The rapid development in aerial digital cameras in combination with the increased availability of high-resolution Digital Elevation Models (DEMs) provides a renaissance for photogrammetry in forest management planning. Tree height, stem volume, and basal area were estimated for forest stands using canopy height, density, and texture metrics derived from photogrammetric matching of digital aerial images and a high-resolution DEM. The study was conducted at a coniferous hemi-boreal site in southern Sweden. Three different data-sets of digital aerial images were used to test the effects of flight altitude and stereo overlap on an area-based estimation of forest variables. Metrics were calculated for 344 field plots (10 m radius) from point cloud data and used in regression analysis. Stand level accuracy was evaluated using leave-one-out cross validation of 24 stands. For these stands the tree height ranged from 4.8 to 26.9 m (17.8 m mean), stem volume 13.3 to 455 m 3 ha (1 (250 m 3 ha (1 mean), and basal area from 4.1 to 42.9 m 2 ha (1 (27.1 m 2 ha (1 mean) with mean stand size of 2.8 ha. The results showed small differences in estimation accuracy of forest variables between the data-sets. The data-set of digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet), showed Root Mean Square Errors (in percent of the surveyed stand mean) of 8.8% for tree height, 13.1% for stem volume and 14.9% for basal area. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.

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Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64°14'N, 19°50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.

Data assimilation techniques were used to estimate forest stand data in 2011 by sequentially combining remote sensing based estimates of forest variables with predictions from growth models. Estimates of stand data, based on canopy height models obtained from image matching of digital aerial images at six different time-points between 2003 and 2011, served as input to the data assimilation. The assimilation routines were built on the extended Kalman filter. The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden (lat. 13˝37 1 N; long. 58˝28 1 E). The assimilation results were compared with two other methods used in practice for estimation of forest variables: the first was to use only the most recent estimate obtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003) to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots, which were carefully measured in the field. The results showed that the data assimilation approach provided better results than the two alternative methods. Data assimilation of remote sensing time series has been used previously for calibrating forest ecosystem models, but, to our knowledge, this is the first study with real data where data assimilation has been used for estimating forest inventory data. The study constitutes a starting point for the development of a framework useful for sequentially utilizing all types of remote sensing data in order to provide precise and up-to-date estimates of forest stand parameters.

The boreal biome exchanges large amounts of carbon (C) and greenhouse gases (GHGs) with the atmosphere and thus significantly affects the global climate. A managed boreal landscape consists of various sinks and sources of carbon dioxide (CO2), methane (CH4), and dissolved organic and inorganic carbon (DOC and DIC) across forests, mires, lakes, and streams. Due to the spatial heterogeneity, large uncertainties exist regarding the net landscape carbon balance (NLCB). In this study, we compiled terrestrial and aquatic fluxes of CO2, CH4, DOC, DIC, and harvested C obtained from tall‐tower eddy covariance measurements, stream monitoring, and remote sensing of biomass stocks for an entire boreal catchment (~68 km2) in Sweden to estimate the NLCB across the land–water–atmosphere continuum. Our results showed that this managed boreal forest landscape was a net C sink (NLCB = 39 g C m−2 year−1) with the landscape–atmosphere CO2 exchange being the dominant component, followed by the C export via harvest and streams. Accounting for the global warming potential of CH4, the landscape was a GHG sink of 237 g CO2‐eq m−2 year−1, thus providing a climate‐cooling effect. The CH4 flux contribution to the annual GHG budget increased from 0.6% during spring to 3.2% during winter. The aquatic C loss was most significant during spring contributing 8% to the annual NLCB. We further found that abiotic controls (e.g., air temperature and incoming radiation) regulated the temporal variability of the NLCB whereas land cover types (e.g., mire vs. forest) and management practices (e.g., clear‐cutting) determined their spatial variability. Our study advocates the need for integrating terrestrial and aquatic fluxes at the landscape scale based on tall‐tower eddy covariance measurements combined with biomass stock and stream monitoring to develop a holistic understanding of the NLCB of managed boreal forest landscapes and to better evaluate their potential for mitigating climate change.