Spectral behavior of Persian oak under compound stress of water deficit and dust storm

• Published in: International journal of applied earth observation and geoinformation Vol. 88; p. 102082
• Main Authors: Darvishi Boloorani, Ali, Ranjbareslamloo, Saba, Mirzaie, Saham, Bahrami, Hossein Ali, Mirzapour, Fardin, Abbaszadeh Tehrani, Nadia
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-06-2020; Elsevier
• Subjects: Dust storm; Narrowband vegetation index; PLSR; Quercus brantii Lindl; SVM; Water stress; Narrowband vegetation index; Quercus brantii Lindl; Dust storm; SVM; Water stress; PLSR
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2020.102082

•A spectral based dust storm and drought impact analysis of Persian oak is developed.•Dust storm and drought impact anaylsis of oak at leaf and canopy levels are modeled.•Strong correlation between Persian oak spectral behaviour and dust storm and drought stresses is revealed.
Persian oak (Quercus Brantii Lindl.) which is the most widely distributed tree in the Zagros Mountain forests is affected by western dust storms, mostly originating in Iraq, and harsh water stress as well. The objective of this research is to analyze the spectral behavior of Persian oak under water and dust stress scenarios, aiming to pave the way for modeling the stresses of drought and dust storms on oak trees using remote sensing images. Experiments were carried out on 54 two-year old oak tree seedlings, using a portable wind tunnel in greenhouse conditions. Water stress was induced on seedlings by means of changes in irrigation practices, i.e. well-watered (100 % field capacity), medium water deficit condition (40 % field capacity), and severe water deficit condition (20 % field capacity) treatments. Dust stress is also investigated by using three different dust particle concentrations, i.e. 350, 750 and 1500 (μg/m³). The spectrometry experiments were carried out at leaf and canopy levels in dark room by Fieldspec-3-ASD spectrometer. Spectral analysis was conducted using four procedures: (i) narrow-band spectral indices analysis, (ii) geometric indicators extraction from absorption features, (iii) Partial Least Squares Regression (PLSR), and SVM classifier. Results show that water stress could be modeled much better using PLSR statistic (R2 = 0.87, RMSE = 0.12), narrow-band indices analysis (R2cv = 0.75, RMSEcv = 0.17), and continuum removal (R2 = 0.71, RMSE = 0.20), respectively. For dust stress, PLSR (R2 = 0.83, RMSE = 0.14) and narrow-band indices (R2cv = 0.7, RMSE cv = 0.30) showed the best results, respectively. SVM could successfully separate stressed and not-stressed samples and also the stress types at both leaf and canopy levels, but it could not distinguish the different levels of stresses.