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Trees Outside Forests (TOF) represent a source of lignocellulosic biomass that has received increasing attention in the recent years. While some studies have already investigated the potential of TOF in Germany, a spatial explicit analysis, specifically for Baden-Wuerttemberg, is still lacking. We used a unique wall-to-wall airborne Light Detection and Ranging (LiDAR) dataset combined with OpenStreetMap (OSM) data to map and classify TOF of the federal state of Baden-Wuerttemberg (∼35.000 km2) in south-western Germany. Furthermore, from annual biomass potentials of TOF areas collected from available literature, we calculated the mean annual biomass supply for all TOF areas in Baden-Wuerttemberg. This combination of remote sensing-based classification and available literature resulted in a mean annual biomass supply between ∼490,000–730,000 t from TOF in Baden-Wuerttemberg. The classification congruence on three reference sites was very high (∼99%) using a simple filter technique applied to the LiDAR data and masking man-made objects using OSM data. In contrast, the available literature revealed a high variability of biomass potentials, supporting the demand for an inventory system. Still, the results demonstrate the applicability of LiDAR based vegetation mapping and the value of OSM data in Baden-Wuerttemberg to detect man-made objects.

Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r2 = 0.56, NRMSE = 9.65%), the approach with balanced training data (r2 = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r2 = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy.

The automatic extraction of single trees from remotely sensed data is approached in numerous studies, but results are still insufficient in areas of dense temperate forest. Common watershed-based algorithms using digital surface models tend to produce erroneous results in difficult constellations because the treetop determination lacks an exact criterion for smoothing. In this article, a new approach is introduced that classifies crown size in advance and uses this information as prior knowledge for single-tree extraction. Crown size is classified from texture with an improved grey-scale granulometry followed by a crown size adapted watershed segmentation of single trees. The method is applied on a large area of 10 km2 and verified on six reference plots reflecting diverse and difficult compositions. The accuracy varies between 64% and 88%, and shows an average improvement of about 30% for deciduous and mixed stands compared to a non-crown-size-dependent algorithm.

This article compares three methods for forest resource estimation based on remote sensing features extracted from Airborne laser scanning and CIR orthophotos. The estimation was made exemplarily for the total stem volume of trees for a given area, measured in cubic metres per hectare [m³ ha−1] (as one of the most important quantitative parameters to characterise a forest stand). The following methods were compared: Regression Analysis (RA), k-NN (nearest neighbour) method and a method that utilises regional yield tables, referred to as the yield table method (YT-method). The estimation of stem volume was examined in a mixed forest in Southern Germany using 300 circular inventory plots, each with a size of 452 m². Remote sensing features relating to vegetation height and structures were extracted and used as input variables in the different approaches. The accuracy of the estimation was analysed using scatter plots and quantified using absolute and relative root mean square errors (RMSE). The comparison was made for all plots, as well as for averaged plot values located within forest stands that have the same age class. On “plot level” the RMSE yielded 79.79 m³ ha−1 (RA), 81.93 m³ ha−1 (k-NN) and 81.78 m³ ha−1 (YT-method) and for the averaged values 35.75 m³ ha−1 (RA), 35.06 m³ ha−1 (k-NN) and 42.98 m³ ha−1 (YT-method). Advantages and disadvantages, as well as requirements, of the methods are discussed.

A literature review of new publications in the field of 3D data for forest applications shows that the application of airborne laser scanner data (ALS) is in the focus of research today due to its great potential for practical applications. While there is a lot of research carried out to derive forest management parameters based on laser metrics deduced from a single tree assessment or a statistical area based assessment, the delineation of stand or sub-stand units derived from laser metrics itself is a rather new approach. In order to describe stand characteristics statistical grid cell approaches or single tree approaches have been developed. The LIDAR based segmentation of stand or sub-stand units is rarely documented. This article provides information on enhanced processes to delineate stand or sub-stand units and to extract different forest information based on airborne laser derived parameters. For the stand delineation an automatic process was developed which provides a stand or sub-stand unit delineation which is according to the first results sufficiently uniform within stands and sufficiently different in species, age class, height class, structure and composition between stands in order to be distinguishable from adjacent areas. With a combined method the stand boundaries as they are established by the mapping units today, as well as sub-stand units which have in common physical characteristics indicating the same management disposition, were assessed. Finally a first validation of the forest stand unit delineation is provided, indicating the high potential of ALS data for separating stand units.

Laser scanning provides a good means to collect information on forest stands. This paper presents an approach to delineate single trees automatically in small footprint light detection and ranging (lidar) data in deciduous and mixed temperate forests. In rasterized laser data possible tree tops are detected with a local maximum filter. Afterwards the crowns are delineated with a combination of a pouring algorithm, knowledge-based assumptions on the shape of trees, and a final detection of the crown-edges by searching vectors starting from the trees’ tops. The segmentation results are assessed by comparison with terrestrial measured crown projections and with photogrammetrically delineated trees. The segmentation algorithm works well for coniferous stands. However, the implemented method tends to merge crowns in dense stands of deciduous trees.

In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4–5 points/m2. Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data.