Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India

Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between ph...

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Autores principales: Nandy, S., Ghosh, Surajit, Singh, S.
Formato: Journal Article
Lenguaje:Inglés
Publicado: Springer 2021
Materias:
Acceso en línea:https://hdl.handle.net/10568/116359
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author Nandy, S.
Ghosh, Surajit
Singh, S.
author_browse Ghosh, Surajit
Nandy, S.
Singh, S.
author_facet Nandy, S.
Ghosh, Surajit
Singh, S.
author_sort Nandy, S.
collection Repository of Agricultural Research Outputs (CGSpace)
description Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between phenology metrics and climatic parameters. Sal, one of the main timber-producing species of India, can be categorized into dry, moist, and very moist sal. The phenological metrics of different types of sal forests were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Enhanced Vegetation Index (EVI) time series data (2002–2015). During the study period, the average start of season (SOS) was found to be 16 May, 17 July, and 29 June for very moist, moist, and dry sal forests, respectively. The spatial distribution of mean SOS was mapped as well as the impact of climatic variables (temperature and rainfall) on SOS was investigated during the study period. In relation to the rainfall, values of the coefficient of determination (R2) for very moist, moist, and dry sal forests were 0.69, 0.68, and 0.76, respectively. However, with temperature, R2 values were found higher (R2 = 0.97, 0.81, and 0.97 for very moist, moist, and dry sal, respectively). The present study concluded that MODIS EVI is well capable of capturing the phenological metrics of different types of sal forests across different biogeographic provinces of India. SOS and length of season (LOS) were found to be the key phenology metrics to distinguish the different types of sal forests in India and temperature has a greater influence on SOS than rainfall in sal forests of India.
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spelling CGSpace1163592025-05-20T05:41:15Z Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India Nandy, S. Ghosh, Surajit Singh, S. forests phenology climatic factors shorea robusta moderate resolution imaging spectroradiometer time series analysis remote sensing temperature rain vegetation index Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between phenology metrics and climatic parameters. Sal, one of the main timber-producing species of India, can be categorized into dry, moist, and very moist sal. The phenological metrics of different types of sal forests were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Enhanced Vegetation Index (EVI) time series data (2002–2015). During the study period, the average start of season (SOS) was found to be 16 May, 17 July, and 29 June for very moist, moist, and dry sal forests, respectively. The spatial distribution of mean SOS was mapped as well as the impact of climatic variables (temperature and rainfall) on SOS was investigated during the study period. In relation to the rainfall, values of the coefficient of determination (R2) for very moist, moist, and dry sal forests were 0.69, 0.68, and 0.76, respectively. However, with temperature, R2 values were found higher (R2 = 0.97, 0.81, and 0.97 for very moist, moist, and dry sal, respectively). The present study concluded that MODIS EVI is well capable of capturing the phenological metrics of different types of sal forests across different biogeographic provinces of India. SOS and length of season (LOS) were found to be the key phenology metrics to distinguish the different types of sal forests in India and temperature has a greater influence on SOS than rainfall in sal forests of India. 2021-09 2021-11-28T13:19:29Z 2021-11-28T13:19:29Z Journal Article https://hdl.handle.net/10568/116359 en Limited Access Springer Nandy, S.; Ghosh, Surajit; Singh, S. 2021. Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India. Environmental Monitoring and Assessment, 193(9):616. [doi: https://doi.org/10.1007/s10661-021-09356-9]
spellingShingle forests
phenology
climatic factors
shorea robusta
moderate resolution imaging spectroradiometer
time series analysis
remote sensing
temperature
rain
vegetation index
Nandy, S.
Ghosh, Surajit
Singh, S.
Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India
title Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India
title_full Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India
title_fullStr Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India
title_full_unstemmed Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India
title_short Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India
title_sort assessment of sal shorea robusta forest phenology and its response to climatic variables in india
topic forests
phenology
climatic factors
shorea robusta
moderate resolution imaging spectroradiometer
time series analysis
remote sensing
temperature
rain
vegetation index
url https://hdl.handle.net/10568/116359
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AT ghoshsurajit assessmentofsalshorearobustaforestphenologyanditsresponsetoclimaticvariablesinindia
AT singhs assessmentofsalshorearobustaforestphenologyanditsresponsetoclimaticvariablesinindia