Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: (1) compare satellite data sources for generating GEDI-fusion models and 30m resolution maps of eight forest structure measures across six western U.S. states; (2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and (3) examine utility of top GEDI-fusion products for characterizing wildlife habitat and biodiversity patterns. Model performance varied across the eight GEDI structure measures although all represented moderate to high predictive performance (model testing R² values ranging from 0.36 to 0.76). Our results showed promise for applying GEDI-fusion models further back in time than our study period starting in 2016, where we found comparable map validation results for hindcasted years versus years of model creation for all structure metrics. Map validation results varied across some forest structures and post-disturbance landscapes. Within our wildlife case studies, modeling encounter rates of three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76 - 0.87 with observed relationships that followed our ecological understanding of the species. We also found promise in the incorporation of GEDI-fusion metrics for studying patterns of diversity for species of management interest.

Deadwood is an important component of the forest ecosystem and represents a central input for carbon storage studies. In addition to information on the presence of deadwood, the volume or biomass as well as the degree of decay are of interest. Usually, deadwood is surveyed in the context of forest inventories at plot level, whereby the lying as well as the standing deadwood is recorded by means of diameter at breast height, and tree height and stem length respectively. Another method is the line-intersect sampling (LIS), where all lying trunks intersecting with a line are recorded. Finally, statistical methods can be used to estimate the total amount of deadwood in a defined area.

Due to the rapid developments in the field of UAV-LiDAR, it is now possible to acquire 3D point clouds with a very high point density even for larger survey areas at favourable prices. This makes it possible to determine the deadwood for large areas.

The lying deadwood is detected and its volume is estimated from UAV-LiDAR and image data (RIEGL-VUX-120, PhaseOne-iXM100) data with a point density of >4000 pts/m² for the Rohrach natural forest reserve with an area of 48 ha. From the aerial images a true-orthopoto is calculated with a pixel size of 5 cm. Voxel-based approaches are used which take into account not only the geometrical properties but also the radiometric properties of the backscattering objects. The results are compared with reference surveys of 48 plots (437 trees) and with LIS (174 trees).

The results show a very high completeness of the detected deadwood. The validation with the plot-based deadwood volumes shows high accuracies. The total deadwood volume for the entire area show some deviations which have to be analysed in more detail in the next months. This study is funded via the Austrian Waldfonds.

Norway has experienced significant loss of natural forests due to centuries of exploitation, leading to significant population declines for numerous species. To limit further loss in biodiversity, the Norwegian government has set a target of protecting 10% of the forest area. However, according to the latest National Forest Inventory (NFI) campaign, natural forests only cover less than two percent of Norway's forested area. To identify forests with high conservation value, we used vertical and horizontal variables derived from airborne laser scanning (ALS) data, acquired from 2007 to 2020, along with NFI plot measurements. Our study focused on predicting the presence of natural forest over three counties located in southeastern Norway, based on three distinct definitions: pristine, near-natural, and semi-natural forests. We evaluated the potential gain from additional field data that specifically targeted natural forests and used a stratification by dominant tree species to improve the models’ performance. Our results showed that semi-natural forests were better predicted, followed by near-natural and pristine forests, with Matthews correlation coefficient (MCC) values of 0.35, 0.28, and 0.23, respectively. Incorporating additional field data into the models had a negative or, at best, no impact on the predictions. However, stratification by species improved predictions of near-natural and semi-natural forests while reducing predictions for pristine forests. Regardless of the definition of natural forest used, models combining both vertical and horizontal variables performed better. Our study highlights the potential of ALS data in identifying forests with high conservation value.

Within a sustainable forest management framework significant importance has been placed on aquatic resource protection and management, specifically riparian zone management, in order to facilitate a more resilient forest ecosystem. Therefore, in addition to information on vegetation, forest practitioners need up-to-date landscape level information on riparian and stream ecosystems in their management areas.

The use of airborne laser scanning (ALS) is well-established in providing information on vegetation for sustainable forest management. As the collection and the availability of ALS data increases there is a growing need to develop and assess the opportunities and limitations associated with the use of ALS systems to provide information on the health and quality of streams and aquatic ecosystems in forested environments. Further, the emerging technology of mobile laser scanning (MLS) is changing the way that validation data is collected. The ability of MLS to produce high spatial resolution, sub-canopy point clouds in a relatively short amount of time coupled with its ease of use, allow for a detailed mapping of stream structure and could be a suitable alternative to traditional stream habitat surveys.

Given this context, this research examines the use of ALS and MLS to characterize stream and vegetation attributes that are important for fish in their freshwater riparian habitats (Figure 1). We propose a framework for watershed-wide fish habitat assessment. The framework is based on deriving key indicators that are important for quality stream habitat including stream width, morphologic units, instream wood features, road density, and potential migration hindrances. Further, we present methodologies for extracting these key components in more detail. By providing comprehensive and accurate data on stream features, lidar can help researchers and managers make informed decisions about the sustainable management of forests and by association, forested watersheds.

Terrestrial Laser Scanning (TLS) is increasingly being replaced by Hand-Held Mobile Laser Scanning (HMLS) technology due to its static measurement type generating long 3D data collection times. This is particularly the case when high measurement accuracies in the single-millimetre range are not required in surveys, and errors in object position and dimension are acceptable at a level of, for example, 1-3cm. The presented study was designed to assess the possibilities of using HMLS technology in the process of mapping the dead wood lying on 1 ha in the "Lipówka" Reserve in Puszcza Niepołomicka (RDSF Kraków; Poland). HMLS point clouds were acquired during the LEAF-OFF period in autumn 2020 using ZEB HORIZON (GeoSLAM). The process of mapping lying deadwood logs consisted of contouring and cross-sectioning the logs in Terrasolild software. With the map of lying wood prepared in the QField application, the wood distribution was determined according to the current methodology in 5 classes. The volume of lying 347 logs and log fragments was determined using various methods based on the cross-sectional area of the so-called log shape complexity classes. A total of around 290.65m3 of deadwood in 5 log size categories was mapped in the study area. The largest number of dead logs (213 pieces; 70%) was in decomposition degree 3 and in degree 4 approx. 20% (88 pcs.). The largest number of log fragments (67%) was identified as oak (232 pieces). The inventory took 20 minutes of work in the field in 1 loop. HMLS technology has proved excellent in this type of work, facilitating the dimensioning of logs, particularly in difficult and unsafe locations for the operator. It is expected to be possible in the near future to geo-reference point clouds from GNSS surveys directly integrated with HMLS.