EVALUATION AND CALIBRATION OF FIXED-WING UAV MOBILE MAPPING SYSTEM EQUIPPED WITH LIDAR AND OPTICAL SENSORS

In this paper, a mobile mapping system mounted on the UAV is presented and evaluated. The NEO3 UAV platform is an 11 kg fixed-wing designed by the MSP company. The UAV is equipped with a Riegl miniVUX-1UAV laser scanner, which is integrated with the GNSS/INS system of Applanix APX-15 UAV and two Sony Alfa 6000 cameras collecting images in the following spectrum: visible for the first camera and near-infrared for the second camera. The UAV mobile system presented is dedicated to the acquisition of multisource data for levee monitoring using active and passive remote sensing data. In this paper, the effectiveness of the ultralight laser scanner, which has not been mounted on the fixed-wing platforms so far, was verified in the experiment with respect to data density and accuracy. The example analyses were conducted using ground control points and surfaces measured with a terrestrial laser scanner and visible in point clouds obtained with a dense image matching algorithm. Analyses showed that the achieved accuracy is much related to trajectory accuracy. The final DTM created from the data collected during the float status of the GNSS measurements of the trajectory provided twice less accurate data than during fixed status (vertical error approximately 20 cm and 10 cm respectively)

Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes

Geospatial analysis of the state of the Marjan Forest Park based on photogrammetric data of the unmanned aerial vehicle

Tijekom 2017. godine u Park šumi Marjan zamijećeno je intenzivno sušenje stabala alepskog bora (Pinus halepensis Mill.) uzrokovano mediteranskim potkornjakom (Orthotomicus erosus Woll.) koje se je intenziviralo i kulminiralo u 2018. i 2019. godini. S ciljem uklanjanja suhih i zaraženih stabala te sprječavanja daljnjeg širenja uzročnika i propadanja stabala, tijekom 2019. godine provedena je sanitarna sječa. Kako bi se dobio brz i detaljan uvid u stanje vegetacije Park šume Marjan, neposredno nakon obavljenih sanitarnih sječa, provedeno je snimanje bespilotnom letjelicom korištenjem klasične fotogrametrijske kamere te multispektralne kamere. Glavni cilj je bio dobiti ‘nulto stanje’ vegetacije koje će predstavljati temelj za uspostavu daljnjeg monitoringa i izvođenje pravovremenih preventivnih stručnih aktivnosti, a u svrhu očuvanja Park šume Marjan. Na temelju snimaka bespilotne letjelice vrlo visoke prostorne i spektralne razlučivosti izrađen je niz fotogrametrijskih i geoinformacijskih proizvoda koji su korišteni za detaljne geoprostorne analize stanja šumskog pokrova Park šume Marjan nakon napada potkornjakom te provedenih zahvata sanitarne sječe. Istraživanje je potvrdilo veliki potencijal bespilotnih letjelica i korištenih senzora za dobivanje kvalitetnih i pouzdanih informacija o stanju šuma u vrlo kratkom vremenu s relativno malih površina. Utvrđena je da zadovoljavajuća pokrovnost površine Park šume Marjan s vegetacijom iznad 5 m visine (45%). Međutim, zabrinjavajuće je mali udio vegetacije u rasponu od 2 – 5 m visine, a koja bi u bliskoj budućnosti trebala preuzeti ulogu visoke vegetacije (nadstojne etaže). Nadalje, detektirane su i izdvojene dvije ‘žarišne’ površine (13,7 ha) koje imaju najveće udjele otvorenih površina (visinska kategorija 0.4) in time of UAV survey (August 2019). Visual interpretation of multispectral images revealed a total of 491 remaining dry and chlorophyll-deficient trees. Since these trees are precisely spatially defined, they should be easily find in the field and removed by sanitary felling

Amélioration de l’inventaire forestier à l’aide de nuages de points à haute densité acquis par drone lidar et lidar mobile : étude de cas en forêts feuillues tempérées

Les exigences en matière d'inventaire forestier évoluent rapidement pour répondre à un ensemble de normes économiques, sociales et environnementales de plus en plus complexes en matière de gestion durable des ressources forestières. Le manque d'informations détaillées sur l'approvisionnement, c'est-à-dire la quantité et les caractéristiques des ressources forestières, constitue un obstacle important à la satisfaction de ces exigences. Avec le développement continu et la démocratisation des capteurs de lidar sur drone (ULS) et de lidar mobile (MLS), de nouveaux types de nuages de points sont de plus en plus accessibles pour appuyer le niveau opérationnel de l’inventaire. Dans la présente thèse, le potentiel et les limites de l’utilisation de nuages de points ULS et MLS pour la numérisation des arbres feuillus en amont de la chaine d’approvisionnement ont été évalués. Des méthodes de traitement ont été développées pour l’estimation d’attributs structuraux clefs tels que le diamètre à hauteur de poitrine (DHP), la hauteur de l'arbre, les dimensions de la couronne et le volume de bois marchand. Dans le premier article, nous nous sommes concentrés sur le développement et l'évaluation de chaînes de traitement automatiques pour la détection et la segmentation des arbres individuels (ITD : Individual Tree Dectection and Delineation) et l'estimation de leurs attributs structuraux. Ceci, à partir de données ULS acquises avec et sans feuilles dans un peuplement naturel hétérogène de feuillus nordiques. Des comparaisons fines avec des nuages de points de lidar aérien (ALS) et terrestre (TLS) ont été réalisées pour mieux comprendre la configuration des données ULS et pour valider l'extraction d’attributs d’inventaire dérivés de l’ULS. Les meilleurs résultats pour la segmentation des arbres et l’estimation de leurs attributs structuraux ont été obtenus hors feuilles via l’utilisation d’une approche de segmentation dite ascendante (« bottom-up »). Les performances globales des capteurs ULS, en termes d'ajustement cylindrique des tiges et de précision géométrique des points le long de la tige, ne sont toutefois pas comparables à celles du TLS. Les incertitudes sont encore trop élevées au niveau de l'arbre individuel pour respecter les normes de l’inventaire terrain. L’acquisition hors feuilles de données ULS à haute densité pourrait toutefois jouer un rôle important dans le développement de modèles allométriques locaux qui font généralement défaut dans les peuplements complexes de feuillus, ainsi que pour la caractérisation des ressources et le soutien des opérations de foresterie de précision. Dans le second article, nous proposons une méthode innovante pour extraire le volume de bois marchand à partir des données MLS-SLAM (localisation et cartographie simultanées). Les approches actuelles pour prédire le volume de bois marchand reposent sur des équations allométriques qui sont indépendantes de la forme et de la géométrie de l'arbre. Il existe des biais et des erreurs connus associés à cette simplification, en particulier pour les arbres feuillus. L'utilisation d'algorithmes de modèles structurels quantitatifs (QSM : Quantitative Structural Model) pour estimer le volume de bois à partir de nuages de points 3D représente une alternative prometteuse aux mesures destructives et un fort potentiel pour améliorer les modèles allométriques. Les résultats ont montré une grande similitude entre les données TLS et MLS pour l'estimation de la hauteur des arbres, des dimensions de la couronne et du DHP. L'application de QSMs sur des nuages de points MLS filtrés pour extraire le volume marchand du tronc principal des arbres feuillus n'a montré aucun biais significatif par rapport aux estimations TLS. Néanmoins, les données MLS sont plus bruitées que les données TLS, ce qui a entraîné une surestimation du volume de bois des branches qui augmente avec l'ordre de ramification. Toutefois, ces erreurs ont été limitées du fait que les branches de 2ème et de 3ème ordre de ramification ne représentaient qu'une faible proportion du volume marchand total. Ces résultats constituent une étape importante vers la prochaine génération d'inventaires forestiers améliorés par lidar mobiles au sol. Compte tenu de l'utilisation accrue des systèmes ULS et MLS dans la gestion forestière, nos développements constituent des étapes importantes pour les futurs inventaires lidar à l’échelle de l’arbre individuel. Nos résultats démontrent des avancées significatives dans l'utilisation des configurations ULS et MLS pour l’estimation des paramètres biophysiques forestiers.Abstract : Forest inventory requirements are rapidly evolving to meet an increasingly complex set of economic, social and environmental standards for sustainable forest resource management. A significant obstacle to support this requirement is the lack of detailed information on the supply, i.e., the quantity and characteristics of forest resources. In recent decades, a substantial effort has been made to reduce the costs of forest inventories by minimizing labor-intensive field surveys and developing inventory systems enhanced by remote sensing. As such, the use of lidar technology in various aerial and terrestrial platforms, such as airborne laser scanning (ALS) and terrestrial laser scanning (TLS) has considerably increased to the point of becoming essential to improve the forest inventories beyond the existing photo-interpretation techniques. With the continuous development and the democratization of UAV-borne laser scanning (ULS) and mobile laser scanning (MLS) sensors, new types of point cloud are increasingly accessible for forest investigations. The level of detail of ULS and MLS point cloud is becoming comparable to that of TLS, decreasing the boundaries between ALS and TLS systems and providing new opportunities to characterize forest resources at the tree level. In the present thesis, the baselines of ULS and MLS point clouds in digitizing hardwood trees up the supply chain were benchmarked and methods were developed to extract critical structural attributes such as diameter at breast height (DBH), tree height, crown dimensions and merchantable wood volume. In the first article, we emphasized on the development and the evaluation of automatic workflows for the detection, the delineation and the estimation of tree structural attributes from leaf-on and leaf-off ULS data collection. These analyses were conducted in a complex heterogeneous natural stand of northern hardwoods. Co-registration process with ALS and TLS point clouds was achieved for a better understanding of ULS data configuration and to validate ULS retrieval of tree structural attributes. In leaf-on condition, no significant differences were observed between ALS and ULS-R raster-based ITD results, where crown delineation errors led to a poor prediction of individual tree DBH using allometry. In contrast, results in leaf-off condition using point cloud-based individual tree detection and delineation (ITD) algorithm outperformed the raster based ITD in terms of tree detection and tree delineation accuracy, revealing the full potential of high-resolution ULS data. DBH estimation from the “bottom-up” point cloud-based ITD also provided accurate results for both methods, namely allometry and cylinder fitting. The latter showed to be more efficient in dealing with forked trees. The overall performance, in terms of stem cylinder fitting and geometric accuracy of stem points from ULS sensors are not yet comparable to TLS. Uncertainties are still too high at the individual tree level to reach the standard of field inventories, but one might expect to get closer to operational requirements with narrower beams and higher ranging accuracy ULS sensors. In leaf-off condition, the use of bottom-up tree segmentation approaches presents a strong potential to overcome ITD limits currently encountered in hardwood stands. Applications requiring accurate tree location and crown size data could greatly benefit from this innovative approach. Leaf-off acquisition of high-density ULS data could play an important role in developing local allometric models that are typically lacking in complex hardwood stands, as well as for resource characterization and supporting precision forestry operations. In the second article, we propose an innovative method to extract merchantable wood volume from MLS data. Current approaches to predict merchantable wood volume rely on allometric equations that are independent of tree form and the geometry of the tree. There are known biases and errors associated with this simplification, particularly for hardwood trees. The use of quantitative structural model (QSM) algorithms to estimate wood volume from 3D point clouds represent a promising alternative to destructive measurement and a strong potential to improve allometric models. However, so far, they were mainly used on TLS point clouds, which are time-consuming to acquire in the field and complex to process. With the rapid technological progress of SLAM-based (simultaneous localization and mapping) MLS systems, new types of ground-based lidar points clouds are available for QSM analysis. SLAM-based MLS systems open new possibilities to support field inventory. In this study, we collected SLAM-based MLS data from a 1 ha leaf-off northern hardwood site and investigated its use for estimating tree structural attributes. Validation was performed on 26 trees using destructive field measurements and multi-scans TLS data. Results showed high similitude of TLS and MLS data for the estimation of the tree height, crown dimensions and DBH. The application of QSM on filtered MLS point clouds to extract the merchantable stem volume of hardwood trees showed no significant bias compared to the TLS estimates. Nevertheless, the MLS data are noisier than the TLS data, primarily due to the propagation of positioning errors and the greater divergence of the sensor beam. This resulted in an overestimation of the branching volume that increases with the branching order. However, these errors were limited by the fact that branches from the 2nd and 3rd branching order represented a small proportion of the total merchantable volume. These findings are an important step towards next generation of forest inventories enhanced by ground-based lidar. Considering the increased use of ULS and MLS systems in forest management, our developments are important steps forward for future individual-tree-based lidar inventories. We believe that our results demonstrate significant advances in the use of ULS and MLS configuration for the retrieval of forest biophysical parameters

Mapping invasive plants using RPAS and remote sensing

The ability to accurately detect invasive plant species is integral in their management, treatment, and removal. This study focused on developing and evaluating RPAS-based methods for detecting invasive plant species using image analysis and machine learning and was conducted in two stages. First, supervised classification to identify the invasive yellow flag iris (Iris pseudacorus) was performed in a wetland environment using high-resolution raw imagery captured with an uncalibrated visible-light camera. Colour-thresholding, template matching, and de-speckling prior to training a random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within each image. The impacts of feature selection prior to training are also explored. Results from this work demonstrate the importance of performing image processing and it was found that the application of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI using spectral and textural features provided the best results. Second, orthomosaicks generated from multispectral imagery were used to detect and predict the relative abundance of spotted knapweed (Centaurea maculosa) in a heterogeneous grassland ecosystem. Relative abundance was categorized in qualitative classes and validated through field-based plant species inventories. The method developed for this work, termed metapixel-based image analysis, segments orthomosaicks into a grid of metapixels for which grey-level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m2 quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground-cover within each metapixel. Analysis of the performance of metapixel-based image analysis in this study suggests that feature optimization and the use of GLCM-based texture features are of critical importance for achieving an accurate classification. Additional work to further test the generalizability of the detection methods developed is recommended prior to deployment across multiple sites.remote sensingremotely piloted aircraft systemsRPASinvasive plant speciesmachine learnin

Entwicklung und Anwendung eines datenbasierten Multikomponenten-Küstenevolutionsmodells am Beispiel der deutschen Nordseeküste

Die deutsche Nordseeküste ist ein komplexes System aus Wattflächen, Ästuaren und Barriereinseln. Innerhalb dieses Systems finden hydro- und morphodynamische Prozesse statt, die durch den Klimawandel beeinflusst werden. Um diese Prozesse und deren Veränderungen im Kontext von Küsten-und Naturschutz sowie wirtschaftlichen Interessen zu untersuchen, werden von staatlichen und wissenschaftlichen Akteuren Modellsysteme verwendet, die diese Prozesse auf der Basis mathematischer Regeln beschreiben und simulieren können. Sie werden daher als prozessbasierte Modellsysteme bezeichnet. Die morphologische Entwicklung und damit auch der Einfluss auf hydrodynamische Eigenschaften hängt stark von der sedimentologischen Zusammensetzung der Gewässerbodenoberfläche und des Untergrundes ab. Informationen hierüber werden in Naturmessungen erhoben und müssen mit speziellen Modellsystemen verarbeitet und in räumlich kontinuierliche Informationen überführt werden. Sie werden als datenbasierte Modellsysteme bezeichnet. Mangels geeigneter datenbasierter Modellierungsansätze bestand in der Berücksichtigung sedimentologischer Basisdaten zur Verwendung in prozessbasierten Modellsystemen bisher jedoch ein Defizit. Diese Lücke wird mit dem in dieser Arbeit entwickelten datenbasierten Multikomponenten-Küstenevolutionsmodell geschlossen. Hierzu werden in der Modellierung von bathymetrischen Daten übliche Approximations- und Interpolationsvorschriften generalisiert und auf funktionale Informationen der Korngrößenverteilung übertragen. In einer neuartigen Kopplung von bestehenden daten- und prozessbasierten Ansätzen werden punktuelle Entwicklungsgleichungen für skalare Eigenschaften einer Kornverteilung auf volle Summenlinien erweitert und in die Fläche übertragen. Hiermit ermöglicht das Küstenevolutionsmodell es, an der gesamten deutschen Nordseeküste zeitvariante sedimentologische Informationen sowohl der Oberfläche als auch des Untergrunds für prozessbasierte Modellsysteme nutzbar zu machen und so die Qualität ihrer Ergebnisse zu verbessern. Neben der Verwendung als Assistenzwerkzeug für prozessbasierte Modellsysteme können aus den einzelnen Komponenten des Küstenevolutionsmodells darüber hinaus küstengeologische Erkenntnisse abgeleitet werden, die so bisher entweder gar nicht beziehungsweise nicht in dem Ausmaß oder Detailgrad möglich waren. Durch einen außergewöhnlich langen Auswertungszeitraum von bis zu sieben Dekaden in Kombination mit einer verhältnismäßig hohen jährlichen Auflösung ist in der bathymetrischen Komponente eine regionale Trendumkehr in der Entwicklung der Höhen der Wattflächen im 21. Jahrhundert erkennbar, die bisher nicht identifiziert wurde und möglicherweise bereits auf Auswirkungen des Klimawandels hindeutet. Erstmals werden darüber hinaus mit der oberflächensedimentologischen Komponente die Auswirkungen starker anthropogener Eingriffe wie dem Bau des Eidersperrwerks auf die Oberflächensedimentologie in der Fläche und im zeitlichen Verlauf quantifizierbar. Die Modellierung des Gewässergrundaufbaus als dritte Komponente ermöglicht non-destruktive Analysen unter Anderem des Ablagerungsalters und schafft so neue Optionen zur zeitlichen Einordnung von Erosionsereignissen in flachseismischen Untersuchungen, die hierdurch auch in geschützten Habitaten ohne Ground-Truthing-Bohrkerne auskommen. Mit der Auswertung dieser Komponente wird zudem die hohe Relevanz der zeitvarianten Analyse des Untergrunds der Nordseeküste verdeutlicht, der genau wie die Bathymetrie und die Oberflächensedimentologie stets im zeitlichen Kontext betrachtet werden muss

Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)

This study reports on a low-cost unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) system called LasUAV, from hardware selection and integration to the generation of three-dimensional point clouds, and an assessment of its performance. Measurement uncertainties were estimated in angular static, angular dynamic, and real flight conditions. The results of these experiments indicate that the point cloud elevation accuracy in the case of angular static acquisition was 3.8 cm, and increased to 3.9 cm in angular dynamic acquisition. In-flight data were acquired over a target surveyed by nine single passages in different flight directions and platform orientations. In this case, the uncertainty of elevation ranged between 5.1 cm and 9.8 cm for each single passage. The combined elevation uncertainty in the case of multiple passages (i.e., the combination of one to nine passages from the set of nine passages) ranged between 5 cm (one passage) and 16 cm (nine passages). The study demonstrates that the positioning device, i.e., the Global Navigation Satellite System real-time kinematic (GNSS RTK) receiver, is the sensor that mostly influences the system performance, followed by the attitude measurement device and the laser sensor. Consequently, strong efforts and greater economic investment should be devoted to GNSS RTK receivers in low-cost custom integrated systems