Aplicación de imágenes de satélites y datos LiDAR en la modelización e inventario de Eucalyptus spp en Uruguay

La integración de información de inventarios de campo, con datos procedentes de sensores remotos y su alta correlación con la estructura de la vegetación, permite ajustar modelos precisos para la estimación de la producción forestal. Esto impacta reduciendo costos, tiempos y sesgos, generando productos que son insumos para procesos como la segmentación y la optimización de la cosecha. En este trabajo se presenta una alternativa a los inventarios forestales y al procesamiento de datos, mediante el uso de sensores LiDAR e imágenes multiespectrales. El objetivo general fue evaluar el uso de LiDAR y datos multiespectrales, en plantaciones de Eucalyptus grandis y Eucalyptus dunnii en Uruguay; para mejorar la calidad y la cantidad de información brindada para optimizar los procesos de gestión forestal con respecto a los sistemas de inventario tradicionales. Los resultados obtenidos demuestran la mejora en la precisión y en la calidad de los datos frente a los inventarios tradicionales. Se proporcionan herramientas que permiten mejorar la precisión en cuatro aspectos para la cuantificación y el manejo de la producción forestal: i) el uso de modelos compatibles y aditivos; ii) el modelado de las variables del rodal a gran escala empleando datos de teledetección; iii) la delimitación de zonas homogéneas dentro del rodal basada en una evaluación no supervisada; y iv) un método de programación lineal que optimiza los planes de corta basado en la disponibilidad de madera, el secuestro de carbono y el Valor Actual Neto. Se concluye que la aplicación de herramientas de geomática en el sector forestal supone un cambio fundamental en las prácticas de inventarios, desde su planificación, ejecución y resolución, así como de la capacidad para generar modelos predictivos y de algoritmos de segmentación con mayor precisión. Se comprobó que el uso de datos procedentes de sensores activos y pasivos incrementa las posibilidades de automatización de inventarios forestales, aumentando la resolución espacial y la temporal de la cartografía forestal. Esto, junto con el uso de técnicas estadísticas paramétricas y no paramétricas, constituyen un avance en el campo del manejo forestal en Uruguay.The integration of information from field inventories, with data from remote sensors, and its high correlation with the structure of the vegetation, allows to adjust precise models for the estimation of forest production. This allows for a reduction in costs, time and bias, producing valuable inputs for processes such as segmentation and optimizing the harvest. Here we present an alternative to forest inventories and data processing through the use of LiDAR and multispectral images. The main objective was to evaluate the use of LiDAR information and high-resolution multispectral data in Eucalyptus plantations in Uruguay, to improve the quality and quantity of information provided to optimize forest management processes with respect to traditional inventory systems. The results obtained demonstrate the improvement in precision and quality of the data compared to traditional inventories. Tools that improve precision in four fundamental aspects for the quantification and management of forest production are provided: i) the use of compatible and additives models; ii) modeling of stand variables on a large scale using remote sensing data; iii) delimitation of homogeneous areas within the stand based on an unsupervised assessment; and iv) a method for optimizing felling plans based on timber availability, carbon prices, and harvest age. The main conclusion is that the application of geomatic tools in the forestry sector represent a fundamental change in inventory practices, from planning, execution and resolution, as well as the ability to generate predictive models and segmentation algorithms with greater precision than those obtained with field inventories. Throughout the thesis, it is shown that the use of data from different active and passive sensors increases the possibilities for automating forest inventories, increasing the spatial and temporal resolution of forest cartography. This, together with the use of parametric and non-parametric statistical techniques, constitutes an advance in the field of forest management in Uruguay

Aerial Laser Scanning for Forest Inventories: Estimation and Uncertainty at Multiple Scales

As remote sensing data continues to proliferate, development and assessment of methods that generate predictions of forest attributes is needed to inform operational use. For several decades, lidar data collected from aerial platforms has informed assessments of forest resources, but many opportunities remain for the technology. We examine and develop methodology for three different problems facing forest management inventories. In our first manuscript, we assess three different classification methods for identifying stands for commercial thinning operations in southwestern Oregon, USA using a set of fixed radius plots and a coincident aerial lidar acquisition. We also assess the impact of sample size using a simulation procedure. We found that random forests and a newly developed gradient boosting algorithm exhibited moderate performance for commercial thinning classification. We also observed that performance of all classification methods stabilized at sample sizes between 200 and 300, which may be an attainable sample size in some operational forest inventories. This study was motivated by the lack of literature that examined prediction of categorical variables such as management classes, rather than prediction of continuous forest structural variables, as a potential method for assisting forest management planning decisions. We anticipate a number of extensions to other management-oriented classifiers, such as pre-commercial thinning and various other silvicultural objectives, as future studies. In the second manuscript we compared a segmentation-based method against an area-based approach method for generating stand-level predictions of forest attributes. Particularly, we leverage small area estimation methods to produce model-based mean squared errors for stand-level predictions. The analysis suggests that the segmentation-based method tends to produce lower mean squared errors in stands where sample sizes increased due to tree segmentation. Furthermore, models based on detected segments were less prone to extrapolation than models produced using the area-based method. However, area-based models generally produced lower mean squared errors for stands that did not contain sampled population units. This study was motivated by a lack of investigation into the use of tree-segmentation methods for producing stand-level predictions of forest attributes, which is a typical objective of many forest management inventories. We believe that this manuscript lays the foundation for continued assessment of alternative tree-segmentation methods with rigorous assessments of prediction error. The final manuscript employed the multivariate Fay-Herriot model, a recent theoretical development in the small area estimation literature, for producing stand-level predictions of forest attributes. We compared bivariate Fay-Herriot models to their univariate counterparts for five forest attributes and observed that, for at least one bivariate pairing, stand-level mean squared errors were reduced for both sampled and unsampled stands. Additionally, we identify the uniformity and strength of correlation among stand-level direct-estimators as an important indicator of the success of a bivariate model over a univariate model. We plan to conduct a future study that leverages the multivariate Fay-Herriot model for use in a time-series analysis to unify remote sensing and field data collected at disparate times

The impact of model and variable selection on estimates of precision

Die letzten zwanzig Jahre haben gezeigt, dass die Integration luftgestützter Lasertechnologien (Light Detection and Ranging; LiDAR) in die Erfassung von Waldressourcen dazu beitragen kann, die Genauigkeit von Schätzungen zu erhöhen. Um diese zu ermöglichen, müssen Feldaten mit LiDAR-Daten kombiniert werden. Diverse Techniken der Modellierung bieten die Möglichkeit, diese Verbindung statistisch zu beschreiben. Während die Wahl der Methode in der Regel nur geringen Einfluss auf Punktschätzer hat, liefert sie unterschiedliche Schätzungen der Genauigkeit. In der vorliegenden Studie wurde der Einfluss verschiedener Modellierungstechniken und Variablenauswahl auf die Genauigkeit von Schätzungen untersucht. Der Schwerpunkt der Arbeit liegt hierbei auf LiDAR Anwendungen im Rahmen von Waldinventuren. Die Methoden der Variablenauswahl, welche in dieser Studie berücksichtigt wurden, waren das Akaike Informationskriterium (AIC), das korrigierte Akaike Informationskriterium (AICc), und das bayesianische (oder Schwarz) Informationskriterium. Zudem wurden Variablen anhand der Konditionsnummer und des Varianzinflationsfaktors ausgewählt. Weitere Methoden, die in dieser Studie Berücksichtigung fanden, umfassen Ridge Regression, der least absolute shrinkage and selection operator (Lasso), und der Random Forest Algorithmus. Die Methoden der schrittweisen Variablenauswahl wurden sowohl im Rahmen der Modell-assistierten als auch der Modell-basierten Inferenz untersucht. Die übrigen Methoden wurden nur im Rahmen der Modell-assistierten Inferenz untersucht. In einer umfangreichen Simulationsstudie wurden die Einflüsse der Art der Modellierungsmethode und Art der Variablenauswahl auf die Genauigkeit der Schätzung von Populationsparametern (oberirdische Biomasse in Megagramm pro Hektar) ermittelt. Hierzu wurden fünf unterschiedliche Populationen genutzt. Drei künstliche Populationen wurden simuliert, zwei weitere basierten auf in Kanada und Norwegen erhobenen Waldinveturdaten. Canonical vine copulas wurden genutzt um synthetische Populationen aus diesen Waldinventurdaten zu generieren. Aus den Populationen wurden wiederholt einfache Zufallsstichproben gezogen und für jede Stichprobe wurden der Mittelwert und die Genauigkeit der Mittelwertschätzung geschäzt. Während für das Modell-basierte Verfahren nur ein Varianzschätzer untersucht wurde, wurden für den Modell-assistierten Ansatz drei unterschiedliche Schätzer untersucht. Die Ergebnisse der Simulationsstudie zeigten, dass das einfache Anwenden von schrittweisen Methoden zur Variablenauswahl generell zur Überschätzung der Genauigkeiten in LiDAR unterstützten Waldinventuren führt. Die verzerrte Schätzung der Genauigkeiten war vor allem für kleine Stichproben (n = 40 und n = 50) von Bedeutung. Für Stichproben von größerem Umfang (n = 400), war die Überschätzung der Genauigkeit vernachlässigbar. Gute Ergebnisse, im Hinblick auf Deckungsraten und empirischem Standardfehler, zeigten Ridge Regression, Lasso und der Random Forest Algorithmus. Aus den Ergebnissen dieser Studie kann abgeleitet werden, dass die zuletzt genannten Methoden in zukünftige LiDAR unterstützten Waldinventuren Berücksichtigung finden sollten.The past two decades have demonstrated a great potential for airborne Light Detection and Ranging (LiDAR) data to improve the efficiency of forest resource inventories (FRIs). In order to make efficient use of LiDAR data in FRIs, the data need to be related to observations taken in the field. Various modeling techniques are available that enable a data analyst to establish a link between the two data sources. While the choice for a modeling technique may have negligible effects on point estimates, different model techniques may deliver different estimates of precision. This study investigated the impact of various model and variable selection procedures on estimates of precision. The focus was on LiDAR applications in FRIs. The procedures considered included stepwise variable selection procedures such as the Akaike Information Criterion (AIC), the corrected Akaike Information Criterion (AICc), and the Bayesian (or Schwarz) Information Criterion. Variables have also been selected based on the condition number of the matrix of covariates (i.e., LiDAR metrics) and the variance inflation factor. Other modeling techniques considered in this study were ridge regression, the least absolute shrinkage and selection operator (Lasso), partial least squares regression, and the random forest algorithm. Stepwise variable selection procedures have been considered in both, the (design-based) model-assisted, as well as in the model-based (or model-dependent) inference framework. All other techniques were investigated only for the model-assisted approach. In a comprehensive simulation study, the effects of the different modeling techniques on the precision of population parameter estimates (mean aboveground biomass per hectare) were investigated. Five different datasets were used. Three artificial datasets were simulated; two further datasets were based on FRI data from Canada and Norway. Canonical vine copulas were employed to create synthetic populations from the FRI data. From all populations simple random samples of different size were repeatedly drawn and the mean and variance of the mean were estimated for each sample. While for the model-based approach only a single variance estimator was investigated, for the model-assisted approach three alternative estimators were examined. The results of the simulation studies suggest that blind application of stepwise variable selection procedures lead to overly optimistic estimates of precision in LiDAR-assisted FRIs. The effects were severe for small sample sizes (n = 40 and n = 50). For large samples (n = 400) overestimation of precision was negligible. Good performance in terms of empirical standard errors and coverage rates were obtained for ridge regression, Lasso, and the random forest algorithm. This study concludes that the use of the latter three modeling techniques may prove useful in future LiDAR-assisted FRIs

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