Analysis of Spectral Vegetation Signal Characteristics as a Function of Soil Moisture Conditions Using Hyperspectral Rem
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RESEARCH ARTICLE
Analysis of Spectral Vegetation Signal Characteristics as a Function of Soil Moisture Conditions Using Hyperspectral Remote Sensing A. Brosinsky & A. Lausch & D. Doktor & C. Salbach & I. Merbach & S. Gwillym-Margianto & M. Pause
Received: 28 October 2012 / Accepted: 27 May 2013 # Indian Society of Remote Sensing 2013
Abstract The assessment and quantification of spatiotemporal soil characteristics and moisture patterns are important parameters in the monitoring and modeling of soil landscapes. Remote-sensing techniques can be applied to characterize and quantify soil moisture patterns, but only when dealing with bare soil. For soils with vegetation, it is only possible to quantify soil-moisture characteristics through indirect vegetation indicators, i.e. the “vitality” of plants. The “vitality” of vegetation is a sum of many indicators, whereby different stress factors can induce similar changes to the biochemical and physiological characteristics of plants. Analysis of the cause and effect of soil-moisture properties, patterns and stress factors can therefore only be carried out using an experimental approach that specifically separates the causes. The study describes an experimental approach and the results from using an imaging hyperspectral sensor AISA-EAGLE (400–970 nm) and a non-imaging spectral sensor ASD (400–2,500 nm) under controlled and comparable conditions in a laboratory to study the spectral response A. Brosinsky Department 1.4 Remote Sensing, Helmholtz Centre Potsdam - GFZ German Research Centre For Geosciences, Telegrafenberg, 14473 Potsdam, Germany A. Brosinsky e-mail: [email protected] A. Lausch (*) : D. Doktor : C. Salbach Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany e-mail: [email protected] D. Doktor e-mail: [email protected] C. Salbach e-mail: [email protected]
compared to biochemical and biophysical vegetation parameters (“vitality”) as a function of soil moisture characteristics over the entire blooming period of Ash trees. At the same time that measurements were taken from the hyperspectral sensors, the following vegetation variables were also recorded: leaf area index (LAI), chlorophyll meter value — SPAD-205, vegetation height, C/N content and leaf water content as indicators of the “vitality” and the state of the vegetation. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VI’s) with relationships for soil moisture characteristics and stress factors. The relationship between vegetation indices and plant “vitality” indicators was analysed using a Generalized Additive Model (GAM). The results show that leaf water content is the most appropriate vegetation indicator for assessing the “vitality” of vegetation. With the Water Index (WI) it was possible to differentiate between the moisture treatments of the control, moisture drought stress and the moisture flooding treatment over the entire growing season of
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