Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment

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1 Remote Sensing Letters ISSN: X (Print) (Online) Journal homepage: Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment A. A. Souza, L. S. Galvão & J. R. Santos To cite this article: A. A. Souza, L. S. Galvão & J. R. Santos (2010) Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment, Remote Sensing Letters, 1:1, 55-64, DOI: / To link to this article: Published online: 22 Jan Submit your article to this journal Article views: 923 Citing articles: 19 View citing articles Full Terms & Conditions of access and use can be found at

2 Remote Sensing Letters Vol. 1, No. 1, March 2010, Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment A.A. SOUZA, L.S. GALVÃO* and J.R. SANTOS Instituto Nacional de Pesquisas Espaciais (INPE), Caixa Postal 515, , São José dos Campos, SP, Brazil (Received 14 May 2009; in final form 8 September 2009) The relationships between 18 narrowband Hyperion vegetation indices and basal area and canopy cover values of eight Brazilian savannah physiognomies were evaluated. The best fitting regression relationships were used to estimate these parameters in the image, which were projected over a digital elevation model (DEM) and compared with an available vegetation map. Results showed that the gradual increase of biophysical parameters, from savannah grassland to semi-deciduous forest, produced large correlation coefficients with most of the indices, especially the near-infrared/red-based ones (e.g. NDVI and simple ratio). Larger canopy cover and basal area values were associated with tall woodland and semi-deciduous forest and smaller values with grasslands and shrub savannah. When projected over the DEM, the predominance of rupestrian fields at high altitudes and of tall woodland and semi-deciduous forest at low altitudes was observed. 1. Introduction The Brazilian savannah (cerrado) comprises grasslands, shrublands, and woodlands with floristic and structural variations and high diversity of species well adapted to fire and to the seasonal contrast between the dry and the rainy seasons (Eiten 1982). Multispectral remote sensing has been used for mapping these physiognomies and for analysing their dynamics using vegetation indices (Sano et al. 2005, Ferreira et al. 2007). The quantification of biophysical attributes using multispectral sensors in savannah environments is challenging because of variation in tree density and soil background influence (Carreiras et al. 2006). The advent of hyperspectral remote sensing has raised new expectations about these estimates based on the assumption that spectral features associated with narrowbands can increase quantification (Cho et al. 2009, Galvão et al. 2009). Despite having a low signal-to-noise ratio in the shortwave infrared (SWIR), Hyperion, on-board the Earth Observing One (EO-1) satellite, provides an opportunity to test the use of narrowband vegetation indices in the study of the Brazilian cerrados. In this investigation, the relationships of narrowband Hyperion vegetation indices with canopy cover and basal area measurements of the cerrado physiognomies were evaluated. Using regression relationships, these biophysical parameters were estimated, plotted over a digital elevation model (DEM), and compared with an available vegetation map. *Corresponding author. lenio@dsr.inpe.br Remote Sensing Letters ISSN X print/issn online # 2010 Taylor & Francis DOI: /

3 56 A.A. Souza et al. 2. Methodology The study area is located in the vicinity of Pirenópolis (GO), central Brazil, between S/ W and S/ W. The climate is tropical with a mean temperature of 23 C and a well-defined rainy season from October to April (1600 mm). Hyperion/EO-1 acquired images on 13 June 2006 (dry season) in 196 radiometrically calibrated bands ( nm) with 30 m spatial resolution. After destriping, the images were converted into surface reflectance using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes algorithm and then geo-referenced. From the classification by Ribeiro and Walter (1998), eight physiognomies were identified: Campo Limpo (savannah grassland), Campo Sujo (shrub savannah), Cerrado Ralo (wooded savannah), Cerrado Rupestre (rupestrian fields), Cerrado Stricto Sensu (savannah woodland), Cerrado Denso (tall savannah woodland), Cerradão (tall woodland), and Mata Seca (semi-deciduous forest). For floristic and structural characterization, 28 transects (50 20 m) were inspected. Only individuals with diameter at breast height 15 cm were sampled for tall woodland and semideciduous forest. Only those with diameter at 30 cm surface height 15 cm were considered for the remaining physiognomies. The FITOPAC algorithm was used for the calculation of basal area and the diversity indices. Canopy cover was measured using a vertical densitometer only in 10 transects of the more dense vegetation (50 measurements per transect). Average values (3 3 pixels) of 18 Hyperion vegetation indices (table 1) were correlated and regressed against basal area (28 transects) and canopy cover (10 transects). This analysis was preceded by the calculation of all possible Table 1. Hyperion-derived vegetation indices. Vegetation index Formula a Simple ratio (SR) R864/R671 Atmospherically resistant vegetation index (ARVI) (R864 - (2 R671 - R467)/ (R864 þ (2 R671 - R467)) Red edge normalized difference vegetation (R752 - R701)/(R752 þ R701) index (RENDVI) Vogelmann red edge index (VOG) R742/R722 Red edge position index (REPI) (Rn þ 1 - Rn)/10, in the nm interval Photochemical reflectance index (PRI) (R529 - R569)/(R529 þ R569) Structure insensitive pigment index (SIPI) (R803 - R467)/(R803 þ R681) Plant senescence reflectance index (PSRI) (R681 - R498)/R752 Carotenoid reflectance index (CRI) (1/R508) - (1/R701) Anthocyanin reflectance index (ARI1) (1/R550)/(1/R700) Water band index (WBI) R905/R973 Normalized difference water index (NDWI) (R854 - R1245)/(R854 þ R1245) Moisture stress index (MSI) R1598/R823 Normalized difference infrared index (NDII) (R823 - R1649)/(R823 þ R1649) Enhanced vegetation index (EVI) 2.5 ((R864 - R671)/ (R864 þ 6 R R467 þ 1)) Normalized difference vegetation (R864 - R671)/(R864 þ R671) index (NDVI) Visible atmospherically resistant (R559 - R640)/(R559 þ R640 - R467) index (VARI) Visible Green Index (VIG) (R559 - R640)/(R559 þ R640) a R is the reflectance of the closest Hyperion bands (nm) to the original equations.

4 Hyperion vegetation indices of the Brazilian savannahs 57 Table 2. Structural and floristic parameters for the studied savannah physiognomies. Parameter Savannah grassland Shrub savannah Wooded savannah Rupestrian fields Savannah woodland Tall savannah woodland Tall woodland Semi-deciduous forest Basal area (m 2 ha -1 ) a Canopy cover (%) a Diameter (cm) b Tree height (m) b Shannon diversity (H) Simpson s diversity (1-D) Pielou s evenness (J) Number of plots (transects) a Minimum and maximum values. b Average and standard deviation.

5 58 A.A. Souza et al. Hyperion band ratios and their correlations with the two parameters to determine the most correlated spectral intervals and their consistency with correlation results from vegetation indices. The best linear regressions with vegetation indices were used to produce images showing biophysical estimates for the savannah physiognomies. Because of the small number of transects, the bootstrapping technique, which in many cases is preferable to cross-validation (Efron 1981), was used with 10,000 times of re-sampling to evaluate the robustness of the relationships. Finally, basal area and canopy cover estimates were compared with a vegetation map (Souza 2009) obtained from a support vector machine (SVM) classification of Hyperion reflectance. To quantify the agreement between the vegetation and the biophysical parameter maps, 525 randomly selected pixels were used to generate error matrices. Results were also plotted over a Shuttle Radar Topography Mission-derived DEM, re-sampled to 30 m, to investigate possible associations between the physiognomies and the elevation data. Figure 1. Profile diagrams representative of (a) semi-deciduous forest; (b) savannah woodland; (c) shrub savannah; and (d) Hyperion reflectance spectra.

6 Hyperion vegetation indices of the Brazilian savannahs Results and discussion Structural and floristic parameters increased from savannah grasslands to semideciduous forest, which displayed the largest values of basal area, canopy cover, diameter, and tree height, and the greatest biodiversity (table 2). Examples of profile diagrams are shown in figures 1(a) (c). During Hyperion image acquisition, the semideciduous forest canopy was dominated by green leaves, as indicated by the reflectance peak at 560 nm and the well-defined chlorophyll and leaf water absorption bands at 667 and 1205 nm, respectively (red colour curve in figure 1(d)). With decreasing canopy cover from semi-deciduous forest (figure 1(a)) to shrub savannah (figure 1(c)), a greater influence of non-photosynthetic vegetation (NPV) over the substrate produced an increase in the red (667 nm) and SWIR reflectance, a decrease in the near-infrared (NIR) response, and well-defined lignin cellulose spectral features at 1760, 2100, and 2300 nm (blue colour curve in figure 1(d)). When correlated with basal area, statistically significant relationships with Hyperion band ratios were observed (figure 2(a)). The largest positive correlations (red colour) were observed for NIR/red ratios (e.g. 864/671 nm), which anticipated the importance of vegetation indices such as NDVI and simple ratio (SR). Because basal area and canopy cover were correlated to each other (r ¼þ0.75), a similar pattern was observed in the contour correlation map of canopy cover (figure 2(b)). Except for REPI, PRI, and ARI, all the other vegetation indices showed large r values for both canopy cover and basal area (table 3), which was consistent with band ratio analysis. The gradual increase in basal area values towards semi-deciduous forest produced a linear relationship for most of the indices, especially SR (figure 3(a)) with r of þ0.92 and a root mean square error (RMSE) of m 2 ha -1. NDVI was also correlated with canopy cover (figure 3(b)) with r of þ0.97 and an RMSE of 7.123%. Bootstrapping histograms confirmed the robustness of the relationships that presented mean r values of þ0.90 and þ0.97 for basal area and canopy cover, respectively (figure 4). Figure 2. Contour correlation map for the relationship of (a) basal area and (b) canopy cover with all possible Hyperion reflectance ratios. Results around 1400 and 1900 nm were omitted because of atmospheric absorption.

7 60 A.A. Souza et al. Table 3. Pearson s correlation coefficients for the relationships between biophysical parameters and Hyperion vegetation indices. SR ARVI RENDVI VOG REPI PRI SIPI PSRI CRI ARI WBI NDWI MSI NDII EVI NDVI VARI VIG Canopy cover 0.96* 0.97* 0.97* 0.97* * -0.96* 0.89* * 0.97* -0.95* 0.96* 0.96* 0.97* 0.97* 0.97* Basal area 0.92* 0.91* 0.90* 0.92* * -0.89* 0.76* * 0.76* -0.74* 0.78* 0.90* 0.87* 0.92* 0.92* *Significant at the 0.01 level.

8 Hyperion vegetation indices of the Brazilian savannahs 61 Y = X r = n = 28 RMSE = Significant at 0.01 level Y = X r = n = 10 RMSE = Significant at 0.01 level Basal area (m 2 ha 1 ) Canopy cover (%) Savannah physiognomies Tall woodland and forest Wooded to tall savannah woodland Grassland to shrub savannah Savannah physiognomies Tall woodland and forest Savannah and tall savannah woodland (a) Simple ratio (SR) (b) Figure 3. NDVI. Regression relationships between (a) basal area and SR and (b) canopy cover and Figure 4. Bootstrapping histograms for the relationships between (a) basal area and SR and (b) canopy cover and NDVI. When the relationships of figure 3 were used to produce biophysical estimates on a per-pixel basis, a close agreement with the vegetation map was observed (figure 5). Larger canopy cover and basal area values (red colours in figures 5(c) and(d)) were associated with tall woodland and semi-deciduous forest (red and brown colours for TW and SF in figure 5(b)), as expected. Smaller values were related to grasslands and shrub savannah. Analysis of pixels for the comparison of the vegetation map with basal area and canopy cover maps showed Kappa values of 0.72 and 0.62, respectively. For the entire study area, an association between the spatial distribution of some physiognomies and elevation was also observed. Rupestrian fields occurred above 900 m of altitude associated with shallow soils or quartzite outcrops that affect

9 62 A.A. Souza et al. Figure 5. (a) False colour composite of the central portion of the study area with Hyperion bands at 864 nm (red), 1648 nm (green), and 640 nm (blue); (b) vegetation map. Other classes are represented in white; (c) canopy cover; and (d) basal area estimates from NDVI and SR. Results are projected over a DEM. Physiognomies: SG, savannah grasslands; SS, shrub savannah; TW, tall woodland; TS, tall savannah woodland; WS, wooded savannah; RF, rupestrian fields; SW, savannah woodland; SF, semi-deciduous forest. vegetation growth, whereas tall woodland and semi-deciduous forest (larger values of canopy cover) were observed at lower elevations (figure 6). No clear trend was observed for the remaining physiognomies. 4. Conclusions The gradual increase in basal area and canopy cover values, from grassland and shrub savannah to tall woodland and semi-deciduous forest, produced high correlation coefficients with Hyperion vegetation indices, especially with NDVI and SR. Most of the indices decreased from close to sparse canopies also because of the influence of NPV over the substrate that produced a red and SWIR reflectance increase and welldefined SWIR lignin-cellulose spectral features. Despite the small number of samples, basal area and canopy cover estimates from linear regression were consistent with mapping of the physiognomies, as indicated by Kappa values. Further research is necessary to investigate the behaviour of the relationships between Hyperion narrowband vegetation indices and biophysical parameters with seasonality.

10 Hyperion vegetation indices of the Brazilian savannahs 63 Figure 6. Estimated canopy cover variation with elevation data of selected physiognomies for training pixels used in SVM classification of the entire Hyperion scene. Results projected over a DEM showed the predominance of rupestrian fields associated with rock outcrops at high altitudes and of tall woodland and semideciduous forest at low altitudes. Acknowledgements The authors are grateful to Embrapa and Agência Ambiental de Goiás for assistance in field survey and to CNPq and CAPES. Thanks are also due to Marcos Adami and three anonymous reviewers. References CARREIRAS, J.M.B., PEREIRA, J.M.C. and PEREIRA, J.S., 2006, Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. Forest Ecology and Management, 223, pp CHO, M.A., SKIDMORE, A.K. and SOBHAN, I., 2009, Mapping beech (Fagus Sylvatica L.) forest structure with airborne hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 11, pp EFRON, B., 1981, Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika, 68, pp EITEN, G., 1982, Brazilian savannas. Ecological Studies, 42, pp

11 64 A.A. Souza et al. FERREIRA, M.E., FERREIRA, L.G., SANO, E.E. and SHIMABUKURO, Y.E., 2007, Spectral linear mixture modelling approaches for land cover mapping of tropical savanna areas in Brazil. International Journal of Remote Sensing, 28, pp GALVÃO, L.S., ROBERTS, D.A., FORMAGGIO, A.R., NUMATA, I. and BREUNIG, F.M., 2009, View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data. Remote Sensing of Environment, 113, pp RIBEIRO, J.F. and WALTER, T.M.B., 1998, Physiognomies of the Cerrado biome. In Cerrado: Environment and Flora, S.M. Sano and S.P. Almeida (Eds), pp (Brasília: Empresa Brasileira de Agropecuária Embrapa) (in Portuguese). SANO, E.E., FERREIRA, L.G. and HUETE, A.R., 2005, Synthetic Aperture Radar (L-Band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: a comparative analysis. Earth Interactions, 9, pp SOUZA, A.A., 2009, A cerrado physiognomy study using Hyperion/EO-1 data. Master thesis. Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP. 107 pp. (in Portuguese).