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Retrieval of leaf area index in different vegetation types using high resolution satellite data
Remote Sensing of Environment 86 (2003) 120 – 131 www.elsevier.com/locate/rse
Retrieval of leaf area index in different vegetation types using high resolution satellite data Roberto Colombo a,*, Dario Bellingeri b, Dante Fasolini c, Carlo M. Marino b a
Institute for Environment and Sustainability, Joint Research Centre of the European Commission, TP 262, Via E. Fermi, s/n 21020 Ispra, Varese, Italy b DISAT Universita` Degli Studi di Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy c ERSAF Ente Regionde per i Servizi all, Agricoltura e alle Foreste della Lombardia, Via Ponchielli 2/4, Milan, Italy Received 21 March 2002; received in revised form 18 February 2003; accepted 8 March 2003
Abstract With the successful launch of the IKONOS satellite, very high geometric resolution imagery is within reach of civilian users. In the 1-m spatial resolution images acquired by the IKONOS satellite, details of buildings, individual trees, and vegetation structural variations are detectable. The visibility of such details opens up many new applications, which require the use of geometrical information contained in the images. This paper presents an application in which spectral and textural information is used for mapping the leaf area index (LAI) of different vegetation types. This study includes the estimation of LAI by different spectral vegetation indices (SVIs) combined with image textural information and geostatistical parameters derived from high resolution satellite data. It is shown that the relationships between spectral vegetation indices and biophysical parameters should be developed separately for each vegetation type, and that the combination of the texture indices and vegetation indices results in an improved fit of the regression equation for most vegetation types when compared with one derived from SVIs alone. High within-field spatial variability was found in LAI, suggesting that high resolution mapping of LAI may be relevant to the introduction of precision farming techniques in the agricultural management strategies of the investigated area. D 2003 Elsevier Science Inc. All rights reserved. Keywords: LAI; IKONOS; SVIs; Texture indices
1. Introduction The high spatial resolution of IKONOS satellite images allows for various environmental applications, such as mapping, agriculture, forestry, and emergency response. The satellite sensor can generate 1-m panchromatic and 4m multiband images with off-nadir viewing of up to 60.25j for better revisit rate and stereo capabilities. The panchromatic imagery has a spectral wavelength interval ranging from 0.45 to 0.9 Am while the multispectral imagery includes four bands in the blue, green, red and near-infrared part of the spectrum (0.45 – 0.52, 0.52 – 0.60, 0.63– 0.69, and 0.76 – 0.90 Am). This very high resolution satellite imagery provides a new source of data for monitoring agricultural production, potentially providing information with respect to the development of crops during the growing season. * Corresponding author. DISAT Universita` Degli Studi di MilanoBicocca, Piazza della Scienza 1, 20126 Milan, Italy. Tel.: +39-0264482848; fax: +39-0264482895. E-mail address: [email protected] (R. Colombo).
The information content of panchromatic and multispectral satellite images may be useful in large-scale quantitative assessment of biophysical attributes, such as leaf area index (LAI), which are key inputs in models describing biosphere processes. The characterisation of the biosphere is a key step for understanding biological and physical processes associated with vegetation, for developing ecosystem productivity models and for computing the mass and energy exchange between soil, vegetation and atmosphere (Bonan, 1993; Liu, Chen, Cihlar, & Park, 1997; Sellers, Mintz, Sud, & Dalcher, 1986). Currently, ecosystem models require either field validation of simulated LAI, or remotely sensed estimates of LAI to initiate them (Running et al., 1999). Leaf area index measurements are critical for improving the performance of such models over large areas and this has prompted investigations of the relationship between ground-measured LAI and spectral vegetation indices (SVIs) derived from satellite-measured data (Chen & Cihlar, 1996; Fassnacht, Gower, MacKenzie, Nordheim, & Lillesand, 1997; Nemani, Pierce, Running, & Band, 1993; Spanner, Pierce, Peterson, & Running, 1990).
0034-4257/03/$ - see front matter D 2003 Elsevier Science Inc. All rights reserved. doi:10.1016/S0034-4257(03)00094-4
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Although LAI can be directly or indirectly measured by several methods (see e.g. Gower, Kucharik, & Norman, 1999; White, Asner, Nemani, Privette, & Running, 2000), its spatial and temporal distribution is usually investigated using remotely sensed data. The simplest and most practical way is to investigate the relationships between LAI and SVI values by means of regression models. Such relationships usually result in different mathematical forms with empirical coefficients that vary, depending primarily on vegetation type (e.g., Chen, Rich, Gower, Norman, & Plummer, 1997; Turner, Cohen, Kennedy, Fassnacht, & Briggs, 1999). Moreover, these relationships are affected by several factors, such as background reflectance, crown closure, orientation and aggregation of leaf elements, branches, stand age and difference in chlorophyll concentration. A considerable scatter in LAI –SVI relationships is usually found when the ground pixels observed by the sensor include a combination of canopy reflectances, originating from different types of vegetation with a variable amount of understory (Chen & Cihlar, 1996; Frankin, 1986). The red (R, 0.63 – 0.69 Am), Near InfraRed (NIR, 0.76– 0.90 Am), Shortwave Infrared (SWIR, 1.55– 1.75 Am) and Middle Infrared bands (MIR, 2.08– 2.35 Am) are the typically used bands in SVI computation, and their response in producing LAI maps is discussed in many studies (e.g., Baret, Guyot, Begue, Maurel, & Podaire, 1988; Brown, Chen, Leblanc, & Cihlar, 2000; Fassnacht et al., 1997; Nemani et al., 1993). When the analysis is to be conducted at field scale, very high resolution satellite data can be a useful input for different environmental applications. Field scale refers to a geometrical resolution able to resolve intra-field variability of crop condition. Traditional agriculture considers a field as a homogeneous unit, while the exploitation of very high geometrical resolution satellite data may allow detecting within-field spatial variability of biophysical parameters, useful in agricultural management (Moran, Inoue, & Barnes, 1997). Information regarding within-field spatial variability is necessary in precision farming techniques in order to increase canopy uniformity, and thus to improve crop yield and quality (Barnes, Pinter, Moran, & Clarke, 1997; Garcia Cidad & Vrindts, 2001; Johnson, Roczen, & Youkhana, 2001). Individual cover types generally show a strong relationship between LAI and SVIs. Therefore, high spatial resolution imagery should allow for the retrieval of better LAI – SVI relationships as compared to low resolution imagery, in which each pixel may be made up of many land cover types. Using IKONOS data, the scattering effect of mixed land cover classes on the spectral data may be reduced. LAI retrieval by means of IKONOS data is usually conducted by regression models with SVIs (e.g. Johnson et al., 2001) without including any additional geometrical information. However, texture information provided by high geometrical resolution aerial and satellite data can potentially be used as additional information related to the spatial canopy architecture that, combined with spectral data, might
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improve the LAI description (Weiss et al., 2001; Wulder, Franklin, & Lavigne, 1996, Wulder, Le Drew, Franklin, & Lavigne, 1998). In this study, different LAI estimates for five vegetation type classes, obtained from spectral vegetation indices and textural information, are discussed. The potential of very high resolution data for LAI retrieval is outlined, and the LAI spatial variability within the different vegetation types is analyzed.
2. Methods Three techniques, based on SVIs coupled with texture indices and geostatistics, are applied in order to retrieve LAI of different agricultural crops using IKONOS data acquired almost simultaneously with ground measurements. LAI values, derived from the LAI-2000 Plant Canopy Analyzer (LI-COR, 1992, Lincoln, NE), were plotted versus SVI data, and versus a combination of SVI and texture information. The test site area is located in the S. Colombano region (Italy), characterized by a landscape encompassing hilly vineyards and flat alluvial areas of the Po river plain mainly devoted to agricultural crops and intensive poplar plantations. 2.1. Ground-based LAI measurements The LAI-2000 instrument was used in different vegetation types to derive LAI indirectly. LAI measurements were collected in a test area investigated in the DUSAF project (Destinazione d’Uso dei Suoli Agricoli e Forestali), coordinated by ERSAL (Ente Regionale di Sviluppo Agricolo della Lombardia) and focused on precise land use and land cover mapping. LAI-2000 measures the gap fraction P(h) in five zenith angle (h) ranges with midpoints of 7j, 23j, 38j, 53j and 67j. LAI is determined by inverting simple radiative transfer model foliage information according to Welles and Norman (1991). This indirect LAI estimate specifically represents an effective leaf area index for the agricultural crops and an effective plant area index, including branch components, for deciduous forests. The assumption of a random spatial distribution of the leaves as made in the model inversion is generally satisfied for these crops. Where a nonrandom spatial distribution of canopy foliage is observed, an accurate description of gap size is essential to avoid large errors in LAI estimation (Chen et al., 1997; Gower et al., 1999). LAI was calculated according to Gower and Norman (1991) from the LAI-2000 gap fraction measurements: LAI ¼ 2
Z
p=2
ln½1=PðhÞcosh sinh dh 0
LAI measurements were collected in late May 2001, under diffuse radiation conditions at sunset in one sensor
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Fig. 1. Example of a corn field with two LAI measurements (left). Schematic presentation of a 3 3 pixel kernel of the multispectral IKONOS image (12 12 m), illustrating the crossing of crop rows (diagonal dashed lines) by the transect.
mode, and using a 45j view cap. LAI was measured along ground transects in five different vegetation types: soybean, corn, vineyards, poplar plantation (plantation) and deciduous forest (forest). Measurements were performed in different fields, at different phenological stages and with different canopy heights, and were also collected within the same field to determine the within-field LAI spatial variation. Twenty-eight crop fields were investigated for a total of 39 LAI measurements with 12, 7, 8, 6, and 6 measurements in corn, soybean, plantations, vineyards, and forest, respectively. Each LAI measurement was obtained by combining two parallel transects, diagonally crossing the crop rows according to the schema presented in Fig. 1. The transect length and the spatial interval between ground samples were fixed for all vegetation types, and a single LAI value was thus obtained by averaging 10 samples, 5 collected along the first 10-m transect and 5 collected coming back along the parallel one. With a distance of 2 m between the samples, consecutive samples of the LAI-2000 instrument are overlapping when the canopy height is greater than 70 cm. The distance between two transects was set to a minimum of 3 m. Such a planning scheme was designed in order to characterise a window of 3 3 pixels on the multispectral IKONOS image (144 m2) and, depending on the canopy height, different crop field areas were sampled. A single IKONOS panchromatic-multispectral image was used for all sites. IKONOS data were acquired on 29 May 2001 under clear sky conditions. The view angle was 15j off-nadir. Raw digital number (DN), re-scaled to the full 11-bit range, was used in this study. As a consequence, in the absence of the re-scaling parameters it was impossible to compute the calibrated radiance from the original data (Holtman and Rigopoulos, Space Imaging, personal com-
munication). IKONOS images were initially georeferenced by an image-to-image technique, using as a master the digital orthophoto provided by ERSAL. A first-order polynomial was used with a nearest neighbour resampling, obtaining a registration accuracy of 0.6 pixel for the multispectral images. The LAI-2000 measurements were geolocated on the IKONOS images as follows. During field work, transect starting points were directly positioned on the printed, georeferenced panchromatic IKONOS image with a compass for measuring their azimuth. Each transect was digi-
Table 1 Spectral vegetation indices used in LAI retrieval SVI
Algorithm
NDVI
NIR R NIR þ R
SR
NIR R
SAVI
NIR aR c ð1 þ LÞ NIR þ R þ L
PVI
NIR aR c pffiffiffiffiffiffiffiffiffiffiffiffiffi a2 þ 1 a2 þ 1
ARVI
NIR rb ; NIR þ rb
EVI
NIR R *G NIR þ C1 *R C2 *B þ C3
rb ¼ R cðB RÞ
B, R, and NIR represent the IKONOS DN data in blue, red and infrared bands. Parameters a, c, L, and c regard respectively the gain and the offset of the soil line, the SAVI term (set equal to 0.5) and the ARVI term (set equal to 1). The coefficients adopted in the EVI algorithm are C1 = 6.0, C2 = 7.5, C3 = 1, and G = 2.5.
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Fig. 2. IKONOS multispectral image (a) and dissimilarity image (b). Highest dissimilarity values correspond to poplar plantations and vineyards, which are well distinguished from other crops.
tised and then located on the georeferenced multispectral image to define the kernel window. It is assumed that these LAI field measurements can be considered as representative of a window of 3 3 pixels in the multispectral IKONOS image. The coefficient of variation in these windows was generally less than 5% for each band, showing little subwindow heterogeneity. 2.2. LAI – SVI relationships Initially, six SVIs were directly computed from DN data to analyse the relationship with LAI (Table 1). Normalised difference vegetation index (NDVI) (Rouse & Haas, 1973), simple ratio (SR) (Jordan, 1969), soil adjusted vegetation index (SAVI) (Huete, 1988), perpendicular vegetation index (PVI) (Richardson & Wiegand, 1977), and atmospherically resistant vegetation index (ARVI) (Kaufman & Tanre´, 1992) were selected as representative of intrinsic, soil adjusted and atmospherically corrected indices. The last three spectral indices were included in the study to analyse their effects on
reducing the background and atmospheric effects in LAI mapping. The enhanced vegetation index (EVI) (Huete, Justice, & van Leeuwen, 1999) used with the coarse resolution MODIS data was also investigated. A series of field-sampled bare soils pixels were selected on the panchromatic band that reflect the broad band albedo of the scene in order to determine the a and b parameters of the so-called ‘‘soil line’’ for computing SAVI and PVI. Initially, all the LAI measurements (N = 39) were plotted against the SVIs in order to compute the coefficient of determination (r2) of the regression equation. A window of 3 3 pixels was initially used in each spectral band to determine the spatial homogeneity by statistical parameters. The analysis was then conducted for each vegetation type, plotting the LAI measurements against the SVIs. The r2 for each LAI – SVI relationship was then recomputed using the SVI values based on mean reflectances of different windows sizes of 6 6 and 9 9 pixels.
Fig. 3. The different spatial variability pattern within the deciduous forest and poplar plantation data is well reproduced by the semivariogram function.
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Fig. 4. Relationship between all measured LAI in different vegetation types and NDVI.
2.3. LAI retrieval by SVIs and texture analysis Image texture is a quantification of the spatial variation of image tone values, which can be related to spatial distribution of vegetation. The above-ground organisation of forest elements is represented in texture, which is supplementary to the spectral image data and may provide an additional information source. Image texture may be related to a variety of statistical measurements that characterise the relationships between neighbouring pixels. Homogeneity, contrast, and entropy are measures related to the specific textural characteristics of the image, while dissimilarity, mean, and standard deviation characterise the complexity and the nature of grey level transitions defined in the co-occurrence matrix (Wulder et al., 1998). The approach for LAI determination based on texture component is subject to the hypothesis that the image spatial information can be represented in simple texture statistics, which are a substitute for the structure of the forest vegetation or the distribution of LAI, and that the definition of the forest structure can result in better estimates of LAI in patchy stands. The texture indices were calculated for the IKONOS panchromatic band at the maximum spatial resolution of 1 m. To appreciate the texture of the vegetation, three different window sizes were employed (3 3, 6 6, 9 9 pixels). A series of textural variables were tested and the best results in terms of r2 were obtained by using the dissimilarity index. Dissimilarity (D) was computed according to the formula: D¼
n1 X
Pi;j ji jj
Dissimilarity was observed to reflect a texture measure that is high when the field region investigated has a high amount of spatial local variation related to structural characteristics of the image (e.g., crops raw and cover, crops height and shadowing effects, discontinuous urban fabric) (Fig. 2). The dissimilarity image was resampled at a 4-m resolution, in order to integrate texture information with the spectral vegetation indices previously derived from multispectral data. The relationship between the combined SVI-texture data versus LAI was tested by performing multiple linear regressions, with LAI as dependent variables and SVI/dissimilarity as independent indices. 2.4. LAI retrieval by SVIs, texture and geostatistical parameters Geostatistics is a useful technique whenever the property of interest behaves as a spatially correlated variable. Several authors have used geostatistical parameters to extract structural, biophysical, and forest damage information (Bruniquel-Pinel & Gastellu-Etchegorry, 1998; Chica Olmo & Abarca Hernandez, 2000; Franklin, Wulder, & Lavigne, 1996; Levesque & King, 1999; Weiss et al., 2001; Wulder Table 2 Summary table of the coefficient of determination (r2) for LAI retrievals computed using the three different approaches Vegetation type
SVI
NDVI NDVI/ dissimilarity
NDVI + sill
NDVI + range
NDVI + range + diss
Forest/ plantation Forest Plantation Vineyards Soybean Corn All data
0.60
0.73
0.65
0.72
0.76
n.s. 0.79 0.74 0.74 0.80 0.33
0.73 0.81 0.98 0.74 0.80 0.62
n.s. 0.79 n.s. – – –
n.s. 0.8 n.s. – – –
n.s. 0.82 n.s. – – –
i;j¼0
where P is the probability for the cell i, j (row and column numbers, respectively). Pi,j is computed as follows Pi;j ¼
Vi;j n 1 X
Vi;j
i;j¼0
where Vi,j is the DN value in the cell i,j of the image window.
Texture analysis Geostatistical parameter
n.s.: not significant at p = 0.05; – : not computed since any additional information is provided by texture.
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Fig. 5. Relationships between LAI and NDVI for individual vegetation types.
et al., 1996; ) from digital data. The central tool of geostatistics is the variogram, which is used to examine the spatial continuity of a regionalized variable as a function of distance and direction. A variogram is a plot of the semivariances at different distances (lag), which is computed as the sum of squared differences between all possible pairs of points separated by the chosen distance. Semivariance (c(h)) was computed according to: m 1 X cðhÞ ¼ ½zðxi Þ zðxi þ hÞ2 2m 1¼1 where m is the number of pairs of pixels separated by the same lag(h) and z(xi) is the DN value at the xi position. Semivariance is thus obtained for each lag and plotted against the lag. Semivariance thus represents the degree of spatial dependence between samples. The main properties defining the variogram are the sill, the range, and the nugget, which relate to the height of the variogram, the distance to reach the sill, and the positive y-intercept, respectively. For measurements of LAI in forest and plantations, a window size of 50 50 pixels on the IKONOS panchromatic band was selected, since it allows analysis of homogeneous areas with respect to crop type and is large enough to allow the calculation of a significant semivariance plot. Omni-directional semivariograms were then computed, and
nugget, range, and sill were determined from the fitted mathematical spherical models. The graphical representation of the average semivariance of several pixel pairs at each lag distance (h) computed for plantations and forest classes is shown in Fig. 3. A spherical model was adopted, even though plantations showed a periodic pattern due to their typical rows (Bruniquel-Pinel & Gastellu-Etchegorry, 1998). The geostatistical parameters were computed and employed in multivariate linear regression with the vegetation indices and the dissimilarity image.
3. Results and discussion For all SVIs, the relationship with LAI shows a positive correlation with a low coefficient of determination. The overall relationship between all LAI measurements and SVIs appears inadequate for mapping LAI. Fig. 4 shows, as an example, the correlation between LAI and NDVI (r2 = 0.33). The large scatter is caused, in part, by the grouping of all vegetation types. The observed degree of uncertainty would greatly compromise the utility of the relationship for LAI mapping at this detailed scale. Due to the heterogeneity of the group of data, one LAI –SVI relationship is not adequate
Fig. 6. Trend of the coefficient of determination computed for the six SVIs used in this study.
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for mapping LAI across all vegetation types. The detected heterogeneity is probably of the same order of magnitude as the heterogeneity detectable within a coarse resolution cell.
When the relationships between SVIs and LAI are analyzed for each vegetation type, the r2 values are much improved (see Table 2). The resulting LAI –NDVI relation-
Fig. 7. Multispectral image with plantations and corn plots outlined; a subset of LAI measurements is also shown (a). The LAI map identifies the spatial variation in different fields (b).
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ships show the great increase in r2 in the agricultural crops (Fig. 5). When the relationship between LAI and NDVI is computed for all vegetation types grouped together, without including the forest class, a decrease in r2 to a value of 0.25 was found. At the same time, the scatter of the relationships is still relatively high in forests, due to their high internal heterogeneity. The investigated forest showed the highest LAI values, with a low SVI variability, and the 3 3 pixel window generally corresponds to an area with different tree heights and canopy architecture. It was difficult to define the most appropriate SVI for mapping LAI of each crop. There is not much variation among SVIs, with relatively high r2 in most cases. EVI was found to have the same performance as NDVI for all crops analyzed. In Fig. 6, the coefficient of determination computed for the different SVIs is reported.
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The low variation among the SVIs in these correlation coefficients may be related to the fact that each crop was sampled at different phenological stages, ranging from very low cover to a high degree of vegetation amount. This may result in a deterioration of the performance for soil linebased indices. No improvement for the background effect was determined using SAVI index, while PVI does worse in plantation stands and better in forest. Moreover, no improvements were found by using SVIs that minimise the atmospheric and background effects. ARVI may not have been successful since the terrain morphology shows low relative relief and, therefore, spatial differences in upwelling sky irradiance and path radiance are low. It should be noted that the regression equations between SVIs and LAI were obtained by fitting a linear relationship, although other studies have shown the nonlinearity of NDVI/LAI (e.g. Chen & Cihlar, 1996; Myneni, Nemani, & Running, 1997).
Fig. 8. On the left, the true colour IKONOS images and on the right, the LAI maps derived for poplar plantation (b) and corn vegetation type (d). The masked areas are shown in black.
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Fig. 9. Coefficient of determination of the relationships LAI – NDVI as estimated from different window sizes for each vegetation type.
The LAI spatial variations of corn and plantations fields, for a sector of the investigated area of about 15 km2, are shown in Fig. 7. LAI is mapped from NDVI data and allows observation of the different behaviour of the plantations in the abandoned meandering river system. A large LAI range characterizes the corn, with fields varying from 0.5 to 4.5 m2/m2. Examples of detailed LAI maps for the intensive plantation and for corn crops show the within-field variation of LAI (Fig. 8). Spatial LAI variations are very evident at the field scale as intra-culture variations characterizing different crop stages. In a corn field, the within-field variation of the ground-based LAI measurements ranged from 0.8 to 2.0. This heterogeneity is related to variations in crop development due to a uniform management (irrigation, chemical treatments) applied to a portion of terrain showing heterogeneous edaphic conditions. Other studies have also shown a high spatial variability of LAI and other biophysical parameters within crop fields detected by remote sensing techniques (Barnes et al., 1997; Castagnoli & Dosso, 2001; Johnson et al., 2001; Yang & Anderson, 1999). These studies suggest that precision farming techniques might usefully incorporate data on local LAI variation, computed from IKONOS data. The performance of areas with low LAI could potentially be improved through a differentiated treat-
ment, and LAI maps could be used to formulate management recommendations during the early stages of growth. The analysis was finally conducted computing different window sizes starting from the SVI values, and a substantial stability was observed when the relationships were computed using window sizes of 3 3, 6 6 and 9 9 pixels. The application of different kernel sizes did not influence the coefficient of determination of the LAI – SVI relationships (Fig. 9). Window sizes greater than nine pixels generally include surrounding fields with different crop types. Thus, the field sampling scheme for measuring LAI may be considered appropriate to represent the vegetation spatial variability detected by high geometric resolution satellite data. Spatial heterogeneity in canopy architecture was analyzed in the LAI mapping by introducing texture indices. The best indicator of texture was found to be the dissimilarity index as computed on a 6 6 pixel window from panchromatic data. The combination of the dissimilarity index and vegetation indices resulted in an increase of the coefficient of determination for most of the vegetation types when compared with that derived from SVIs alone (Fig. 10). A large increase in r2 for the overall relationship (from 0.33 to 0.62) was achieved when texture information was included in the analysis, suggesting a useful way to avoid possible problems related to class definitions with an
Fig. 10. Values of coefficient of determination computed for the different vegetation types using spectral and textural information of IKONOS data.
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automatic land cover stratification. The addition of a texture index as a covariate in the multiple regressions has an effect similar to doing a classification before developing the LAI – SVI relationships. The outstanding improvement in the r2 for the forest class when adding the texture information might be explained by the nonuniform and nonrandom spatial distribution of natural vegetation. The forest was a mosaic of dense and sparse vegetation cells even within a single 3 3
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pixel window and that heterogeneity weakened the LAI – SVI relationships. An example of the LAI map for the forest class is shown in Fig. 11. The substantially homogeneous texture of corn and soybean viewed at the spatial resolution of 1 m did not add appreciable information to LAI determination. Geostatistical tool data also strengthened LAI –SVI relationships in some cases. The semivariogram computed for plantation and forest yielded information about their structure. In particular, a linear sill in the semivariogram is typical of the forest class, while the periodic shape of the plantation’s semivariogram shows the repetitive pattern of the plantation (see Fig. 4). As shown in Table 2, the parameters extracted by geostatistical analysis, calculated only for forest and plantation, showed marginal improvements in some cases for the empirical fits to LAI. In particular, the inclusion of the range and sill values into a multivariate linear regression permits derivation of LAI with an r2 similar to the case when the texture band is added to SVIs. This small improvement might be due to the additional information regarding the density of the crowns.
4. Conclusions
Fig. 11. IKONOS panchromatic image with the forest class indicated in black (a); LAI map of the corresponding class superimposed on the true color multispectral IKONOS image (b).
Field measurements of the leaf area index (LAI) of different vegetation types and spectral/textural indices, using high spatial resolution IKONOS data, were analyzed for the purposes of mapping LAI in a patchy agricultural area. The analysis between LAI and different spectral/ textural indices was investigated by means of regression models. It was shown that an overall relationship between all LAI measurements and spectral vegetation indices (SVIs), covering all different vegetation types, was inadequate for mapping LAI. When land cover was stratified, relationships were considerably better and LAI spatial variability was accurately mapped for different crop types. Little difference was found among the different SVIs. The best coefficients of determination were obtained by using linear relationships; however, they should be understood as valid only within the LAI range measured in the present study. Moreover, a substantial stability of the coefficient of determination was observed when the relationships were computed using different kernel sizes. When texture information was included in the multivariate regression model, a general increase was observed in r2, in each cover class and for the overall relationship. In particular, where the spectral information is heterogeneous and patchy, the textural information is useful. The dissimilarity index computed from the panchromatic band of IKONOS was found to be a useful textural parameter, especially for forest, plantations, and vineyards. Geostatistical information also improved the LAI – SVI correlations in some cases.
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The derived relationships allowed highlighting of high within-field spatial variability of LAI in the investigated area. High spatial resolution mapping of LAI with IKONOS data may be particularly useful in applications such as precision agriculture, where information on LAI variation is relevant to management decisions.
Acknowledgements This work was supported by Regione Lombardia (Italy) grant to Dr. Fasolini. We gratefully acknowledge Michele Meroni (Di.S.A.F.Ri, Universita´ della Tuscia, Viterbo) for his suggestions and Tracy d’Alton who helped improve the manuscript. We would like to thank the anonymous reviewers for their useful comments.
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Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI). Remote Sens. Environ., 64, 64 – 76. Yang, C., & Anderson, G. L. (1999). Airborne videography to identify spatial plant growth variability for grain sorghum. Precis. Agric., 1(1), 67 – 79.
Retrieval of leaf area index in different vegetation types using high resolution satellite data Roberto Colombo a,*, Dario Bellingeri b, Dante Fasolini c, Carlo M. Marino b a
Institute for Environment and Sustainability, Joint Research Centre of the European Commission, TP 262, Via E. Fermi, s/n 21020 Ispra, Varese, Italy b DISAT Universita` Degli Studi di Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy c ERSAF Ente Regionde per i Servizi all, Agricoltura e alle Foreste della Lombardia, Via Ponchielli 2/4, Milan, Italy Received 21 March 2002; received in revised form 18 February 2003; accepted 8 March 2003
Abstract With the successful launch of the IKONOS satellite, very high geometric resolution imagery is within reach of civilian users. In the 1-m spatial resolution images acquired by the IKONOS satellite, details of buildings, individual trees, and vegetation structural variations are detectable. The visibility of such details opens up many new applications, which require the use of geometrical information contained in the images. This paper presents an application in which spectral and textural information is used for mapping the leaf area index (LAI) of different vegetation types. This study includes the estimation of LAI by different spectral vegetation indices (SVIs) combined with image textural information and geostatistical parameters derived from high resolution satellite data. It is shown that the relationships between spectral vegetation indices and biophysical parameters should be developed separately for each vegetation type, and that the combination of the texture indices and vegetation indices results in an improved fit of the regression equation for most vegetation types when compared with one derived from SVIs alone. High within-field spatial variability was found in LAI, suggesting that high resolution mapping of LAI may be relevant to the introduction of precision farming techniques in the agricultural management strategies of the investigated area. D 2003 Elsevier Science Inc. All rights reserved. Keywords: LAI; IKONOS; SVIs; Texture indices
1. Introduction The high spatial resolution of IKONOS satellite images allows for various environmental applications, such as mapping, agriculture, forestry, and emergency response. The satellite sensor can generate 1-m panchromatic and 4m multiband images with off-nadir viewing of up to 60.25j for better revisit rate and stereo capabilities. The panchromatic imagery has a spectral wavelength interval ranging from 0.45 to 0.9 Am while the multispectral imagery includes four bands in the blue, green, red and near-infrared part of the spectrum (0.45 – 0.52, 0.52 – 0.60, 0.63– 0.69, and 0.76 – 0.90 Am). This very high resolution satellite imagery provides a new source of data for monitoring agricultural production, potentially providing information with respect to the development of crops during the growing season. * Corresponding author. DISAT Universita` Degli Studi di MilanoBicocca, Piazza della Scienza 1, 20126 Milan, Italy. Tel.: +39-0264482848; fax: +39-0264482895. E-mail address: [email protected] (R. Colombo).
The information content of panchromatic and multispectral satellite images may be useful in large-scale quantitative assessment of biophysical attributes, such as leaf area index (LAI), which are key inputs in models describing biosphere processes. The characterisation of the biosphere is a key step for understanding biological and physical processes associated with vegetation, for developing ecosystem productivity models and for computing the mass and energy exchange between soil, vegetation and atmosphere (Bonan, 1993; Liu, Chen, Cihlar, & Park, 1997; Sellers, Mintz, Sud, & Dalcher, 1986). Currently, ecosystem models require either field validation of simulated LAI, or remotely sensed estimates of LAI to initiate them (Running et al., 1999). Leaf area index measurements are critical for improving the performance of such models over large areas and this has prompted investigations of the relationship between ground-measured LAI and spectral vegetation indices (SVIs) derived from satellite-measured data (Chen & Cihlar, 1996; Fassnacht, Gower, MacKenzie, Nordheim, & Lillesand, 1997; Nemani, Pierce, Running, & Band, 1993; Spanner, Pierce, Peterson, & Running, 1990).
0034-4257/03/$ - see front matter D 2003 Elsevier Science Inc. All rights reserved. doi:10.1016/S0034-4257(03)00094-4
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Although LAI can be directly or indirectly measured by several methods (see e.g. Gower, Kucharik, & Norman, 1999; White, Asner, Nemani, Privette, & Running, 2000), its spatial and temporal distribution is usually investigated using remotely sensed data. The simplest and most practical way is to investigate the relationships between LAI and SVI values by means of regression models. Such relationships usually result in different mathematical forms with empirical coefficients that vary, depending primarily on vegetation type (e.g., Chen, Rich, Gower, Norman, & Plummer, 1997; Turner, Cohen, Kennedy, Fassnacht, & Briggs, 1999). Moreover, these relationships are affected by several factors, such as background reflectance, crown closure, orientation and aggregation of leaf elements, branches, stand age and difference in chlorophyll concentration. A considerable scatter in LAI –SVI relationships is usually found when the ground pixels observed by the sensor include a combination of canopy reflectances, originating from different types of vegetation with a variable amount of understory (Chen & Cihlar, 1996; Frankin, 1986). The red (R, 0.63 – 0.69 Am), Near InfraRed (NIR, 0.76– 0.90 Am), Shortwave Infrared (SWIR, 1.55– 1.75 Am) and Middle Infrared bands (MIR, 2.08– 2.35 Am) are the typically used bands in SVI computation, and their response in producing LAI maps is discussed in many studies (e.g., Baret, Guyot, Begue, Maurel, & Podaire, 1988; Brown, Chen, Leblanc, & Cihlar, 2000; Fassnacht et al., 1997; Nemani et al., 1993). When the analysis is to be conducted at field scale, very high resolution satellite data can be a useful input for different environmental applications. Field scale refers to a geometrical resolution able to resolve intra-field variability of crop condition. Traditional agriculture considers a field as a homogeneous unit, while the exploitation of very high geometrical resolution satellite data may allow detecting within-field spatial variability of biophysical parameters, useful in agricultural management (Moran, Inoue, & Barnes, 1997). Information regarding within-field spatial variability is necessary in precision farming techniques in order to increase canopy uniformity, and thus to improve crop yield and quality (Barnes, Pinter, Moran, & Clarke, 1997; Garcia Cidad & Vrindts, 2001; Johnson, Roczen, & Youkhana, 2001). Individual cover types generally show a strong relationship between LAI and SVIs. Therefore, high spatial resolution imagery should allow for the retrieval of better LAI – SVI relationships as compared to low resolution imagery, in which each pixel may be made up of many land cover types. Using IKONOS data, the scattering effect of mixed land cover classes on the spectral data may be reduced. LAI retrieval by means of IKONOS data is usually conducted by regression models with SVIs (e.g. Johnson et al., 2001) without including any additional geometrical information. However, texture information provided by high geometrical resolution aerial and satellite data can potentially be used as additional information related to the spatial canopy architecture that, combined with spectral data, might
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improve the LAI description (Weiss et al., 2001; Wulder, Franklin, & Lavigne, 1996, Wulder, Le Drew, Franklin, & Lavigne, 1998). In this study, different LAI estimates for five vegetation type classes, obtained from spectral vegetation indices and textural information, are discussed. The potential of very high resolution data for LAI retrieval is outlined, and the LAI spatial variability within the different vegetation types is analyzed.
2. Methods Three techniques, based on SVIs coupled with texture indices and geostatistics, are applied in order to retrieve LAI of different agricultural crops using IKONOS data acquired almost simultaneously with ground measurements. LAI values, derived from the LAI-2000 Plant Canopy Analyzer (LI-COR, 1992, Lincoln, NE), were plotted versus SVI data, and versus a combination of SVI and texture information. The test site area is located in the S. Colombano region (Italy), characterized by a landscape encompassing hilly vineyards and flat alluvial areas of the Po river plain mainly devoted to agricultural crops and intensive poplar plantations. 2.1. Ground-based LAI measurements The LAI-2000 instrument was used in different vegetation types to derive LAI indirectly. LAI measurements were collected in a test area investigated in the DUSAF project (Destinazione d’Uso dei Suoli Agricoli e Forestali), coordinated by ERSAL (Ente Regionale di Sviluppo Agricolo della Lombardia) and focused on precise land use and land cover mapping. LAI-2000 measures the gap fraction P(h) in five zenith angle (h) ranges with midpoints of 7j, 23j, 38j, 53j and 67j. LAI is determined by inverting simple radiative transfer model foliage information according to Welles and Norman (1991). This indirect LAI estimate specifically represents an effective leaf area index for the agricultural crops and an effective plant area index, including branch components, for deciduous forests. The assumption of a random spatial distribution of the leaves as made in the model inversion is generally satisfied for these crops. Where a nonrandom spatial distribution of canopy foliage is observed, an accurate description of gap size is essential to avoid large errors in LAI estimation (Chen et al., 1997; Gower et al., 1999). LAI was calculated according to Gower and Norman (1991) from the LAI-2000 gap fraction measurements: LAI ¼ 2
Z
p=2
ln½1=PðhÞcosh sinh dh 0
LAI measurements were collected in late May 2001, under diffuse radiation conditions at sunset in one sensor
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Fig. 1. Example of a corn field with two LAI measurements (left). Schematic presentation of a 3 3 pixel kernel of the multispectral IKONOS image (12 12 m), illustrating the crossing of crop rows (diagonal dashed lines) by the transect.
mode, and using a 45j view cap. LAI was measured along ground transects in five different vegetation types: soybean, corn, vineyards, poplar plantation (plantation) and deciduous forest (forest). Measurements were performed in different fields, at different phenological stages and with different canopy heights, and were also collected within the same field to determine the within-field LAI spatial variation. Twenty-eight crop fields were investigated for a total of 39 LAI measurements with 12, 7, 8, 6, and 6 measurements in corn, soybean, plantations, vineyards, and forest, respectively. Each LAI measurement was obtained by combining two parallel transects, diagonally crossing the crop rows according to the schema presented in Fig. 1. The transect length and the spatial interval between ground samples were fixed for all vegetation types, and a single LAI value was thus obtained by averaging 10 samples, 5 collected along the first 10-m transect and 5 collected coming back along the parallel one. With a distance of 2 m between the samples, consecutive samples of the LAI-2000 instrument are overlapping when the canopy height is greater than 70 cm. The distance between two transects was set to a minimum of 3 m. Such a planning scheme was designed in order to characterise a window of 3 3 pixels on the multispectral IKONOS image (144 m2) and, depending on the canopy height, different crop field areas were sampled. A single IKONOS panchromatic-multispectral image was used for all sites. IKONOS data were acquired on 29 May 2001 under clear sky conditions. The view angle was 15j off-nadir. Raw digital number (DN), re-scaled to the full 11-bit range, was used in this study. As a consequence, in the absence of the re-scaling parameters it was impossible to compute the calibrated radiance from the original data (Holtman and Rigopoulos, Space Imaging, personal com-
munication). IKONOS images were initially georeferenced by an image-to-image technique, using as a master the digital orthophoto provided by ERSAL. A first-order polynomial was used with a nearest neighbour resampling, obtaining a registration accuracy of 0.6 pixel for the multispectral images. The LAI-2000 measurements were geolocated on the IKONOS images as follows. During field work, transect starting points were directly positioned on the printed, georeferenced panchromatic IKONOS image with a compass for measuring their azimuth. Each transect was digi-
Table 1 Spectral vegetation indices used in LAI retrieval SVI
Algorithm
NDVI
NIR R NIR þ R
SR
NIR R
SAVI
NIR aR c ð1 þ LÞ NIR þ R þ L
PVI
NIR aR c pffiffiffiffiffiffiffiffiffiffiffiffiffi a2 þ 1 a2 þ 1
ARVI
NIR rb ; NIR þ rb
EVI
NIR R *G NIR þ C1 *R C2 *B þ C3
rb ¼ R cðB RÞ
B, R, and NIR represent the IKONOS DN data in blue, red and infrared bands. Parameters a, c, L, and c regard respectively the gain and the offset of the soil line, the SAVI term (set equal to 0.5) and the ARVI term (set equal to 1). The coefficients adopted in the EVI algorithm are C1 = 6.0, C2 = 7.5, C3 = 1, and G = 2.5.
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Fig. 2. IKONOS multispectral image (a) and dissimilarity image (b). Highest dissimilarity values correspond to poplar plantations and vineyards, which are well distinguished from other crops.
tised and then located on the georeferenced multispectral image to define the kernel window. It is assumed that these LAI field measurements can be considered as representative of a window of 3 3 pixels in the multispectral IKONOS image. The coefficient of variation in these windows was generally less than 5% for each band, showing little subwindow heterogeneity. 2.2. LAI – SVI relationships Initially, six SVIs were directly computed from DN data to analyse the relationship with LAI (Table 1). Normalised difference vegetation index (NDVI) (Rouse & Haas, 1973), simple ratio (SR) (Jordan, 1969), soil adjusted vegetation index (SAVI) (Huete, 1988), perpendicular vegetation index (PVI) (Richardson & Wiegand, 1977), and atmospherically resistant vegetation index (ARVI) (Kaufman & Tanre´, 1992) were selected as representative of intrinsic, soil adjusted and atmospherically corrected indices. The last three spectral indices were included in the study to analyse their effects on
reducing the background and atmospheric effects in LAI mapping. The enhanced vegetation index (EVI) (Huete, Justice, & van Leeuwen, 1999) used with the coarse resolution MODIS data was also investigated. A series of field-sampled bare soils pixels were selected on the panchromatic band that reflect the broad band albedo of the scene in order to determine the a and b parameters of the so-called ‘‘soil line’’ for computing SAVI and PVI. Initially, all the LAI measurements (N = 39) were plotted against the SVIs in order to compute the coefficient of determination (r2) of the regression equation. A window of 3 3 pixels was initially used in each spectral band to determine the spatial homogeneity by statistical parameters. The analysis was then conducted for each vegetation type, plotting the LAI measurements against the SVIs. The r2 for each LAI – SVI relationship was then recomputed using the SVI values based on mean reflectances of different windows sizes of 6 6 and 9 9 pixels.
Fig. 3. The different spatial variability pattern within the deciduous forest and poplar plantation data is well reproduced by the semivariogram function.
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Fig. 4. Relationship between all measured LAI in different vegetation types and NDVI.
2.3. LAI retrieval by SVIs and texture analysis Image texture is a quantification of the spatial variation of image tone values, which can be related to spatial distribution of vegetation. The above-ground organisation of forest elements is represented in texture, which is supplementary to the spectral image data and may provide an additional information source. Image texture may be related to a variety of statistical measurements that characterise the relationships between neighbouring pixels. Homogeneity, contrast, and entropy are measures related to the specific textural characteristics of the image, while dissimilarity, mean, and standard deviation characterise the complexity and the nature of grey level transitions defined in the co-occurrence matrix (Wulder et al., 1998). The approach for LAI determination based on texture component is subject to the hypothesis that the image spatial information can be represented in simple texture statistics, which are a substitute for the structure of the forest vegetation or the distribution of LAI, and that the definition of the forest structure can result in better estimates of LAI in patchy stands. The texture indices were calculated for the IKONOS panchromatic band at the maximum spatial resolution of 1 m. To appreciate the texture of the vegetation, three different window sizes were employed (3 3, 6 6, 9 9 pixels). A series of textural variables were tested and the best results in terms of r2 were obtained by using the dissimilarity index. Dissimilarity (D) was computed according to the formula: D¼
n1 X
Pi;j ji jj
Dissimilarity was observed to reflect a texture measure that is high when the field region investigated has a high amount of spatial local variation related to structural characteristics of the image (e.g., crops raw and cover, crops height and shadowing effects, discontinuous urban fabric) (Fig. 2). The dissimilarity image was resampled at a 4-m resolution, in order to integrate texture information with the spectral vegetation indices previously derived from multispectral data. The relationship between the combined SVI-texture data versus LAI was tested by performing multiple linear regressions, with LAI as dependent variables and SVI/dissimilarity as independent indices. 2.4. LAI retrieval by SVIs, texture and geostatistical parameters Geostatistics is a useful technique whenever the property of interest behaves as a spatially correlated variable. Several authors have used geostatistical parameters to extract structural, biophysical, and forest damage information (Bruniquel-Pinel & Gastellu-Etchegorry, 1998; Chica Olmo & Abarca Hernandez, 2000; Franklin, Wulder, & Lavigne, 1996; Levesque & King, 1999; Weiss et al., 2001; Wulder Table 2 Summary table of the coefficient of determination (r2) for LAI retrievals computed using the three different approaches Vegetation type
SVI
NDVI NDVI/ dissimilarity
NDVI + sill
NDVI + range
NDVI + range + diss
Forest/ plantation Forest Plantation Vineyards Soybean Corn All data
0.60
0.73
0.65
0.72
0.76
n.s. 0.79 0.74 0.74 0.80 0.33
0.73 0.81 0.98 0.74 0.80 0.62
n.s. 0.79 n.s. – – –
n.s. 0.8 n.s. – – –
n.s. 0.82 n.s. – – –
i;j¼0
where P is the probability for the cell i, j (row and column numbers, respectively). Pi,j is computed as follows Pi;j ¼
Vi;j n 1 X
Vi;j
i;j¼0
where Vi,j is the DN value in the cell i,j of the image window.
Texture analysis Geostatistical parameter
n.s.: not significant at p = 0.05; – : not computed since any additional information is provided by texture.
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Fig. 5. Relationships between LAI and NDVI for individual vegetation types.
et al., 1996; ) from digital data. The central tool of geostatistics is the variogram, which is used to examine the spatial continuity of a regionalized variable as a function of distance and direction. A variogram is a plot of the semivariances at different distances (lag), which is computed as the sum of squared differences between all possible pairs of points separated by the chosen distance. Semivariance (c(h)) was computed according to: m 1 X cðhÞ ¼ ½zðxi Þ zðxi þ hÞ2 2m 1¼1 where m is the number of pairs of pixels separated by the same lag(h) and z(xi) is the DN value at the xi position. Semivariance is thus obtained for each lag and plotted against the lag. Semivariance thus represents the degree of spatial dependence between samples. The main properties defining the variogram are the sill, the range, and the nugget, which relate to the height of the variogram, the distance to reach the sill, and the positive y-intercept, respectively. For measurements of LAI in forest and plantations, a window size of 50 50 pixels on the IKONOS panchromatic band was selected, since it allows analysis of homogeneous areas with respect to crop type and is large enough to allow the calculation of a significant semivariance plot. Omni-directional semivariograms were then computed, and
nugget, range, and sill were determined from the fitted mathematical spherical models. The graphical representation of the average semivariance of several pixel pairs at each lag distance (h) computed for plantations and forest classes is shown in Fig. 3. A spherical model was adopted, even though plantations showed a periodic pattern due to their typical rows (Bruniquel-Pinel & Gastellu-Etchegorry, 1998). The geostatistical parameters were computed and employed in multivariate linear regression with the vegetation indices and the dissimilarity image.
3. Results and discussion For all SVIs, the relationship with LAI shows a positive correlation with a low coefficient of determination. The overall relationship between all LAI measurements and SVIs appears inadequate for mapping LAI. Fig. 4 shows, as an example, the correlation between LAI and NDVI (r2 = 0.33). The large scatter is caused, in part, by the grouping of all vegetation types. The observed degree of uncertainty would greatly compromise the utility of the relationship for LAI mapping at this detailed scale. Due to the heterogeneity of the group of data, one LAI –SVI relationship is not adequate
Fig. 6. Trend of the coefficient of determination computed for the six SVIs used in this study.
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for mapping LAI across all vegetation types. The detected heterogeneity is probably of the same order of magnitude as the heterogeneity detectable within a coarse resolution cell.
When the relationships between SVIs and LAI are analyzed for each vegetation type, the r2 values are much improved (see Table 2). The resulting LAI –NDVI relation-
Fig. 7. Multispectral image with plantations and corn plots outlined; a subset of LAI measurements is also shown (a). The LAI map identifies the spatial variation in different fields (b).
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ships show the great increase in r2 in the agricultural crops (Fig. 5). When the relationship between LAI and NDVI is computed for all vegetation types grouped together, without including the forest class, a decrease in r2 to a value of 0.25 was found. At the same time, the scatter of the relationships is still relatively high in forests, due to their high internal heterogeneity. The investigated forest showed the highest LAI values, with a low SVI variability, and the 3 3 pixel window generally corresponds to an area with different tree heights and canopy architecture. It was difficult to define the most appropriate SVI for mapping LAI of each crop. There is not much variation among SVIs, with relatively high r2 in most cases. EVI was found to have the same performance as NDVI for all crops analyzed. In Fig. 6, the coefficient of determination computed for the different SVIs is reported.
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The low variation among the SVIs in these correlation coefficients may be related to the fact that each crop was sampled at different phenological stages, ranging from very low cover to a high degree of vegetation amount. This may result in a deterioration of the performance for soil linebased indices. No improvement for the background effect was determined using SAVI index, while PVI does worse in plantation stands and better in forest. Moreover, no improvements were found by using SVIs that minimise the atmospheric and background effects. ARVI may not have been successful since the terrain morphology shows low relative relief and, therefore, spatial differences in upwelling sky irradiance and path radiance are low. It should be noted that the regression equations between SVIs and LAI were obtained by fitting a linear relationship, although other studies have shown the nonlinearity of NDVI/LAI (e.g. Chen & Cihlar, 1996; Myneni, Nemani, & Running, 1997).
Fig. 8. On the left, the true colour IKONOS images and on the right, the LAI maps derived for poplar plantation (b) and corn vegetation type (d). The masked areas are shown in black.
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Fig. 9. Coefficient of determination of the relationships LAI – NDVI as estimated from different window sizes for each vegetation type.
The LAI spatial variations of corn and plantations fields, for a sector of the investigated area of about 15 km2, are shown in Fig. 7. LAI is mapped from NDVI data and allows observation of the different behaviour of the plantations in the abandoned meandering river system. A large LAI range characterizes the corn, with fields varying from 0.5 to 4.5 m2/m2. Examples of detailed LAI maps for the intensive plantation and for corn crops show the within-field variation of LAI (Fig. 8). Spatial LAI variations are very evident at the field scale as intra-culture variations characterizing different crop stages. In a corn field, the within-field variation of the ground-based LAI measurements ranged from 0.8 to 2.0. This heterogeneity is related to variations in crop development due to a uniform management (irrigation, chemical treatments) applied to a portion of terrain showing heterogeneous edaphic conditions. Other studies have also shown a high spatial variability of LAI and other biophysical parameters within crop fields detected by remote sensing techniques (Barnes et al., 1997; Castagnoli & Dosso, 2001; Johnson et al., 2001; Yang & Anderson, 1999). These studies suggest that precision farming techniques might usefully incorporate data on local LAI variation, computed from IKONOS data. The performance of areas with low LAI could potentially be improved through a differentiated treat-
ment, and LAI maps could be used to formulate management recommendations during the early stages of growth. The analysis was finally conducted computing different window sizes starting from the SVI values, and a substantial stability was observed when the relationships were computed using window sizes of 3 3, 6 6 and 9 9 pixels. The application of different kernel sizes did not influence the coefficient of determination of the LAI – SVI relationships (Fig. 9). Window sizes greater than nine pixels generally include surrounding fields with different crop types. Thus, the field sampling scheme for measuring LAI may be considered appropriate to represent the vegetation spatial variability detected by high geometric resolution satellite data. Spatial heterogeneity in canopy architecture was analyzed in the LAI mapping by introducing texture indices. The best indicator of texture was found to be the dissimilarity index as computed on a 6 6 pixel window from panchromatic data. The combination of the dissimilarity index and vegetation indices resulted in an increase of the coefficient of determination for most of the vegetation types when compared with that derived from SVIs alone (Fig. 10). A large increase in r2 for the overall relationship (from 0.33 to 0.62) was achieved when texture information was included in the analysis, suggesting a useful way to avoid possible problems related to class definitions with an
Fig. 10. Values of coefficient of determination computed for the different vegetation types using spectral and textural information of IKONOS data.
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automatic land cover stratification. The addition of a texture index as a covariate in the multiple regressions has an effect similar to doing a classification before developing the LAI – SVI relationships. The outstanding improvement in the r2 for the forest class when adding the texture information might be explained by the nonuniform and nonrandom spatial distribution of natural vegetation. The forest was a mosaic of dense and sparse vegetation cells even within a single 3 3
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pixel window and that heterogeneity weakened the LAI – SVI relationships. An example of the LAI map for the forest class is shown in Fig. 11. The substantially homogeneous texture of corn and soybean viewed at the spatial resolution of 1 m did not add appreciable information to LAI determination. Geostatistical tool data also strengthened LAI –SVI relationships in some cases. The semivariogram computed for plantation and forest yielded information about their structure. In particular, a linear sill in the semivariogram is typical of the forest class, while the periodic shape of the plantation’s semivariogram shows the repetitive pattern of the plantation (see Fig. 4). As shown in Table 2, the parameters extracted by geostatistical analysis, calculated only for forest and plantation, showed marginal improvements in some cases for the empirical fits to LAI. In particular, the inclusion of the range and sill values into a multivariate linear regression permits derivation of LAI with an r2 similar to the case when the texture band is added to SVIs. This small improvement might be due to the additional information regarding the density of the crowns.
4. Conclusions
Fig. 11. IKONOS panchromatic image with the forest class indicated in black (a); LAI map of the corresponding class superimposed on the true color multispectral IKONOS image (b).
Field measurements of the leaf area index (LAI) of different vegetation types and spectral/textural indices, using high spatial resolution IKONOS data, were analyzed for the purposes of mapping LAI in a patchy agricultural area. The analysis between LAI and different spectral/ textural indices was investigated by means of regression models. It was shown that an overall relationship between all LAI measurements and spectral vegetation indices (SVIs), covering all different vegetation types, was inadequate for mapping LAI. When land cover was stratified, relationships were considerably better and LAI spatial variability was accurately mapped for different crop types. Little difference was found among the different SVIs. The best coefficients of determination were obtained by using linear relationships; however, they should be understood as valid only within the LAI range measured in the present study. Moreover, a substantial stability of the coefficient of determination was observed when the relationships were computed using different kernel sizes. When texture information was included in the multivariate regression model, a general increase was observed in r2, in each cover class and for the overall relationship. In particular, where the spectral information is heterogeneous and patchy, the textural information is useful. The dissimilarity index computed from the panchromatic band of IKONOS was found to be a useful textural parameter, especially for forest, plantations, and vineyards. Geostatistical information also improved the LAI – SVI correlations in some cases.
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The derived relationships allowed highlighting of high within-field spatial variability of LAI in the investigated area. High spatial resolution mapping of LAI with IKONOS data may be particularly useful in applications such as precision agriculture, where information on LAI variation is relevant to management decisions.
Acknowledgements This work was supported by Regione Lombardia (Italy) grant to Dr. Fasolini. We gratefully acknowledge Michele Meroni (Di.S.A.F.Ri, Universita´ della Tuscia, Viterbo) for his suggestions and Tracy d’Alton who helped improve the manuscript. We would like to thank the anonymous reviewers for their useful comments.
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