Jörgen Wallerman

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.

Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods

Individual tree crown segmentation from airborne laser scanning (ALS) data often fails to detect all trees depending on the forest structure. This paper presents a new method to produce tree lists consistent with unbiased estimates at area level. First, a tree list with height and diameter at breast height (DBH) was estimated from individual tree crown segmentation. Second, estimates at plot level were used to create a target distribution by using a k-nearest neighbour (k-NN) approach. The number of trees per field plot was rescaled with the estimated stem volume for the field plot. Finally, the initial tree list was calibrated using the estimated target distribution. The calibration improved the estimates of the distributions of tree height (error index (EI) from 109 to 96) and DBH (EI from 99 to 93) in the tree list. Thus, the new method could be used to estimate tree lists that are consistent with unbiased estimates from regression models at field plot level.

Tree species discrimination using Z/I DMC imagery and template matching of single trees

Abstract Early results from automatic tree species discrimination, using the Z/I DMC digital aerial photographic camera are reported. The position of single trees is identified using template matching techniques and spectral data are extracted from the sunlit part of each detected tree crown. The imagery was acquired in mid-October 2003 for a test site in western Sweden. A discriminant analysis based on canopy colour yielded 88.7% overall accuracy when discriminating between Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and deciduous trees. The promising results are of great practical interest since the Swedish National Land Survey has recently acquired a Z/I DMC camera.

Estimating forest biomass and height using optical stereo satellite data and a DTM from laser scanning data

This paper investigates the possibilities of improving aboveground forest biomass and basal area weighted mean height estimates from optical multispectral satellite data by adding Canopy Height Models (CHMs) obtained from matching multiple view-angle satellite data. The analysis was carried out using data collected over the Remningstorp test site in southern Sweden from 2008–2011 and used training and validation data from airborne laser scanning and field data. CHMs were produced by subtracting a Digital Elevation Model (based on airborne laser scanning data) from the Digital Surface Models created by matching multiple view-angle SPOT-5 HRS and ALOS PRISM data. By modeling biomass and height using regression analysis on multispectral data from SPOT-5 HRG in combination with height metrics from the CHMs, an improved root mean squared error (RMSE) was attained, compared with using the individual satellite data sources alone. A comparison between SPOT-5 HRS and ALOS PRISM CHMs in combination with multispectral data was made at stand level using biomass and height estimates from laser scanning data as reference data. For biomass, the relative RMSE improved from 32.9% when using only multispectral data, to 29.2% and 22.4% when adding the CHM from SPOT-5 HRS and ALOS PRISM, respectively. The corresponding improvements for height were from 16.1% to 15.3% with the SPOT-5 HRS CHM and to 12.9% with the ALOS PRISM CHM. A further analysis of combining the ALOS PRISM CHM and multispectral data was made at sub-stand level with field measurements as reference data. This combination gave a relative RMSE of 20.6% for biomass and 10.5% for height. In conclusion, the estimation accuracy for aboveground biomass and basal area weighted mean height was improved by adding CHM data to multispectral data from optical satellites.

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.

Estimating stem diameter distributions from airborne laser scanning data and their effects on long term forest management planning

Data obtained from airborne laser scanning (ALS) are frequently used for acquiring forest data. Using a relatively low number of laser pulses per unit area (≤5 pulses per m2), this technique is typically used to estimate stand mean values. In this study stand diameter distributions were also estimated, with the aim of improving the information available for effective forest management and planning. Plot level forest data, such as stem number and mean height, together with diameter distributions in the form of Weibull distributions, were estimated using ALS data. Stand-wise tree lists were then estimated. These estimations were compared to data obtained from a field survey of 124 stands in northern Sweden. In each stand an average of seven sample plots (radius 5–10 m) were systematically sampled. The ALS approach was then compared to a mean value approach where only mean values are estimated and tree lists are simulated using a forest decision support system (DSS). The ALS approach provided a better match to observed diameter distributions: ca. 35% lower error indices used as a measure of accuracy and these results are in line with the previous studies. Moreover – which is unique compared to earlier studies – suboptimal losses were assessed. Using the Heureka DSS the suboptimal losses in terms of net present value due to erroneous decisions were compared. Although no large difference was found, the ALS approach showed smaller suboptimal loss than the mean value approach.

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.

Estimating annual cuttings using multi-temporal satellite data and field data from the Swedish NFI

Many countries have ongoing national forest inventories (NFIs) that provide reliable information on current forest conditions and changes in the forest landscape. These inventories are often based on data collected using field inventory procedures and the results are presented in terms of forest statistics for different geographical areas. The Swedish NFI has decided to combine their field data with optical satellite data by using post-stratification to obtain improved and unbiased estimates of forest variables. The method has been shown to reduce the sampling error (standard error) by 10–35% for variables such as stem volume and forest area. The objective of this study is to investigate the effect on sampling error for the estimated annual clear-felled area when the NFI plots are post-stratified by cuttings mapped from multi-temporal satellite images. Clear-felled areas mapped by the Swedish Forest Agency using image pairs (SPOT and Landsat) from the years 2001/2002, 2002/2003, 2003/2004, and 2004/2005 were used to post-stratify the NFI plots. The study area covers approximately a 1.3 Mha forest land area in Coastal Västerbotten. It was found that the sampling error (standard error) for the annually clear-felled area was reduced by 31% using post-stratification compared to use of field data alone.

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.

Estimation of stem volume in hemi-boreal forests using airborne low-frequency Synthetic Aperture Radar and lidar data

Synthetic Aperture Radar (SAR) backscatter data from the Swedish airborne CARABAS-II and LORA systems were used to estimate stem volume at stand level. The study was performed in hemi-boreal forests at the Remningstorp test site, located in southern Sweden. In total, ten 80 m × 80 m stands, where all trees were measured in situ, with stem volumes in the range of 70-530 m3 ha-1 (on average 347 m3 ha-1) were analyzed. SAR data from CARABAS-II and LORA were acquired from two different years, with nine unique flight headings that were repeated for each system and year. Regression analysis was used to estimate stem volume and the accuracy was assessed in terms of Root Mean Square Error (RMSE). As a first step, stem volume was estimated for each flight heading separately. The accuracy assessment was then performed by weighting the separate estimates for each system and year inversely proportionally to the variance about the regression function. The best results for CARABAS-II and LORA showed a relative RMSE of 7% and 24% of the mean stem volume, respectively. In a previous study, stem volume was estimated using LiDAR data and the same forest stands, resulting in an RMSE of about 12%. In conclusion, the estimation accuracy of stem volume using combined low-frequency CARABAS SAR images was found to be superior to that from using LiDAR data for the stands investigated.