ArticleScientific reports2020
Improved NDVI based proxy leaf-fall indicator to assess rainfall sensitivity of deciduousness in the central Indian forests through remote sensing.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Quantifying the leaf-fall dynamics in the tropical deciduous forest will help in modeling regional energy balance and nutrient recycle pattern, but the traditional ground-based leaf-fall enumeration is a tedious and geographically limited approach. Therefore, there is a need for a reliable spatial proxy leaf-fall (i.e., deciduousness) indicator. In this context, this study attempted to improve the existing deciduousness metric using time-series NDVI data (MOD13Q1; 250 m; 16 days interval) and investigated its spatio-temporal variability and sensitivity to rainfall anomalies across the central Indian tropical forest over 18 years (2001-2018). The study also analysed the magnitude of deciduousness during extreme (i.e., dry and wet) and normal rainfall years, and compared its variability with the old metric. The improved NDVI based deciduousness metric performed satisfactorily, as its observed variations were in tandem with ground observations in different forest types, and for different pheno-classes. This is the first kind of study in India revealing the spatio-temporal character of leaf-fall in different ecoregions, elevation gradients and vegetation fraction.
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