Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM

Abstract

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.

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

“…However, the visual interpretation of forest attributes, such as tree species, health status, and maturity, cannot currently be estimated from LiDAR data (White et al 2013). To utilise the advantages of each method, the option to combine photogrammetry and LiDAR for forest inventory purposes has attracted the attention of many researchers (Popescu et al 2004, StOnge et al 2004, 2008, Holmgren et al 2008, Bohlin et al 2012, Nurminen et al 2013. For example, St-Onge et al (2004) proposed the photogrammetric-LiDAR ('photo-LiDAR') approach to estimate tree height by calculating the difference between the photogrammetrically detected tree tops and tree bottoms derived from LiDAR DTM.…”

“…Based on a review of the research on the applications of digital photogrammetric method in forest inventories, differentiated between (i) manual methods (e.g., Korpela 2004, Magnusson et al 2007, 2012, Hoxha 2012, Ferdinent & Padmanaban 2013; (ii) automated methods of photogrammetric measurement and interpretation of digital aerial images using a computer or DPW (e.g., Naesset 2002a, Korpela 2004, Korpela & Anttila 2004, Zagalikis et al 2005, Bohlin et al 2012. Disadvantages of manual methods, such as the required expertise of the workforce, need for interpreters, increased time requirements, and subjectivity of interpreters (Meyer et al 1996, Anttila 2005, Morgan et al 2010, have made automated methods more attractive and interesting for researchers (Ke & Quackenbush 2011).…”

Inventory of Small Forest Areas Using an Unmanned Aerial System

“…However, airborne photogrammetric data has proven to be a viable data source alternative to ALS to model forest biophysical properties [30,31], suggesting the possibility to apply such methods to UAS-SfM data. Thus, the objectives of our study were to:…”

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

“…We can also create digital surface models (DSMs), which are similar to those derived from airborne LiDAR, from a pair of stereo aerial photographs using stereo matching algorithms. The DSM derived from aerial photographs have significant potential to accurately estimate tree height, stem volume and basal area [24,25]. Recent progress in computer science enables the production of DSMs using the Structure from Motion (SfM) approach with a much higher level of automation and much greater ease of use [26].…”