Segment-based stem volume retrieval in boreal forests using ...

Can. J. Remote Sensing, Vol. 34, No. 1, pp. 46–55, 2008

Segment-based stem volume retrieval in boreal forests using multitemporal ERS-1/2 InSAR data Marcus E. Engdahl, Jouni Pulliainen, and Martti Hallikainen Abstract. In this study, we demonstrate a method for the combined land cover classification and boreal forest stem volume retrieval using multitemporal interferometric synthetic aperture radar (InSAR) data. The method utilizes the mean InSAR coherence image that is segmented into quasihomogeneous segments, and the land cover classes of the segments are determined based on their multitemporal InSAR signatures. Continuous stem volume estimates for the forest segments are then produced by inverting a semi-empirical backscattering coherence model. A group of forest stands with known stem volumes are required as training areas for determining the values of the model parameters. The performance of the method was studied by estimating the stem volumes of 4176 forest segments using 134 training stands. The results were compared with stem volume estimates produced by ground-based measurements and the satellite-based operational National Forest Inventory (NFI) of Finland. The performance of the method in stem volume estimation for stands larger than 1.5 ha (average stand size 3.2 ha) achieves a correlation coefficient of 0.87 and a root mean square error (RMSE) of 54% in comparison with ground-based reference data. The results are better than the estimates of the operational NFI that saturate at around 200 m3/ha. Markedly higher accuracy is to be expected when applying the method to larger stands in a large-area forest inventory. Résumé. Dans cette étude, nous présentons une méthode pour la classification du couvert et l’extraction du volume des tiges en forêt boréale à l’aide de données interférométriques multitemporelles InSAR (« interferometric SAR »). La méthode utilise l’image de cohérence moyenne InSAR, qui est segmentée en segments quasi-homogènes, et les classes de couvert des segments sont déterminées sur la base de leurs signatures multitemporelles InSAR. Des estimations en continu du volume des tiges sont alors produites pour les segments de forêt en inversant un modèle semi-empirique de rétrodiffusion-cohérence. Un groupe de peuplements forestiers avec des volumes de tiges connus est nécessaire comme sites d’entraînement pour déterminer les valeurs des paramètres du modèle. La performance de la méthode a été étudiée en estimant les volumes des tiges de 4176 segments de forêt en utilisant 134 peuplements d’entraînement. Les résultats ont été comparés avec des estimations de volumes des tiges dérivées de mesures au sol et des données de l’inventaire opérationnel NFI (« National Forest Inventory ») basé sur des données satellitaires de Finlande. La performance de la méthode dans l’estimation des volumes des tiges pour des peuplements plus grands que 1,5 ha (dimension moyenne des peuplements de 3,2 ha) atteint un coefficient de corrélation de 0,87 et une valeur de RMSE = 54% comparativement aux données de référence acquises au sol. Les résultats sont meilleurs que les estimations de l’inventaire opérationnel NFI, qui saturent à environ 200 m3/ha. Des niveaux de précision nettement plus élevés devraient être atteints si l’on appliquait la méthode à des peuplements plus étendus dans le contexte d’un inventaire forestier en région plus vaste. [Traduit par la Rédaction]

Introduction

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The potential of C-band spaceborne repeat–pass interferometric synthetic aperture radar (InSAR) in land cover classification is well established (Wegmüller and Werner, 1997; Dammert et al., 1999; Strozzi et al., 2000; Weydahl, 2001; Engdahl and Hyyppä, 2003; Matikainen et al., 2006). C-band InSAR performs especially well in forest/non-forest classification, where classification accuracies from 80% to well over 90% have been reported (Wegmuller and Werner, 1997;

Strozzi et al., 2000; Castel et al., 2000). Furthermore, previous studies have shown that C-band repeat–pass interferometric coherence is sensitive to boreal forest stem volume, even though environmental and weather conditions have a very strong influence on both the InSAR coherence and the correlation between the InSAR coherence and stem volume (Wegmüller and Werner, 1997; Askne et al., 1997; 2003; Hyyppä et al., 2000; Fransson et al., 2001; Koskinen et al., 2001; Santoro et al., 2002; Pulliainen et al., 2003; Askne and Santoro, 2005). Despite the strong influence of environmental

Received 7 April 2006. Accepted 7 February 2008. Published on the Canadian Journal of Remote Sensing Web site at http://pubs.nrc-cnrc.gc.ca/cjrs on 11 July 2008. M.E. Engdahl.1 Directorate of Earth Observation Programmes, ESA–ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati, Italy. J. Pulliainen. Arctic Research Centre, Finnish Meteorological Institute, Tähteläntie 62, FI-99600 Sodankylä, Finland. M. Hallikainen. Laboratory of Space Technology, Helsinki University of Technology, Otakaari 5A, P.O. Box 3000, Espoo FIN-02015 HUT, Finland. 1

Corresponding author (e-mail: [email protected]).

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and weather conditions, spaceborne C-band repeat–pass InSAR has proved itself to be a highly valuable tool in the estimation of boreal forest stem volume over large areas (Wagner et al., 2003; Gaveau et al., 2003; Tansey et al., 2004; Santoro et al., 2007). In boreal forests, the above-ground dry biomass measured in tons/ha is approximately 0.6 times the stem volume measured in m3/ha (i.e., 100 m3/ha stem volume corresponds to 60 tons/ha above-ground dry biomass) (Häme et al., 1992). Therefore, C-band InSAR is a suitable technique for above-ground dry biomass retrieval in boreal forests, and, in the rest of this paper, the term “biomass” refers to aboveground dry biomass. Remote-sensing-based forest inventories are often not very accurate at the pixel level; that is, at the spatial scale from a few metres to a few tens of metres, depending on the instrument. A basic forest mapping and description unit is the forest stand, which can be defined as a relatively homogeneous patch of forest in terms of tree composition and forest structure. In the boreal forests of Finland, the size of forest stands varies typically from 0.5 ha to tens of hectares, whereas in Michigan, for example, homogeneous forest stands are usually larger than 10 ha (Dobson et al., 1995; Hyyppä and Hyyppä, 2001). Hence, several instrument pixels fall within a stand, and the accuracy of remote-sensing-based forest inventories can be increased by dealing with stand averages instead of the values of individual pixels. Presently, stand boundaries and forest attributes within a stand are usually measured in a field survey. This is a necessary procedure when the attributes of the training stands are determined, but for larger areas the effort and cost involved in determining just the stand boundaries for the remote-sensingbased inventories may be prohibitive. Therefore, it is desirable to be able to derive applicable stand boundaries from the remotely sensed data itself. In this study, we demonstrate a method for the combined land cover classification and stem volume retrieval using multitemporal ERS-1/2 Tandem InSAR data. The stem volume retrieval is based on an inversion of a semi-empirical backscattering coherence model, and its performance in stem volume retrieval is assessed by comparing the results against ground-based stem volume measurements and stem volume estimates produced by the Finnish National Forest Inventory (NFI).

Mean stem vol. (m3/ha) Std. of stem vol. (m3/ha) Min. stem vol. (m3/ha) Max. stem vol. (m3/ha) Mean area (ha) Std. of area (ha) Min. area (ha) Max. area (ha) Total area (ha)

All 210 stands

134 stands >1.5 ha

4176 segmentsa

156 160 0 539 2.4 1.9 0.2 13.8 506

174 176 0 539 3.2 2.0 1.5 13.8 429

101 64 0 281 5.4 3.3 0.3 35.1 22 345

Note: Stand statistics are based on ground-based measurements, and segment statistics on National Forest Inventory estimates. a Segmentation is based on radar data.

are very heterogeneous, as the soil types range from mineral soils to peatlands, and the soil fertility class from poor to rich. The ground vegetation layer is also highly heterogeneous, including bare mineral soils, grasses, shrubs, and mosses. Reference data The reference data include ground-based forest inventory data measured at the stand level, as well as pixel-based stem volume estimates from the operative NFI. The NFI is based on multiple data sources, and it employs Landsat TM data in addition to ground-based sampling in estimating forest stem volume with the k-nearest neighbor (k-NN) method (Katila and Tomppo, 2001). Table 1 presents the forest characteristics in the study area according to both reference datasets. The standbased forest inventory data based on ground measurements covers a subset of the area covered by the segment-averaged NFI. Hyyppä and Hyyppä (2001) have shown that the NFI was not very accurate in stem volume estimation at the stand level on the Kalkkinen test site in Southern Finland, but it should be kept in mind that although NFI is sometimes used in forest inventory at the stand level, it is an operational system developed for large-area forest inventory. In addition to stem volume reference data, high-resolution aerial orthophotos were used in the supervised classification step to identify samples of land cover classes. SAR data

Test site and data Test site Combined land cover classification and stem volume estimation was studied on a 366 km2 area at the Tuusula test site (60°N, 25°E) in Southern Finland. The land cover in the area is a mixture of agricultural, forested, and urban land, and the topography of the area is quite flat with some small rugged hills that are interspersed with boulders and open rock surfaces. Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and birch (Betula pendula, Betula pubescens) are the main tree species in the southern-type boreal forests of the study area (area covers 38%, 33%, and 20%, respectively). The Finnish boreal forests © 2008 CASI

Table 1. Forest characteristics of the study area.

The SAR data used in this study consisted of 14 ERS-1/2 Tandem image pairs (28 single-look-complex (SLC) images in total) acquired with the C-band SARs onboard the European Space Agency’s ERS-1 and ERS-2 satellites, between 1995 and 1996. During the ERS-1/2 Tandem mission, the two satellites were flown in the same orbital plane so that ERS-2 imaged the same area on the ground 24 h after ERS-1. Owing to the relatively short temporal baseline of 24 h and the short interferometric baseline lengths, the image pairs collected during the Tandem mission are particularly useful in interferometric studies of natural targets, which decorrelate quite rapidly in C-band. The image data used in this study span 47

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a whole year, from July 1995 to July 1996. All images were acquired from a descending orbit at 12:35 local time, and the perpendicular interferometric baselines are between 2 and 239 m. Nine of the 14 Tandem pairs were acquired in snow-free conditions, four pairs in conditions with dry snow, and one pair when the snowpack was wet and melting. The highest and lowest air temperatures at the time of the image acquisitions were 21 and –15 °C, and precipitation between the acquisitions in a Tandem pair ranged between 0 and 15 mm. In two of the wettest snow-free cases, the trees might have been wet during one or both image acquisitions. A more detailed description of the imaging characteristics and weather conditions can be found in Pulliainen et al. (2003). Processing of SAR data The SLC SAR images were processed into radiometrically calibrated 5-look intensity images (multilooking in the azimuth direction) and 5-look Tandem interferograms. Tandem coherence images were produced using a 7 × 7 pixel Gaussian estimator window, which achieves a rather good compromise between resolution and estimation bias on the small forest stands we were studying. If the emphasis is on large-area forest inventory with large forest stands, a bigger estimator window can be used. Two coherence images with longer temporal baselines (35 and 245 days) were produced in addition to the Tandem coherence images. All image data were orthorectified into map coordinates in 20 m pixel size using an InSAR-generated digital elevation model (DEM) produced from two high-coherence wintertime Tandem pairs. The intensity images were corrected for the true pixel size using the DEM, which reduces the effect of terrain slope on backscattered intensity. The achieved highquality coregistration between all images allowed us to reduce image noise in the intensity- and coherence image-series with a simple multitemporal filter described in Quegan and Yu (2001). A detailed description of the way SAR data are processed can be found in Pulliainen et al. (2003) and Engdahl and Hyyppä (2003).

Modelling and inversion In this section, we describe the Helsinki University of Technology (HUT) backscattering coherence model and how to invert it. The inversion technique is a three-step minimization procedure, introduced in Pulliainen et al. (2003) and described in more detail in Pulliainen et al. (2004). For an in-depth review of the theoretical basis of the method, see, for example, Anderson (1958). The modelling approach Semi-empirical modelling of boreal forest backscatter can be used as a basis for the empirical modelling of InSAR-observed coherence (Askne et al., 1997; 2003; Askne and Santoro, 2005; Koskinen et al., 2001; Pulliainen et al., 2003; Santoro et al., 2002). The applied backscattering model must be able to distinguish between the backscattering contributions of the 48

forest canopy and that of the ground layer. The response of radar backscatter to an increase in forest biomass or stem volume is a nonlinear curve. The level and shape of this curve are affected by the forest type (structure) and by the water content of forest canopy constituents and soil. Additionally, the ground surface roughness properties as well as surface humus layer and ground vegetation have an effect on the level of backscatter. In this investigation, we apply the HUT boreal forest backscattering model that models the C-band backscattering coefficient of forested areas as a function of forest stem volume and takes into account the effects of vegetation water content, top-soil moisture, and the effective surface roughness (Pulliainen et al., 1994: 1996; 1999; 2003; Koskinen et al., 2001). The model predicts that the two backscattering contributions required for C-band InSAR coherence modelling are 0 σ 0 (V , θ, m v,can , σ 0ground ) = σ can + t 2 (V , θ, m v,can ) σ 0ground

≡ σ 0can + σ 0floor

(1)

where σ 0ground is the backscatter of the ground layer (the temporal variations of it are related to soil moisture variations), σ 0can is the canopy backscattering contribution, and t2 is the two-way forest canopy transmissivity. V is the total stem volume of forest (forest stand), θ is the angle of incidence, and mv,can is the effective vegetation (forest canopy) volumetric water content. The HUT model is based on the first-order radiative transfer equation, and it reduces the influence of parameters mv,can and σ 0ground into two empirical coefficients that can be estimated from reference (training) data (Pulliainen et al., 1994; 1996; 1999). When the absolute σ0 levels of ERS-1/2 SAR observations have been determined, the solution of the radiative transfer equation is given by  aV   σ 0 (V , θ) = 0131 . ⋅ a cos θ 1 − exp  −512 . × 10 −3  cos θ    aV   0 0 + b exp  −512 . × 10 −3  ≡ σ can + σ floor θ cos  

(2)

where parameter a is related to the volumetric vegetation water content (under dry summer conditions the value of a is close to 1) and parameter b ≡ σ 0ground . Formula (2) excludes the effect of trunk-ground reflections, which appears to be a valid approximation for ERS-1/2 SAR observations in C-band (Pulliainen et al., 1999; 2003). Previous investigations have indicated that the level of interferometric coherence as a function of forest stem volume V is approximately linearly dependent on the ratio σ 0can (V ) / σ 0 (V ), especially for relatively short interferometric baselines (Koskinen et al., 2001; Pulliainen et al., 2003). Hence, the interferometric coherence | γ | can be modelled as © 2008 CASI

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 σ 0 ( a, V )  | γ | = c 0 + c1  0can   σ ( a, b, V ) 

(3)

where c0 and c1 are empirical coefficients that have to be determined from reference data. As stated in Koskinen et al. (2001), c0 corresponds to the level of coherence for open areas (i.e., clear-cut or sapling areas (V = 0 m3/ha)), and c1 corresponds to the difference between coherence levels of very dense forests and open areas. In practice, the coherence estimates contain also the estimation bias, which may be significant at low coherence values. Model inversion The inversion technique is a three-step minimization procedure introduced in Pulliainen et al. (2003). Stem volume reference data are required for a set of training stands. The actual minimization problems are solved with constrained iteration algorithms. The first two steps are performed separately for each interferometric pair. If the total number of images is denoted by m, then the total number of estimated model parameters is 4 × m. First step of the inversion algorithm The first step is fitting the backscattering model (Equation (2)) into the observed mean σ0 values for all training stands with a and b as the parameters to be optimized. Second step of the inversion algorithm The second step is fitting the coherence model (Equation (3)) to observed interferometric coherences for all training stands with c0 and c1 as the parameters to be optimized. This step uses the values estimated for parameters a and b in the first step of the algorithm. Third step of the inversion algorithm The actual stem volume estimation is based on consideration of the multidimensional Gaussian probability density distribution of modelling errors. In practice, the cost function to be minimized is obtained from the conditional probability of stem volume, given the set of multitemporal observations. Minimization of the cost function yields the stem volume estimate for the j forest segments:   1 1 V$ j = min  ⌫ TC −1⌫ + 2 (V j − VREF ) 2    2 2σ REF where  | γ |1, obs   | γ (V )|1, mod    ⌫ =  M  −  M     | γ | m, obs   | γ (V )| m, mod  © 2008 CASI

    

| γ | m, obs denotes the observed coherence in image m for the forest segment under investigation, | γ (V )| m, mod is the model prediction according to Equation (3), and C is the covariance matrix of the modelling errors in the multitemporal channels 1…m. The covariance matrix is directly determined from the m residual vectors obtained in the fitting of Equation (3) to reference observations in step two of the inversion algorithm. VREF is the mean stem volume of the reference/training data. The latter term in Equation (4) is an optional factor that shifts the estimates toward the mean value of the training data using a weigh factor related to the standard deviation of the training dataset. It can be applied to reduce the retrieval bias, and it is a mathematically exact term if the distribution of reference data is Gaussian. If the modelling errors in different images are independent, then C is a diagonal matrix including the variances of error in coherence modelling. In that case, the minimization problem defined by Equation (4) for estimating the stem volume of forest segment j, reduces to  m 1 2 V$ j = min  ∑ 2 [| γ (V j )| k , mod − | γ | k , obs ]  k =1 2σ k +

1 2σ 2REF

 (V j − VREF ) 2  

(5)

such that Vj ≥ 0 m3/ha. The standard deviation of the coherence modelling error for image k (estimated from the reference/training data) is denoted with σ 2k , and VREF is the mean value of the stem volume in the reference/training dataset.

Combined land-cover classification and stem volume estimation method Our proposed method is the following: (1) Segmentation of the study area into quasi-homogeneous segments based on the mean interferometric coherence. (2) Classification of the segments into forest and other land cover classes based on the multitemporal InSAR signatures of the segments. (3) Estimation of forest stem volume for any forest segment by inverting the HUT backscattering coherence model. The above-mentioned steps are now described in more detail.

(4)

Segmentation into quasi-homogeneous segments Stem volume retrieval accuracy improves considerably when it is possible to deal with segment averages instead of values of single pixels. The needed segment boundaries can be derived from auxiliary data sources like existing land cover databases or conventional stand-based forest inventory data, but deriving them from the remotely sensed data itself is often preferable because of cost reasons. 49

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In this study, the mean ERS Tandem coherence image (mean of 14 images) was segmented into quasi-homogeneous segments. The rationale for using the mean Tandem coherence image as the basis for segmentation is that the Tandem coherence is sensitive to forest stem volume. The mean coherence image has a very low noise level compared with single coherence images, and segmenting such an image is straightforward. The segmentation itself was performed with the commercial software package eCognition, in which the resulting segment size, homogeneity, and boundary smoothness could be controlled. A total of 4806 segments with an average size of 7.6 ha were created on the 366 km2 test area. The more Tandem coherence images are available, the less noisy the image to be segmented will be. To achieve a highquality segmentation on forested areas, one should ideally average only those coherence images that are most sensitive to differences in stem volume. These “best” coherence images from the stem volume retrieval point-of-view can be identified through the use of interferometric coherence contrast (ICC), which measures the difference in coherence between the sparsest and densest forest stands (Engdahl et al., 2004). Classification of segments into forest and other land cover classes Once the study area has been segmented, the segments need to be classified into forest and other land cover classes based on their multitemporal InSAR characteristics. The classification method can be either supervised or unsupervised, and if segments from auxiliary data sources are utilized, the classification step is not necessary as in that case the classes of the segments are already known. In this study, the segments were classified into four classes in eCognition (forest, water, agricultural/open, and urban). In this supervised classification step, a human operator chose a few training segments for each of the four classes using high-resolution aerial orthophotos as reference, and eCognition performed a nearest neighbor (NN) classification of all the segments. The feature space for the NN classification consisted of the segment means in the following six image channels: (1) Mean Tandem coherence (mean of all 14 images) (2) Mean backscattered intensity (mean of 14 pairs = 28 images) (3) Mean longtime coherence (mean of 35-day and 245-day coherences) (4) First principal component of the Tandem coherence timeseries (5) Second principal component of the Tandem coherence time-series (6) First principal component of the backscattered intensity time-series In this study, 4176 segments of the total of 4806 segments created in the previous step were classified as forest segments. 50

The accuracy of the classification was not assessed, but with the same InSAR dataset, an overall classification accuracy of 90% into six land cover classes was achieved with an unsupervised pixel-based approach in Engdahl and Hyyppä (2003), and with a segmentation-based supervised classification approach using eCognition, the overall classification accuracy rose to 97% in Matikainen et al. (2006). Only forest/non-forest classification of the segments is strictly necessary for stem volume retrieval, and using just the mean Tandem coherence to perform such a classification should be sufficient, as forest segments exhibit markedly lower Tandem coherence than urban or agricultural/open segments. Estimation of forest stem volume for any forest segment by inverting the backscattering coherence model for multitemporal InSAR data The model inversion follows the steps described in the previous section. During model inversion, all 134 forest stands larger than 1.5 ha from a total of 210 stands were used as training stands (see Table 1). Practically identical inversion performance was achieved in a test using a randomly selected 25% of the 134 stands for training and the rest of the stands for testing. This illustrates that having less than 40 training stands was enough to achieve good stem volume retrieval performance on our test site. To find the best subsets of Tandem data for stem volume retrieval, a forward selection rule was used in the selection from the total set of 14 Tandem pairs. The performance of both Equations (4) and (5) was compared in stem volume retrieval, both with and without the regularization term. When regularization was not used, the maximum allowed stem volume in the inversion was set equal to the maximum stem volume (539 m3/ha) found in the ground-based reference/training data (see Table 1). This approach is justified, as it is known that higher stem volumes are very rare in Finnish boreal forests, and because the reference data can be assumed to be a representative sample of the forests in the study area.

Results The performance of the proposed InSAR-based method in stem volume estimation was assessed by comparing the InSAR estimates against ground-based stem volume measurements and stem volume estimates produced by the NFI. In addition, NFI estimates were compared against the ground-based data. The combined land cover and stem volume map produced with the presented method is illustrated in Figure 1. Pixels, segments, and stands The NFI estimates are pixel-based, the InSAR estimates segment-based, and the ground-based inventory data standbased. In addition, the stand and segment boundaries are not identical. Nevertheless, these different kinds of data need to be compared on equal spatial units. When the pixel-based NFI was compared against ground-based and InSAR data, the values of NFI pixels falling inside ground-based stands and InSAR © 2008 CASI

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Figure 1. Combined land cover classification and stem volume estimation result. Shades of green indicate stem volume of forest segments, other colors indicate non-forest land cover classes (see legend).

segments were averaged to produce stand- and segmentaveraged NFI estimates. Similarly, when InSAR estimates were compared against ground-based stands, the segmented InSAR estimates falling inside a ground-based stand were averaged before comparison. Model inversion In this study, the stem volume retrieval performance was practically the same when using the best 2, 3, or 4 Tandem pairs, and adding more pairs started to decrease the performance, probably because coherence of the worst Tandem pairs is only weakly correlated with stem volume (Pulliainen et al., 2003; Askne and Santoro, 2005). Use of the full error covariance matrix in the cost function (Equation (4)) did not improve the results compared with using the simpler diagonalized version of Equation (5) in this case. Furthermore, © 2008 CASI

stem volume retrieval without the use of the regularization term in Equation (5) reduced the estimation bias (see Table 2) on this test site. A possible reason for the failure of the regularization in improving estimation accuracy is that it presupposes that the stem volume distribution in the study area is Gaussian, whereas in reality the distribution is more like the exponential distribution. In this study, the best results were obtained with using just the two best image pairs and doing the minimization by Equation (5) without using the regularization term. Results obtained with the best configuration are reported, unless stated otherwise. The two best Tandem pairs for stem volume retrieval were acquired during wintertime with dry snow cover (12–13 February 1996 and 2–3 March 1996). The Tandem coherence images of these two pairs also scored high values of the ICC measure (0.35 and 0.25, the first and third highest in the dataset). 51

Vol. 34, No. 1, February/février 2008 Table 2. Comparison of InSAR- and NFI-based stem volume estimates with ground-based reference data (210 forest stands total, 134 stands ≥1.5 ha).

InSAR with regularization InSAR without regularization InSAR without regularization on stands ≥1.5 ha NFI NFI on stands ≥1.5 ha

Bias (m3/ha)

RMSE (m3/ha)

RMSE (%)

Unbiased RMSE (m3/ha)

r

!36 !25 !30 !27 !44

107 103 95 118 130

69 66 54 76 74

101 100 90 115 122

0.79 0.78 0.87 0.67 0.67

Note: The InSAR estimates are based on the use of the two best Tandem pairs. NFI, National Forestry Inventory.

InSAR vs. ground-based measurements The InSAR-based stem volume estimates were compared with ground measurements on the 210 reference forest stands (see Table 1), and the results are presented in Figure 2 and Table 2, both for all stands and stands larger than 1.5 ha (average stand size 2.4 ha and 3.2 ha, respectively). For the 134 forest stands larger than 1.5 ha, the performance of the presented method (r = 0.87, RMSE = 54%) is close to the results (r = 0.89, RMSE = 48%) reported in Pulliainen et al. (2003), who used the same 134 stands in their analysis. The difference between these two studies is that, in this study, the InSAR-based stem volume estimates were produced for InSAR-generated forest segments, whose boundaries are not identical to the forest stand boundaries in the ground-based reference data. If all forest stands including the

ones smaller than 1.5 ha are used, then the results are somewhat worse than those obtained for larger stands, as indicated in Figure 2 and Table 2. On stands smaller than 1.5 ha, the correlation between InSAR-estimated stem volume and reference data drops to 0.48, so it can be concluded that the presented method does not perform well on the smallest forest stands. This is not surprising, as the resolution of the InSAR coherence images is dictated by the use of the 7 × 7 pixel Gaussian coherence estimator window, and no buffer zones were used at stand boundaries to prevent the influence of nearby pixels or of small stand localization errors on stand coherence. Since InSAR-generated forest segments smaller than 1.5 ha cover just 1.4% of the forested area on the test site, the poor performance of the method on the smallest forest stands has a negligible effect on the overall stem volume retrieval performance.

Figure 2. Comparison between InSAR-estimated stem volumes and ground-based measurements on 210 forest stands. The stands have been divided into two groups based on their size.

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Figure 3. Comparison between National Forest Inventory (NFI) stem volume estimates and ground-based measurements on 210 forest stands. The stands have been divided into two groups based on their size.

NFI vs. ground-based measurements Stem volume estimates produced by the pixel-based NFI were compared with the ground measurements on the 210 forest stands and the results are presented in Figure 3 and Table 2. The results indicate that NFI underestimates high stem volumes on this test site, as the stem volume estimates saturate at about 200 m3/ha. Peculiarly, the NFI-based estimates did not improve when only the 134 stands larger than 1.5 ha were included in the analysis, but the stem volume estimation bias increased considerably. This is probably an artefact of the distribution of stand sizes and stem volumes in the groundbased reference data, there being only four forest stands smaller than 1.5 ha with stem volumes larger than 300 m3/ha. When the small stands are not included in the analysis, a larger proportion of the stands have high stem volumes, which NFI underestimates. Based on Figure 3, it may be concluded that, on the smallest stands, the dispersion of the NFI estimates increases somewhat, which is to be expected, but otherwise the estimates behave similarly as on the larger stands. NFI vs. InSAR The performance of our InSAR-based method was compared against the operationally used NFI on the 4176 InSARgenerated forest segments (see Table 1). The results are presented in Figure 4, which is in excellent agreement with Figure 3. These results indicate that, on this site, InSAR-based © 2008 CASI

stem volume estimations perform significantly better than NFI, which gives estimates that clearly saturate at around 200 m3/ha. According to the InSAR estimates, the characteristics of the 4176 forest segments (compare with Table 1) are as follows: mean stem volume 127 m3/ha, standard deviation of stem volume 123 m3/ha, minimum stem volume 0 m3/ha, and maximum stem volume 539 m3/ha.

Discussion and conclusions The presented InSAR-based method was demonstrated on a 366 km2 test area in Southern Finland, and its performance in stem volume retrieval was assessed by comparing the estimates against ground-based measurements on 210 forest stands, and stem volume estimates produced of the pixel-based NFI. The main advantage of the InSAR-based method is that, apart from the training stands, it is not necessary to have information about applicable forest stand boundaries, but this information is generated from the remotely sensed InSAR data itself by segmenting the mean Tandem coherence image. The mean coherence image is a low-noise single-channel dataset, and its segmentation is therefore a straightforward operation. After segmentation, the segments are classified based on their multitemporal InSAR characteristic with a supervised or unsupervised method. Even though a multitemporal Tandem InSAR dataset has potential for high-accuracy classification of the segments into several land cover classes (Engdahl and 53

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Figure 4. Comparison between NFI and InSAR-based stem volume estimates on 4176 InSAR-generated forest segments. Note that InSAR estimates are limited to 539 m3/ha, which is the highest stem volume in the training data.

Hyyppä, 2003; Matikainen et al., 2006), a simple forest/nonforest classification is sufficient for stem volume retrieval purposes. During model inversion, a search was performed to identify the best combinations of Tandem pairs for stem volume retrieval. The performance was found to be practically the same when using data from the best 2, 3, or 4 Tandem pairs, and adding more data started to decrease the performance, probably because coherence of the worst Tandem pairs is only weakly correlated with stem volume (Pulliainen et al., 2003; Askne and Santoro, 2003). The best results were achieved using two Tandem pairs, acquired during wintertime with dry snow cover. These are the conditions when InSAR-based stem volume retrieval generally works best (Hyyppä et al., 2000; Koskinen et al., 2001; Fransson et al., 2001; Santoro et al., 2002; Pulliainen et al., 2003; Askne et al., 2003; Engdahl et al., 2004; Askne and Santoro, 2005). Suitable Tandem pairs can be identified using the ICC that measures the difference in coherence between sparse and dense forests (Engdahl et al., 2004), and the two best pairs in this study had the first and third highest ICC values of the whole dataset. Ground-based standwise forest inventory data from 210 forest stands were used to assess the stem volume retrieval performance of the InSAR-based method. On stands larger than 1.5 ha (average stand size 3.2 ha), the performance (r = 0.87, RMSE = 54%) is very close to the results reported in Pulliainen et al. (2003), where InSAR-based segmentation was not used. This shows that deriving forest segment boundaries from InSAR data did not have a large negative impact on stem volume 54

retrieval performance, even though the segment boundaries and the stand boundaries in ground-based reference data were not identical. On stands smaller than 1.5 ha, the performance of the method is poor. This is mainly due to the resolution of the coherence estimator (7 × 7 pixel Gaussian) and the larger influence of boundary effects and possible localization errors on small stands. A drawback of this study is that no buffer zones were used on the boundaries of forest stands. The NFI stem volume estimates were compared against both the InSAR estimates and ground-based reference data. Both comparisons show that, on this test site, InSAR-based stem volume retrieval performs better than NFI, as the latter saturates at about 200 m3/ha. The presented InSAR-based method could be suitable for operational stem volume retrieval in Finland, in the sense that at least on this test site it performs better in stem volume retrieval of dense forests. It should be noted that, in this study, the reported accuracies in stem volume retrieval were attained on small forest stands (see Table 1). Hyyppä and Hyyppä (2001) have shown that forest stand size has a dramatic impact on stem volume estimation using remotely sensed imagery, with accuracies improving with increasing stand size. For example, the correlation of ERS-1/2 Tandem coherence with stem volume increased from r = 0.49 to r = 0.92 as the average stand size increased from 1 ha to 20 ha on the Kalkkinen test site in Southern Finland. Recent results by Askne and Santoro (2005) confirm the large impact of the stand size in InSAR-based stem volume retrieval. Therefore, it should be kept in mind that although the accuracies reported in this study are acceptable for forest inventory over small stands, markedly better results are to be expected when the method is applied to larger stands in a large-scale forest inventory. Acquiring spaceborne C-band InSAR data with a short temporal baseline has been impossible since the loss of ERS-1 in the year 2000. This situation will change in the coming years with the planned spaceborne C-band SAR missions and constellations (RADARSAT-2, the Sentinel-1 constellation, and the planned Canadian C-band SAR constellation). If these future SAR satellites are operated in such a way that they will provide short temporal baseline InSAR data, then it is foreseen that these data will have a large impact on the mapping of forest biophysical parameters over the whole boreal forest zone.

Acknowledgements The authors wish to thank the ESA for providing the SAR data through the ESA Announcement of Opportunity studies AOT-SF.301 and A03–277, as well as the town of Vantaa for the high-resolution aerial orthophotos. The authors wish to thank the anonymous reviewers for the valuable comments received during the evolution of this manuscript.

References Anderson, T.W. 1958. An introduction to multivariate statistical analysis. Wiley, New York. © 2008 CASI

Canadian Journal of Remote Sensing / Journal canadien de télédétection Askne, J.I.H., Dammert, P.B.G., Ulander, L., and Smith, G. 1997. C-band repeat-pass interferometric SAR observations of the forest. IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 1, pp. 25–35. Askne, J., Santoro, M., Smith, G., and Fransson, J.E.S. 2003. Multitemporal repeat-pass SAR interferometry of boreal forests. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 7, pp. 1540–1550. Askne, J., and Santoro, M. 2005. Multitemporal repeat-pass SAR interferometry of boreal forests. IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 6, pp. 1219–1228. Castel, T., Martinez, J.-M., Beaudoin, A., Wegmüller, U., and Strozzi, T. 2000. ERS InSAR data for remote sensing hilly forested areas. Remote Sensing of Environment, Vol. 73, No. 1, pp. 73–86. Dammert, P.B.G., Askne, J.I.H., and Kühlmann, S. 1999. Unsupervised segmentation of multitemporal interferometric SAR images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, pp. 2259– 2271. Dobson, M.C., Ulaby, F.T., Pierce, L.E., Sharik, T.L., Bergen, K.M., Kellndorfer, J., et al. 1995. Estimation of forest biophysical characteristics in Northern Michigan with SIR-C/X-SAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 4, pp. 877–895. Engdahl, M.E., and Hyyppä, J. 2003. Land-cover classification using multitemporal ERS-1/2 InSAR data. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 6, pp. 1620–1628. Engdahl, M.E., Pulliainen, J., and Hallikainen, M. 2004. Boreal forest coherence-based measures of interferometric pair suitability for operational stem volume retrieval. IEEE Geoscience and Remote Sensing Letters, Vol. 1, No. 3, pp. 228–231. Fransson, J.E.S., Smith, G., Askne, J., and Olsson, H. 2001. Stem volume estimation in boreal forests using ERS-1/2 coherence and SPOT XS optical data. International Journal of Remote Sensing, Vol. 22, No. 14, pp. 2777– 2791. Gaveau, D.L.A., Balzer, H., and Plummer, S. 2003. Forest woody biomass classification with satellite-based radar coherence over 900 000 km2 in Central Siberia. Forest Ecology and Management, Vol. 174, Nos. 1–3, pp. 65–75. Häme, T., Salli, A., and Lahti, K. 1992. Estimation of carbon storage in boreal forests using remote sensing data. In The Finnish research programme on climate change, progress report. Edited by M. Kanninen and P. Anttila. Academy of Finland, pp. 43–47. Hyyppä, H.J., and Hyyppä, J.M. 2001. Effects of stand size on the accuracy of remote sensing-based forest inventory. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 12, pp. 2613–2621. Hyyppä, J.M., Hyyppä, H.J., Inkinen, M., Engdahl, M., Linko, S., and Zhu, Y.-H. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, Vol. 128, Nos. 1–2, pp. 109–120. Katila, M., and Tomppo, E. 2001. Selecting estimation parameters for the Finnish multisource national forest inventory. Remote Sensing of Environment, Vol. 76, No. 1, pp. 16–32. Koskinen, J., Pulliainen, J., Hyyppä, J., Engdahl, M. E., and Hallikainen, M. 2001. The seasonal behavior of interferometric coherence in boreal forest. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 4, pp. 820–829. Matikainen, L., Hyyppä, J., and Engdahl, M. 2006. Mapping built-up areas from multitemporal interferometric SAR images — a segment-based © 2008 CASI

approach. Photogrammetric Engineering and Remote Sensing, Vol. 72, No. 6, pp. 701–714. Pulliainen, J., Heiska, K., Hyyppä, J., and Hallikainen, M. 1994. Backscattering properties of boreal forests at the C- and X-band. IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 5, pp. 1041–1050. Pulliainen, J.T., Mikkelä, P.J., Hallikainen, M.T., and Ikonen, J.-P. 1996. Seasonal dynamics of C-band backscatter of boreal forests with applications to biomass and soil moisture estimation. IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 3, pp. 758–770. Pulliainen, J.T., Kurvonen, L., and Hallikainen, M.T. 1999. Multitemporal behavior of L- and C-band SAR observations of boreal forests. IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 2, pp. 927–937. Pulliainen, J., Engdahl, M.E., and Hallikainen, M. 2003. Feasibility of multitemporal interferometric SAR data for stand-level estimation of boreal forest stem volume. Remote Sensing of Environment, Vol. 85, No. 4, pp. 397–409. Pulliainen, J., Vepsäläinen, J., Kaitala, S., Hallikainen, M., Kallio, K., Fleming, V., and Maunula, P. 2004. Regional water quality mapping through the assimilation of spaceborne remote sensing data to ship-based transect observations. Journal of Geophysical Research C Oceans, Vol. 109, No. 12, C12009, doi:10.1029/2003JC002167. Quegan, S., and Yu, J. 2001. Filtering of multichannel SAR images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 11, pp. 2373–2379. Santoro, M., Askne, J., Smith, G., and Fransson, J.E.S. 2002. Stem volume retrieval in boreal forests from ERS-1/2 interferometry. Remote Sensing of Environment, Vol. 81, No. 1, pp. 19–35. Santoro, M., Shivdenko, A., McCallum, I., Askne, J., and Schmullius, C. 2007. Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval. Remote Sensing of Environment, Vol. 106, No. 2, pp. 154–172. Strozzi, T., Dammert, P.G.B., Wegmüller, U., Martinez, J.-M., Askne, J.I.H., Beaudoin, A., and Hallikainen, M.T. 2000. Land use mapping with ERS SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 2, pp. 766–775. Tansey, K.J., Luckman, A.J., Skinner, L., Balzter, H., Strozzi, T., and Wagner, W. 2004. Classification of forest volume resources using ERS tandem coherence and JERS backscatter data. International Journal of Remote Sensing, Vol. 25, No. 4, pp. 751–768. Wagner, W., Luckman, A., Vietmeier, J., Tansey, K., Balzter, H., Schmullius, C., et al. 2003. Large-scale mapping of boreal forest in SIBERIA using ERS Tandem coherence and JERS backscatter data. Remote Sensing of Environment, Vol. 85, No. 2, pp. 125–144. Wegmüller, U., and Werner, C.L. 1995. SAR interferometric signatures of forest. IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 5, pp. 1153–1161. Wegmüller, U., and Werner, C.L. 1997. Retrieval of vegetation parameters with SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 1, pp. 18–24. Weydahl, D.J. 2001. Analysis of ERS SAR coherence images over vegetated areas and urban features. International Journal of Remote Sensing, Vol. 22, No. 14, pp. 2811–2830. 55