Jonas Bohlin

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 m3 ha−1 (250 m3 ha−1 mean), and basal area from 4.1 to 42.9 m2 ha−1 (27.1 m2 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.

Forest height estimation using semi-individual tree detection in multi-spectral 3D aerial DMC data

The increasing availability of accurate Digital Elevation Models (DEMs) of nation-wide cover has opened new possibilities to produce accurate forest variable estimation using 3D data acquired from aerial imagery. Such data can be produced by automatic matching of stereo images and photogrammetric modeling of the forest canopy height. Using existing accurate DEM information, the forest canopy height above ground is then easily assessed. Today, Airborne Laser Scanning (ALS) is frequently used to capture data for accurate estimation of variables to be used in forest management planning. Recent studies in Scandinavia show estimation accuracies almost as accurate as ALS, using 3D data obtained from standard aerial imagery, at least for the most important forest variables. So far mainly area-based estimation methods at field plot or raster cell level have been studied. This paper reports early results from applying a single-tree modeling approach, corresponding to the Semi-ITC (Individual Tree Crown) method, commonly used in ALS-based applications, using 3D data acquired from aerial DMC imagery. Here, a simplified Semi-ITC method was used to estimate tree height at segment level. The Root Mean Square Error of estimating the maximum tree height was 34% (of the true mean maximum tree height). Clearly, the methodology used shows promising results and has potential to be used in forest management planning.

Combining point clouds from image matching with SPOT 5 multispectral data for mountain vegetation classification

There is a need to replace outdated vegetation maps over Sweden’s mountain region; the ability and accuracy of creating such maps with automated methods and remotely sensed data has been a topic of recent research. While spectral information is a key data input for mapping mountain vegetation, the addition of three-dimensional (3D) data has also proven useful in classification. Point clouds from photogrammetric image matching (IM) or from airborne laser scanning (ALS) are potential 3D data sources. In this study, vegetation height and density metrics from IM and ALS data were classified both alone and in combination with SPOT 5 (Système Probatoire d’Observation de la Terre) satellite data and elevation data (elevation, slope, and a wetness index). A Random Forest classification was used to map alpine and subalpine vegetation over Abisko, Sweden. The most notable result in this study was higher producer’s accuracy of the mountain birch classification when using IM metrics alone (98%) as compared to ALS data alone (89%). Classification of IM, SPOT, and elevation data combined gave the same overall accuracy (83%) as when using ALS, SPOT, and elevation data combined (also 83%). While most of the alpine vegetation classes were poorly classified using either the IM or ALS metrics alone, the IM point cloud appeared to contain more information for lower-growing (<2 m) vegetation than the ALS point cloud.

Forest mapping using 3D data from SPOT-5 HRS and Z/I DMC

The nation-wide Airborne Laser Scanning (ALS) currently performed by the Swedish National Land Survey will provide a new and accurate Digital Elevation Model (DEM). These data will enable new and cost-efficient assessments of vegetation height using Canopy Height Models (CHMs) derived as the difference between a Digital Surface Model (DSM) and the DEM. In this context, the High Resolution Stereoscopic (HRS) sensor onboard SPOT-5 and the airborne Z/I Digital Mapping Camera (DMC) used for operational aerial photography by the Swedish National Land Survey are of main interest. Previous research has shown that reliable tree height data are a powerful source of information for forest management planning. This study investigated the possibilities to map forest variables using CHMs derived from either the SPOT-5 HRS or Z/I DMC sensor together with ALS DEM data, in combination with spectral data from the SPOT-5 High Resolution Geometric (HRG) sensor. The results when using the Z/I DMC CHM in combination with SPOT-5 HRG data showed Root Mean Square Errors for standwise prediction of mean tree height, stem diameter, and stem volume of 7.3%, 9.0%, and 19%, respectively. The SPOT-5 HRS CHM in combination with SPOT-5 HRG data improved the SPOT HRG based estimates from 13% to 10%, 15% to 13%, and 31% to 23%, for tree height, stem diameter, and stem volume, respectively. Adding CHM data to a SPOT-5 HRG based prediction model improved the mapping accuracy between 13% to 44%. In conclusion, the obtained accuracies may be sufficient for operational forest management planning.

Deciduous forest mapping using change detection of multi-temporal canopy height models from aerial images acquired at leaf-on and leaf-off conditions

ABSTRACT Discrimination of deciduous trees using spectral information from aerial images has only been partly successfully due to the complexity of the reflectance at different view angles, times of acquisition, phenology of the trees and inter-tree radiance. Therefore, the objective was to evaluate the accuracy of estimating the proportion of deciduous stem volume (P) utilizing change detection between canopy height models (CHMs) generated by digital photogrammetry from leaf-on and leaf-off aerial images instead of using spectral information. The study was conducted at a hemi-boreal study area in Sweden. Using aerial images from three seasons, CHMs with a resolution of approximately 0.5 m were generated using semi-global matching. For training plots, metrics describing the change between leaf-on and leaf-off conditions were calculated and used to model the continuous variable P, using the Random Forest approach. Validated at sub-stands, the estimation accuracy of P in terms of root mean square error and bias was found to be 18% and −6%, respectively. The overall classification accuracy, using four equally wide classes, was 83% with a kappa value of 0.68. The validation plots in classes of high proportion of coniferous or deciduous stem volume were well classified, whereas the mixed forest classes showed lower classification accuracies.

Estimating forest age and site productivity using time series of 3D remote sensing data

Three-dimensional (3D) data about forest captured by airborne laser scanning (ALS) have revolutionized forest management planning. Accurate, updated large-scale maps of forest variables produced with low costs today support greatly improved decisions about silvicultural treatments compared to the past practice based on field surveyed data only. These maps usually lack important information about forest age and site productivity, as this cannot be accurately assessed from the available ALS data. In Sweden, ALS has recently been performed nation-wide, except the mountainous area, to produce a new and accurate digital terrain model (DTM). This DTM enables extremely cost-efficient extraction of 3D data about the forest from other sources than ALS, such as automatic stereo-matching of aerial images as well as from single-pass spaceborne interferometric synthetic aperture radar (InSAR). In contrast to ALS, these data sources can provide low-cost time-series of 3D data. Aerial images of Sweden are often available in archives back to approximately 1960, and the TanDEM-X SAR system has the potential to provide new data every second week over large areas. These data have a potentially high value for forest management planning, since they may provide missing and highly important information - forest site productivity, Site Index (SI) and forest age. This pilot study explores a least-squares minimization approach to estimate forest age and SI from time series of 3D data produced by 1) image matching of DMC aerial images, and 2) TanDEM-X SAR data.