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Rai, Ishwari Datt
- Neottia pinetorum (Orchidaceae): A New Record to the Flora of Himachal Pradesh, India
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Indian Forester, Vol 139, No 6 (2013), Pagination: 566-567Abstract
No Abstract
- A Rare and Endangered Root Parasite: Balanophora Involucrata Hook. F.&Thompson
Abstract Views :227 |
PDF Views:0
Authors
Affiliations
1 Department of Habitat Ecology, Wildlife Institute of India, P.O. Box # 18, Chandrabani, Dehradun, Uttarakhand
2 Ecosystem Services, International Centre for Integrated Mountain Development, GPO Box 3226, Kathmandu, Nepal
1 Department of Habitat Ecology, Wildlife Institute of India, P.O. Box # 18, Chandrabani, Dehradun, Uttarakhand
2 Ecosystem Services, International Centre for Integrated Mountain Development, GPO Box 3226, Kathmandu, Nepal
Source
Indian Forester, Vol 140, No 4 (2014), Pagination: 435-436Abstract
No Abstract- New Distributional Record of Gastrochilus affinis (Orchidaceae) from Western Himalaya with Notes on A New Synonym and Typification
Abstract Views :219 |
PDF Views:106
Authors
Affiliations
1 Botanical Survey of India, Western Regional Centre, Pune- 411001, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun - 248001, IN
3 Central National Herbarium, Botanical Survey of India, Botanic Garden, Howrah - 711 103, IN
4 Uttarakhand Space Application Centre, Upper Aamwala, Tapowan-Nalapani, Dehradun-248008, IN
1 Botanical Survey of India, Western Regional Centre, Pune- 411001, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun - 248001, IN
3 Central National Herbarium, Botanical Survey of India, Botanic Garden, Howrah - 711 103, IN
4 Uttarakhand Space Application Centre, Upper Aamwala, Tapowan-Nalapani, Dehradun-248008, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 62, No 1 (2020), Pagination: 12-15Abstract
Gastrochilus affinis (King & Pantl.) Schltr. is reported here for the first time from Western Himalaya. Gastrochilus nepalensis Raskoti, a species from Nepal described in 2015 is reduced to synonymy under G. affinis. A note on typification of G. affinis is also provided in the present communication.Keywords
Epiphyte, Gastrochilus nepalensis, Lectotype, Orchid, Uttarakhand.Full Text
References
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- TURLAND, N.J., J.H. WIERSEMA, F.R. BARRIE, W. GREUTER, D.L. HAWKSWORTH, P.S. HERENDEEN, S. KNAPP, W.H. KUSBER, D.-Z. LI, K. MARHOLD, T.W. MAY, J. MCNEILL, A.M. MONRO, J. PRADO, M.J. PRICE, & G.F. SMITH (eds.). 2018. International Code of Nomenclature for algae, fungi, and plants (Shenzhen Code) adopted by the Nineteenth International Botanical Congress Shenzhen, China, July 2017. Regnum Vegetabile 159. Glashütten: Koeltz Botanical Books. DOI https://doi.org/10.12705/Code.2018.
- Deciphering Tropical Tree Communities Using Earth Observation Data and Machine Learning
Abstract Views :122 |
PDF Views:76
Authors
Rahul Bodh
1,
Hitendra Padalia
1,
Divesh Pangtey
1,
Ishwari Datt Rai
1,
Subrata Nandy
1,
C. Sudhakar Reddy
2
Affiliations
1 Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
1 Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
Source
Current Science, Vol 124, No 6 (2023), Pagination: 704-712Abstract
Publicly available EO datasets offer new possibilities to generate biodiversity information at the community composition level, an essential biodiversity variable, beyond forest type. We demonstrated the potential of Sentinel-2, GEDI LiDAR canopy height and ALOSDEM in discriminating and classifying tropical tree communities in the Western Himalayas, India. For this, tree communities were first identified based on the ordination of field data and subsequently classified using satellite data applying machine learning, i.e. random forest (RF). From the three forest types in the study area, eight distinct tree communities were identified for which classification accuracy increased from single date (75.17%) to multi-date images (85.33%) and further by applying feature selection (88.17%). Whereas the best classification accuracy of 94.66% was achieved when canopy height and topographic variables were also considered. The findings suggest that RF is suitable for mapping tree communities by combining Sentinel-2 with GEDI and DEM parameters.Keywords
Biodiversity, Canopy Height, Machine Learning, Remote Sensing, Tropical Forest.Full Text
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