spatial resolution. LST climatology of Dal catchment (Figure 15) depicted that the annual average temperature of this region varied from 2.6 to 24.7 °C. The lowest temperature ranges from 2 to 10 °C was seen in the highly elevated region of the area whereas the highest LST (15 to 24 °C was observed in Northwestern and West southern region of catchment which has moderate vegetation and lakes. The central part of the catchment with dense vegetation has moderate LST ranging between 10 to 15 °C.

Figure 15 LST-climatology for the multi-year data over Dal catchment

Normalized difference vegetation index (NDVI) for Pindari A long-term (2001-2016) Climatology of NDVI is derived using Landsat 5 and 8 datasets for the vigor of vegetation estimation of Pindari catchment. In the present study, 30 m spatial and 32-day composite temporal product of Landsat 5 and 8 was used for climatology derivation. NDVI is a compilation product of visible and near- infrared bands, which ranges from -1.0 to 1.0. The generated map of Pindari catchment NDVI ranged between -0.13 and 0.49. North and Central part of catchment had highest NDVI (0.31 to 0.49) whereas area under glacier water body had negative NDVI (Figure 16). The NDVI between 0.03 to 0.3 shows built up and barren land and NDVI above 0.21 shows the area under vegetation.

Figure 16 NDVI-climatology for the multi-year data over Pindari catchment

Land Surface Temperature (LST) for Pindari Land Surface Temperature (LST) Climatology has been derived using version 6 Moderate Resolution Imaging Spectroradiometer (MOD11A2) product. The MOD11A2 product provides 8-day LST with 1000 m spatial resolution. LST climatology of Dal catchment (Figure 17) depicted that the annual average temperature of this region varied from -27 °C to 26 °C. The lowest temperature ranges from -27 to -7.8 °C was seen in the highly elevated region of the area whereas the highest LST (12 to 26 °C was observed in Southwestern and West southern region of catchment which has moderate vegetation and lakes. The temperature varies from -1.5 °C to 4.6 °C in the area comprising of dense vegetation. The central part of the catchment with dense vegetation has moderate LST ranging between 4.7 to 11 °C.

Figure 17 LST-climatology for the multi-year data over Pindari catchment

Leaf Area Index (LAI) for Pindari Leaf Area Index (LAI) Climatology has been derived using version 6 Moderate Resolution Imaging Spectroradiometer (MOD11A2) product. The MOD11A2 product provides 8-day LST with 1000 m spatial resolution. LAI climatology of Pindari (Figure 18) depicted that the annual density of plant canopies of this region varied from 0 to 3.1. The lowest LAI ranges from 0 to 0.21 was seen in the highly elevated region of the area whereas the highest LAI (1 to 3.1 was observed in Northwestern and West southern region of catchment which has moderate vegetation and lakes. The central part of the catchment with dense vegetation has moderate LAI ranging between 0.22 to 1.8.

Figure 18 LAI for the multi-year data over Pindari catchment

Weather Research and Forecasting (WRF) model downscaling for temperature over Dachigam and Pindari For downscaling NCEP final reanalysis datasets at 6 hours’ time interval has selected at 25 km spatial resolution. Model simulations are performed daily for the study period. The WRF single-moment (WSM) 6-class microphysics and Kain-Fritsch scheme are used for cumulus parameterization. This scheme has been evolved at the National Center for Atmospheric Research (NCAR) and shows good performance in WRF (Hong and Lim 2006). To achieve the accurate result under the clear sky including upward and downward cloud radiation fluxes, the terrestrial radiation is parameterized by the radiative transfer (RRTM scheme) model (Mlawer, Taubman, et al. 1997). Further, to retrieve the better result in shortwave radiation we selected Dudhia scheme (Chen and Dudhia 2001). Planetary boundary layer option helps in the assessment of the boundary layer fluxes (such as moisture, heat, momentum) the vertical diffusion within the entire column for this YSU scheme was selected to constitute near- surface weather operation (Kim and Wang 2011). Weiss and Wilson (1957) find out that objects and structures affect the measurement of rain. Atmospheric factors like wind contribute to error and inconsistencies in the rain measurement using the rain gauge observations. In this regard, WRF downscaling plays a significant role to monitor the small-scale meteorological variables from global datasets to a regional scale like the precipitation, wind, relative humidity, and temperature. To perform downscaling the model domain is set up over the Dachigam as well as Pindari catchment to get meteorological variables (Figure 13). The model has applied to Dachigam as well as Pindari catchment and is represented in Figure 19 and 20 respectively from 2001 till 2015. From Figure 19, it is clear that the change in the temperature lies between 9.15 °C to 2.70 °C for Dachigam, while for Pindari, it is clear that the change in the temperature lies between 12.52 °C to -0.11 °C (Figure 20).

Figure 19 WRF output for the multi-year temperature data downscaling over Dachigam

Figure 20 WRF for the multi-year data over Pindari catchment

Analysis of Plant Species found in Pindari A statistical analysis of plant species is carried out for Pindari region and some interesting observations were made. The observations are listed in Table 4 and is represented in Figures 21-24. Some eight species identified are represented in Figure 25.

Table 4: Medicinal Benefits of the Plants in Pindari

Name of Plant Family Parts used as medicine the plant local name

1. Malva Sutsal Malvaceae Seeds boiled in sugary (sharbat) is taken to cure cough and fever rotundifolia

2. Urtica Soi Urticaceae The plant is used for the Treatment prostatic hyperplasia, arthritis dioica and increase free testosterone

3.Prinula Kalwauth Prinulaceae The hot water bath of flowering tops is used to cure headache, vulgaris fever, and body muscular pain

4. Datura Datur Solanaceae The fruits and leaves are considered good for pain in the chest. stramonium The powdered leaves are applied to hemorrhoids, gastrointestinal problems, arthritis, rattlesnake bites and tumors.

5. Brarigasa Lamiaceae Infusion of leaves is used in the treatment of itches and skin Labiateae eruptions; leaf juice is applied to treat baldness (alopecia). Seed powder is given to children against worm infection.

6. Allium Rohun Alliaceae Infusion of leaves is used in the treatment of itches and skin sativum eruptions; leaf juice is applied to treat baldness (alopecia). Seed powder is given to children against worm infection

The Status of medicinal plants reported in the literature from Pindari area Uttarakhand can be visualized as:

50 42 37 a 40 x

a t

f 30 o

27 r e

b 20 m u N 10

0

Species Genus Families Taxonomic level

Figure 21 Taxonomic diversity of medicinal plants reported in published in literature from Pindari area Uttarakhand

16 1 14 4

s 12 e

i c

e 10

p s

8 f 6 o

. 6 o

N 4 3 2 2

0 CAMP IUCN CITES RDB Threaten ed catagory

Figure 22 Conservation status of medicinal plants reported from Pindari area Uttarakhand

Whole Plant 11%

Fruit/flower 18% Roots 44%

Leaf 27%

Figure 23 Ethnobotanical usage pattern of medicinal plants in Pindari area Uttarakhand Non- endemic 43%

Endemic 57%

Figure 24 Nativity of the medicinal plants in the Pindari area Uttarakhand

Plant species identification based on reflectance of a proxy site (Sariska Forest, Figure 26) is done for calibration of spectra and development of discrimination algorithm.  Acacia catechu  Anogeissus pendula  Boswelia serrata  Acacia leucophloea  Grewia flavescens  Zizyphus mauritiana

Rhododendron Podophyllu Jurinea Delphinium macrocep

Rheum Arnebia Dactylorhi Angeli bentham

Figure 25 List of Eight high value medicinal plants species

Figure 26 Spatial distribution of plant species as conducted on a proxy site for optimizing the species discrimination algorithm. The spectrum with the help of spectroradiometer is ready for the species with the location as mentioned in Table 5.

Table 5: Species, Site and Spectra Details (Kashmir Valley) Chinar (Platanus Orientalis) chl a chl b carotenoids (microgram/ml) (microgram/ml) (microgram/ml)

N 74 Site 001 white 12.66962 8.9012 15.02218525 ˚53.38’. 1 reference E 34 ˚ 002-007 sample 12.27’. N 74 Site 041 white 9.95961 2.2805 10.73226114 ˚54.44’. 3 reference E 34 ˚ 042- 046 11.40’ Sample N 74 Site No white 11.70618 5.9668 13.48013588 ˚54.50’. 4 reference E 34 ˚ 008-012 sample 11.29’. N 74 Site 013 white 14.1394 7.13 16.44518256 ˚53.18’. 28 reference E 34 ˚ 014-018 sample 11.44’.

Pinus (Pinus Sabiniana) chl a chl b caroteniods (microgram/ml) (microgram/ml) (microgram/ml)

N 74 Site 031-034 1.21803 2.4013 1.632609913 ˚54.31’. 1 sample E 34 ˚ 12.29’

N 74 Site 026 to 030 3.3225 0.6506 3.757902722 ˚54.30’. 2 sample E 34 ˚ 12.23’

N 74 Site 020 white 1.29484 0.5592 1.681829653 ˚51.12’. 3 reference E 34 ˚ 021-025 sample 11.49’

Figures from 27-35 depicts the spectra for the sites specified in Table 5.

Figure 27 DN for the species Platanus Orientalis and Pinus Sabiniana

Figure 28 Reflectance for the species Platanus Orientalis and Pinus Sabiniana

Figure 29 Reflectance for the species Platanus Orientalis at site 1

Figure 30 Reflectance for the species Platanus Orientalis at site 3

Figure 31 Reflectance for the species Platanus Orientalis at site 4

Figure 32 Reflectance for the species Platanus Orientalis at site 28

Figure 33 Reflectance for the species Pinus Sabiniana at site 1

Figure 34 Reflectance for the species Pinus Sabiniana at site 2

Figure 35 Reflectance for the species Pinus Sabiniana at site 3

There are a number of other statistical pre-processing techniques which can be utilized through the ViewSpec software. There is also a provision to store reflectance data in the textual or tabular format.