Remote sensing based characterisation of community level phenological variations in a regional forest landscape of Western Ghats, India-Reference-Cited by-同舟云学术

Remote sensing based characterisation of community level phenological variations in a regional forest landscape of Western Ghats, India

Published:2022-08-19 Issue:27 Volume:37 Page:16620-16635
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Ayushi Kurian¹²^ORCID,Babu Kanda Naveen²³^ORCID,Reddy C. Sudhakar⁴^ORCID,Mayamanikandan T.⁴^ORCID,Barathan Narayanan²,Debabrata Behera¹²^ORCID,Ayyappan Narayanan²^ORCID

Affiliation:

1. Centre for Doctoral Studies, Directorate of Research, Manipal Academy of Higher Education, Manipal, India

2. Department of Ecology, French Institute of Pondicherry, Puducherry, India

3. Department of Ecology and Environmental Sciences, School of Life Sciences, Pondicherry University, Puducherry, India

4. National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad, India

Funder

Department of Biotechnology and Department of Space

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2112304

Reference40 articles.

1. CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery

2. A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant

3. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

4. FAO. 1996. Agro-ecological zoning: guidelines. FAO Soils Bull.73, FAO, Rome. p. 78.

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