International Journal of Remote Sensing Vol. 30, No. 6, 20 March 2009, 1651–1658
Letter Spectral vegetation indices for estimating shrub cover, green phytomass and leaf turnover in a sedge-shrub tundra K. KUSHIDA*{, YONGWON KIM{, S. TSUYUZAKI§ and M. FUKUDA{ {Institute of Low Temperature Science, Hokkaido University, W8 N19, Kita-ku, Sapporo 060-0819, Japan {International Arctic Research Center, University of Alaska Fairbanks, 930 Koyukuk Drive, Fairbanks, Alaska, 99775-7340, USA §Graduate School of Environmental Science, Hokkaido University, W5 N10, Kita-ku, Sapporo 060-0810, Japan (Received 1 July 2008; in final form 2 September 2008 ) Using field observations, we determined the relationships between spectral indices and the shrub ratio, green phytomass and leaf turnover of a sedge-shrub tundra community in the Arctic National Wildlife Refuge, Alaska, USA. We established a 50-m 6 50-m plot (69.73uN 143.62uW) located on a floodplain of the refuge. The willow shrub (Salix lanata) and sedge (Carex bigelowii) dominated the plot vegetation. In July to August 2007, we established ten 0.5m 6 0.5-m quadrats on both shrub-covered ground (shrub quadrats) and on ground with no shrubs (sedge quadrats). The shrub ratio was more strongly correlated with the normalized difference vegetation index (NDVI, R2 of 0.57) than the normalized difference infrared index (NDII), the soil-adjusted vegetation index (SAVI) or the enhanced vegetation index (EVI). On the other hand, for both green phytomass and leaf turnover, the strongest correlation was with NDII (R2 of 0.63 and 0.79, respectively).
1.
Introduction
Previous studies have demonstrated that the field-measured spectra of tussock tundra (Hope et al. 1993) and four tundra vegetation types (Riedel et al. 2005) are correlated with the ground phytomass. Furthermore, IKONOS and Landsat ETM + data have been used to assess the percentage vegetation cover in a tundra landscape (Laidler et al. 2008). These results indicate that the normalized difference vegetation index (NDVI) is more sensitive than other indices for estimating phytomass and percentage vegetation cover. Basic field measurements, such as vegetation spectra and phytomass are necessary for extending the above estimates to larger temporal and spatial scales. The Arctic National Wildlife Refuge (ANWR) is a national wildlife refuge located between the Brooks Range and the Beaufort Sea, covering an 80 000 km2 area of north-eastern Alaska. The notable feature of this refuge is that large-scale ecological and evolutionary processes have continued here, free from human control or manipulation. The Coastal Plain tundra area comprises a 13 000 km2 area on the
*Corresponding author. Email:
[email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2009 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160802502632
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northern edge of the ANWR. To date, no field spectra analyses of the ANWR tundra vegetation, that we are aware of, have been undertaken. This letter summarizes an investigation of the correlation of vegetation attributes and field spectral measurements. Field spectral measurements allow a more detailed study over finer scales, which is especially useful in heterogeneous environments such as tundra vegetation. We first compared the reflectance factors of shrubs and sedges in an area of sedge-shrub tundra in the Coastal Plain using wavelengths corresponding to the Landsat ETM + bands. Secondly, we analysed the relationships between the spectral indices, such as the NDVI and the normalized difference infrared index (NDII), and vegetation variables, such as shrub ratio, green phytomass and leaf turnover. The spectral reflectances in the broad bands were treated as the simple averages of those in the narrow bands. 2. 2.1
Materials and methods Site specifications and field observations
We established a 50650 m plot (69.73u N, 143.62u W; 152 m a.s.l.) located on the lowland floodplain of the Jago River in the Coastal Plain of ANWR. The climate of the Coastal Plain is harsh, with average monthly temperatures of 5uC in July and 220uC in February, and with a maximum summer temperature of 30uC. The average annual precipitation is 250 mm. The lowland floodplain vegetation is dominated by the willow shrub Salix lanata and the sedge Carex bigelowii. Within the plot, the heights of the willow shrub and the sedge were 15–40 cm and 12–20 cm, respectively. The willow shrub covered 20% of the ground and the sedge was distributed over the entire ground surface. Non-Sphagnum mosses covered 30% of the soil surface. Other willows, such as S. pulchra, were established at a low density. Other than these, S. reticulata and Equisetum arvense were established, but covered less than 5% of the ground surface. Ten 0.560.5 m quadrats were established in the plot on both the shrub-covered ground (shrub quadrats) and on the ground with no shrubs (sedge quadrats). Leaf area indices (LAI) (average ¡1 standard deviation (s.d.)), determined using an LAI-2000 plant canopy analyzer (LI-COR, Lincoln, USA) were 1.1¡0.5 and 0.49¡0.33 for the shrub and sedge quadrats, respectively. All the shrubs in the shrub quadrats were harvested, and the photosynthetic and non-photosynthetic parts were separated. The LAI of the shrub quadrats following the harvest was 0.48¡0.29. After harvesting the shrubs, the remaining green phytomass was also harvested. The shrub quadrats were measured spectrally both before and after harvesting the shrubs and after harvesting the other green phytomass. The sedge quadrats were also measured spectrally. The harvested phytomass samples (the photosynthetic and non-photosynthetic parts of the shrubs and the other green phytomass for each of the 10 shrub quadrats) were packed in a paper bag, sealed in a plastic bag, placed in a cool container to avoid water loss as much as possible, and were oven-dried in the laboratory at 65uC for at least 48 h and then weighed. The samples were also weighed before drying and water content per unit dry matter calculated. In 2007, between the dates of 28 July and 4 August, the quadrats were measured spectrally using a GER-2600 spectroradiometer (GER Corporation, New York, USA) at a wavelength of 350 to 2500 nm, following the method described by Kushida et al. (2004). Reflectance factor measurements were made of a 0.15 m diameter circle at the centre of each quadrat. The radiance measurements were made in a nadir direction, and with a spectral sampling of 1.5 and 11.5 nm over the ranges
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of 350–1050 nm and 1050–2500 nm, respectively. A Spectralon panel was used for the spectral normalization required for calculating reflectance factors. Measurements of both the panel and the sample were replicated five times. Solar illumination was totally diffuse under cloudy skies. The coefficients of variation of the repeated measurements of the Spectralon panel for each of the Landsatsimulated band passes were 2%–4% on average. From the dry weight of the samples, the shrub ratio (SR (%)), green phytomass (GP (g m22)) and leaf turnover (LT (g m22)) were obtained. These variables were defined as follows: wps |100, wps zwpn
ð1Þ
wps zwpn A
ð2Þ
rls cls wps zrln cln wpn : A
ð3Þ
SR~
GP~ and LT~
Here, wps (g) and wpn (g) are the observed weights of the photosynthetic parts of the shrubs and the non-shrubs in the quadrats, rls (yr21) and rln (yr21) are the leaf turnover rates of the shrubs and the non-shrubs, respectively, cls (gC g21) and cln (gC g21) are the carbon content per unit leaf (shoot) weight of the shrubs and the non-shrubs, respectively, and A (m2) is the area of the sample harvesting and equal to 0.25 m2 for this study. We assumed cls5cln50.45 gC g21 (Atjay et al. 1979). S. lanata is a deciduous plant. C. bigelowii is a biennial plant; however, its shoots can live up to 5–7 years with low mortality in arctic-alpine regions (Bernard 1990). Consequently, we assumed the average life spans of the photosynthetic parts of the shrubs, composed of nearly 100% Salix lanata leaves, and the non-shrubs, composed of more than 95% C. bigelowii shoots, to be 1 and 2.5 years, respectively. From this, we obtain rls51.0 yr21 and rln50.4 yr21. 2.2
Spectral indices
The following spectral indices (the quotients of bi-band difference and summation (SI), the soil adjusted vegetation index (SAVI) (Huete 1988) and the enhanced vegetation index (EVI) (Huete et al. 1997)) were used for testing the linear correlations between the spectral indices and the shrub ratio, green phytomass and leaf turnover. SIðx, yÞ~
Bx {By , Bx zBy
SAVI~ð1zL1 Þ
B4 {B3 B4 zB3 zL1
ð4Þ
ð5Þ
and EVI~G
B4 {B3 : B4 zC1 B3 {C2 B1 zL2
ð6Þ
Here, Bx and By are the reflectance factors of the wavelengths corresponding to Landsat ETM + bands x (x51, 2, 3, 4, 5 and 7) and y (y51, 2, 3, 4, 5 and 7; y?x),
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respectively, L151 or 0.5 or 0.25, L251, C156, C257.5 and G52.5 (Huete et al. 1997). The NDVI and NDII are defined as follows: B4 {B3 ~SIð4, 3Þ B4 zB3
ð7Þ
B4 {B5 ~SIð4, 5Þ: B4 zB5
ð8Þ
NDVI~ and NDII~
3.
Results
3.1
Differences in the spectra of shrubs and sedges
There was no significant difference between ‘the sedge quadrats’ and ‘the shrub quadrats after harvesting the shrubs’ in terms of the average spectral reflectance factors in all six ETM + bands, based on the results of Student’s t-test at the 5% level. These two variables were grouped as ‘greenpart of non-shrub + senescent non-shrub (B)’ and compared with the average reflectance factors of ‘the shrub quadrats before harvesting (A)’ and ‘the shrub quadrats after harvesting the shrubs and the other green phytomass (C)’ (see table 1). There were significant differences among these three groups in ETM + bands 3 and 5. In bands 1 and 2, the average value of A was different from that of B and C, whilst the average values of B and C did not differ. In bands 4 and 7, the average value of C was different to that of A and B, and the average values of A and B did not differ. 3.2
Relationships between spectral indices and canopy variables
For the 10 shrub quadrats, there was no significant relationship between the green phytomass of the shrubs and the sedges, and the green phytomass and the total ground phytomass of the shrubs were almost proportional, with a slope of 10.8 Table 1. Spectral reflectance factors of the field samples (average ¡1 standard deviation (s.d.)).
Band 1 2 3 4 5 7
Shrub + greenpart of Greenpart of nonnon-shrub + senescent shrub + senescent Senescent Wavelength non-shrub (A) non-shrub (B) non-shrub (C) (mm) (n510) (%) (n520) (%) (n510) (%) 0.45–0.52 0.53–0.61 0.63–0.69 0.75–0.90 1.55–1.75 2.09–2.35
3.6¡0.7 6.8¡1.0 6.3¡1.6 28.8¡5.7 21.4¡2.2 22.8¡3.0
4.4¡0.9 7.8¡1.5 8.4¡1.8 26.6¡4.5 24.4¡3.0 23.7¡3.4
4.9¡1.0 8.1¡1.3 10.3¡1.7 22.4¡3.0 28.2¡2.3 36.1¡10.7
Difference A–BC* A–BC* A–B–C*** AB–C** A–B–C*** AB–C**
*The average value of A differs from that of B or C, and the average values of B and C do not differ, based on the results of Student’s t-test at the 5% level. **The average value of C differs from that of A or B, and the average values of A and B do not differ based on the results of Student’s t-test at the 5% level. ***The average values of A, B and C are different compared to each other based on the results of Student’s t-test at 5% level.
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(R250.96). These observations indicate that the sedge green phytomass in the shrub quadrats was not affected by the overlying shrub phytomass and that the estimates of the shrub green phytomass contribute to estimating the total ground phytomass of the shrubs. Among SI(x,y) (x51, 2, 3, 4, 5 and 7; y51, 2, 3, 4, 5 and 7; y?x), SAVI (L50.25, 0.5 and 1) and EVI, the most sensitive spectral indices to SR, GP, and LT estimations were the NDVI, NDII and NDII, respectively. Figure 1 shows the relationships between the NDVI and NDII and SR, GP and LT. Since the sedge areas with no shrubs were distributed in the objective area, we added ‘Sedge’ for estimating the shrub ratio. Table 2 shows the coefficients of determination (R2) for the linear estimations of SR, GP and LT from the NDVI, NDII, SAVI (L50.25, 0.5 and 1) and EVI. The R2 of SR estimates from the NDVI was 0.57. The R2 of GP and LT estimates from the NDII were 0.63 and 0.79, respectively. The water content of the photosynthetic parts of the shrubs, the nonphotosynthetic parts of the shrubs and the photosynthetic parts of the non-shrubs were 13.0¡4.2%, 33.5¡5.4% and 44.1¡9.6% (average ¡1 s.d.), respectively. There were no significant correlations between the water content of the photosynthetic parts of the shrubs or non-shrubs and the canopy NDVI or NDII. The nonphotosynthetic part of the shrubs was weakly correlated with the NDVI (R250.18) and NDII (R250.19). This could be explained by the correlation between the phytomass and water content of the non-photosynthetic parts of the shrubs (R250.22). 4.
Discussions and conclusions
In the present study, the NDII was demonstrated to be the most sensitive to linear estimations of the green phytomass, whereas the NDVI was most sensitive to linear estimation of the shrub ratio in the photosynthetic part among the SI(x,y) (x51, 2, 3, 4, 5 and 7; y51, 2, 3, 4, 5 and 7; y?x), SAVI (L50.25, 0.5 and 1) and EVI. These results are somewhat inconsistent with those of previous studies that demonstrated an advantage of the NDVI compared to other spectral indices for phytomass estimation in tundras. Since willow shrub and sedge were the main vegetation components of our study site, the characteristics of the site favoured the use of the NDII for green phytomass estimation. Nevertheless, this vegetation composition is distributed over the lowland floodplain of the Coastal Plain, ANWR, and thus this research suggests that the NDII is useful for estimating the green phytomass in this Table 2. Coefficients of determination (R2) of linear estimations of the canopy variables from spectral indices. Canopy variables Shrub ratio (%) Green phytomass (g m22)
Leaf turnover (gC m22)
NDVI NDII
0.57 0.44
0.39 0.63
0.68 0.79
SAVI (L50.25) SAVI (L50.5) SAVI (L51) EVI
0.44 0.37 0.32 0.37
0.52 0.54 0.54 0.52
0.71 0.68 0.65 0.66
Note. Bold type shows where R2 is the highest among the spectral indices.
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Figure 1. Relationships between the NDVI and NDII and shrub ratio (weight), green phytomass and leaf turnover: (a) shrub ratio (weight) versus NDVI, (b) shrub ratio (weight) versus NDII, (c) green phytomass versus NDVI, (d) green phytomass versus NDII, (e) leaf turnover versus NDVI and (f) leaf turnover versus NDII. The solid circles and squares correspond to ‘shrub with sedge’ (shrub quadrats before harvesting) and ‘sedge’ (shrub quadrats after harvesting), respectively.
area. The NDII is correlated with leaf and canopy water content under water stress conditions (Hardisky et al. 1983). In this study, no significant correlations were found between the water content of the photosynthetic parts of the shrubs
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or non-shrubs and the NDVI or NDII. This implies that the leaf water content was not one of the main factors for the determination of the NDVI or NDII here. The R2 of the leaf turnover estimation was higher than that of the green phytomass estimation using both the NDII and NDVI. The shrub contributes more than the sedge to the reflectance spectra, as the shrub layer obscures the underlying sedge. Furthermore, since the shrub sheds its leaves once a year, whereas the shoots of the sedge senesce once every 2 or more years, the shrub green phytomass contributes to the leaf turnover to a greater extent than that of the sedge. The leaf turnover is one of the main factors in the annual carbon accumulation in the tundra soil. Combined with its higher detectability from the spectral data than green phytomass, the leaf turnover may be one of the key variables for monitoring. We used a linear model to explore the relationships between the spectral indices and the vegetation variables. In attempting to extend the field measurements to a larger scale, the spatial heterogeneity detected at the satellite observation scale can be problematic. However, even in the spatially heterogeneous cases, with the application of a spatial linear mixture model of the shrub canopy over sedge and the exposed sedge, the vegetation variables can be predicted for each pixel. The observed significant difference in the spectra of the shrub canopy over the sedge and the sedge also supports the validity of this estimation. Acknowledgements We thank Fran Maurer and Yoriko Freed for their field support and Timothy Warner of West Virginia University and the anonymous reviewers for their valuable suggestions. This study was supported by the Arctic Research Projects using the IARC-JAXA Information System of the Japan Aerospace Exploration Agency (JAXA). References ATJAY, G.L., KETNER, P. and DUVIGNEAUD, P., 1979, Terrestrial primary production and phytomass. In The Global Carbon Cycle, SCOPE 13 B. Bolin, E.T. Degens, S. Kempe, and P. Detner (Eds), pp. 129–181 (New York: John Wiley & Sons). BERNARD, J.M., 1990, Life-history and vegetative reproduction in Carex. Canadian Journal of Botany, 68, pp. 1441–1448. HARDISKY, M.A., KLEMAS, V. and SMART, R.M., 1983, The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing, 49, pp. 77–84. HOPE, A.S., KIMBALL, J.S. and STOW, D.A., 1993, The relationship between tussock tundra spectral reflectance properties and biomass and vegetation composition. International Journal of Remote Sensing, 14, pp. 1861–1874. HUETE, A.R., 1988, A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, pp. 295–309. HUETE, A.R., LIU, H.Q., BATCHILY, K. and VAN LEEUWEN, W., 1997, A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59, pp. 440–451. KUSHIDA, K., KIM, Y., TANAKA, N. and FUKUDA, M., 2004, Remote sensing of net ecosystem productivity based on component spectrum and soil respiration observation in a boreal forest, interior Alaska. Journal of Geophysical Research Atmospheres, 109, pp. D06108. LAIDLER, G.J., TREITZ, P.M. and ATKINSON, D.M., 2008, Remote sensing of arctic vegetation: relations between the NDVI, spatial resolution and vegetation cover on Boothia Peninsula, Nunavut. Arctic, 61, pp. 1–13.
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RIEDEL, S.M., EPSTEIN, H.E. and WALKER, D.A., 2005, Biotic controls over spectral reflectance of arctic tundra vegetation. International Journal of Remote Sensing, 26, pp. 2391–2405.
