APPLICATIONS OF THE RAPIDEYE RED EDGE BAND

Transcription

1 APPLICATIONS OF THE RAPIDEYE RED EDGE BAND Introduction The Red Edge band is a unique feature that distinguishes RapidEye satellites from most other multispectral satellites. The Red Edge band is located between the Red band and the NIR band, without overlap. In a typical spectral response of green vegetation (green line in Fig. 2), the Red Edge band covers the portion of the spectrum where reflectance drastically increases from the red towards the NIR regions. The relevance of the Red Edge spectral region for vegetation characterization is widely accepted. Several studies have proven that the transition between the red absorption and the Near-Infrared (NIR) reflection in plants can provide valuable additional information about vegetation and its characteristics. The red portion of the electromagnetic spectrum is one of the areas where chlorophyll (green line in Fig. 2) strongly absorbs light and the NIR is where the leaf cell structure reflects strongly. To illustrate this, the three segments in Fig. 3 show the reflectance values as recorded by the satellite s Red Edge band for each of the three plant spectra shown. These spectra come from plants with varying levels of vitality, which is reflected by the values at the Red Edge band, together with the other two bands. The different reflectance values show how the Red Edge band provides additional information to characterize plant spectra at different vitality levels. Red Edge band sensitivity to differences in leaf structure and Chlorophyll content can have useful applications on precision agriculture and resource monitoring and management. Figure 1: Spectral bands of the RapidEye satellites Wavelength (nm) Applications of the RapidEye Red Edge Band Introduction Page 1

2 Figure 2: Typical spectral reflectance curves of selected surfaces in relation to the RapidEye spectral bands. Notice how distinct vegetation is from other surfaces, particularly at the Red, Red Edge and NIR wavelengths. Figure 3: Differences in reflectance at the Red Edge wavelengths between three plant spectra with varying levels of vitality. Applications of the RapidEye Red Edge Band Introduction Page 2

3 Agriculture Crop yield strongly depends on an adequate supply of nitrogen (N). Therefore, knowledge about N-status represents an important factor for agricultural management. Additionally, N-fertilizer is one of the largest input cost factors of many crops such as corn, wheat and rice, which makes the assessment of the N-status of a crop critical for optimal fertilizer management. N-status cannot be directly measured through the imagery, but it can be estimated through indirect indicators. The N-status of crops can be assessed through Chlorophyll (Chl) measurements, given the strong correlation between the two in several major crop types (Daughtry et al. 2000). The use of spectral measurements and spectral indicators for the determination of Chl content is based on the fact that Chl, as well as a number of other pigments, are strong absorbers of light in certain well-defined wavelengths, specifically in the blue and the red spectral region (Fig. 2.) Many studies have been conducted to investigate the relationship between plant health status and their spectral responses, particularly in the Red Edge domain. Among others, Pinar and Curran (1996), and Filella and Peñuelas (1994) found that the Red Edge region is sensitive to Chl content and N-status. Some studies indicate that a broad Red Edge band, like RapidEye s, is also suitable for obtaining information about the Chl and N content of plants (Eitel et al. 2007, Ramoelo et al. 2012, and Schelling 2010); as well as for other important vegetation parameters such as leaf area index estimation (Asam et al. 2013; Richter et al. 2012; and Viña et al. 2011) According to these findings, it is possible to produce a Relative Chlorophyll Map, as an indicator of the nutritional status of the crop, using the Red Edge band to show the spatial variation of the relative Chl content within a field (Fig. 4). Figure 4: Example of a Relative Chlorophyll Map. High Low Chlorophyll (relative content) Applications of the RapidEye Red Edge Band Agriculture Page 3

4 Vegetation Classification Land cover classification is a fundamental tool for monitoring environmental quality and land productivity. Reliable information on the extent and distribution of the main landscape plant types constitutes the basis for assessing crop productivity, environmental degradation, and the effects of land management practices. Since the RapidEye Red Edge band is sensitive to Chl status and leaf and canopy structure, it is expected that this band would contribute to the characterization of different plant cover types. Schuster et al. (2012) tested the Red Edge band in a land use classification project encompassing sixteen land use/land cover classes including two forest classes (deciduous and coniferous). They found that with the Red Edge band, the overall classification accuracy was consistently higher than without it. In terms of individual classes, the most significant improvements were obtained with classes comprised of open landscape vegetation. When comparing RapidEye images with other satellite and aerial multispectral data to classify cropland and grassland, Recio et al. (2011) found that the best results were obtained with RapidEye data (either alone or combined with images from other sources) and that the highest accuracy could be achieved by involving variables derived from the Red Edge band. Conrad et al. (2012) used indices combining the Red Edge, Red and Near-Infrared bands in a multi temporal approach to separate several crop types. The authors concluded that using the three bands to characterize this sensitive portion of the reflectance spectrum allowed for accurate separation of the crop types under investigation. Similar results were obtained by Ustuner et al. (2014), and Zillmann and Weichelt (2014). Scatterplots in Fig. 5 show how vegetation classes can be better separated by using the Red Edge band. Each oval represents a particular ground feature class, e.g. red indicates grassland, green indicates forest, and yellow and orange represent two different agricultural crop types. Using only the red and NIR bands, a significant overlap between the agricultural classes and grassland occurs (Fig. 5, top image), whereas the introduction of the Red Edge band (Fig. 5, bottom image) contributes to better class discrimination, in particular between agricultural and grassland vegetation types. Figure 5: Scatterplots of different combinations of RapidEye bands showing the spectral location of selected land cover classes (ovals). Black-and-white background indicates number of pixels at each location (brighter means higher quantity). Land Cover Classes Grassland Crop type 1 Crop type 2 Forest Bare soil Applications of the RapidEye Red Edge Band Vegetation Classification Page 4

5 Forestry Forest stands and plantations are subject to a wide range of pests and diseases. Monitoring and early detection of infestations such as bark beetles are important for the protection of the forest. Traditional ground-based monitoring techniques or airborne surveys are costly, and the results are often subjective. In addition, early indications of disease or infestation are usually not visible on the ground. Several pilot studies conducted by Planet in the last years indicate that the most accurate vegetation indices used for damage detection include the Red Edge band (Marx 2010). In a study by Eitel et al. (2011), coniferous trees were girdled to produce stress symptoms and three different vegetation indices calculated from a time series of sixteen RapidEye images were tested and assessed. Results showed that the Normalized Difference Red Edge Index (NDRE) was able to detect stress symptoms earlier than the other vegetation indices. Given the sensitivity of the NDRE, this index can be used for a variety of forest health applications such as drought stress, bark beetle damage, fire damage, and disease mapping. In fact, Planet s operational service Forest Vitality & Change Monitoring, is based on this index. Water Monitoring The amount of Chlorophyll-a (Chl-a) in water and water turbidity are two of the main parameters used to assess lake water quality under the European and German Water Framework Directive. Chl-a content is a measurement of the abundance of photosynthetically active organisms in lakes. Water turbidity is assessed by measuring Secchi depth, which is related to visibility in water under specific conditions. The RapidEye Red Edge band can be utilized to assess these two water quality parameters in productive inland water bodies with relatively high phytoplankton content. Studies of productive freshwater systems showed that the reflected signal between 670 and 740 nm is particularly sensitive to Chlorophyll (Chl) concentration (Ruddick et al. 2001, Thiemann 1999, Schalles et al. 1998, Gitelson 1993) (Fig. 6). Accordingly, analyses with RapidEye imagery have shown a relatively high Chl prediction capacity when the Red Edge band is used in rivers (Choe et al. 2015) and lakes (Wen et al. 2014). Figure 6: Chlorophyll-a reflectance for different concentration in water in the visible and NIR spectrum. Notice the differences in spectral values in the Red Edge band region. Chlorophyll-a Concentration (µg/l) Applications of the RapidEye Red Edge Band Forestry Page 5

6 The Secchi depth, a variable related to water turbidity is mainly a function of the light attenuation coefficient (Reigber et al. 2012). Since light at a wavelength between 690 and 730 nm is more attenuated by water than, for example, blue light, the Red Edge band can be used for the estimation of Secchi depth. Studies using RapidEye imagery have indicated that depth variations can be accurately estimated with these images (Giardino et al. 2014). Using the prediction capabilities of the Red edge band, it is possible to generate maps that indicate the spatial distribution of Chl content and turbidity in water (Fig. 7). These maps constitute a valuable tool for water monitoring. Figure 7: Maps of Chlorophyll-a concentration and Secchi depth for two lakes in Brandenburg, Germany, produced with RapidEye images. Chlorophyll-a Concentration (µg/l) Secchi Depth (m) 0-9,7 > > > > > > > > > 6.0 Applications of the RapidEye Red Edge Band Water Monitoring Page 6

7 References ASAM, S., H. FABRITIUS, D. KLEIN, C. CONRAD, AND S. DECH. (2013). Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data. International Journal of Remote Sensing 34: CHOE, E.; LEE, J; CHEON, S. (2015). Monitoring and modelling of chlorophyll-a concentrations in rivers using a high-resolution satellite image: a case study in the Nakdong river, Korea. International Journal of Remote Sensing, Vol. 36, 6. CONRAD, C., FRITSCH, S., LEX, S., LÖW, F., RÜCKER, G., SCHORCHT, G., SULTANOV, M., LAMERS, J. (2012): Potenziale des Red Edge Kanals von RapidEye zur Unterscheidung und zum Monitoring landwirtschaftlicher Anbaufrüchte am Beispiel des usbekischen Bewässerungssystems Khorezm. Borg, Daedelow, Johnson (Eds.), Rapid- Eye Science Archive (RESA) - Vom Algorithmus zum Produkt, 4. RESA Workshop, March 21-22, Neustrelitz, Germany, pp DAUGHTRY, C.S.T.; WALTHALL, C.L.; KIM, M.S.; BROWN, E. AND MCMURTREY, J.E.III (2000): Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74 (2): EITEL, J. U. H.; LONG, D. S.; GESSLER, P. E.; SMITH, A. M. S. (2007): Using in-situ measurements to evaluate the new RapidEye satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, Vol. 28, 2007, 1-8. EITEL, J.U.H., VIERLING, L.A., LITVAK, M.E., LONG, D.S., SCHULTHESS, U., AGER, A.A., KROFCHECK, D.J., STOSCHECK, L. (2011): Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sensing of Environment, 115, FILELLA, I., PEÑUELAS, J. (1994): The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, S GIARDINO, C.; BRESCIANI, M; CAZZANIGA, I.; SCHENK, K.; RIEGER, P.; BRAGA, F.; MATTA, E.; BRANDO, V.E Evaluation of multi-resolution satellite sensors for assessing water quality and bottom depth of Lake Garda. Sensors, 14, GITELSON, A. (1993): The nature of the peak near 700 nm on the radiance spectra and its application for remote estimation of phytoplancton pigments in inland waters. Optical Engineering and Remote Sensing SPIE MARX, A. (2010): Erkennung von Borkenkäferbefall in Fichtenreinbeständen mit multi-temporalen RapidEyeSatellitenbildern und Datamining-Techniken. PFG 2010, 4, S PINAR, A., CURRAN, P.J. (1996): Grass chlorophyll and the reflectance red edge. International Journal of Remote Sensing, 17, S RAMOELO, A., A.K. SKIDMORE, M.A. CHO, M. SCHLERF, R. MATHIEU, AND I.M. HEITKÖNIG Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation 19: RECIO, J.A., HELMHOLZ, P., MÜLLER, S. (2011): Potential evaluation of different types of images and their combination for the classification of GIS objects cropland and grassland. The Internat. Archives of the Photogramm., Remote Sens. and Spatial Info. Sc., Vol XXXVIII-4/W19, 6pp. Applications of the RapidEye Red Edge Band References Page 7

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