Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Abstract

Highlights• 3D aerial imaging provides a feasible method for estimating forest variables in the form of thematic maps in large area inventories.• Photogrammetric 3D data based on aerial imagery that was originally acquired for orthomosaic production was tested in estimating stand variables.• Photogrammetric 3D data highly improved the accuracy of forest estimates compared to traditional 2D remote sensing imagery. AbstractOptical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials.On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower shortrange correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making

“…In recent years, the DAP has been proved to be a very promising alternative to the ALS Tuominen et al 2017;Puliti et al 2017). In all studies the accuracy of the DAP has been lower than that of ALS, but in the most recent studies the difference has been quite small.…”

“…Typically forest parameters (e.g. volume, basal area, height, diameter) estimated from DAP data have been less accurate than those estimated from ALS data (Bohlin et al 2012;Vastaranta et al 2013;Straub et al 2013;Rahlf et al 2014;Yu et al 2015), but some studies have demonstrated that similar accuracies may be obtained with the two data sources Tuominen et al 2017;Puliti et al 2017).…”

Remote sensing and forest inventories in Nordic countries – roadmap for the future

“…The 3D photogrammetric point clouds might, depending on the forest structure, produce estimates that are almost as good as those made from ALS point cloud data (Gobakken Rahlf et al 2014;Puliti et al 2017;Tuominen et al 2017). Ali-Sisto and Packalen (2017) noted that changes can be equally well detected from both image matching data and ALS data.…”

“…Main data users are forest industry for timber procurement and numerous research projects. At the pixel level, the RMSE of total volume is roughly 60% (Tuominen et al 2017). …”

“…However, while the primary purpose of NFI plots is to produce statistical estimates, they are today often also used as training data for producing nationwide wall-to-wall raster-type forest resources maps based on either satellite images, laser scanning, or photogrammetric point cloud data (Tomppo 1990;Nord-Larsen and Schumacher 2012;Nilsson et al 2017;Bohlin et al 2017;Tuominen et al 2017). They are typically less accurate and/or less detailed than FMI data, but freely available also for other users than the forest owner.…”