VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Developing Allometric Equations for Teak Plantations Located in the Coastal Region of Ecuador from Terrestrial Laser Scanning Data

Traditional studies aimed at developing allometric models to estimate dry above-ground biomass (AGB) and other tree-level variables, such as tree stem commercial volume (TSCV) or tree stem volume (TSV), usually involves cutting down the trees. Although this method has low uncertainty, it is quite costly and inefficient since it requires a very time-consuming field work. In order to assist in data collection and processing, remote sensing is allowing the application of non-destructive sampling methods such as that based on terrestrial laser scanning (TLS). In this work, TLS-derived point clouds were used to digitally reconstruct the tree stem of a set of teak trees (Tectona grandis Linn. F.) from 58 circular reference plots of 18 m radius belonging to three different plantations located in the Coastal Region of Ecuador. After manually selecting the appropriate trees from the entire sample, semi-automatic data processing was performed to provide measurements of TSCV and TSV, together with estimates of AGB values at tree level. These observed values were used to develop allometric models, based on diameter at breast height (DBH), total tree height (h), or the metric DBH2 × h, by applying a robust regression method to remove likely outliers. Results showed that the developed allometric models performed reasonably well, especially those based on the metric DBH2 × h, providing low bias estimates and relative RMSE values of 21.60% and 16.41% for TSCV and TSV, respectively. Allometric models only based on tree height were derived from replacing DBH by h in the expression DBH2 x h, according to adjusted expressions depending on DBH classes (ranges of DBH). This finding can facilitate the obtaining of variables such as AGB (carbon stock) and commercial volume of wood over teak plantations in the Coastal Region of Ecuador from only knowing the tree height, constituting a promising method to address large-scale teak plantations monitoring from the canopy height models derived from digital aerial stereophotogrammetry

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes

Scaling Near-Surface Remote Sensing To Calibrate And Validate Satellite Monitoring Of Grassland Phenology

Phenology across the U.S. Great Plains has been modeled at a variety of field sites and spatial scales. However, combining these spatial scales has never been accomplished before, and has never been done across multiple field locations. We modeled phenocam Vegetation Indices (VIs) across the Great Plains Region. We used coupled satellite imagery that has been aligned spectrally, for each imagery band to align with one another across the phenocam locations. With this we predicted the phenocam VIs for each year over the six locations.Using our method of coupling the phenocam VIs and the meteorological data we predicted 38 years of phenocam VIs. This resulted in a coupled dataset for each phenocam site across the four VIs. Using the coupled datasets, we were able to predict the phenocam VIs, and examine how they would change over the 38 years of data. While imagery was not available for modeling the 38 years of weather data, we found weather data could act as an acceptable proxy. This means we were able to predict 38 years of VIs using weather data. A main assumption with this method, it that no major changes in the vegetation community took place in the 33 years before the imagery. If a large change did take place, it would be missed because of the data lacking to represent it. Using the phenocam and satellite imagery we were able to predict phenocam GCC, VCI, NDVI, and EVI2 and model them over a five-year period. This modeled six years of phenocam imagery across the Great Plains region and attempted to predict the phenocam VIs for each pixel of the satellite imagery. The primary challenge of this method is aggregating grassland predicted VIs with cropland. This region is dominated by cropland and managed grasslands. In many cases the phenology signal is likely driven by land management decisions, and not purely by vegetation growth characteristics. Future models that take this into account may provide a more accurate model for the region

MAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSION

This dissertation explored the combined use of lidar and other remote sensing data for improved forest structure and habitat mapping. The objectives were to quantify aboveground biomass and canopy dynamics and map habitat characteristics with lidar and /or fusion approaches. Structural metrics from lidar and spectral characteristics from hyperspectral data were combined for improving biomass estimates in the Sierra Nevada, California. Addition of hyperspectral metrics only marginally improved biomass estimates from lidar, however, predictions from lidar after species stratification of field data improved by 12%. Spatial predictions from lidar after species stratification of hyperspectral data also had lower errors suggesting this could be viable method for mapping biomass at landscape level. A combined analysis of the two datasets further showed that fusion could have considerably more value in understanding ecosystem and habitat characteristics. The second objective was to quantify canopy height and biomass changes in in the Sierra Nevada using lidar data acquired in 1999 and 2008. Direct change detection showed overall statistically significant positive height change at footprint level (ΔRH100 = 0.69 m, +/- 7.94 m). Across the landscape, ~20 % of height and biomass changes were significant with more than 60% being positive, suggesting regeneration from past disturbances and a small net carbon sink. This study added further evidence to the capabilities of waveform lidar in mapping canopy dynamics while highlighting the need for error analysis and rigorous field validation Lastly, fusion applications for habitat mapping were tested with radar, lidar and multispectral data in the Hubbard Brook Experimental Forest, New Hampshire. A suite of metrics from each dataset was used to predict multi-year presence for eight migratory songbirds with data mining methods. Results showed that fusion improved predictions for all datasets, with more than 25% improvement from radar alone. Spatial predictions from fusion were also consistent with known habitat preferences for the birds demonstrating the potential of multi- sensor fusion in mapping habitat characteristics. The main contribution of this research was an improved understanding of lidar and multi-sensor fusion approaches for applications in carbon science and habitat studies