Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring initiative tasked with characterizing and understanding ecological phenomenology over a 30-year time frame. To support this mission, NEON collects ground truth measurements, such as organism counts and characterization, carbon flux measurements, etc. To spatially upscale these plot-based measurements, NEON developed an airborne observation platform (AOP), with a high-resolution visible camera, next-generation AVIRIS imaging spectrometer, and a discrete and waveform digitizing light detection and ranging (lidar) system. While visible imaging, imaging spectroscopy, and discrete lidar are relatively mature technologies, our understanding of and associated algorithm development for small-footprint full-waveform lidar are still in early stages of development. This work has as its primary aim to extend small-footprint full-waveform lidar capabilities to assess vegetation biophysical structure. In order to fully exploit waveform lidar capabilities, high fidelity geometric and radio-metric truth data are needed. Forests are structurally and spectrally complex, which makes collecting the necessary truth challenging, if not impossible. We utilize the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, which provides an environment for radiometric simulations, in order to simulate waveform lidar signals. The first step of this research was to build a virtual forest stand based on Harvard Forest inventory data. This scene was used to assess the level of geometric fidelity necessary for small-footprint waveform lidar simulation in broadleaf forests. It was found that leaves have the largest influence on the backscattered signal and that there is little contribution to the signal from the leaf stems and twigs. From this knowledge, a number of additional realistic and abstract virtual “forest” scenes were created to aid studies assessing the ability of waveform lidar systems to extract biophysical phenomenology. We developed an additive model, based on these scenes, for correcting the attenuation in backscattered signal caused by the canopy. The attenuation-corrected waveform, when coupled with estimates of the leaf-level reflectance, provides a measure of the complex within-canopy forest structure. This work has implications for our improved understanding of complex waveform lidar signals in forest environments and, very importantly, takes the research community a significant step closer to assessing fine-scale horizontally- and vertically-explicit leaf area, a holy grail of forest ecology

Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

3D terrain mapping and filtering from coarse resolution data cubes extracted from real-aperture 94 GHz radar

William D. Harcourt was funded by the Engineering and Physical Sciences Research Council (EPSRC; grant number: EP/R513337/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES). The data sets analysed in this paper were collected through a grant awarded by the National Centre for Earth Observation (NCEO) and in collaboration with the University of Glasgow (who collected short-range Terrestrial Laser Scanner (TLS) data), Lancaster University, and the CGG company. The authors are grateful to Eric Murphy, Breedon Aggregates Ltd. for arranging access to the quarryPeer reviewe

3D terrain mapping and filtering from coarse resolution data cubes extracted from real-aperture 94 GHz radar

Funding: William D. Harcourt was funded by the Engineering and Physical Sciences Research Council (EPSRC; grant number: EP/R513337/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES).Accurate, high-resolution 3-D mapping of environmental terrain is critical in a range of disciplines. In this study, we develop a new technique, called the PCFilt-94 algorithm, to extract 3-D point clouds from coarse-resolution millimeter-wave radar data cubes and quantify their associated uncertainties. A technique to noncoherently average neighboring waveforms surrounding each AVTIS2 range profile was developed to reduce speckles and was found to reduce point cloud uncertainty by 13% at long range and 20% at short range. Furthermore, a Voronoi-based point cloud outlier removal algorithm was implemented, which iteratively removes outliers in a point cloud until the process converges to the removal of 0 points. Taken together, the new processing methodology produces a stable point cloud, which means that: 1) it is repeatable even when using different point cloud extraction and filtering parameter values during preprocessing and 2) is less sensitive to overfiltering through the point cloud processing workflow. Using an optimal number of ground control points (GCPs) for georeferencing, which was determined to be 3 at close ranges (3 km), point cloud uncertainty was estimated to be approximately 1.5 m at 1.5 km to 3 m at 3 km and followed a Lorentzian distribution. These uncertainties are smaller than those reported for other close-range radar systems used for terrain mapping. The results of this study should be used as a benchmark for future application of millimeter-wave radar systems for 3-D terrain mapping.Peer reviewe

Implementing Unmanned Aerial Systems Within a Field-Based Maize (Zea mays L.) Breeding Program: Improving Yield Prediction and Understanding Temporal QTL Expression of Plant Height

Unmanned aerial system (UAS) technologies are becoming common place within field-based agriculture programs allowing breeders to evaluate greater numbers of genotypes, reducing resource inputs and maintaining unbiased data collection. A comprehensive evaluation was conducted focused on the implementation of UAS technologies within a field-based maize breeding program using the plant height phenotype as a proof of concept in implementation and validation. A robust data processing pipeline was developed to extract height measurements from RGB structure from motion (SfM) point clouds. The 95th percentile (P95) height estimates exceeded 70% correlation to manual ground truth measurements across diverse germplasm groups of hybrid (F1) and inbred lines. Sigmoidal functions were developed to model the overall growth and trajectory of hybrids (R^2 : >98%; RMSE: 99%; RMSE: 1.5- fold similar to functional growth parameters. A ~4-fold improvement in indirect selection of hybrid grain yield was achieved using functional growth parameters compared to conventional manual, terminal plant height (PHTTRML). We expanded our implementation of UAS phenotyping to evaluate three inbred line mapping populations aimed at studying functional QTL and temporal QTL expression. Functional growth parameters identified 34 associations explaining 3 to 15% genetic variation. Height was estimated at one-day intervals to 85 DAS using the Weibull function, identifying 58 unique temporal peak QTL locations. Temporal QTL demonstrated all of the identified significant QTL had dynamic expression patterns. In all, UAS technologies improved phenotypic selection accuracy and have capacity to monitor traits on a temporal scale furthering our understanding of crop development and biological trajectories

Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies

Analyse von full-waveform Flugzeuglaserscannerdaten zur volumetrischen Repräsentation in Umweltanwendungen

Wissenschaftliche Untersuchungen von terrestrischen und aquatischen Ökosystemen erfordern präzise Informationen über die dreidimensionale Struktur des ökologischen Systems. Full-waveform Flugzeuglaserscannerdaten eignen sich hervorragend zur Charakterisierung von Ökosystemen und bilden eine ideale Basis für die vollständige volumetrische Repräsentation der Vegetations- und Gewässerstruktur in einem Voxelraum. Die Voxelattribute werden dabei aus der digitalisierten Wellenform abgeleitet. Jeder emittierte Laserpuls wird von Dämpfungseffekten beeinflusst, die durch Teilreflexionen auf seinem Weg durch die unterschiedlichen Vegetations- oder Wasserschichten entstehen. Dadurch ist die Struktur im unteren Bereich der empfangenen Rohsignale unterrepräsentiert. Die im Rahmen dieser Arbeit entwickelten innovativen Methoden zur Analyse von full-waveform Daten ermöglichen die Generierung einer radiometrisch korrigierten Voxelraumrepräsentation. Voraussetzung dafür ist die numerisch stabile Rekonstruktion des effektiven differentiellen Rückstreuquerschnitts mit geeigneten Entfaltungs- und Regularisierungsverfahren. Das Kernstück der Analyse bildet die Beschreibung der Signaldämpfung mit Hilfe geeigneter Modelle. Auf Grundlage dieser Modelle wurden neuartige Korrekturverfahren zur Kompensation der Signaldämpfung erarbeitet, wobei der Korrekturterm direkt aus dem differentiellen Rückstreuquerschnitt abgeleitet wird. Die Grundidee der entwickelten Methode ist das schrittweise Anheben der Signalintensität in Abhängigkeit von der individuellen Historie jedes Laserpulses. Die Resultate der vorliegenden Arbeit tragen dazu bei, die in full-waveform Daten enthaltenen Informationen über die Vegetations- und Gewässerstruktur zugänglich zu machen. Weiterhin zeigen die hier präsentierten Ergebnisse, dass die Limitierungen bestehender Auswertemethoden, welche weitgehend auf die Extraktion diskreter Maxima und die Erzeugung volumetrischer Repräsentationen aus diskreten 3D Punktwolken beschränkt sind, überwunden werden können.The scientific investigation of terrestrial and aquatic ecosystems requires precise information on the three-dimensional structure of the ecologic system. Full-waveform airborne laser scanner data are an ideal basis for the complete volumetric representation of vegetation and water structure in a voxel space. Due to attenuation effects, caused by partial reflections during the laser pulse propagation through the vegetation or water column, each individual laser pulse echo is significantly modified. As a result, the structure in the lower parts of the vegetation or water column is underrepresented in the digitized waveform. Within this research, novel and innovative methods were developed, which enable the generation of a radiometrically correct voxel space representation. Therefore, a numerically stable reconstruction of the effective differential backscattering cross section utilizing appropriate deconvolution and regularization techniques is required. The essential element of the analysis is the description of the signal attenuation using applicable mathematical models. For this purpose, novel correction methods compensating the signal attenuation based on these models were developed. The correction term is directly derived from the differential backscatter cross section. The basic idea is a gradually increase of the signal amplitudes depending on the individual history of each laser pulse. The results gained in this work contribute to an improved access to the information on vegetation and water structure, contained in full-waveform laser scanner data. Furthermore, it is possible to overcome limitations of existing approaches, which are mainly based on the extraction of discrete maxima

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application