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Title (Primary) Analysis of spectral vegetation signal characteristics as a function of soil moisture conditions using hyperspectral remote sensing
Author Brosinsky, A.; Lausch, A.; Doktor, D.; Salbach, C.; Merbach, I.; Gwillym-Margianto, S.; Pause, M.;
Journal Journal of the Indian Society of Remote Sensing
Year 2014
Department CLE; BZF;
Volume 42
Issue 2
Language englisch;
POF III (all) T11; T31;
Keywords Imaging hyperspectral sensor; AISA; ASD; Laboratory; Spectral index; Leaf area index; Chlorophyll content; Leaf gravimetric water content; C/N content; Ash tree; GAM; Monitoring
UFZ wide themes TERENO; RU1;
Abstract

The assessment and quantification of spatio-temporal 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 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 the plants (R 2 = 0.94). There is a correlation between the “vitality” vegetation parameters (LAI, C/N content and vegetation height) and the indicators NDVI, WI, PRI and Vog2. In our study with Ash trees the vegetation parameter chlorophyll was found not to be a suitable indicator for detecting the “vitality” of plants using the spectral indicators. There is a possibility that the sensitivity of the indicators selected was too low compared to changes in the chlorophyll content of Ash trees. Adding the co-variable ‘time’ strengthens the correlation, whereas incorporating time and moisture treatment only improves the model very slightly. This shows that changes to the biochemical and biophysical characteristics caused by phenology, overlay a differentiation of the moisture treatments.

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Within this Article

  1. Introduction
  2. Materials and Methods
  3. Results
  4. Discussion
  5. Conclusion
  6. References
  7. References

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ID 14165
Persistent UFZ Identifier http://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=14165
Brosinsky, A., Lausch, A., Doktor, D., Salbach, C., Merbach, I., Gwillym-Margianto, S., Pause, M. (2014):
Analysis of spectral vegetation signal characteristics as a function of soil moisture conditions using hyperspectral remote sensing
J. Indian Soc. Remote Sens. 42 (2), 311 - 324