Are phenological variations in natural teak (Tectona grandis) forests of India governed by rainfall? A remote sensing based investigation.

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2020
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Abstract
Monitoring and assessment of vegetation phenology at the regional to global scale are essential to understand the characteristics of various biophysical parameters in terrestrial ecosystems. Passive optical remote sensing data have been used extensively in the recent past to study phenology of vegetation, also called land surface phenology, at diverse landscapes across the globe. In the present study, the moderate resolution imaging spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) time series data (2000-2013) was used to study the phenology of dry and moist teak (Tectona grandis) forests of different biogeographic provinces of India. Four phenology metrics, viz., start of season (SOS), end of season (EOS), peak of season (POS) and length of season (LOS) were derived using the TIMESAT tool. The SOSs' of dry and moist teak were found during July-August. LOS of moist teak was found to be much longer (~ 48 days) than dry teak. Also, a significant difference of leaf area index (LAI) (~ 2.8) of dry and moist teak forests was noticed during peak season from MODIS LAI product (MOD15A2). Vegetation phenology is greatly responsive to the fluctuation of climatic parameters such as rainfall. Hence, pre-season cumulative rainfall data were analysed to understand the control of rainfall over phenological variations in natural teak forests of India. It was noticed that rainfall was reasonably well correlated with SOS (R = 0.57-0.72) for both types of teak forests. The study highlighted the efficacy of time series MODIS EVI data to study the phenological variations in different teak forest types of India in a data-limited situation.
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ghosh2020areenvironmental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ghosh, Surajit;Nandy, Subrata;Mohanty, Srutisudha;Subba, Rupesh;Kushwaha, S P S;
Journal Environmental monitoring and assessment
Year 2020
DOI
10.1007/s10661-019-7680-0
URL
Keywords
rainfall phenology modis evi modis lai timesat tool tectona grandis

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