It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (D_g) and Lorey’s mean height (H_g) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m²) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for H_g, 11.7% (3.0 cm)–20.6% (5.3 cm) for D_g, 14.8% (4.0 m²/ha)–25.8% (6.9 m²/ha) for G, 15.9% (43.0 m³/ha)–31.2% (84.2 m³/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for H_g and D_g, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for H_g, 20.6% to 19.2% for D_g, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

Climate change has increased the occurrence of heavy storms that cause damage to forests. After a storm, it is necessary to obtain knowledge about the injured trees quickly in order to detect and aid in collecting the fallen trees and estimate the total damage. The objective in this study was to develop an automatic method for storm damage detection based on comparisons of digital surface models (DSMs), where the after-storm DSM was derived by automatic image matching using high-altitude photogrammetric imagery. This DSM was compared to a before-storm DSM, which was computed using national airborne laser scanning (ALS) data. The developed method was tested using imagery collected in extreme illumination conditions after winter storms on 8 January 2012 in Finland. The image matching yielded a high-quality surface model of the forest areas, which were mainly coniferous and mixed forests. The entire set of major damage forest test areas was correctly classified using the method. Our results showed that airborne, high-altitude photogrammetry is a promising tool for automating the detection of forest storm damage. With modern photogrammetric cameras, large areas can be collected efficiently, and the imagery also provides visual, stereoscopic support for various forest storm damage management tasks. Developing methods that work in different seasons are becoming more important, due to the increase in the number of natural disasters.

In this research, we developed and tested a remote sensing-based approach for stand age estimation. The approach is based on changes in the forest canopy height measured from a time series of photo-based digital surface models that were normalized to canopy height models using an airborne laser scanning derived digital terrain model (DTM). Representing the Karelian countryside, Finland, CHMs from 1944, 1959, 1965, 1977, 1983, 1991, 2003, and 2012 were generated and allow for characterization of forest structure over a 68-year period. To validate our method, we measured stand age from 90 plots (1256 m²) in 2014, whereby producer's accuracy ranged from 25.0% to 100.0% and user's accuracy from 16.7% to 100.0%. The wide range of accuracy found is largely attributable to the quality and characteristics of archival images and intrastand variation in stand age. The lowest classification accuracies were obtained for the images representing the earliest dates. For forest managers and agencies that have access to long-term photo archives and a detailed DTM, the estimation of stand age can be performed, improving the quality and completeness of forest inventory databases.