AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of ~170. Our deep learning experiments showed high classification accuracies (~82% coniferous and ~90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level

RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization

We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud (rendered as depth images). Prior works were demonstrated on small datasets and did not lend themselves to scaling up for large-scale applications. To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images. We propose a novel joint embedding based method that effectively combines the appearance and semantic cues from both modalities to handle drastic cross-modal variations. Experiments on the proposed dataset show that our model achieves a strong result of a median rank of 5 in matching across a large test set of 50K location pairs collected from a 14km^2 area. This represents a significant advancement over prior works in performance and scale. We conclude with qualitative results to highlight the challenging nature of this task and the benefits of the proposed model. Our work provides a foundation for further research in cross-modal visual localization.Comment: ACM Multimedia 202

Deep learning methods applied to digital elevation models: state of the art

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103

Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration

Sensing Mountains

Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change

Determining peak altitude on maps, books and cartographic materials : multidisciplinary implications

Mountain peaks and their altitude have been of interest to researchers across disciplines. Measurement methods and techniques have changed and developed over the years, leading to more accurate measurements and, consequently, more accurate determination of peak altitudes. This research transpired due to the frequency of misstatements found in existing sources including books, maps, guidebooks and the Internet. Such inaccuracies have the potential to create controversy, especially among peak‐baggers in pursuit of climbing the highest summits. The Polish Sudetes Mountains were selected for this study; 24 summits in the 14 mesoregions were measured. Measurements were obtained employing the global navigation satellite system (GNSS) and light detection and ranging (LiDAR), both modern and highly precise techniques. Moreover, to determine the accuracy of measurements, several of the summits were measured using a mobile phone as an additional method. We compare GNSS vs. LiDAR and verify the level of confidence of peak heights obtained by automatic methods from LiDAR data alone. The GNSS receiver results showed a discrepancy of approximately 10 m compared with other information sources examined. Findings indicate that the heights of peaks presented in cartographic materials are inaccurate, especially in lesser‐known mountain ranges. Furthermore, among all the mountain ranges examined, the results demonstrated that five of the summits were no longer classed as the highest, potentially impacting tourist percep-tions and subsequent visitation. Overall, due to the topographical relief characteristics and varying vegetation cover of mountains, we argue that the re‐measuring procedure should comprise two steps: (1) develop high‐resolution digital elevation models (DEMs) based on LiDAR; (2) assumed heights should be measured using precise GNSS receivers. Unfortunately, due to the time constraints and the prohibitive costs of GNSS, LiDAR continues to be the most common source of new altitude data

The life cycle of anvil cirrus clouds from a combination of passive and active satellite remote sensing

Anvil cirrus clouds form in the upper troposphere from the outflow of ice crystals from deep convective cumulonimbus clouds. By reflecting incoming solar radiation as well as absorbing terrestrial thermal radiation, and re-emitting it at significantly lower temperatures, they play an important role for the Earth’s radiation budget. Nevertheless the processes that govern their life cycle are not well understood and, hence, they remain one of the largest uncertainties in atmospheric remote sensing and climate and weather modelling. In this thesis the temporal evolution of the anvil cirrus properties throughout their life cycle is investigated, as is their relationship with the meteorological conditions. For a comprehensive retrieval of the anvil cirrus properties, a new algorithm for the remote sensing of cirrus clouds called CiPS (Cirrus Properties from SEVIRI) is developed. Utilising a set of artificial neural networks, CiPS combines the large spatial coverage and high temporal resolution of the imaging radiometer SEVIRI aboard the geostationary satellites Meteosat Second Generation, with the high vertical resolution and sensitivity to thin cirrus clouds of the lidar CALIOP aboard the polar orbiting satellite CALIPSO. In comparison to CALIOP, CiPS detects 71 % and 95 % of all cirrus clouds with an ice optical thickness (IOT) of 0.1 and 1.0 respectively. Furthermore, CiPS retrieves the corresponding cloud top height, IOT, ice water path (IWP) and, by parameterisation, effective ice crystal radius. This way, macrophysical, microphysical and optical properties can be combined to interpret the temporal evolution of the anvil cirrus clouds. Together with a tool for identifying convective activity and a new cirrus tracking algorithm, CiPS is used to analyse the life cycle of 132 anvil cirrus clouds observed over southern Europe and northern Africa in July 2015. Although the anvil cirrus clouds grow optically thick during the convective phase, they become thinner at a rapid pace as convection ceases. Two hours after the last observed convective activity, 92±7 % of the anvil cirrus area has IOT_CiPS < 1 and IWP_CiPS < 30 g m−2 on average, with highest probability density around 0.1–0.2 and 1.5–3 g m−2 respectively. During the same time period, the cloud top height is observed to decrease. Since this is observed for both long-lived and short-lived anvil cirrus, it is deduced that in this life phase the amount of ice in the anvil is mainly controlled by sedimentation. This is in line with a corresponding decrease in the estimated effective radius. While the convective strength has no evident effect on the IOT and IWP, stronger vertical updraught is clearly correlated with higher cloud top height and larger effective radius. Larger ice crystals are, however, observed to be removed effectively within 2-3 h after convection has ceased, suggesting that the convective strength has no impact on the ice crystal sizes in ageing anvils. In this life stage, upper tropospheric relative humidity, as derived from ERA5 reanalysis data, is shown to have a larger impact on the anvil cirrus life cycle, where higher relative humidity govern larger and especially more long-lived anvil cirrus clouds