Article Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region Ayushi Gupta 1 , Prashant K. Srivastava 1, * , George P. Petropoulos 2 and Prachi Singh 1   Citation: Gupta, A.; Srivastava, P.K.; Petropoulos, G.P.; Singh, P. Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region. Forests 2021, 12, 1726. https://doi.org/10.3390/f12121726 Received: 8 October 2021 Accepted: 1 December 2021 Published: 7 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Uttar Pradesh 221005, India; ayushi.gupta10@bhu.ac.in (A.G.); prachisngh246@gmail.com (P.S.) 2 Department of Geography, Harokopio University of Athens, EI. Venizelou 70, Kallithea, 17671 Athens, Greece; gpetropoulos@hua.gr * Correspondence: prashant.just@gmail.com Abstract: Taxol drugs can be extracted from various species of the taxaceae family. It is an alkaloid (metabolic product) used for the treatment of various types of cancer. Since taxol is a metabolic product, multiple aspects such as edaphic, biochemical, topographic factors need to be assessed in determining the variation in Taxol Content (TC). In this study, both sensor-based hyperspectral reflectance data and absorption-based indices were tested together for the development of an ad- vanced statistical unfolding approach to understand the influencing factors for TC in high altitude Himalayan region. Seriation analysis based on permutation matrix was applied with complete linkage and a multi-fragment heuristic scaling rule along with the common techniques such as Principal Component Analysis (PCA) and correlation to understand the relationship of TC with various factors. This study also tested the newly developed taxol indices to rule out the possibility of overlapping of TC determining bands with the foliar pigment’s wavelengths in the visible region. The result implies that T. wallichiana with a high TC is found more in its natural habitat of deep forest, relating it indirectly to elevation in the case of the montane ecosystem. Taxol is the most varying parameter among the measured variables, followed by hyperspectral Taxol content (TC) indices such as TC 2, TC 5, and carotenoids, which suggests that the indices are well versed to capture variations in TC with elevation. Keywords: taxol; sensor-based indices; biophysical variables; biochemical variables; hyperspectral; principal component analysis; seriation analysis 1. Introduction Recent studies have shown that the turnover in tree species composition across edaphic and elevational gradients can be strongly correlated with the functional traits [1]. These factors affect plant growth via various means and can be used to characterize differ- ent ecosystems. The major determining components of vegetation include biochemical constituents that are central to their physiological form and function, along with water, chlorophyll, and accessory pigments, nitrogen, cellulose, starch, sugars, lignin, and pro- tein. These are the mandatory parameters for describing the nutritional status of any tree of a particular ecosystem [2,3], while the secondary metabolites such as terpenes, sesquiterpenes, phytosterols, etc., are more useful to humans [4], which makes the plant economically valuable. The majority of studies have been carried out to acknowledge and retrieve these determining variables and the effects on vegetation using various models and remote sensing techniques, but the relative effects of all these factors have not been addressed intricately with proper research findings [5]. The spatial and temporal variation of these properties offers great help in understanding and evaluating physiological conditions such as photosynthesis, evapotranspiration, secondary metabolites formation, and deriving Forests 2021, 12, 1726. https://doi.org/10.3390/f12121726 https://www.mdpi.com/journal/forests