Airborne and satellite remote sensing plays an important role in mapping of large remote areas. Satellite images, such as Landsat have been used to generate wall-to-wall information from field data. Several countries, such as Finland, Sweden, USA, Norway, Austria, New Zealand, China, Germany, and Italy employ or have tested this kind of approach in addition to the solely field measurement-based national forest inventory (NFI) [1
]. Though there have been major advances in satellite remote sensing technologies in recent years, it has been challenging to overcome the saturation problem that makes it hard to detect forests with high above-ground biomass (AGB) or stem volume. Tuominen and Haakana [2
] used Landsat 7 ETM images to predict forest attributes. Sample plots of 9th NFI of Finland were used in the calibration and validation. Mean height was among the predicted variables, and the prediction accuracy was 7.1 m (root mean square error, RMSE). Franco-Lopez et al.
] obtained plot-level RMSE-accuracies of 46% and 65% for basal area and stem volume, respectively, using Landsat imagery. Holmström and Fransson [4
] obtained plot-level RMSE-accuracy of 64% for stem volume using SPOT (French: Satellite Pour l’Observation de la Terre) 4-XS imagery. Thus, due to saturation, stem volume or biomass estimation accuracy at the plot level is close to RMSE of 70% when optical satellite images are used with field data for prediction of forest attributes.
The saturation problem in AGB estimation can be overcome by adopting airborne laser scanning (ALS). When ALS is used, laser pulses penetrate even through a dense multi-layered canopy, and there is a strong correlation between airborne laser height profiles or point clouds and AGB or stem volume (e.g., [5
]). Lefsky et al.
] showed that a single profiling laser-derived metric, such as the quadratic mean of the canopy height, could explain 80% of the variance in AGB. Næsset [12
] used regression methods to estimate AGB for 143 sample plots in young and mature coniferous forests. In the study, regression methods explained 92% of the variability of the AGB.
In wall-to-wall forest inventories in intensively managed forest areas, a two-phase procedure using ALS data and field plots, i.e.
, an area-based approach (ABA, [13
]), has become common [14
]. The ABA is capable of providing wall-to-wall estimates and maps of forest inventory attributes, such as basal area or stem volume, with an accuracy that is better than in traditional mapping inventories [14
]. However, ALS surveys required for ABA are carried out at relatively low altitudes, usually from 0.5 to 3 km, which makes data acquisition expensive per area unit. Thus, other remotely sensed data will still be needed, especially when updated information is required annually or consistently over large areas.
Asner et al.
], Andersen et al.
], Gregoire et al.
], Ståhl et al.
], and Gobakken et al.
] have all investigated ALS-based sampling to obtain reliable forest resource estimates for totals. In addition to sampling, other cost-efficient use of ALS is in collection of calibration and validation data for wall-to-wall predictions using optical image data [21
]. While optical image data, such as Landsat, provide useful information on the horizontal distribution of forest canopy structure, ALS provides information on its vertical distribution. When ALS-derived forest characteristics are used in the modeling instead of traditional ground plots, more information can be acquired with the same costs. Thus, compared to ground plots, ALS plots provide more information of the distribution of the forest characteristics in the inventory area. However, some amount of ground plots is required to predict forest characteristics of the ALS plots in this approach. With this kind of procedure, stand mean height has been estimated with app. 3 m RMS-accuracy [22
]. Recently, ALS has been used for calibration and validation of forest characteristic predictions using optical imagery [24
]. Chen et al.
] tested integration of Landsat imagery and ALS to estimate tree height variables. The estimation errors for mean, dominant and Lorey’s height were 4.9 m, 4.1 m and 4.7 m, respectively, validated at the plot level (625 m2
). Mora et al.
] used very high spatial resolution (VHSR, <1 m) optical imagery calibrated and validated with ALS to estimate forest characteristics. Stand and tree objects were delineated, followed by modeling of stand height, stem volume, and AGB using metrics derived from the stand and tree crown objects. In Mora et al.
], only stand height was modeled, and RMSE accuracy of 2.3 m (21%) was obtained in British Columbia, Canada, using a k
-nearest neighbor (k
-NN) approach. Mora et al.
] obtained an RMSE accuracy of 1.95 m (11.6%) for stand height, 9.6 m3
/ha (12.8%) for stand volume and 22.2 t/ha (15.8%) for AGB. In both of these studies, accuracies were reported at the stand level (mean size 9.6 ha in [26
]). Mora et al.
] concluded that VHSR and ALS data provide an opportunity for monitoring in areas for which there is no detailed forest inventory information available.
Besides optical images, commercial radar satellite data have rapidly improved in recent years in terms of spatial resolution, thanks to the latest very-high-resolution synthetic aperture radar (SAR) satellites (e.g., TerraSAR-X, COSMO-SkyMed, Radarsat-2, and TanDEM-X). SAR is able to provide images with a resolution of about 1 m from satellites orbiting at altitudes of several hundreds of kilometers. A major advantage of radar images, compared with optical region satellite images, has been their availability (temporal resolution) under all imaging conditions. This makes radar imaging, especially the SAR approach that is conducted by satellites, an intriguing option in developing methods for operational inventory and monitoring of large areas of forest attributes. Respectively to the optical images, saturation problems also exist with SAR data if the estimation is based solely on the basis of SAR backscatter intensity (e.g., [27
]). The saturation level is dependent on the radar wavelength that is used. Perhaps the most promising approach to determining forest attributes by radar imaging is via canopy height information, which is similar to that obtained from ALS. Recent studies have shown that elevation information extracted from stereo SAR data can be used in the estimation of forest attributes, with results emerging that are close to those of ALS data [10
There are two approaches to extracting detailed elevation information from SAR images: interferometry and radargrammetry. If the elevation values of the ground surface are accurately known, such as by using an ALS-derived digital terrain model (DTM), then the X-band’s or C-band’s interferometric or radargrammetric height can be related to the forest canopy height [5
]. Radargrammetry is based on the stereoscopic measurement of SAR images (see [33
]) in which, analogously to photogrammetric forward intersection, two or more radar images with different viewing perspectives are used to extract 3D information from the target area. Although radargrammetry has been a well-known technique for many decades, it has gained new recognition due to recent SAR satellites with enhanced spatial resolution [34
]. Interferometric height measurements are based on the phase-differences of two or more SAR data acquisitions with slightly different view angles.
Perko et al.
] used TerraSAR-X stereo radargrammetry to derive elevation models over forested areas and compared them with ALS data. They concluded that radar-based elevation values correlated with forest canopy height values at the stand level and that the underestimation of the canopy height was dependent on the characteristics of the forest stand. Karjalainen et al.
] used TerraSAR-X spotlight mode SAR stereo radargrammetry and were able to derive relatively favorable estimates for the mean height and stem volume (the relative RMSE of 34% for stem volume) at the plot level in Finland’s boreal forest zone. Vastaranta et al.
] evaluated the boreal forest AGB and stem volume prediction accuracy at the plot level when ALS and TerraSAR-X stereo radargrammetry-derived point-height metrics were used as predictors in the NN estimation approach. They obtained a RMSEs of 29.9% (41.3 t/ha) and 30.2% (78.1 m3
/ha) for AGB and stem volume, respectively, when using radargrammetry-derived metrics. The respective ALS estimation accuracy values were 21.9% (32.3 t/ha) and 24.8% (64.2 m3
/ha). Persson and Fransson [32
] obtained RMSE of 22.9% for AGB and 9.4% for height at the stand level when using TerraSAR-X stereo radargrammetry-derived metrics in the regression modeling. Solberg et al.
] tested interferometric X-band SAR heights (from the Tandem-X mission) in the estimation of spruce stem volume and AGB. They obtained RMSE values of 43%–44% at the plot level and 19%–20% at the stand level using a nonlinear, mixed model.
The use of TerraSAR-X stereo radargrammetry could be an efficient method for mapping and monitoring forest attributes for large areas if an accurate DTM is available. This study is a continuation of the Karjalainen et al.
] study, described above, where a basic suite of forest inventory attributes were predicted and validated at the plot level. Our objective is to better understand the strengths and limitations of radargrammetry for estimating forest inventory attributes at the stand level. We used stereo-SAR imagery and radargrammetry to predict forest stands’ Lorey’s height, basal area, stem volume, and AGB and evaluated the prediction accuracy using ALS-based ABA inventory as a validation data. In addition, we investigated capabilities of radargrammetry in capturing the variation in the most commonly estimated forest attributes and canopy structure. The results obtained with stereo-SAR were also compared to the forest attribute maps provided by Finnish multi-source NFI, which is the current operational large area map source in Finland.
Stereo-SAR is capable of producing 3D metrics that can be used in the mapping of forest stand attributes. Stereo-SAR is especially suitable for mapping of forest height-related variables, such as Lorey’s height, which was predicted here. It should be noted, however, that stereo-SAR requires a spatially detailed DTM to normalize obtained heights to heights above ground level. However, as stereo-SAR-derived elevations appear to be linearly correlated with forest height, it could be possible to detect forest biomass changes even without the existing DTM [29
]. With ALS-derived DTM, stereo-SAR-based predictions of Lorey’s height, basal area, stem volume and AGB were more accurate than results obtained with MS-NFI. Stand-level prediction accuracies were also in line or even slightly better than obtained in stand-wise field inventories based on relascope measurements [15
]. Overall, the performance of stereo-SAR in the prediction of forest stand attributes would be promising for large-area monitoring applications, but we remain circumspect on recommendations regarding the use of stereo-SAR because the predictions were biased. The source of bias was investigated, and several factors may have contributed. Part of the bias stems from stereo-SARs’ limited capability to detect the small canopy openings. For example, ALS-based vegetation density, which is highly correlated with canopy cover, was 60% on average in our evaluation stands, as it was 94% based on stereo-SAR. When canopy openings are not detected, basal area, stem volume and AGB are overestimated. The variation in our plot-level training should have represented the validation stands as well as possible because our predictions were validated only in those stands that included at least one training plot. However, less vegetated areas in our test stands were included than in our training data. The vegetation density was on average 52% in our study area, based on ALS. However, the respective mean value in our training plots was 60%, meaning that our training plots were located in denser forests than average. In addition, the vegetation density was below 20% in areas covering 5% of the validation area, as only one training plot was located in such a sparsely vegetated stand. In addition, the number of the used neighbors also clearly affected the amount of bias. The lowest biases were always obtained with a k
value of one.
ALS-based predictions provided the stand-level validation data, and it should be noted that those predictions include also estimation error. At the stand level, accuracy of the ALS-based forest inventory is expected to vary between RMSE of 5% and 10% in stem volume, basal area and Lorey’s height [46
]. Our comparison to the MS-NFI should be carefully interpreted. ALS-based predictions provided the validation data, and those were based on the same field plot data that were used with stereo-SAR. Naturally, this procedure favored stereo-SAR. Nevertheless, MS-NFI could not predict stem volumes of over 300 m3
/ha. Stereo-SAR saturation was not detected, and, in general, this is the major advantage of all 3D methods (e.g., [50
We obtained an RMSE accuracy of 7.0% (1.1 m) for Lorey’s height. The prediction accuracy is close to accuracies obtained using ALS data, which are ∼1 m (e.g., [10
]). When ALS has been used for calibration of forest characteristic predictions using medium resolution optical satellite imagery (for example, Landsat images), stand height prediction RMSE accuracy drops to between two and three meters [22
]. With high resolution IKONOS imagery, obtained height prediction accuracies have also been ∼3 m (e.g., [53
]). Thus, it can be concluded that radargrammetry appears to be a suitable method for mapping of stand height over large areas. Stereo-SAR-based predictions for basal area and stem volume were also more accurate compared to respective predictions by Peuhkurinen et al.
] using IKONOS imagery validated using approximately same sized stands. Stereo-SAR-based prediction RMSE accuracies were 12.0% and 16.3% for basal area and stem volume, and the respective accuracies using IKONOS imagery were 25.3% and 31.3%. Mora et al.
] obtained similar accuracies for forest stand attributes, but results were validated using larger stands (9.6 ha). Stand size has a notable effect on the accuracy of the remote sensing-based forest inventory [54
]. Mainly, there are two approaches to extracting detailed elevation information from SAR images. Thus, it is intriguing to compare results obtained with interferometry and radargrammetry. Solberg et al.
] predicted spruce stem volume and AGB using an interferometric-derived digital surface model. The obtained stand level prediction accuracies were slightly imprecise, considering the RMSE percentages (19%–20% for stem volume and AGB). Respective results were obtained by Persson and Fransson [32
] for AGB (also for height) with radargrammetry in Sweden.
Based on these results, the accuracy level that can be obtained by means of stereo-SAR seems slightly worse than can be obtained using low density (<1 pulse/m2
) ALS data [10
] or digital stereo imagery derived DSM [55
], but is far more accurate than can be obtained with 2D methodologies [2
]. Thus, if large areas can be first covered with ALS to produce detailed DTMs, stereo-SAR can be used to provide consistent forest maps or detailed forest monitoring.